69874 The World Bank Final Report GLOBAL STUDY FOR PURPOSE OF GLOBAL WORLD BANK GUIDANCE DEVELOPMENT SOLID WASTE MANAGEMENT HOLISTIC DECISION MODELING Japan Country-Tied Fund JUNE 2008 submitted by Nippon Koei Co., Ltd. in association with Nippon Koei UK Co., Ltd. With subcontracting to Research Triangle Institute Solid Waste Management Holistic Decision Modeling Final Report Table of Contents Pages ABBREVIATIONS GLOSSARY CHAPTER 1 INTRODUCTION 1.1 Background of the Project ................................................................. 1-1 1.2 Goals and Scope of the Study ........................................................... 1-2 1.3 Consulting Team Formation ............................................................... 1-3 1.4 Selection of Target Cities ................................................................... 1-4 CHAPTER 2 DATA AND METHODOLOGY 2.1 General Approach .............................................................................. 2-1 2.2 Waste Management Scenarios Analyzed .......................................... 2-4 2.3 Data Collection and Key City Characteristics..................................... 2-11 2.3.1 Data Requirements ................................................................. 2-11 2.3.2 Site Visits to Collect Data ....................................................... 2-11 2.3.3 Key City Characteristics ......................................................... 2-14 2.4 Data Analysis ..................................................................................... 2-28 2.4.1 Review of Other Sources of Existing Solid Waste Data .......... 2-28 2.4.2 Review of JICA Study Reports ................................................ 2-28 2.5 Summary of Key Input Parameters, Default Model Data, Assumptions, and Limitations.......................................................................................... 2-34 CHAPTER 3 SCENARIO RESULTS 3.1 Open Dumping and Open Burning (Base Case) ................................ 3-1 3.2 Mixed Waste Collection and Management Using One Primary Technology ........................................................................................................... 3-5 3.2.1 Simulation Scenario Results Using One Primary Technology . 3-5 3.2.2 Recycling Using Manual and Mechanical Sorting ................... 3-9 3.2.3 Mixed Waste Composting Using Manual Turning and Mechanical Windrow Turner ...................................................................... 3-27 3.2.4 Incineration Without and With Energy Recovery .................... 3-43 3.2.5 Landfill with Gas Venting, Gas Collection and Flaring, and Gas Collection and Energy Recovery ............................................ 3-51 3.3 Optimization Scenario Results ........................................................... 3-59 3.3.1 Cost Results ........................................................................... 3-63 3.3.2 Energy Results ....................................................................... 3-80 3.3.3 Emissions Results .................................................................. 3-92 3.4 Option Comparison per City............................................................... 3-106 3.4.1 Amman ................................................................................... 3-106 3.4.2 Buenos Aires .......................................................................... 3-116 i Solid Waste Management Holistic Decision Modeling Final Report 3.4.3 Conakry .................................................................................. 3-125 3.4.4 Kathmandu ............................................................................. 3-134 3.4.5 Lahore .................................................................................... 3-143 3.4.6 Sarajevo ................................................................................. 3-152 3.4.7 Shanghai ................................................................................ 3-161 3.4.8 Kawasaki ................................................................................ 3-170 3.4.9 Atlanta .................................................................................... 3-179 CHAPTER 4 CONCLUSIONS 4.1 Key Findings of the Scenario Modeling Exercise ............................... 4-1 4.2 Discussion of Sensitivity for Parameters of Interest ........................... 4-4 4.3 Appropriateness of Future Holistic analysis of Waste Management in Bank Member Countries ............................................................................. 4-6 ii Solid Waste Management Holistic Decision Modeling Final Report APPENDICES: Appendix 2.3 Field Survey Result (Presentation Summary) 1. Amman, Jordan 2. Buenos Aires, Argentina 3. Conakry, Guinea 4. Kathmandu, Nepal 5. Lahore, Pakistan 6. Sarajevo, Bosnia And Herzegovina 7. Shanghai, China 8. Kawasaki, Japan 9. Atlanta, Georgia, USA Appendix 2.4.1 Composition Rate for Compostable materials vs. Economic Indicator (GDP or GNI) Appendix 2.4.2 Compostable: kitchen waste and yard waste(wood and grass), by regional areas Appendix 2.4.3 Composition Rate for Combustible materials vs. Economic Indicator (GDP or GNI) Appendix 2.4.4 Composition Rate for Combustible materials vs. Economic Indicator (GDP or GNI), by regional areas Appendix 2.4.5 Composition Rate for Recyclable materials. Economic Indicator (GDP or GNI) Appendix 2.4.6 Composition Rate for Recyclable materials vs. Economic Indicator (GDP or GNI), by regional areas Appendix 2.5 Analysis Assumptions Appendix 2.5.1 General Data Input by City Appendix 2.5.2 Generation Inputs by City – Metric Appendix 2.5.3 Collection Inputs by City – Metric Appendix 2.5.4 MRF Inputs by City – Metric Appendix 2.5.5 Compost Inputs by City – Metric Appendix 2.5.6 Incineration Inputs by City – Metric Appendix 2.5.7 Landfill Inputs by City – Metric Appendix 2.5.8 Energy Inputs by City Appendix 2.5.9 Constants Data – Metric Appendix 3.3 Optimization Scenarios Mass Flows by City and Management Process Appendix 3.4 Simulation and Optimization Scenario Results by City iii Solid Waste Management Holistic Decision Modeling Final Report List of Tables Table 1.3-1 Responsibility of Field Visit by NK and NK UK Team ................. 1-4 Table 1.4-1 Comparative Information on the Countries of the 9 Target Cities .......................................................................................... 1-5 Table 2.2-1 Waste Management Scenarios Analyzed ................................... 2-5 Table 2.4-1 List of JICA Development Study for Solid Waste Management .. 2-29 Table 2.5-1 Summary of Key Input Parameters, Assumptions and Data Sources by Process ................................................................... 2-35 Table 2.5-2 Cost Data Availability by City ...................................................... 2-38 Table 2.5-3 MSW DST Default Pre/Combustion Energy Factors (Fuel Type Used to Produce Electricity) ....................................................... 2-40 Table 2.5-4 MSW DST Default Emission Factors (Fuel Type Used to Produce Electricity) .................................................................... 2-41 Table 2.5-5 MSW DST Default Energy Savings (Recycling Individual Materials) ................................................................................... 2-41 Table 2.5-6 MSW DST Default Emission Savings (Recycling Individual Materials*) .................................................................................. 2-41 Table 2.5-7 MSW DST Default Landfill Gas Generation (Individual Waste Items*) ........................................................................................ 2-42 Table 3.1-1 Unit Gas Emissions from Open Dumping of Waste .................... 3-2 Table 3.1-2 Annual Gas Emissions from Open Dumping of Waste ............... 3-2 Table 3.1-3 Unit Emissions of Key Water Pollutants from Open Dumping of Waste ....................................................................................... 3-2 Table 3.1-4 Annual Emissions of Key Water Pollutants from Open Dumping of Waste ..................................................................................... 3-3 Table 3.1-5 Open Burning Non-Metal Emissions Factors Applied ................. 3-3 Table 3.1-6 Unit Non-Metal Emissions from Open Burning of Waste ............ 3-4 Table 3.1-7 Annual Non-Metal Emissions from Open Burning of Waste ....... 3-4 Table 3.1-8 Dioxin/Furan Emissions from Open Burning of Waste................ 3-4 Table 3.2-1 Summary of the Significance of Key Input Parameters to the Scenario Results by Process* .................................................... 3-7 Table 3.2-2 Percentage of Available Recyclables* and Flow to the Different Processes .................................................................................. 3-9 Table 3.2-3 Percentage of Recyclable Items Recovered for Remanufacturing by City (Sorted by Percentage of Total Metals) ....................................................................................... 3-18 iv Solid Waste Management Holistic Decision Modeling Final Report Table 3.2-4 Percentages of Remanufactured Recyclables by City (Sorted by Total Aluminum and Plastic) .................................................. 3-22 Table 3.2-5 Percentage of Compostable Organics and Waste Flow by City (Sorted by Percent of Organics Composted).............................. 3-28 Table 3.2-6 Percentage of Waste Going to the Different Processes (Sorted by Average Heat Content) ............................................. 3-43 Table 3.2-7 Percentage of Each Waste Category Incinerated by City (Sorted by Percentage of Plastics) ............................................. 3-49 Table 3.2-8 Percentage of Cost Attributed to the Different Landfill Scenario Processes (Sorted by Disposal Cost) ......................................... 3-51 Table 3.2-9 Percentage of Energy Attributed to the Different Landfill Scenario Processes (Sorted by Disposal Energy Consumption) 3-55 Table 3.3-1 Waste Management Processes Selected by the MSW DST for Each Optimization Scenario ....................................................... 3-60 Table 3.3-2 Summary of Cost Variation by Scenario and Scenario Settings . 3-64 Table 3.3-3 Group 3 Scenarios- Percentage of Net Total Cost Attributed to the Different Processes .............................................................. 3-76 Table 3.3-4 Group 4 Scenarios- Percentage of Net Total Cost Attributed to the Different Processes .............................................................. 3-77 Table 3.3-5 Group 5 (Carbon) Scenarios- Percentage of Net Total Cost Attributed to the Different Processes .......................................... 3-78 Table 3.3-6 Group 5 (PM) Scenarios- Percentage of Net Total Cost Attributed to the Different Processes .......................................... 3-79 Table 3.3-7 Summary of Energy Variation by Scenario and Scenario Settings ...................................................................................... 3-81 Table 3.3-8 Group 3 Scenarios- Percentage of Net Total Energy Attributed to the Different ............................................................................ 3-88 Table 3.3-9 Group 4 Scenarios- Percentage of Net Total Energy Attributed to the Different Processes .......................................................... 3-89 Table 3.3-10 Group 5 (Carbon) Scenarios- Percentage of Net Total Energy Attributed to the Different Processes .......................................... 3-90 Table 3.3-11 Group 5 (PM) Scenarios- Percentage of Net Total Energy Attributed to the Different Processes .......................................... 3-91 Table 3.3-12 Summary of Carbon and PM Emissions Variation by Scenario and Scenario Settings ................................................................ 3-94 Table 3.3-13 Group 3 Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes .......................................... 3-101 Table 3.3-14 Group 4 Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes .......................................... 3-102 v Solid Waste Management Holistic Decision Modeling Final Report Table 3.3-15 Group 5 (Carbon) Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes......................... 3-103 Table 3.3-16 Group 5 (PM) Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes......................... 3-104 Table 3.3-17 Group 5 (PM) Scenarios- Percentage of Net Total PM Emissions Attributed to the Different Processes......................... 3-105 vi Solid Waste Management Holistic Decision Modeling Final Report List of Figures Figure 1.3-1 Consulting Team Formation ....................................................... 1-3 Figure 2.1-1 Life Cycle Inputs and Outputs of a Waste Management Process (Note: Some materials leaving a waste management process may loop back into the process.) .................................. 2-2 Figure 2.2-1 Boundaries for Group 1- Base Case .......................................... 2-9 Figure 2.2-2 Boundaries for Group 2 Scenarios ............................................. 2-10 Figure 2.2-3 Boundaries for Group 3 Scenarios ............................................. 2-10 Figure 2.2-4 Boundaries for Groups 4 and 5 Scenarios ................................. 2-10 Figure 2.3-1 Simplified Waste Flow in Amman ............................................... 2-14 Figure 2.3-2 Waste Composition in Amman ................................................... 2-15 Figure 2.3-3 Simplified Waste Flow in Atlanta ................................................ 2-16 Figure 2.3-4 Waste Composition in Atlanta..................................................... 2-16 Figure 2.3-5 Simplified Waste Flow in Buenos Aires ...................................... 2-17 Figure 2.3-6 Waste Composition in Buenos Aires .......................................... 2-18 Figure 2.3-7 Simplified Waste Flow in Conakry .............................................. 2-19 Figure 2.3-8 Waste Composition in Conakry .................................................. 2-19 Figure 2.3-9 Simplified Waste Flow in Kathmandu ......................................... 2-20 Figure 2.3-10 Waste Composition in Kathmandu ............................................. 2-21 Figure 2.3-11 Simplified Waste Flow in Kawasaki ............................................ 2-22 Figure 2.3-12 Waste Composition in Kawasaki ................................................ 2-22 Figure 2.3-13 Simplified Waste Flow in Lahore ................................................ 2-23 Figure 2.3-14 Waste Composition in Lahore .................................................... 2-24 Figure 2.3-15 Simplified Waste Flow in Sarajevo ............................................. 2-25 Figure 2.3-16 Waste Composition in Sarajevo ................................................. 2-25 Figure 2.3-17 Simplified Waste Flow in Shanghai ............................................ 2-26 Figure 2.3-18 Waste Composition in Shanghai ................................................ 2-27 Figure 2.4-1 Unit Generation Rate vs. Economic Indicator of Cities of JICA Study and this MSW HDM Study (Sorted by Economic Indicator) .................................................................................... 2-32 Figure 2.4-2 Unit Generation Rate vs. Economic Indicator of Cities of JICA Study and this MSW HDM Study (Sorted by Regional Area) ...... 2-33 Figure 3.2-1 Net Total Cost by City (with Land Price) ..................................... 3-12 Figure 3.2-2 Unit Cost by City and Process (with Land Price: Manual MRF Design) ....................................................................................... 3-13 vii Solid Waste Management Holistic Decision Modeling Final Report Figure 3.2-3 Unit Cost by City and Process (with Land Price: Mechanical MRF Design) .............................................................................. 3-14 Figure 3.2-4 Net Total Cost by City (without Land Price) ................................ 3-15 Figure 3.2-5 Unit Cost by City and Process (without Land Price: Manual MRF Design) .............................................................................. 3-16 Figure 3.2-6 Unit Cost by City and Process (without Land Price: Mechanical MRF Design) .............................................................................. 3-17 Figure 3.2-7 Net Total Energy Consumption by City ....................................... 3-19 Figure 3.2-8 Energy Consumption by City and Process (Manual MRF Design) ....................................................................................... 3-20 Figure 3.2-9 Energy Consumption by City and Process (Mnecanical MRF Design) ....................................................................................... 3-21 Figure 3.2-10 Net Total Carbon Emissions by City ........................................... 3-23 Figure 3.2-11 Carbon Emissions by City and Process (Manual MRF Design) 3-24 Figure 3.2-12 Carbon Emissions by City and Process (Mnecanical MRF Design) ....................................................................................... 3-25 Figure 3.2-13 Net Total Cost by City (with Land Price) ..................................... 3-29 Figure 3.2-14 Unit Cost by City and Process (with Land Price: Manual Composting Design) ................................................................... 3-30 Figure 3.2-15 Unit Cost by City and Process (with Land Price: Mechanical Composting Design) ................................................................... 3-31 Figure 3.2-16 Net Total Cost by City (without Land Price) ................................ 3-32 Figure 3.2-17 Unit Cost by City and Process (without Land Price: Manual Composting Design) ................................................................... 3-33 Figure 3.2-18 Unit Cost by City and Process (without Land Price: Mechanical Composting Design) ................................................ 3-34 Figure 3.2-19 Net Total Energy Consumption by City ....................................... 3-36 Figure 3.2-20 Energy Consumption by City and Process (Manual Composting Design) ................................................................... 3-37 Figure 3.2-21 Energy Consumption by City and Process (Mnecanical Composting Design) ................................................................... 3-38 Figure 3.2-22 Net Total Carbon Emissions by City ........................................... 3-40 Figure 3.2-23 Carbon Emissions by City and Process (Manual Composting Design) ....................................................................................... 3-41 Figure 3.2-24 Carbon Emissions by City and Process (Mnecanical Composting Design) ................................................................... 3-42 Figure 3.2-25 Net Total Cost by City (with Land Price) ..................................... 3-45 Figure 3.2-26 Net Total Cost by City (without Land Price) ................................ 3-46 viii Solid Waste Management Holistic Decision Modeling Final Report Figure 3.2-27 Net Total Energy Consumption by City ....................................... 3-48 Figure 3.2-28 Net Total Carbon Emissions by City ........................................... 3-50 Figure 3.2-29 Net Total Cost by City (with Land Price) ..................................... 3-53 Figure 3.2-30 Net Total Cost by City (without Land Price) ................................ 3-54 Figure 3.2-31 Net Total Energy Consumption by City ....................................... 3-56 Figure 3.2-32 Net Total Carbon Emissions by City ........................................... 3-58 Figure 3.3-1 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Cost by City(with Land Price) .......................................................................................... 3-65 Figure 3.3-2 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Cost by City(without Land Price) ................................................................................. 3-66 Figure 3.3-3 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Cost by City(with Land Price) .......................................................................................... 3-67 Figure 3.3-4 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Cost by City(without Land Price) ................................................................................. 3-68 Figure 3.3-5 Group 4: Maximizing Energy Recovery- Net Total Cost by City(with Land Price) .................................................................. 3-69 Figure 3.3-6 Group 4: Maximizing Energy Recovery- Net Total Cost by City(without Land Price) ............................................................. 3-70 Figure 3.3-7 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Cost by City(with Land Price) ............................................ 3-71 Figure 3.3-8 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Cost by City(without Land Price)........................................ 3-72 Figure 3.3-9 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Cost by City(with Land Price).................... 3-73 Figure 3.3-10 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Cost by City(without Land Price)............... 3-74 Figure 3.3-11 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Energy Consumption by City ............................................................................................. 3-82 Figure 3.3-12 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Energy Consumption by City ............................................................................................. 3-83 Figure 3.3-13 Group 4: Maximizing Energy Recovery- Net Total Energy Consumption by City .................................................................. 3-84 ix Solid Waste Management Holistic Decision Modeling Final Report Figure 3.3-14 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Energy Consumption by City ............................................. 3-85 Figure 3.3-15 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- - Net Total Cost by City ............................................ 3-86 Figure 3.3-16 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Carbon Emissions by City ............................................................................................. 3-95 Figure 3.3-17 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Carbon Emissions by City ............................................................................................. 3-96 Figure 3.3-18 Group 4: Maximizing Energy Recovery- Net Total Carbon Emissions by City ....................................................................... 3-97 Figure 3.3-19 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Carbon Emissions by City .................................................. 3-98 Figure 3.3-20 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Carbon Emissions by City ......................... 3-99 Figure 3.3-21 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total PM Emissions by City ............................... 3-100 Figure 3.4-1 Cost Results of Simulation Scenarios (Amman: with Land Price) .......................................................................................... 3-108 Figure 3.4-2 Cost Results of Simulation Scenarios (Amman: without Land Price) .......................................................................................... 3-109 Figure 3.4-3 Energy Recovery Results of Simulation Scenarios (Amman) ..... 3-110 Figure 3.4-4 Carbon Emissions Results of Simulation Scenarios (Amman) ... 3-111 Figure 3.4-5 Cost Results of Optimizations Scenarios (Amman: with Land Price) .......................................................................................... 3-112 Figure 3.4-6 Cost Results of Optimizations Scenarios (Amman: without Land Price) ................................................................................. 3-113 Figure 3.4-7 Energy Recovery Results of Optimizations Results (Amman) .... 3-114 Figure 3.4-8 Carbon Emissions Results of Optimizations Results (Amman) .. 3-115 Figure 3.4-9 Cost Results of Simulation Scenarios (Buenos Aires: with Land Price) .......................................................................................... 3-117 Figure 3.4-10 Cost Results of Simulation Scenarios (Buenos Aires: without Land Price) ................................................................................. 3-118 Figure 3.4-11 Energy Recovery Results of Simulation Scenarios (Buenos Aires) .......................................................................................... 3-119 Figure 3.4-12 Carbon Emissions Results of Simulation Scenarios (Buenos Aires) .......................................................................................... 3-120 x Solid Waste Management Holistic Decision Modeling Final Report Figure 3.4-13 Cost Results of Optimizations Scenarios (Buenos Aires: with Land Price) ................................................................................. 3-121 Figure 3.4-14 Cost Results of Optimizations Scenarios (Buenos Aires: without Land Price) .................................................................... 3-122 Figure 3.4-15 Energy Recovery Results of Optimizations Results (Buenos Aires) .......................................................................................... 3-123 Figure 3.4-16 Carbon Emissions Results of Optimizations Results (Buenos Aires) .......................................................................................... 3-124 Figure 3.4-17 Cost Results of Simulation Scenarios (Conakry: with Land Price) .......................................................................................... 3-126 Figure 3.4-18 Cost Results of Simulation Scenarios (Conakry: without Land Price) .......................................................................................... 3-127 Figure 3.4-19 Energy Recovery Results of Simulation Scenarios (Conakry) .. 3-128 Figure 3.4-20 Carbon Emissions Results of Simulation Scenarios (Conakry) .. 3-129 Figure 3.4-21 Cost Results of Optimizations Scenarios (Conakry: with Land Price) .......................................................................................... 3-130 Figure 3.4-22 Cost Results of Optimizations Scenarios (Conakry: without Land Price) ................................................................................. 3-131 Figure 3.4-23 Energy Recovery Results of Optimizations Results (Conakry) ... 3-132 Figure 3.4-24 Carbon Emissions Results of Optimizations Results (Conakry) . 3-133 Figure 3.4-25 Cost Results of Simulation Scenarios (Kathmandu: with Land Price) .......................................................................................... 3-135 Figure 3.4-26 Cost Results of Simulation Scenarios (Kathmandu: without Land Price) ................................................................................. 3-136 Figure 3.4-27 Energy Recovery Results of Simulation Scenarios (Kathmandu) .............................................................................. 3-137 Figure 3.4-28 Carbon Emissions Results of Simulation Scenarios (Kathmandu) .............................................................................. 3-138 Figure 3.4-29 Cost Results of Optimizations Scenarios (Kathmandu: with Land Price) ................................................................................. 3-139 Figure 3.4-30 Cost Results of Optimizations Scenarios (Kathmandu: without Land Price) ................................................................................. 3-140 Figure 3.4-31 Energy Recovery Results of Optimizations Results (Kathmandu) .............................................................................. 3-141 Figure 3.4-32 Carbon Emissions Results of Optimizations Results (Kathmandu) .............................................................................. 3-142 Figure 3.4-33 Cost Results of Simulation Scenarios (Lahore: with Land Price) 3-144 Figure 3.4-34 Cost Results of Simulation Scenarios (Lahore: without Land Price) .......................................................................................... 3-145 xi Solid Waste Management Holistic Decision Modeling Final Report Figure 3.4-35 Energy Recovery Results of Simulation Scenarios (Lahore) .... 3-146 Figure 3.4-36 Carbon Emissions Results of Simulation Scenarios (Lahore) .... 3-147 Figure 3.4-37 Cost Results of Optimizations Scenarios (Lahore: with Land Price) .......................................................................................... 3-148 Figure 3.4-38 Cost Results of Optimizations Scenarios (Lahore: without Land Price) .......................................................................................... 3-149 Figure 3.4-39 Energy Recovery Results of Optimizations Results (Lahore) ..... 3-150 Figure 3.4-40 Carbon Emissions Results of Optimizations Results (Lahore) ... 3-151 Figure 3.4-41 Cost Results of Simulation Scenarios (Sarajevo: with Land Price) .......................................................................................... 3-153 Figure 3.4-42 Cost Results of Simulation Scenarios (Sarajevo: without Land Price) .......................................................................................... 3-154 Figure 3.4-43 Energy Recovery Results of Simulation Scenarios (Sarajevo) . 3-155 Figure 3.4-44 Carbon Emissions Results of Simulation Scenarios (Sarajevo) . 3-156 Figure 3.4-45 Cost Results of Optimizations Scenarios (Sarajevo: with Land Price) .......................................................................................... 3-157 Figure 3.4-46 Cost Results of Optimizations Scenarios (Sarajevo: without Land Price) ................................................................................. 3-158 Figure 3.4-47 Energy Recovery Results of Optimizations Results (Sarajevo) .. 3-159 Figure 3.4-48 Carbon Emissions Results of Optimizations Results (Sarajevo) 3-160 Figure 3.4-49 Cost Results of Simulation Scenarios (Shanghai: with Land Price) .......................................................................................... 3-162 Figure 3.4-50 Cost Results of Simulation Scenarios (Shanghai: without Land Price) .......................................................................................... 3-163 Figure 3.4-51 Energy Recovery Results of Simulation Scenarios (Shanghai) 3-164 Figure 3.4-52 Carbon Emissions Results of Simulation Scenarios (Shanghai) 3-165 Figure 3.4-53 Cost Results of Optimizations Scenarios (Shanghai: with Land Price) .......................................................................................... 3-166 Figure 3.4-54 Cost Results of Optimizations Scenarios (Shanghai: without Land Price) ................................................................................. 3-167 Figure 3.4-55 Energy Recovery Results of Optimizations Results (Shanghai) . 3-168 Figure 3.4-56 Carbon Emissions Results of Optimizations Results (Shanghai).................................................................................. 3-169 Figure 3.4-57 Cost Results of Simulation Scenarios (Kawasaki: with Land Price) .......................................................................................... 3-171 Figure 3.4-58 Cost Results of Simulation Scenarios (Kawasaki: without Land Price) .......................................................................................... 3-172 Figure 3.4-59 Energy Recovery Results of Simulation Scenarios (Kawasaki) 3-173 xii Solid Waste Management Holistic Decision Modeling Final Report Figure 3.4-60 Carbon Emissions Results of Simulation Scenarios (Kawasaki) 3-174 Figure 3.4-61 Cost Results of Optimizations Scenarios (Kawasaki: with Land Price) .......................................................................................... 3-175 Figure 3.4-62 Cost Results of Optimizations Scenarios (Kawasaki: without Land Price) ................................................................................. 3-176 Figure 3.4-63 Energy Recovery Results of Optimizations Results (Kawasaki) . 3-177 Figure 3.4-64 Carbon Emissions Results of Optimizations Results (Kawasaki) ................................................................................. 3-178 Figure 3.4-65 Cost Results of Simulation Scenarios (Atlanta: with Land Price) 3-180 Figure 3.4-66 Cost Results of Simulation Scenarios (Atlanta: without Land Price) .......................................................................................... 3-181 Figure 3.4-67 Energy Recovery Results of Simulation Scenarios (Atlanta) .... 3-182 Figure 3.4-68 Carbon Emissions Results of Simulation Scenarios (Atlanta) .... 3-183 Figure 3.4-69 Cost Results of Optimizations Scenarios (Atlanta: with Land Price) .......................................................................................... 3-184 Figure 3.4-70 Cost Results of Optimizations Scenarios (Atlanta: without Land Price) .......................................................................................... 3-185 Figure 3.4-71 Energy Recovery Results of Optimizations Results (Atlanta) ..... 3-186 Figure 3.4-72 Carbon Emissions Results of Optimizations Results (Atlanta) ... 3-187 xiii Solid Waste Management Holistic Decision Modeling Final Report ABBREVIATIONS GENERAL BHC: Biweekly High Capture BLC: Biweekly Low Capture BOD: Biochemical Oxygen Demand BTU: British Thermal Unit CH4: Methane CO: Carbon Monoxide CO2: Carbon Dioxide DHC: Daily High Capture DLC: Daily Low Capture DST: Decision Support Tool D/S: Dumping Site ER: Energy Recovery FCA: Full Cost Accounting GHG: Greenhouse Gas GNI: Gross Nation Income GNP: Gross Nation Product HDPE: High Density Polyethylene LCA: Life Cycle Assessment LCI: Life Cycle Inventory LDPE: Low Density Polyethylene LF: Landfill MRF: Material Recovery Facility MSW: Municipal Solid Waste MSW DST: Municipal Solid Waste Decision Support Tool MTCE: Metric Tons of Carbon Equivalents NOx: Nitrogen Oxides O & M: Operation and Management PDU3: 3rd Urban Development Project PET: Polyethylene Terephthalate PM: Particulate Material SOx: Sulfur Oxides SPTD: Service Public de Tranfert des Dechets xiv Solid Waste Management Holistic Decision Modeling Final Report SWM: Solid Waste Management SWM HDM: Solid Waste Management Holistic Decision Modeling T/S: Transfer Station WDI: World Development Indicator WTE: Waste to Energy ORGANIZATION EPA: United States Environmental Protection Agency NK: Nippon Koei Co., Ltd. NK-UK: Nippon Koei UK Co., Ltd. RTI: Research Triangle Institute WB: The World Bank (Amman) GAM: Greater Amman Municipality (Buenos Aires) AIDIS: Inter-American Association of Sanitary Engineers ARS: Solid Waste Association CBA: City of Buenos Aires CEAMSE: Metropolitan Area Ecologic Coordination Society GBA: Government of Province of Buenos Aires IATASA: Engineering Technical Support Argentina Co. UBA: University of Buenos Aires (Conakry) SME: Guinéenne d’Assainissement (Kathmandu) BKM: Bhaktapur Municipality KMC: Kathmandu Metropolitan City KRM: Kirtipur Municipality LSMC: Lalitpur Sub-Metropolitan City MTM: Madhyapur Thimi Municipality SchEMS: School of Environmental Management and Sustainable Development SWM RMC: Solid Waste Management & Resource Mobilization Center (Lahore) CDGL: City District Government of Lahore (Shanghai) xv Solid Waste Management Holistic Decision Modeling Final Report SCAESAB: Shanghai City Appearance Environmental Sanitation Administrative Bureau SCEID: Shanghai Chengtou Environment Industry Development Co., Ltd. SIDREE: Shanghai Institute for Design and Research in Environmental Engineering xvi Solid Waste Management Holistic Decision Modeling Final Report GLOSSARY Collection: The process of picking up wastes from residences, businesses, or a collection point, loading them into a vehicle, and transporting them to a processing site, transfer station or landfill. Collection Frequency: The number of MSW collections made from a specific location within a given time period. Collection Timing. The pre-determined time period when MSW is collected from a location or pick-up point. Combustion Process Energy: the electricity consumed in producing the product and the energy associated with the amount of fuel combusted in the production process. An example of this type of fuel combustion is the use of coal in process boilers to produce process steam. Combustion Transportation Energy: the energy consumed to transport the various intermediate products or materials to the next unit process in the system. Commercial Waste: All municipal solid waste emanating from business establishments such as stores, markets, office buildings, restaurants, shopping centers, and entertainment centers. Commingled MRF: it is a material recovery facility that receives recyclables from a commingled recyclables (i.e.., segregated collection) collection program. All fiber recyclables are collected in one compartment and non-fiber recyclables are collected in a separate compartment on the collection vehicle. Commingled Recyclables: Mixed recyclables that are collected together. Compost: The relatively stable humus material that is produced from a composting process of putrescible fraction of MSW in which bacteria in soil mixed with it break down the mixture into organic fertilizer. Energy Recovery: Obtaining energy from MSW through a variety of processes (eg combustion). Greenhouse Gases: Components of the atmosphere that contribute to greenhouse effect which is the process in which the emission of infrared radiation by the atmosphere warms a planet's surface. The name comes from an incorrect analogy with the warming of air inside a greenhouse compared to the air outside the greenhouse. xvii Solid Waste Management Holistic Decision Modeling Final Report Gross National Income : Economic indicator that comprises the total value of goods and services produced within a country (i.e. its Gross Domestic Product), together with its income received from other countries (notably interest and dividends), less similar payments made to other countries. Landfill: Generally used to mean the same as sanitary landfill, though it may be used to indicate any disposal site where waste is deposited on land, rather than specifying the standard of the operation. Landfill Gas: Gases arising from the decomposition of the organic (putrescible) fraction of MSW; principally methane, carbon dioxide, and hydrogen sulfide. Such gases may cause explosions at landfills if not properly managed. Leachate: Wastewater that collects contaminants as it trickles through MSW disposed in a landfill. Leaching may result in hazardous substances entering surface water, ground water or soil. LCI: Life Cycle Inventory: “Life Cycle Inventory� involves model of the product system, data collection, as well as description and verification of data for life cycle assessment which is the assessment of environmental impact of a given product or service throughout its lifespan. This implies data for inputs and outputs for all affected unit processes that compose the product system. Manufacturing Emissions: the total air, water, and solid waste emissions associated with both the production process and transportation energy consumption. This includes emissions from process, transportation, and pre-combustion activities. Manufacturing Energy Consumption: the total energy consumed in the manufacturing process, including combustion and pre-combustion, as well as process and transportation related energy consumption. Materials Recovery Facility (MRF): Facility that processes residentially collected mixed recyclables into new products. Metric Tons of Carbon Equivalent: CO2eq or CO2e, is an internationally accepted measure that expresses the amount of global warming of greenhouse gases (GHGs) in terms of the amount of carbon dioxide (CO2) that would have the same global warming potential. Mixed Waste MRF: material recovery facilities (MRF) are used to recover recyclables from the municipal waste stream. The process flow in a MRF depends xviii Solid Waste Management Holistic Decision Modeling Final Report on the recyclables processed and the manner in which they are collected (e.g., segregated vs. non-segregated collection). A mixed waste MRF processes mixed municipal solid waste from non-segregated collection. Non-segregated Collection: Collection of mixed refuse in a single compartment truck. Open Burning: Uncontrolled fires in a dump. Open Dump: A site used for disposal of waste without any management and/or environmental controls (see also dump). Pre-combustion Process Energy: the energy consumed in mining and transportation steps required to produce fuels used in the manufacturing process. Examples of this type of energy are the use of energy to extract petroleum, transport it to a refinery, and produce natural gas that is combusted at a manufacturing facility for process steam. Pre-combustion Transportation Energy: the energy consumed in mining and transportation steps required to produce fuels for transportation. Examples of this type of energy are the use of energy to extract petroleum, transport it to a refinery, and produce diesel fuel for truck, ocean freighters, locomotives, etc. Primary Collection: The means by which municipal solid waste is collected from its source (domestic and commercial premises) and transported to communal stations, transfer points or disposal sites. Usually primary collection is characterized in developing countries by hand carts, bicycles or small vehicles. Recycle/Reuse: Recovering and re-processing useable MSW that might otherwise end disposed in landfills (ie, recycling of aluminium cans, paper, and bottles, etc.). Residuals: Unintended outputs of production processes. These include municipal solid waste and wastewater. Sanitary Landfill: A US term for land MSW disposal site that is located to minimize water pollution from runoff and leaching. MSW is spread in thin layers, compacted, and covered with a fresh layer of soil each day to minimize pest, aesthetic, disease, air pollution, and water pollution problems (see also: landfill). Segregated Collection: collection of commingled recyclables in a vehicle with two compartments. xix Solid Waste Management Holistic Decision Modeling Final Report Tonnage: The amount of waste that a landfill accepts, usually expressed in tons per month. The rate at which a landfill accepts waste is limited by the landfill's permit. Transfer Station: A facility at which municipal solid waste from collection vehicles is consolidated into loads that are transported by larger trucks or other means to more distant landfill sites. Waste Stream: The total flow of MSW from homes, businesses, institutions, and manufacturing plants that are recycled, burned, or disposed of in landfills, or segments thereof such as the ‘residential waste stream’ or the ‘recyclable waste stream’. Yard Waste (Yard Trimmings): The part of MSW composed of grass clippings, leaves, twigs, branches and garden refuse. xx Solid Waste Management Holistic Decision Modeling Final Report CHAPTER 1 INTRODUCTION 1.1 Background of the Project Solid waste management is the most labor intensive service in the cities of developing countries and its costs comprise the largest expenditure in most cities. Optimizing costs by helping cities choose correct technologies is critically important. Everyday cities are besieged by private vendors selling inappropriate technologies and they have limited technical capacity and analytical tools for assessing their claims and viability. Many times inappropriate systems have been built, only to close within months of costly start up operations. The variables affecting Local Government’s decision-making on solid waste technology and management choices have become more complicated, especially when we take into consideration carbon finance for green house gas reduction and avoidance, economic instruments that provide incentives for energy-reduction, power grid use of renewable enrgy, and landfill-minimization, emissions trading, recycling targets, land reclamation and irrigation water reduction needs, and both fuel and emission impacts for transporting recyclables and by-products. As waste characteristics, costing factors, economic incentives, and local environmental conditions vary by region and income level, it is useful for the Bank’s policy dialogue with client countries to have explicit analytical information to discuss the technology choices. The US Environmental Protection Agency (US EPA) worked for 10 years with the Research Triangle Institute (RTI), a non-profit organization, in a public/private partnership to develop a comprehensive holistic model that is able to compare technologies holistically regarding all of these issues. Dozens of organizations were involved in peer review and the model has been extensively tested, calibrated, and used in over [] cities. The model, officially known as the Municipal Solid Waste Decision Support Tool (MSW DST), uses a materials balance flow logical framework to evaluate all waste management processes and systems, including collection, transfer, recycling, source segregation, composting, waste-to-energy, anaerobic digestion, and sanitary landfill. The model also assesses the use of these technologies at different levels of environmental control and emission standards. The model provides a comprehensive and standard method to screen solid waste management alternatives, and ultimately bridge the gap between cost and environmental objectives. The main categories of cost and environmental parameters presented in the model results include annual estimates of cost, energy consumption, air emissions, water pollutants, and solid waste residuals. The model evaluates life-cycle environmental tradeoffs from start-up operations through post-closure emissions, including all air and water emissions, energy requirements, and impacts to the environment, including land use and climate change impacts. The model provides full cost accounting of life-cycle capital, debt, and recurrent expenditures. 1-1 Solid Waste Management Holistic Decision Modeling Final Report The type of data entered to the model is site-specific for each country, and often site-specific for individual cities in each country. The data includes: waste generation for each sector of activity, including households; waste composition for each sector; process specific operation and consumption data for each technology; emission data for each technology, local conditions affecting operations. All mass flows are then identified and quantified. All analysis conducted on a life-cycle basis is translated to present worth, with capacity for sensitivity analysis. This study provides support to the Bank’s ability to conduct client dialogue on solid waste management technology selection, and will contribute to client decision-making. The study was financed by Japanese Country-Tied Trust Funds and was conducted by a combined team of Japanese and US consultants. The Japanese firm of NIPPON KOEI Co., Ltd., was the prime contractor. RTI and the individual consultant, Nancy Cunningham Wilson, were the subcontractors. 1.2 Goals and Scope of the Study The goal of the study was to fully explore the use of the EPA/RTI holistic decision model to study alternative solid waste systems in a wide array of waste management conditions using data collected from cities selected in each region of the world. Alternative scenarios included different arrangements of technical systems, and also included scenarios that optimized energy, global warming emission reduction, and global dimming emission reduction. Target waste was the combined mixture of municipal solid waste identified in each of the selected cities. Target technologies were landfill, composting, materials recovery, and incineration. Collection and transportation systems were included in each scenario analyzed, and different scenarios of separate or mixed collection of various recyclable, combustible or compostable materials were additionally analyzed. 1-2 Solid Waste Management Holistic Decision Modeling Final Report 1.3 Consulting Team Formation The following organization chart shows the consulting team formation for this study. NIPPON KOEI (NK) NK Tokyo Team with NK LAC Mr. Shungo Soeda (Leader) Mr. Satoshi Higashinakagawa Mr. Takahiro Kamishita Mr. Juan Martin Coutoudjian NK UK Team Mr. Mitsuhiro Doya Mr. Pirran Driver Research Triangle Institute (RTI) Individual Consultant Mr. Keith A. Weitz Ms. Nancy Cunningham-Wilson Ms. Alexandra M. Zapata F. Figure 1.3-1 Consulting Team Formation As the prime contractor, NIPPON KOEI (NK) was responsible for fully addressing the Terms of Reference for the study including reconnaissance of solid waste conditions in each of the target cities and collection of all data to be used in the modeling effort. Nancy Cunningham-Wilson worked with various operations staff of the World Bank to facilitate country visits and data access, and she obtained the available pre-reconnaissance background information for the target cities. RTI was responsible for all modeling activities, and for adequately training NK Team on the data requirements of the model. RTI was responsible to provide in-depth output from the model, together with analysis in figures, charts and text, to enable NK to provide a comprehensive analysis of each city investigated. The full team worked together to develop the framework of the scenarios to be modeled and dealt with inconsistencies and calibration of the data, including additional research of literature, including master plans from various cities, to address data gaps or questions. The full team jointly developed the reports to the Bank. All study travel was conducted by two NK field teams that utilized staff from NK Tokyo Headquarters, NK UK and NK LAC (Latin America-Caribbean). Table 1.3-1 shows the demarcation of field visits by various NK staff on the field teams 1-3 Solid Waste Management Holistic Decision Modeling Final Report Table 1.3-1 Responsibility of Field Visit by NK and NK UK Team Kawasaki (Japan) Kathmandu Shanghai Lahore Buenos Aires Sarajevo Conakry Amma n NK S. Soeda X X X X X S. Higashinakagawa X X X X T. Kamishita X X NK-LAC J. Koutoudjian X NK-UK M. Doya X X X P. Driver X X X The consulting team worked under the World Bank direction of Ms. Sandra Cointreau, Solid Waste Management Advisor. The team was regularly supported by technical guidance from Dr. Susan Thorneloe, the US EPA Project Manager who conceived development of the holistic decision support tool and continues to manage its use and continuous improvement, as well as . and continues to manage the public/private partnership use of the holistic decision support tool, as well as Ms. Ozge Kaplan, both from the US EPA Office of Research and Development in Raleigh- Durham, North Carolina. 1.4 Selection of Target Cities Seven cities were selected from the different regions of development countries served by the World Bank. They were: (1) Buenos Aires, Argentina; (2) Conakry Guinea; (3) Shanghai, China; (4) Kathmandu, Nepal; (5) Lahore, Pakistan; (6) Amman, Jordan; and (7) Sarajevo, Bosnia and Herzegovina. These cities presented study conditions where data was expected to be reasonably competent and complete, and where cooperation with the team’s field reconnaissance and data collection efforts were confirmed. They represented a wide array of economic development conditions and costing factors, in addition to their different physical settings and conditions. Some were very low-income and with minimal industrial and commercial activity, while others were on the other end of the spectrum of upper middle income and significant industrial and commercial activity. Some were in cool wet climates; others were from hot dry climates. The diversity of conditions were selected to determine whether and, if so, how these conditions would effect the outcome of the modeling analysis Each of the selected cities is one of the largest within its country. However, a city that is large in Nepal and Guinea, is comparatively small when compared to Argentina and China. Despite the wide range in city size of these selected cities, it is important to note that most cities in developing 1-4 Solid Waste Management Holistic Decision Modeling Final Report countries fall into the category of secondary cities, and are much smaller that these cities. Such secondary cities commonly would have much less industrial and commercial activity than primary cities, and therefore market prices for recyclables could be lower and transport distances to markets could be longer. This aspect of the modeling needs to be weighed when the reader attempts to extrapolate from analysis for these primary cities to secondary cities. In addition to the 7 target cities from developing countries, Kawasaki, Japan and Atlanta, Georgia were selected for comparative purposes. The following table provides some comparative information on the countries wherein the 9 cities are located. For perspective of these countries relative to all other countries in the world, the reader is referred to the annual World Development Reports published by the World Bank, and available at http://www.worldbank.org/[]. Table 1.4-1 Comparative Information on the Countries of the 9 Target Cities %Below Average Life Country Country %Urban %Literacy, GNI, $ per Poverty Expectancy, City, Country, GDP, Population, Population ages 15 and capita, Level, 2005*1 Region $ millions, Billions, in Country, older, 2000- *1 *1 *2 2006 below $1 2006 2006 2004 05*1 Male Female a day*1 Buenos Aires, 214,058 6.6 39 90 5,150 97 71 79 Argentina (2004) Conakry, 3,317 9 36 410 -- 29 54 54 Guinea Shanghai, 9.9 2,668,071 1,312 40 2,010 91 70 74 China (2004) Kathmandu, 24.1 8,052 28 15 290 49 62 63 Nepal (2003-04) Lahore, 17.0 128,830 159 34 770 50 64 65 Pakistan (1998-99) Amman, <2.0 14,176 6 79 2,660 91 71 74 Jordan (2002-03) Sarajevo, Bosnia and 11,296 4 45 2,980 -- 97 72 77 Herzegovina Kawasaki, 4,340,133 128 66 38,410 -- -- 79 86 Japan Atlanta, 13,201,81 299 80 44,970 -- -- 75 81 United States 9 Data Source: *1: World Development Report 2008, Agriculture for Development: http://siteresources.worldbank.org/INTWDR2008/Resources/WDR_00_book.pdf *2: 2006 World Development Indicators: http://siteresources.worldbank.org/DATASTATISTICS/Resources/table3-10.pdf 1-5 Solid Waste Management Holistic Decision Modeling Final Report CHAPTER 2 DATA AND METHODOLOGY 2.1 General Approach Total annual cost, energy consumption, and emissions (air, water) were calculated using the US EPA/RTI Municipal Solid Waste Decision Support Tool (MSW DST). The MSW DST is populated with average facility default data, which has been modified for this study to include site-specific data collected by Nippon Koei for each of the nine cities. Numerous MSW management options were evaluated to identify the options that appear economically and environmentally efficient. The MSW DST employs the principles of Full Cost Accounting (FCA) and Life Cycle Assessment (LCA). FCA is a systematic approach for identifying, quantifying, and reporting the costs of all stages of MSW management from the point of waste collection to its final disposition. It also takes into account past and future outlays, overhead (oversight and support services) costs, and operating costs by process. The cost of some activities is shared between waste management processes. For example, the cost of recycling can be shared by a source-segregated collection system and materials recovery facility (MRF). Understanding the costs of the individual MSW management processes enables understanding the costs of the entire solid waste system, and the relationship between processes. LCA is a type of systems analysis that accounts for the complete set of upstream and downstream (cradle-to-grave) energy and environmental aspects associated with industrial systems. The technique examines the inputs and outputs from every stage of the life cycle from the extraction of raw materials, through manufacturing, distribution, use/reuse, and waste management. In the context of integrated waste management systems, an LCA tracks the energy and environmental aspects associated with all stages of waste management from waste collection, transfer, materials recovery, treatment, and final disposal or reuse. For each waste management process, energy and material inputs and emissions and energy/material outputs are calculated (see Figure 2.1.1). In addition, energy and emissions associated with the manufacturing of the fuels, energy, and material inputs are captured. Likewise, the potential benefits associated with energy and/or materials recovery displacing energy and/or materials production from virgin resources are captured in the life cycle results. Additional information about the MSW DST can be found at: https://webdstmsw.rti.org/resources.htm. Solid Waste Management Holistic Decision Modeling Final Report Energy Materials Energy (power/steam) Solid Waste Waste Management Process Recovered Materials Air Residual Emissions Wastes Water Pollution Figure 2.1-1 Life Cycle Inputs and Outputs of a Waste Management Process (Note: Some materials leaving a waste management process may loop back into the process.) Taking a life-cycle perspective encourages waste management planners to consider the environmental aspects of the entire system including activities that occur outside of the traditional framework of activities from the point of waste collection to final disposal. With these considerations in mind, the MSW DST generates cost, energy consumption, and multimedia (e.g., air and water) emission results that a waste management planner can use to evaluate a given system. The MSW DST can be used in two basic modes: simulation and optimization. Simulation refers to the ability of the MSW DST to model predefined (e.g., current or future planned) waste management systems. Optimization refers to the ability of the MSW DST to identify the “best� waste management system based on a defined objective, such as minimizing cost or minimizing greenhouse gas (GHG) emissions. Cost The cost modeled by the MSW DST is consistent with “full cost accounting� principles. It includes the capital, operating and maintenance, and labor costs over the life of the facilities included in each strategy. Therefore, the cost is not necessarily representative of the price or tip fee charged by any facility. The cost values include all waste management activities from collection through final disposition. For facilities that recover energy and/or materials and sell them to create revenue, this revenue stream is netted out of the total cost. The cost results therefore represent a net annual total cost. It should be noted that for the recycling scenarios, the revenue obtained from the sale of recyclables is dependent on available markets for recyclables. This is important because the revenue stream from the sale of recyclables can significantly lower the net cost of the recycling scenarios. Solid Waste Management Holistic Decision Modeling Final Report Energy Consumption Energy is consumed by all waste management activities (e.g., landfill operations), as well as by the processes to produce energy and material inputs (e.g., diesel fuel, landfill liner) that are included in the analysis. Energy can also be produced by some waste management activities (e.g., landfill gas-to-energy, waste-to-energy technologies) and can be offset or avoided by other activities (e.g., recycling). An additional activity in the holistic model analysis involves assessing the energy required for collection and transport of mixed or source segregated materials. If the energy produced and/or offset by the waste management system is greater than the energy consumed, then there is a net energy savings. Energy consumption is an important parameter in life-cycle studies, because it often drives the results of the study due to the significant amounts of air and water emissions associated with energy production. Therefore, energy consumption is also a good indicator of the emissions of key air pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), particulate material (PM), and sulfur oxides (SOx). Carbon Emissions • Recycling of materials offsets carbon emissions by avoiding the consumption of energy that otherwise would be used in materials production processes. • Production of electrical energy offsets carbon emissions from the generation of electrical energy using fossil fuels in the utility sector. • Diversion of organics from landfills avoids methane gas generation. In this study carbon emissions are reported in units of metric tons of carbon equivalent (MTCE), derived as follows: MTCE = [(Fossil CO2 x 1 + CH4 x 21) x 12/44] / 2200 Solid Waste Management Holistic Decision Modeling Final Report 2.2 Waste Management Scenarios Analyzed In this study, simulation and optimization scenarios were defined and analyzed. The same scenarios were applied to all cities to aid in the identification and understanding of how city- specific factors affect scenario results. It should be noted that all scenarios are hypothetical, meaning that they are not a representation of the actual waste management situation in any of the cities included in the study. The scenarios modeled in this study include the following: Simulation Scenarios: • Group 1 includes base case simulation scenarios consist of 2 scenarios, one sending all the waste to an open dump and the other open burning. • Group 2 includes nine different scenarios, each sending all the waste to one primary technology/waste management process. Optimization Scenarios: • Group 3 optimization scenarios are set to maximize the amount of material recovered (or diverted from landfill disposal) using non-incineration processes. • Group 4 consists of maximizing the energy recovered. • Group 5 seeks to minimize climate change related emissions including carbon (global warming) and PM (global dimming) emissions. For the optimization scenarios, variations in the type of MRF and Composting facility (manual sorting and pile turning vs. mechanical sorting and pile turning), the frequency of waste collection (daily vs. biweekly), and the percent of recyclables recovered (high capture vs. low capture) were considered for each of the optimization scenarios to gauge their impact on the LCI results. Table 2.2.1 presents additional detail about the selected scenarios and each of the waste management processes (collection, recycling, composting, incineration with and without energy recovery, and landfill disposal) considered in each scenario. The waste management scenarios assume that the processes employ modern designed facilities and equipment that meet U.S. and EU design and operation requirements. Other key assumptions include: • Unless otherwise specified, all residual waste is disposed of in a landfill with gas flaring and 70% gas collection efficiency. • Both segregated and non-segregated collection systems were considered in the optimization scenarios. Holistic Decision Modeling Solid Waste Management Table 2.2-1 Waste Management Scenarios Analyzed Non Scenario Segregated Landfill Scenario Segregated Recycling Composting Incineration Variations Collection** Disposal Collection* SIMULATION SCENARIOSIOS Group 1: Open dumping X X Base Case Open burning X X Recycling - manual sort X X Recycling - mechanical sort X X Composting - manual turning X X Group 2: Composting – mechanical X X Mixed Waste Collection windrow turner And Management Using Incineration X X One Primary Incineration with energy Technology X X recovery Landfill - vent X X 2-5 Landfill - flare X X Landfill- energy recovery X X OPTIMIZATION SCENARIOS Daily - High X X X X Capture Daily - Low X X X X Capture Biweekly Manual MRF and Compost Collection - X X X X High Capture Group 3: Biweekly Maximize Materials Collection - X X X X Recovery (via recycling Low Capture and composting) Daily - High X X X X Capture Daily - Low Mechanical MRF and X X X X Capture Compost Final Report Biweekly Collection - X X X X High Capture Holistic Decision Modeling Solid Waste Management Non Scenario Segregated Landfill Scenario Segregated Recycling Composting Incineration Variations Collection** Disposal Collection* Biweekly Collection- Low X X X X Capture Daily - High X X X X X Capture Daily - Low X X X X X Capture Run DST in Optimization Group 4: Mode to Minimize Energy Maximize Energy Biweekly Consumption (non- Recovery Collection - X X X X X incineration options only) High Capture Biweekly Collection - X X X X X Low Capture Daily - High X X X X X Capture Daily - Low X X X X X 2-6 Capture Minimize Carbon (Global Warming) Emissions Biweekly Collection - X X X X X High Capture Biweekly Collection - X X X X X Group 5: Low Capture Optimize Reduction of Global Warming and Dimming Emissions Daily - High X X X X Capture Daily - Low X X X X Capture Minimize PM (Global Dimming ) Emissions Biweekly Collection - X X X X High Capture Biweekly Collection - X X X X Final Report Low Capture *Non- segregated collection: mixed waste collection only **Segregatedcollection:separaterecyclables,organics,andresidualscollection. Solid Waste Management Holistic Decision Modeling Final Report • Both yard waste and mixed MSW composting were considered. • The incineration facility modeled is assumed to be a modern mass burn type of facility with 17,500 BTU/kWh heat rate and 70% ferrous recovery rate from ash. EU Incinerator emissions standards were used to estimate facility emissions. • The landfill facility modeled is a modern U.S.EPA Subtitle D type landfill with 100-year as the time period for calculating emissions. For scenarios that included gas collection, an assumed gas collection efficiency of 70% was used. • Daily waste collection is assumed to be 6 times/week. • Biweekly waste collection is assumed to be 2 times/week. • High capture is defined by 75% participation factor1, 75% capture rate2, and 75% separation efficiency of mixed waste at the MRF. Low capture is defined by 50% participation factor, 50% capture rate, and 55% separation efficiency. Unless otherwise specified low capture settings were used. Scenario Analysis Boundaries The boundaries of the scenarios included in this study are largely defined by the waste management processes included under each scenario and the mass flow among processes (see Figures 2.2.1 to 2.2.4). In addition, the boundaries considered for the cost analysis are different from those of the life cycle environmental (energy and emissions) assessment as mentioned below. Boundaries for Cost Analysis Costs have also included in this study because they play such a crucial role in making decisions about MSW management strategies. The system boundaries for the cost analysis differ from that of the life cycle environmental assessment because they are designed to provide a relative comparison of annual cost among alternative MSW management strategies as incurred by the public sector (e.g., municipal government). These costs are intended to provide a relative ranking of the different alternatives as part of a screening tool to narrow the range of options associated with integrated MSW management. No distinction is made between public and private sector costs. All MSW management activities are assumed to occur in the public sector and therefore costs are calculated as though they are accruing to the public sector. The cost analysis is intended to reflect the full costs associated with waste management alternatives based on U.S. EPA guidance from Full Cost Accounting for Municipal Solid Waste Management: A Handbook (U.S. EPA, 1997). 1 The participation factor indicates the average percentage of households that set out recycling bins for each collection cycle within a region in a source-segregated recyclables collection program. 2 The capture rate is the fraction of recyclable material removed by households from the waste and put in the recyclables collection bin. 2-7 Solid Waste Management Holistic Decision Modeling Final Report In focusing the cost analysis on publicly accrued costs, the costs associated with electricity production, for instance, are not included in the study because the public sector only pays the price for electricity consumed. In cases where recyclables are shipped from a MRF, the cost analysis ends where the public sector receives revenue (or incurs a cost) in exchange for the recyclables. The cost analysis does not include the costs associated with the remanufacturing processes for different materials (e.g., recycled office paper). These costs are borne by the manufacturing sector and not by municipal or county governments. The same procedure is applied to the generation and sale of electricity derived from incineration facilities or landfills. Where waste is produced as part of a waste management facility, the cost of waste disposal or treatment is included in the cost analysis of that facility. For example, we include the cost of leachate treatment in our cost analysis of landfills. We also include the cost of training, educational, or other materials associated with source reduction or other aspects of MSW management. In addition, we compare the landfill cost with and without the land price in the cost analysis because usually the government does not pay for land in case the land is the government land. Cost parameters and results are allocated to individual MSW components. Thus, the result of the cost analysis can illustrate, for example, the capital and operating costs attributed to a MRF versus a composting facility. Boundaries for Environmental Assessment The boundaries for life cycle environmental assessment include all activities that have a bearing on the management of MSW, from collection through transportation, recovery and separation of materials, treatment, and disposal. Collection begins whenever and wherever waste is put outside the generation source for removal by the municipality or private collectors. As observed in Figures 2.2.1 to 2.2.4, we assume that MSW enters the system boundaries when it is set out for collection; thus, the production of garbage bags, garbage cans, and recycling bins were not included in the study. Similarly, the transport of waste by residents to a collection point (for example, drop-off facility) was not included. The functional elements of MSW management include numerous pieces of capital equipment, from refuse collection vehicles to balers for recycled materials to major equipment at combustion facilities. Resource and energy consumption and environmental releases associated with the operation of equipment and facilities were included in the study. For example, energy (fuel) consumed during the operation of waste collection vehicles was included in the study. We included in the study electricity consumed for operation of the office through which the vehicle routes are developed and the collection workers are supervised. Activities associated with the fabrication of capital equipment, however, were not included. For material and energy inputs to various processes, the resource and energy consumption and environmental releases associated with producing the material and energy inputs were 2-8 Solid Waste Management Holistic Decision Modeling Final Report included in the study. For example, the resources and environmental releases associated with the production of diesel fuel consumed by collection vehicles were included. Where a material was recovered and recycled, the resource and energy consumption and environmental releases associated with the manufacture of a new product were calculated and included in the study. We assumed closed-loop recycling processes. These parameters were then compared against parameters for manufacturing the product using virgin resources to estimate net resource and energy consumption and environmental releases. This procedure was also applied to energy recovery from other unit processes, including incineration with energy recovery and landfill gas recovery projects. Another system boundary was set at the waste treatment and disposal. Where liquid wastes are generated and require treatment (usually in a publicly owned treatment works), the resource and energy consumption and environmental releases associated with the treatment process were included. For example, if BOD is treated in an aerobic biological wastewater treatment facility, then energy is consumed to supply adequate oxygen for waste treatment. If a solid waste is produced that requires burial, energy is consumed in the transport of that waste to a landfill during its burial (for example, bulldozer) and after its burial (for example, gas collection and leachate treatment systems) in the landfill. Also, where compost was applied to the land, volatile and leachate emissions were included in the study. Similar to the cost analysis, environmental parameters such as carbon, PM and other air emissions as well as water emissions are also allocated to individual MSW components. Thus, the result of the life cycle environmental assessment can illustrate, for example, the environmental aspects of recycling versus composting of paper waste. MSW Open Dumping Open Burning MSW Figure 2.2-1 Boundaries for Group 1- Base Case 2-9 Solid Waste Management Holistic Decision Modeling Final Report Ferrous to Remanufacturin Recyclables to Mixed Combustion Remanufacturing Waste Facility Mixed Waste Mixed Waste MRF Ash to a Landfill Collection Residuals to LF Using Gas Collection and Recycling Flaring Incineration With and Without Energy Recovery Residuals to LF Using Gas Mixed Waste Collection and Mixed Waste Composting Flaring Collection Facility Compost Product Mixed LF Waste Composting Disposal at a Landfill Figure 2.2-2 Boundaries for Group 2 Scenarios Mixed Waste and Commingled Recyclables to Waste MRF Remanufacturing Commingled Waste Collection Mixed Waste Compost Product Mixed Waste and Yard Waste Composting Residuals to LF Using Gas Facilities Collection and Flaring Figure 2.2-3 Boundaries for Group 3 Scenarios Commingled Recyclables to Waste MRF Remanufacturing Mixed Waste Commingled and Yard Waste Waste Composting Compost Product Collection Facilities Yard waste and mixed waste residuals Ferrous to Remanufacturing Incineration Facility Ash to Landfill Figure 2.2-4 Boundaries for Groups 4 and 5 Scenarios 2-10 Solid Waste Management Holistic Decision Modeling Final Report 2.3 Data Collection and Key City Characteristics 2.3.1 Data Requirements To conduct Solid Waste Management Holistic Decision Modelling (SWM HDM) calculation reflecting the actual conditions of SWM in target cities of this study, it was required to collect city-specific data, as well as some regional contextual data on economic, social and natural environmental condition s. This supplemented the model’s extensive default data (e.g., defaults on calorific values for each type of waste constituent, and defaults on the emissions from processing of each type of waste constituent) . As emissions data is continuously developed through the monitoring and research of the US EPA, the defaults on emissions are routinely updated. In addition to the city-specific input data, the SWM system within each country was fully described through actual field reconnaissance and interviews conducted in each city by the Nippon Koei team. As needed, the SWM HDM data input sheets needed to be modified to address unique solid waste system situations in the target cities. For example, barge transfer needed to be added to address the unique transfer system used in Shanghai, China. Additionally, systems of informal sector recycling and use of carts for primary collection are unique to many cities in developing countries. 2.3.2 Site Visits to Collect Data For the requirement of data collection and reconnaissance of the solid waste systems in the target cities, as described in 2.3.1, the consultant team visited in each target city on the following schedule. Schedule Visited organization and facility Amman, Jordan Nov. 14 – Nov. (1) Organization 26, 2006 GAM (Greater Amman Municipality), Jordan Biogas Co, Arab Paper Converting & Trading Co, Jordan Paper and Cardboard Factories Co, Friends of Environment Society, Ministry of Environment, Ministry of Municipal Affairs, Ministry of Health, Ministry of Planning and International Cooperation, UNHABITAT (2) Facilities Al Ghabawy Landfill, Al Rusaifah Landfill and Biogas facility, Al Sha’er TS, Al Yarmook TS, Ain Gazal TS, Slaughterhouse, Atlanta, - � Georgia, USA 2-11 Solid Waste Management Holistic Decision Modeling Final Report Schedule Visited organization and facility Buenos Aires, Sep. 24 – Oct. (1) Organization Argentina 6, 2006 CBA, WB, ARS (Solid Waste Association), CEAMSE (Metropolitan Area Ecologic Coordination Society), AIDIS (Inter-american Association of Sanitary Engineers), UBA, Cliba, IATASA, El Ceibo (2) Facilities Pompeya T/S, Relleno Sanitario Norte III, Relleno Sanitario Villa Dominico Conakry, Dec. 6 – Dec. (1) Organization Guinea 18, 2006 SPTD (Service Public de Transfert des Dechets), PDU3 (3rd Urban development Project), Guinéenne d'Assainissement (SME), Electricité de Guinée, Ministry of Public Health (2) Facilities La Minière Landfill, Transfer points, Pilot compost, Slaughterhouse Kathmandu, Dec. 11 – Dec. (1) Organization Nepal 23, 2006 SWMRMC, KMC, LSMC, BKM, MTM, KRM, SchEMS, Watsan (2) Facilities Teku T/S, Sisdol Landfill, BKM’s Segregated Collection and Compost Kawasaki, Oct 2006 – Feb (1) Organization Japan 2007 Environmental Dept. of Kawasaki City (2) Facilities Kase Transfer Station, Sikine Incinerator, Ukisima Final Disposal Site (sea reclamation), Ukisima Incinerator, Nanbu MRF, Shikine MRF Lahore, Nov. 25 – Dec. (1) Organization Pakistan 10, 2006 The Urban Unit, CDGL, University of Engineering and Technology (2) Facilities Mahamood Booti LF, Saggian D/S, Nashitar D/S, CDGL’s workshops, Lahore Composting, Waste Busters, Children Hospital Sarajevo, Sep. 26 – Oct. (1) Organization Bosnia And 9, 2006 Cantonal Public Utility Rad, Cantonal Public Utility Herzegovina Park, Papir Servis, Ministry of Environment and urban development (2) Facilities Smiljevići Landfill (& MRF), Papir Servis, Hospital, Slaughterhouse 2-12 Solid Waste Management Holistic Decision Modeling Final Report Schedule Visited organization and facility Shanghai, Oct. 26 – Nov. (1) Organization China 13, Dec. 18 – SCEID, SIDREE, SCAESAB, Shanghai 22. 2006 Electric Power Design Institute, Tongji University (2) Facilities Huangpu Transfer Station, Jiangqiao incinerator, Yangpu Transfer Station, Huling Dock, Gacu dumping site, Minghan dumping site, Laogang Landfill Site Each consultant field reconnaissance team consisted of 2 members and spent at last two weeks in each target city. The site visit were successfully implemented with the support of relevant government organizations, facilitated by the World Bank. 2-13 Solid Waste Management Holistic Decision Modeling Final Report 2.3.3 Key City Characteristics The key city characteristics of the selected cities are summarized as follows. Basic size, population and economic information is provided, as well as a description of the solid waste system in each city. Annex [] provides a summary of solid waste systems, together with illustrative photos. Amman, Jordan Amman is the capital city of Jordan and its area is 688 km2, located in the northern part of Jordan. Amman’s population is approximately 2,125,000 and rapidly has been increasing over the past years, partly due to the large influx of refugees that have arrived from the surrounding countries. The GNI per capita is approximately US$2,500 in 2005. The climate of Amman is characterized by sharp seasonal variations in both precipitation and temperature. Its rainy season is in winter and the annual precipitation is about 230 mm. the average winter temperature is above 7°C and the average summer temperature is 26°C. Its unit generation rate of MSW is 1.02 kg/person/day, means about 2,174 tonnes of MSW are generated every day from the city. The hilly nature of the city makes collection of MSW a difficult task, as many areas of town have roads that are too steep, narrow or winding for conventional collection vehicles to use. The majority of Amman’s MSW is disposed at Al Rusaifah landfill site which has landfill gas collection as shown in Figure 2.3-1. Figure 2.3-1 Simplified Waste Flow in Amman Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-2 shows typical composition of municipal solid waste in Amman that includes 42% of food waste, 11% of corrugated cardboard, 9% of paper and 16% of plastic waste. 2-14 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-2 Waste Composition in Amman 2% 2% 2% 14% 2% 42% 16% 9% 11% Yard Trimmings, Leaves Food Waste Corrugated Cardboard Mixed Paper Plastic Glass Metal Textile and Rubber Others Atlanta, Georgia, USA Atlanta city which was selected as a representative US city has the area of 343 km2, located in the southern part of America. The population of the city roughly is 442,000 and is rapidly increasing in the northern portions in Metro Atlanta. A large number of residents commute to this area and commercial and industry activity is concentrating in this area and GNPper capita is approximately US$43,740. Atlanta has a humid subtropical climate, with hot, humid summers and mild winters by the standards of the United States. The annual precipitation is about 1,270 mm and the average temperature is approximately 16°C. Rear-loading compactor trucks are utilized for single family waste collection and front- loading compactor trucks are utilized for the collection from multi-family, commercial and institutional sectors. The average unit generation rate of MSW is 1.72 kg/person/day, which means that about 2,174 tonnes of MSW are generated every day. The landfill site in Metro Atlanta was closed in 2004 and the city began taking the waste to two existing transfer stations in metro Atlanta. Transfer stations accept the waste from primary collection vehicles and then load it on to secondary transportation vehicles that take it to landfills outside of metro Atlanta. Figure 2.3-3 shows simplified waste flow in which people can understand no incinerator operated in Atlanta. 2-15 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-3 Simplified Waste Flow in Atlanta Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-4 shows typical composition of municipal solid waste in Atlanta that includes 41% of food waste, 7% of yard trimmings, 4% of paper, 6% of metals and 15% of plastic waste. Figure 2.3-4 Waste Composition in Atlanta 15% 7% 12% 4% 41% 6% 15% Yard Trimmings Paper Plastic Metals Glass Food Waste Miscellaneous 2-16 Solid Waste Management Holistic Decision Modeling Final Report Buenos Aires, Argentina The City of Buenos Aires (CBA) is the capital city of Argentina and its area is 203 km2. It is located on the southern shore of the Río de la Plata, on the southeastern coast of the South American continent. The population in 2001 was 2,776,138 and has been increased around 3 millions. GNP per capita in Argentina was US$4,470 in 2005, and income level in in Buenos Aires would be higher. The climate of Buenos Aires is characterized by South Temperate Zone, "Humid Subtropical", with average temperatures ranging from 35°C in January to 10°C in July, 17°C annually. Annual precipitation is about 1,200 mm and the heaviest rain falls during the winter months, though rain can be expected at any time of year. The unit generation rate of MSW is 0.979 kg/person/day, means about 4,300 tonnes of MSW are generated every day from the city. Almost all MSW is collected and transported to a designated landfill via three transfer stations or directly. Buenos Aires is divided into 6 zones for waste management and three private haulers are contracted for the primary collection at 5 of those zones. Small scale composting and MRFs are operated in or around the landfill site. Figure 2.3-5 shows simplified waste flow. There is no incinerator operated in Buenos Aires. Figure 2.3-5 Simplified Waste Flow in Buenos Aires Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-6 shows typical composition of municipal solid waste in Buenos Aires that includes 35% of food waste, 24% of paper, and 14% of plastic waste. 2-17 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-6 Waste Composition in Buenos Aires 6% 1% 4% 5% 1% 3% 2% 4% 24% 35% 14% 1% Yard Trimmings, Leaves Woods Paper Plastic Rubber, Leather Food Waste Diapers Metal Glass Demolition Hazardous Misc. Conakry, Guinea The Conakry is the capital of Guinea and includes five municipalities and is located on a peninsular of 308 km2. The population is estimated at over 2 million in 2006. The population has risen to its present level extremely rapidly; in 1958 the city had only 120,000. The rapid rise in population in such a physically restricted area has resulted in extreme pressure on the city’s infrastructure and housing. Most people live in very poor and overcrowded makeshift housing, few have access to safe water and power (around 60% and 20% respectively). There is significant evidence of inadequate solid waste collection, with uncollected waste throughout the city. Guinea’s GNP per capita is US$370, evidence that it is one of the poorest countries in Africa. The climate of Conakry is tropical with the an annual average temperature of 27°C。The rainy season usually lasts from April or May to October or November. The total generation of MSW is approximately 800 tonnes/day and the unit generation rate is approximately 0.4 kg/person/day. The generated waste is transported to La Miniere disposal site through transfer stations or by direct haul. Much of the uncollected waste is burned openly or dumped illegally. Figure 2.3-7 shows simplified waste flow in Conakry. Informal sector involves collecting recyclables from the waste. Pilot scale composting plant is operated to produce the compost from the kitchen waste. 2-18 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-7 Simplified Waste Flow in Conakry Collection Composting Ash MSW Recycling Incineration* (pilot) Landfill Landfill (informal) Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-8 shows typical composition of municipal solid waste in Conakry that includes 40% of food waste, 19% of paper, 12% of textile/Rubber 8% of yard waste and 5% of plastic waste. Figure 2.3-8 Waste Composition in Conakry 5% 7% 8% 12% 1% 3% 40% 5% 19% Yard Waste Food Waste Paper Plastics Metal Glass Textile & Rubber Leather & Animal Manure Others 2-19 Solid Waste Management Holistic Decision Modeling Final Report Kathmandu, Nepal Kathmandu Valley has five municipalities including Kathmandu Municipality, which is the capital of Nepal. The area of the valley is 580km2. The Kathmandu Valley is located at an average attitude of 1,350m above sea level and surrounded by mountains. The total population of five municipalities is 1,099,158 in 2004. The economy in Kathmandu depends on the tourism and the GNI per capita is US$270 in 2005. The climate of Kathmandu Valley is temperature monsoon type with rainy season lasting from June to September. The annual precipitation is 2,000 mm. The temperature is 1°C in winter to 25°C in summer on average and approximately 13°C on average. The total waste generation of Kathmandu’s five municipalities is 435 tonnes/day and the generation ratio is approximately 0.4 kg/person/day in five municipalities on average. Traditionally, much of the generated waste has been dumped on the bank’s of Bagmati River. Sisdol sanitary landfill site commenced operation in 2006 and waste is transported there through the Teku transfer station. Because of the relatively high composition of organic materials in the waste, a variety of small scale composting activities have been tried in the Kathmandu Valley. There are also a number of small scale recycling activities. Figure 2.3-9 shows simplified waste flow in Kathmandu. Small scale composting plant is operated to produce the compost from the kitchen waste. Figure 2.3-9 Simplified Waste Flow in Kathmandu Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-10 shows typical composition of municipal solid waste in kahtmandu that includes 71% of food waste, 12% of paper and 8% of plastic waste. 2-20 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-10 Waste Composition in Kathmandu 0% 1% 2% 0% 3% 12% 8% 3% 0% 71% Yard Trimmings, Leaves Paper Plastic Textile Rubber, Leather Food Waste Metal Glass Ceramics Misc. Kawasaki, Japan Kawasaki city in Kanagawa prefecture, Japan, is located between the Capital Tokyo and Yokohama that is the biggest population city in Japan. The reclaimed land beside Tokyo bay is occupied by heavy chemical industry complexes at the centre of the Keihin Industrial Area. The eastern end of the area is flat, and much of it consists of heavily industrialized and densely built working-class areas. In contrast, its western suburbs occupy an area of hills known as Tama hills and are mostly pleasant, often newly developed residential areas for people commuting to metropolis Tokyo area. As of 2006, the population of the city is 1,330,309 and the density is 9,216 persons/ km² and the total area is 144.35 km². Kawasaki city is located in the temperate humid climate area. Climate monitoring at Yokohama local observing station located close to Kawasaki city shows that precipitation was 1,932 mm/year in 2005, and average temperature was around 17°C. Residential waste generation was 0.7 kg/person/day in 2005. Waste generation rate of MSW including commercial waste was 1.01 kg/person/day in 2005, and the total generation amount is 1,399 tonnes/day. A part of municipal waste is transported by train, general and bulky waste, resource material and residual ash from incinerators which is generated in the northern part of Kawasaki city. The difficulty of suitable site for landfill causes the historical promotion of incineration as well as other cities in Japan as shown in Figure 2.3-11. 2-21 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-11 Simplified Waste Flow in Kawasaki Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-12 shows typical composition of municipal solid waste in kawasaki that includes 36% of food waste, 33% of paper and 14% of plastic waste. Figure 2.3-12 Waste Composition in Kawasaki 1% 4% 3% 33% 36% 5% 4% 14% Wood and leaves Paper Plastic Metals Glass Food Waste Fabric Misc. 2-22 Solid Waste Management Holistic Decision Modeling Final Report Lahore, Pakistan The Republic of Pakistan is in the South Asia region and is bordered by India to the east, China to the north east, and Iraq to the west. Lahore is a historical city, is the second largest city in the country, and is capital of the province of Punjab, located in the north-east of Pakistan. The total area is 1,772 km². The total population of the city including 9 districts is approximately 8,000,000 in 2006. Lahore city and near the area is the most fertile area of Pakistan and chief producer of agricultural products for the country as well as software producing, tourism and handmade manufacturing. The climate of Lahore is semi arid and the temperature is approximately 20°C on average and annual precipitation is around 500 mm/year. The GNI of the Republic of Pakistan in 2005 was roughly US$690/person. The total amount of generation is 5,200 tonnes/day and the unit generation ratio of solid waste is approximately 0.65 kg/person/day on average in Lahore city. In the collection and transportation system, arm roll vehicle, tipper and dump truck is utilized as well as handcarts for primary collection. The main treatment and disposal method is landfilling; and there is composting by the city near existing landfill site, as well as composting plant by NGO combined with materials recovery. Figure 2.3-13 shows waste flow in Lahore. Figure 2.3-13 Simplified Waste Flow in Lahore Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-14 shows typical composition of municipal solid waste in Lahore that includes 28% of food waste, 19% of yard wastes, 4% of paper, 9% of plastic waste and 25% of dust, dirt and others. 2-23 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-14 Waste Composition in Lahore 0% 25% 28% 0% 1% 19% 2% 9% 4% 1% 11% Food / Kitchen Leaves & Grass, Straw & Wood Paper Plastic & Rubber & Polyethylene Bags Clothes/ Rags Bones Animal Waste Glass Metal Dust, Dirt, Ashes, Stones, Bricks, etc Other Sarajevo, Bosnia And Herzegovina Sarajevo Canton is the capital of Bosnia and Herzegovina, which constitutes 9 municipalities, located between the surrounding mountains and the area is 1,227 km2. The Center Sarajevo Municipality, the oldest part of Sarajevo, has a population of about 410,000. Sarajevo’s economy is largely based on secondary and tertiary industries. Its manufacturing activities include production of foods, beverages, tobacco, textiles furniture, automobiles, pharmaceuticals and metal working. The service sectors include tourism, communications, banking and public administrations. The GNP per capita is approximately US$2,440. The climate of Sarajevo is continental climate and the average year-round temperature is 10°C. Summers are warm, with temperatures of over 30°C not being uncommon. The temperatures in winter go down to below minus 10°C and the annual precipitation is around 900 mm. Despite this common trend for the capital city to produce, on average, more waste per capita than in rural areas, it was noticed during the fieldwork that per capita generation rates calculated on the basis of the amount of waste landfilled were higher than expected, and were far higher than other comparable capital cities in the region, equaling the US average of 1.2 kg/person/day, which is rough estimation based on population data and on total mass of waste delivered to the landfill. The total generation amount is 492 tonnes/day. The vast majority of Sarajevo’s municipal and commercial solid waste ends up at landfill site as shown in Figure 2.3-15. 2-24 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-15 Simplified Waste Flow in Sarajevo Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. Figure 2.3-16 shows typical composition of municipal solid waste in Sarajevo that includes 37% of food waste, 17% of paper, 13% of plastic waste and 18% of miscellaneous waste such as dust and sand. Figure 2.3-16 Waste Composition in Sarajevo 18% 1% 37% 8% 6% 13% 17% Yard Trimmings, Leaves Food Waste Paper Plastics Metal Glass Misc. 2-25 Solid Waste Management Holistic Decision Modeling Final Report Shanghai, China Shanghai city is located in east coast of China and is one of most urbanized cities in China. The area is 6,340km2. It has 19 districts which are divided into three categories which are urban including Pu Dong district, suburban and outskirt area. Geographically, the area is flat and there are some water courses such as canals or Huang Pu River, which is a tributary of Yangtze River. The total population is approximately 17,800,000 not including up to three million visitors commonly present in the city. The GDP of Shanghai alone grew 11.1 percent to over US$109 billion in 2005, accounting for over five percent of China's total output. GNI per capita in Shanghai is US$1,740. Shanghai city belongs to typical subtropical monsoon climate. The temperature is from 5°C in winter to 25°C in summer and the average is about 15°C. The rainfall is 1,440 mm/year on average. Shanghai is the industrial, financial, and commercial center of China. It hosts a concentration of manufacturing activities as well as commercial or tourism sectors. The total amount of generation is 17,000 tonnes/day and the unit generation ratio of solid waste is approximately 0.96 kg/person/day on average in Shanghai city. The collection and transportation is variously based on the areas. The area far from existing landfill site, the collected waste is transported by a barge or secondary transfer vehicles through a transfer station. As shown in Figure 2.3-17, the treatment and disposal method is mainly landfilling but incinerators have been introduced as well as composting facilities including separating system. Figure 2.3-17 Simplified Waste Flow in Shanghai Collection Composting MSW Incineration* Ash Recycling Landfill Landfill Compost Product Remanufacturing End Use Facility * Incineration can be with or without energy recovery. 2-26 Solid Waste Management Holistic Decision Modeling Final Report Figure 2.3-18 shows typical composition of municipal solid waste in Shanghai that includes 63% of food waste, 8% of paper and 19% of plastic waste. Figure 2.3-18 Waste Composition in Shanghai 2% 0% 3% 0% 3% 2% 8% 19% 63% Kitchen and Fruits Plastic Papers Cloth Bamboo & wood Metal Glass Slag & stones Hazardous and others 2-27 Solid Waste Management Holistic Decision Modeling Final Report 2.4 Data Analysis 2.4.1 Review of Other Sources of Existing Solid Waste Data MSW DST was originally developed based on the waste compositions, solid waste management systems and operating conditions in the US. While solid waste compositions in developing countries are different, each separate constituent (paper, plastic, metal, glass, etc.) behaves the same way when processed developing countries as it does in the US. Reliable waste composition data is not well available in developing countries, and most cities do not have complete weight records of all wastes being handled. Some of the target cities had good data, from actual weighbridge recordings or from recent surveys done in master plans. But, overall, data was poor. In order to fill data gaps and determine whether the actual data collected was reasonable, the published literature was examined for regional data and also data from cities of comparable economic levels. The studies that were reviewed for the most comprehensive waste quantity and composition data were prepared by: • JICA: Japan International Cooperation Agency • METAP: Mediterranean Environmental Technical Assistance Program • PAHO: Pan American Health Organization 2.4.2 Review of JICA Study Reports The government of Japan has carried out many major planning efforts for cities in developing countries. Most of these were city-wide master plans. These studies typically lasted 18-24 months and included extensive field efforts to survey, sample, and analyze waste quantity and compostion in a wide range of neighborhoods of various income levels and economic activities. In addition to JICA master planning studies, other studies named “basic design study� also conducted by JICA were also reviewed. Most of these various studies are available in the JICA library, and many are posted on the JICA website at : [] As Table 2.4.1 shows, more than 20 JICA studies were completed after 1995 over the world. While there were other studies in the previous two decades, it was decided to focus on those done since 1995. 2-28 Solid Waste Management Holistic Decision Modeling Final Report Table 2.4-1 List of JICA Development Study for Solid Waste Management Project Name Year Target of Solid Waste 1 The Study on the Improvmente of the 1995.05 domestic waste, market waste, X Solid Waste Manegment System for commercial waste, road cleaning the City of Managua in Nicaragua waste and public institution waste 2 The Study on the Solid Waste 1995.12 municipal waste, medical waste, X Management System for Bucharest and construction waste Municipality in Rumania 3 The Study on Wastewater and Solid 1996.03 Domestic waste, commercial X Waste Management for the City of waste, factory waste, hospital Ujung Pandang in the Republic of waste, road and drain waste Indonesia 4 The study on the National Guidelines 1996.08 Municipal waste, industrial waste X for Solid Saste Management for the and medical waste Kingdom of Morocco 5 The Study on the Solid Waste 1997.09 Domestic waste, commercial X Management for Dar es Salaam City waste, market waste and road in Tanzania cleaning waste 6 The Study on Solid Waste 1998.08 Domestic waste, market waste, X Management in Nairobi City in the commercial waste and road waste Republic of Kenya 7 The study on Solid Waste 1999.03 Domestic waste, commercial X Management for Metro Manila in the waste, market waste, road Republic of Philippines cleaning waste medical waste and industrial waste 8 The Study on Solid Waste 1999.03 Domestic waste, commercial Management of the Urban Area of waste, market waste and road Tegucigalpa's Central District in cleaning waste Honduras 9 The Study on Solid Waste 1999.05 Domestic waste, commercial X Management for Mexico City in the waste, market waste, road United Mexican States cleaning waste and medical waste 10 The Study on Solid Waste 1999.05 Domestic waste, commercial Management for Male' City in the waste, market waste, public area Republic of Maldives waste, industrial and medical waste 11 The Study on Regional Solid Waste 2000.01 Domestic waste, commercial X Management for Adana-Mersin in waste, market waste, road the Republic of Turkey cleaning waste and medical waste 12 The Study on solid Waste 2000.01 X Management for Almaty City in the Republic of Kazakhstan 13 The study on environmental 2000,7 Domestic Waste, Medical Waste, X improvement for Hanoi City in the Industrial Waste Socialist Republic of Vietnam 14 The Study on Regional Solid Waste 2000.11 Domestic waste, commercial Management for San Salvador waste, institution waste, road Metropolitan Area in the Republic cleaning waste and medical waste of El Salvador 2-29 Solid Waste Management Holistic Decision Modeling Final Report Project Name Year Target of Solid Waste 15 The Study on sanitation Improvement 2001.12 Domestic waste, industrial waste X for the Niamey City in the Republic and medical waste of Niger 16 The Study on Solid Waste 2002.01 Domestic waste, commercial X Management at Local Cities in the waste, park and road waste, Syrian Arab Republic medical waste and industrial waste 17 The Study on Solid Waste 2003.03 Domestic waste, commercial X Management Plan for Municipality of waste, market waste, road Panama in the Republic of Panama cleaning waste and public institution waste 18 The Study on Improvement of Solid 2003.11 domestic waste, commercial X Waste Management in Secondary waste, and public institution waste Cities in Sri Lanka 19 The Study on the Solid Waste 2005.03 Municipal waste (sludge, industrial Management for the Phnom Penh in and medical waste) Cambodia 20 The study on the solid waste 2005.03 Domestic waste, industrial and management in Dhaka City in medical waste Bangladesh 21 The Study on Solid Waste 2005.09 Domestic waste Management for Kathmandu Valley in the Kingdom of Nepal 22 The Study on Integrated Management 2006.09 Domestic waste Plan of Municipal Solid Waste in Havana City in the Republic of Cuba The result of comparison between the data of JICA past studies and data collected from the target cities for MSW HDM are summarized as follows; • Unit Generation Rate (kg/person/day) vs. Economic Indicator (GDP or GNI) • Ditto, by regional areas • Composition Rate for Compostable materials* vs. Economic Indicator (GDP or GNI) * Compostable: kitchen waste and yard waste (wood and grass) • Ditto, by regional areas • Composition Rate for Combustible materials** vs. Economic Indicator (GDP or GNI) ** Combustible: compostable waste, paper and plastics • Ditto, by regional areas • Composition Rate for Recyclable materials*** vs. Economic Indicator (GDP or GNI) *** Recyclable: paper, plastic, metals and glass • Ditto, by regional areas 2-30 Solid Waste Management Holistic Decision Modeling Final Report Comparisons between the unit generation rate and economic indicator are shown in Figure 2.4.1 and 2.4.2 and other results are shown in Appendix 2.4.1 to 6. Comparing those data, it could be said the data obtained from the field survey at each target city for MSW HDM was within the range representing the regions with similar geographical, metrological and economic conditions. 2-31 2-32 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Tanzania/Dar Es Salaam (39 wards)(1996) Niger/Niamey(2001) Low Laos/Vientian(1991) kg/person/day Kenya/Nairobi(1997) Nepal/Kathmandu(***) Egypt/Alexandria(1984) Guinea/Conakry(***) Vietnam/Hanoi (2000) Nicaragua/Managua(1995) Pakistan/Rawalpindi(1995) Pakistan/Quetta(1996) Indonesia/Jakarta(1986) GDP, GNI or RGDP Indonesia/Surabaya(1992) Egypt/Alexandria(1994) Philippines/Metropolitan Manila(1997) Pakistan/Lahore(***) Sry Lanka/Moratuwa (1997) High Indonesia/Ujungpandang(1994) Guatemala/Metropolitan(1992) Bulgaria/Sofia(1993) Syria/Latakia and other three cities(2001) Syria/Aleppo(1997) Peru/Lima (7 districts in northan areas) (1984) Syria/Damascus(1995) Waste generation (MSW)(kg/person/day) Morocco/Safi (Three urban communes)(1996) Kazakhstan /Almaty(1999) Poland/Poznan(1992) (Sorted by Economic Indicator) Dominica/Santo Domingo(2006) Romania/Bucharest(1994) Peru/Kajayo (1995) Paraguay/Asunción Metropolitan(1994) Malaysia/Penan(1988) Bosnia/Sarajevo(***) Jordan/Amman(***) Panama/Panama(2002) Turkey/Adana metropolitan(1998) GDP or GNI(dollors/person/year) Turkey/Mersin Metropolitan(1998) Hungary/Budapest(1992) Mexico/Mexico city (1998) Argentine/Buenos Aires(***) China/Shanghai(***) 0 Figure 2.4-1 Unit Generation Rate vs. Economic Indicator of Cities of JICA Study and this MSW HDM Study 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 dollors/person/year Final Report Holistic Decision Modeling Solid Waste Management 2-33 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Tanzania/Dar Es Salaam (39 wards)(1996) Niger/Niamey(2001) Africa Kenya/Nairobi(1997) kg/person/day Guinea/Conakry(***) Lao PDR/Vientian(1991) Vietnam/Hanoi (2000) Indonesia/Jakarta(1986) Indonesia/Surabaya(1992) Philippines/Metropolitan Manila(1997) Indonesia/Ujungpandang(1994) East Asia & Pacific Malaysia/Penan(1988) China/Shanghai(***) Bulgaria/Sofia(1993) Kazakhstan/Almaty(1999) Poland/Poznan(1992) Romania/Bucharest(1994) Bosnia/Sarajevo(***) Turkey/Adana metropolitan(1998) Turkey/Mersin Europe and Central Asia Metropolitan(1998) Hungary/Budapest(1992) Nicaragua/Managua(1995) Waste generation (MSW)(kg/person/day) Guatemala/Metropolitan(1992) Peru/Lima (7 districts in northan areas) (1984) Dominica/Santo Domingo(2006) Peru/Kajayo (1995) Paraguay/Asunción Metropolitan(1994) (Sorted by Regional Area) Panama/Panama(2002) Mexico/Mexico city (1998) Latin America and Caribbean Argentine/Buenos Aires(***) Egypt/Alexandria(1984) Egypt/Alexandria(1994) Syria/Latakia and other three cities(2001) Syria/Aleppo(1997) Syria/Damascus(1995) GDP or GNI(dollors/person/year) Morocco/Safi (Three urban communes)(1996) Jordan/Amman(***) Middle East and North Africa Nepal/Kathmandu(***) Pakistan/Rawalpindi(1995) Pakistan/Quetta(1996) South Asia Pakistan/Lahore(***) Sri Lanka/Moratuwa (1997) 0 Figure 2.4-2 Unit Generation Rate vs. Economic Indicator of Cities of JICA Study and this MSW HDM Study 1000 2000 3000 4000 5000 6000 7000 8000 9000 dollors/person/year 10000 Final Report Holistic Decision Modeling Solid Waste Management Solid Waste Management Holistic Decision Modeling Final Report 2.5 Summary of Key Input Parameters, Default Model Data, Assumptions, and Limitations Key Input Parameters and Assumed Data for the Model In any complex modeling exercise, there is a considerable amount of data and assumptions that can significantly affect model outcomes. In this study, the key data and assumptions used include a mixture of city-specific data that was collected through site visits and default data and assumptions that are built into the MSW DST. In this section, key data inputs, default MSW DST data, and assumptions are presented and discussed. The full consulting team met to work together at RTI after the completion of data collection, and the analysis assumptions were discussed and developed as per attached in Appendix 2.5. The World Bank and US EPA participated in this phase of the work, wherein there was agreement on assumptions and how to handle data gaps and the design of scenarios. From RTI, the full team when to the World Bank headquarters and held a workshop with various Bank staff, including many of those who were involved with projects in the target cities. Table 2.5-1 presents a summary of the most important input parameters from those described in Sections 2.3 and 2.4 based on their impact on the cost, energy consumption, and environmental emission results. This table also characterizes the input parameters according to their data sources (i.e., field data or based on assumptions) and provides references to the tables where the specific data can be found. Key assumptions and limitations common to all data are subsequently described. Table 2.5-1 shows how the data inputs were a mix of city specific values and assumptions. For some parameters city specific data were not found and the model used default values to cover the data gaps. Cost data in particular were difficult to obtain. Therefore, cost results may not be an accurate reflection of actual practice in a given city. Table 2.5-2 shows the available cost data for all cities and waste management processes including energy cost data. The blanks in the table indicate lack of data and the zeroes indicate values that were set to zero. Available cost data are carefully reviewed and some of them are modified for the model run. For example, cost for equipment and maintenance for the landfill in Conakry is omitted and operation cost of incinerator in Shanghai is replaced to the US default data. Calculated costs represent average costs for these types of processes and not are specific facility costs. For facilities that recover energy and/or materials and sell them for revenue, as mentioned in Section 2.1, this revenue is netted out of the cost. The cost results therefore represent a net annual total cost. It should be noted that for the recycling scenarios, the revenue obtained from the sale of recyclables is dependent on available markets for recyclables. In addition, as previously mentioned, we prepare two input data, one is with land prices for landfill cost and the other is without those to see how the land price affect the net total cost. 2-34 Solid Waste Management Holistic Decision Modeling Final Report Table 2.5-1 Summary of Key Input Parameters, Assumptions and Data Sources by Process. Input Parameter Data Sources and Assumptions Common Energy sources breakdown City specific (utility grid mix of fuels)—See Appendix Electrical Energy Offset 2.5.8 Energy Data Inputs Electricity Cost- purchase Electricity Price- sale City specific—See Appendix 2.5.8 Energy Data Inputs. Diesel Fuel Generation Waste Generation City specific—See Appendix 2.5.2 MSW Generation Data Waste Composition Inputs. Fraction of residential waste that is Assumed 10% for all cities except Kawasaki and Atlanta single family (50%)—See Appendix 2.5.9 Constants Data. Fraction of residential waste that Assumed 90% for all cities except Kawasaki and Atlanta multi family (50%)—See Appendix 2.5.9 Constants Data. Collection Sectors Residential and Multifamily Set to be the same value per income category—See Collection cost Appendix 2.5.3 Collection Data Inputs. Low participation factor* 50%—See Appendix 2.5.9 Constants Data. High participation factor* 75%—See Appendix 2.5.9 Constants Data. Low capture rate** 50%—See Appendix 2.5.9 Constants Data. High capture rate** 75%—See Appendix 2.5.9 Constants Data. Daily: 6 times/week, Biweekly: 2 times/week—See Collection frequency Appendix 2.5.9 Constants Data. Houses per stop in the residential sector Household size in the residential Assumed the same for all cities—See Appendix 2.5.9 sector Constants Data. Usable capacity in open trucks Vehicle travel speeds Vehicle travel distances 30 km one way—See Appendix 2.5.9 Constants Data. Families per stop in the multifamily sector Household size in the multifamily sector Number of multifamily collection locations Fraction of compactor vehicles Income category- specific—See Appendix 2.5.3 Fraction of open trucks Collection Data Inputs. Usable capacity of compactor vehicles Waste density in compactor vehicles- compacted Waste density in compactor vehicles- uncompacted 2-35 Solid Waste Management Holistic Decision Modeling Final Report Table 2.5-1 Summary of Key Input Parameters, Assumptions and Data Sources by Process. (continued) MRF Basic design Accepts mixed MSW, manual/ mechanical designs Low separation efficiency 55%—See Appendix 2.5.9 Constants Data. High separation efficiency 75%—See Appendix 2.5.9 Constants Data. Recyclables prices Separation efficiencies Working day length Driver and operator requirement Capacity of manual bag opener Labor overhead rate City specific—See Appendix 2.5.4 MRF Data Inputs. Management wages Picker wage rate Driver and operator wage rate Bin cost rate Rolling stock cost rate Baler cost rate Vehicle travel distances to 10 km one way—See Appendix 2.5.9 Constants Data. remanufacturing Residuals management Landfill with gas collection flaring Compost Basic design Accepts mixed MSW, manual/mechanical designs Number of operating hours Number of days per week Operating days per year Wage for operator Wage for manager City specific—See Appendix 2.5.5 Compost Data Inputs. Compost residence time Curing stage residence time Compost pile turning frequency Compost product prices Residuals management Landfill with gas flaring Incineration Basic design Modern Mass Burn Plant heat rate 17,500 BTU/kWh Ferrous recovery rate 70% Unit WTE capital cost City specific—See Appendix 2.5.6 Incineration Data Unit WTE O & M cost Inputs. 2-36 Solid Waste Management Holistic Decision Modeling Final Report Table 2.5-1 Summary of Key Input Parameters, Assumptions and Data Sources by Process. (continued) Landfill Basic design Modern (U.S.EPA Subtitle D type) Time period for calculating 100 years emissions 0% for venting; 70% for gas control—See Appendix 2.5.9 Gas collection efficiency Constants Data. Landfill gas management Vent, flare, and energy recovery Active life of facility Number of cells Type of liner LF gas composition Precipitation Minimum labor cost Maximum daily waste handled by City-specific—See Appendix 2.5.7 Landfill Data Inputs. minimum labor costs Both cases with and without land prices are analyzed. Utility rate Overhead costs Equipment and maintenance Capital cost of internal combustion engine Land prices Landfill depth Assumed the same for all cities—See Appendix 2.5.9 Landfill slope Constants Data. *Participation factor: percent of population that participates in recycling **Capture rate: percent of recyclables placed in bins by those participating 2-37 Holistic Decision Modeling Solid Waste Management Table 2.5-2 Cost Data Availability by City. Buenos Cost Input Parameters Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Energy Data Electricity Cost- purchase X X X X X X X Electricity Price- sale X X X X X X X Diesel Fuel X X X X X X X X X MRF Data Old News Print X X X Corrugated Cardboard X X X X X Office Paper X X X X X Phone Books X X X Books X X X 2-38 Old Magazines X X X 3rd Class Mail X X X Recyclables prices Mixed paper X X X X X X X HDPE - Translucent X X X X HDPE - Pigmented X X X X PET X X X X X X Mixed plastics X X X X X Ferrous Cans X X X X X X Ferrous Metal - Other X X X X X X Aluminum Cans X X X X X X X Aluminum Other #1 0 Aluminum Other #2 0 Final Report Glass - Clear X X X X X X X Glass - Brown X X X X Glass - Green X X X X Holistic Decision Modeling Solid Waste Management Buenos Cost Input Parameters Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Energy Data Mixed Glass X X X X X Picker wage rate X X X X X X Driver and operator wage rate X X X X X X Bin cost rate X X Rolling stock cost rate X X Baler cost rate X X Composting Data Wage for operator X X X X X Wage for manager X X X Value of compost product X X X X X X X X X Incineration Data 2-39 Unit combustor capital cost X X X Unit combustor O & M cost X X X Landfill Disposal Data Labor cost X X X Overhead costs X X X Equipment and maintenance X X X X Capital cost of internal combustion X X X engine Land Prices X X X X X X X X X Note x: City specific data were found for that parameter by field survey or on the web site. Some of them are not used for the model input because of their data reliability. 0: The value was set to zero as for the recyclables prices. Final Report Blank cells mean no data available by the field survey, therefore defaults or other related values are applied. Zeroes are also set for the recyclables prices in case of no market for those recyclables. Solid Waste Management Holistic Decision Modeling Final Report Key MSW DST Default Data Employed In addition to the key input parameters presented in Tables 2.5.1 and 2.5.2, there are key default data used in the MSW DST that can significantly affect scenario results and are key to understanding the behavior of the results for each city. In particular, energy production, materials production, and landfill gas production data are highly sensitive parameters. In the context of their significance on the scenario results presented in Section 3 of this report, the following default data are highlighted: • Electrical energy production total energy consumption factors • Electrical energy production fuel-specific emission factors • Energy savings factors associated with materials recycling • Emission savings (or burdens) factors associated with materials recycling • Landfill gas production parameters by waste item. Defaults used in the MSW DST for each of these items are presented in Tables 2.5.3 to 2.5.7 below. It should be noted that each table is sorted in descending order according to the values highlighted in bold to aid illustrating the impact of these values in the results. Carbon dioxide and methane emission factors are presented as these pollutants are the main contributors to the total carbon emissions from the waste management processes. Table 2.5-3 MSW DST Default Pre/Combustion Energy Factors (Fuel Type Used to Produce Electricity) Fuel Units Pre- Total Energy Total Combustion** Consumed per Fuel Type (fuel combustion* Factor (BTU/ Energy Energy Factor Electric kWh units) Energy Factor fuel unit Factor (BTU/fuel unit) delivered (fuel (BTU/fuel unit) consumed) (BTU/kWh) unit/kWh) Natural Gas (ft3) 129 1,022 1,151 13.535 15,578 Distillate Oil 19,300 138,700 158,000 0.090 14,181 (gal) Residual Oil 21,000 149,700 170,700 0.075 12,781 (gal) Uranium (lb) 50,600,000 985,321,000 1,035,921,000 0.000 11,585 Wood (lb) 0 10,350 10,350 1.015 10,504 Other 0 10,350 10,350 1.015 10,504 Coal (lb) 264 10,402 10,666 0.979 10,438 Hydro 0 3,413 3,413 1.000 3,413 *Pre-combustion energy is the energy used to mine and process a unit of fuel. **Combustion energy is the energy value of the fuel as combusted in a utility boiler. 2-40 Solid Waste Management Holistic Decision Modeling Final Report Table 2.5-4 MSW DST Default Emission Factors (Fuel Type Used to Produce Electricity) Emission Factors (lb/kWh) Fuel Type (fuel units) Carbon Dioxide Methane PM Residual Oil 2.75E+00 4.09E-04 4.21E-04 Coal 2.18E+00 4.76E-03 2.90E-03 Distillate Oil 1.98E+00 3.28E-04 2.66E-04 Natural Gas 1.47E+00 4.07E-03 4.79E-05 Uranium 7.51E-02 1.66E-04 4.86E-04 Hydro 0.00E+00 0.00E+00 0.00E+00 Wood 0.00E+00 0.00E+00 8.63E-05 Table 2.5-5 MSW DST Default Energy Savings (Recycling Individual Materials) Energy Savings Factors (BTU/ ton of remanufactured Recyclables Category material) Aluminum 201,882,712 STEEL 171,726,050 LDPE 25,978,843 PET 23,785,635 HDPE 20,233,296 Newspaper 17,353,633 Corrugated Boxes 13,001,578 Phone Books 12,341,360 OFFICE PAPER 11,184,559 Glass 2,361,566 Text Books 1,082,049 Magazines/3rd Class Mail 711,964 Table 2.5-6 MSW DST Default Emission Savings (Recycling Individual Materials*) Emission Factors (lb/ ton of remanufactured material) Recyclables Category Carbon Dioxide Methane PM Aluminum 2.12E+04 3.26E+01 5.92E+01 LDPE 3.70E+03 1.60E-01 4.31E+00 PET 3.58E+03 4.84E-02 5.69E+00 HDPE 2.82E+03 -1.67E-01 2.59E+00 Steel 2.06E+03 1.71E+00 1.00E+01 Newspaper 1.90E+03 4.15E+00 2.82E+00 Glass 6.94E+02 4.49E+00 6.92E+00 Phone Books 3.36E+02 1.88E+00 5.09E+00 Text Books 1.18E+02 -1.65E+00 -3.48E-01 Magazines/3rd Class Mail 3.70E+01 4.27E-02 2.99E-01 Corrugated Boxes -2.27E+02 -7.20E-01 2.34E+01 Office Paper -5.80E+02 -6.74E-01 4.94E+00 * These values are calculated based on production of materials using recycled vs. virgin resources. Waste collection, separation, and transport are not included. A negative value denotes a net positive emission, or additional burden. 2-41 Solid Waste Management Holistic Decision Modeling Final Report Table 2.5-7 MSW DST Default Landfill Gas Generation (Individual Waste Items*) CH4 Yield Waste Category % moisture Gas Production Rate (yr-1) (L CH4/ dry kg) Food Waste 0.70 300.70 0.11 Office Paper 0.06 217.30 0.02 Books (used Office Paper) 0.06 217.30 0.02 Corr. Cardboard 0.05 152.30 0.03 3rd Class Mail 0.06 150.85 0.06 Yard Trimmings, Grass 0.60 136.00 0.31 Mixed Paper 0.06 103.67 0.06 Paper - Non-recyclable 0.06 103.67 0.06 Magazines 0.06 84.40 0.16 Newsprint 0.06 74.30 0.04 Phone Books 0.06 74.30 0.04 Yard Trimmings, Branches 0.60 62.60 0.12 Yard Trimmings, Leaves 0.60 30.60 0.25 * These values were selected to represent typical waste from the cities under analysis according to personal communications with Professor Morton A. Barlaz, North Carolina State University and Susan Thorneloe, US EPA. 2-42 Solid Waste Management Holistic Decision Modeling Final Report CHAPTER 3 SCENARIO RESULTS 3.1 Open Dumping and Open Burning (Base Case) This section of the report presents and explains the results of base case waste management simulation including open dumping and open burning. The MSW DST originally developed based on SWM experiences in the US does not contain open dumping and open burning models per se. The conventional landfill and incineration models were tailored to represent unmanaged landfill disposal and incineration, respectively. It is assumed that there is no collection, transportation, or open dump/burn related cost or energy consumption. Waste is either taken by households to an open dump site or burned on-site or at a common burn site. Emissions have been estimated using a combination of the MSW DST landfill and incineration models and additional emission factors data for open burning of solid waste available from the U.S. EPA. Due to the difficulty in characterizing open dumping and burning practices and emissions, results should be taken as rough approximations and used as a relative guide to compare against the more conventional MSW management practices and scenarios analyzed in Sections 3.2 and 3.3. These MSW management scenarios are also hypothetical, meaning that they are not a representation of the waste management situation in any of the cities included in this study, but are tailored to meet the objectives of this study. Open Dumping In many locations, waste is disposed of in unmanaged dumps or piles. To approximate an open dumping scenario, the following modifications were implemented to the MSW DST’s landfill model: • Energy consumption factors were zeroed out. • Liner system removed • Daily and final covers removed • Leachate collection and treatment systems removed • Assumed zero oxidation of methane through cover soil (i.e., there is no cover soil) • Assumed all gas produced is released to the atmosphere (i.e,. vented). Estimates for unit and anual gas emissions associated with open dumping of waste are shown in Table 3.1.1 and Table 3.1.2. Table 3.1.3 and Table 3.1.4 contains estimates for selected key emissions of water pollutants (i.e., leachate) associated with open dumping of waste. 3-1 Solid Waste Management Holistic Decision Modeling Final Report Table 3.1-1 Unit Gas Emissions from Open Dumping of Waste Total Carbon Total Gas Emissions (kg/ton) City Emissions (kg/ton) CO2 CH4 C-eq Kathmandu 156.77 53.11 0.15 Conakry 188.28 41.23 0.12 Lahore 69.96 25.06 0.07 Sarajevo 180.50 38.41 0.11 Amman 214.16 43.54 0.13 Buenos Aires 177.00 38.97 0.11 Shanghai 113.66 43.80 0.13 Kawasaki 308.48 51.40 0.15 Atlanta 479.30 50.69 0.15 Table 3.1-2 Annual Gas Emissions from Open Dumping of Waste Total Carbon Total Gas Emissions (kg/year) City Emissions (kg/year) CO2 CH4 C-eq Kathmandu 25,131,710 8,513,320 24,423 Conakry 17,675,735 3,870,470 11,113 Lahore 92,950,012 33,294,527 95,750 Sarajevo 31,247,946 6,649,108 19,096 Amman 167,749,561 34,105,656 97,915 Buenos Aires 662,330,435 145,844,940 418,864 Shanghai 684,101,219 263,611,212 756,667 Kawasaki 161,212,661 26,860,029 77,091 Atlanta 380,333,797 40,219,898 115,464 Table 3.1-3 Unit Emissions of Key Water Pollutants from Open Dumping of Waste Unit Emissions (kg/ton) City TDS TSS BOD COD Ammonia Phosphate Kathmandu 2.49E-03 2.87E-03 3.79E+00 4.99E+00 1.61E-02 4.71E-04 Conakry 2.77E-03 2.22E-03 2.84E+00 3.74E+00 1.21E-02 3.53E-04 Lahore 2.75E-03 1.49E-03 1.81E+00 2.38E+00 7.72E-03 2.25E-04 Sarajevo 2.90E-03 2.12E-03 2.68E+00 3.52E+00 1.14E-02 3.33E-04 Amman 2.85E-03 2.39E-03 3.06E+00 4.03E+00 1.31E-02 3.81E-04 Buenos Aires 2.93E-03 2.02E-03 2.54E+00 3.34E+00 1.08E-02 3.15E-04 Shanghai 2.66E-03 2.43E-03 3.14E+00 4.13E+00 1.34E-02 3.90E-04 Kawasaki 2.98E-03 2.64E-03 3.40E+00 4.48E+00 1.45E-02 4.23E-04 Atlanta 3.28E-03 2.86E-03 3.69E+00 4.85E+00 1.57E-02 4.58E-04 3-2 Solid Waste Management Holistic Decision Modeling Final Report Table 3.1-4 Annual Emissions of Key Water Pollutants from Open Dumping of Waste Unit Emissions (kg/year) City TDS TSS BOD COD Ammonia Phosphate Kathmandu 399 461 607,357 799,533 2,588 75 Conakry 260 209 266,574 350,930 1,136 33 Lahore 3,659 1,986 2,404,820 3,165,949 10,252 299 Sarajevo 502 367 463,196 609,778 1,974 58 Amman 2,234 1,872 2,400,433 3,160,028 10,230 298 Buenos Aires 10,948 7,565 9,488,471 12,491,228 40,443 1,180 Shanghai 16,009 14,620 18,903,639 24,885,359 80,561 2,350 Kawasaki 1,558 1,379 1,777,544 2,340,022 7,575 221 Atlanta 2,599 2,272 2,926,014 3,851,912 12,470 364 Open Burning In addition to open dumping of waste, in many locations waste is also burned in open piles or barrels. To approximate an open burning scenario, the following modifications were implemented to the MSW DST’s incineration model: • Energy consumption and production zeroed out. • Air pollution controls were zeroed out. • Environmental burdens associated with the production of air pollution control agents (e.g., lime) were removed. • For metal air emissions, emission removal efficiencies were zeroed out. • For non-metal air emissions, emissions factors for uncontrolled solid waste combustion were taken from the U.S. EPA’s AP-42 Emission Factors for Open Burning and PCDD/F Emissions From Uncontrolled, Domestic Waste Burning1. The emission factors used are shown in Table 3.1.5. Estimates for total non-metal gas emissions associated with open burning of waste are shown in Table 3.1.6. Table 3.1.7 contains estimates for emissions of dioxins/furans associated with open burning of waste. There likely are water pollutants associated with the ash residues from open burning. However, no emission factors were found to characterize these potential emissions. Table 3.1-5 Open Burning Non-Metal Emissions Factors Applied Pollutant Emission Factors Units PM 7.3 kg/ton SOx 0.5 kg/ton NOx 2.7 kg/ton CO 38.6 kg/ton CH4 5.9 kg/ton Dioxin/Furans 35,196 Ng/kg 1 Gullett, B. K. , P. Lemieux, C . Winterrowd, D . Winters. 2000. PC DD/F Emissions from Uncontrolled, Domestic Waste Burning. Presented at Dioxin ’00, 20th International Symposium on Halogenated and Environmental Organic Pollutants & POPs, held Aug 13-17 at Monterey, CA. Corrected revision of short paper in Organohalogen Compounds 46: 193-196. 3-3 Solid Waste Management Holistic Decision Modeling Final Report Table 3.1-6 Unit Non-Metal Emissions from Open Burning of Waste Total Carbon Unit Non-Metal Emissions (kg/ton) City Emissions (MTCE/yr) PM SOx NOx CO CO2 CH4 Carbon Kathmandu 7.94 0.50 2.98 42.16 184.76 6.45 0.09 Conakry 7.92 0.50 2.97 42.08 127.80 6.44 0.08 Lahore 7.92 0.50 2.97 42.08 207.38 6.44 0.10 Sarajevo 7.95 0.50 2.98 42.23 121.84 6.46 0.07 Amman 7.94 0.50 2.98 42.19 374.95 6.45 0.14 Buenos Aires 7.94 0.50 2.98 42.20 318.40 6.45 0.13 Shanghai 7.92 0.49 2.97 42.06 455.06 6.43 0.17 Kawasaki 7.93 0.50 2.97 42.12 240.99 6.44 0.11 Atlanta 7.88 0.49 2.96 41.89 406.05 6.41 0.15 Table 3.1-7 Annual Non-Metal Emissions from Open Burning of Waste Total Carbon Unit Non-Metal Emissions (lb pollutant/year) City Emissions (MTCE/yr) PM SOx NOx CO CO2 CH4 Carbon Kathmandu 1,272,306 79,519 477,115 6,759,124 29,618,537 1,033,748 14,709 Conakry 743,515 46,470 278,818 3,949,925 11,997,781 604,106 7,133 Lahore 10,522,512 657,657 3,945,942 55,900,845 275,531,539 8,549,541 130,075 Sarajevo 1,376,330 86,021 516,124 7,311,755 21,093,032 1,118,268 12,896 Amman 6,220,246 388,765 2,332,592 33,045,055 293,694,361 5,053,950 112,930 Buenos Aires 29,722,406 1,857,650 11,145,902 157,900,284 1,191,484,239 24,149,455 481,246 Shanghai 47,645,928 2,977,871 17,867,223 253,118,993 2,738,875,886 38,712,317 999,796 Kawasaki 4,143,514 258,970 1,553,818 22,012,416 125,944,770 3,366,605 56,027 Atlanta 6,256,433 391,027 2,346,162 33,237,299 322,206,706 5,083,352 120,971 Table 3.1-8 Dioxin/Furan Emissions from Open Burning of Waste Unit Dioxin/Furan Annual Dioxin/Furan City Emissions (kg/ton) Emissions (kg/year) Kathmandu 3.93E 6 Conakry 3.83E 4 Lahore 3.83E 51 Sarajevo 3.90E 7 Amman 3.85E 30 Buenos Aires 3.85E 144 Shanghai 3.83E 230 Kawasaki 3.87E 20 Atlanta 3.80E 30 3-4 Solid Waste Management Holistic Decision Modeling Final Report 3.2 Mixed Waste Collection and Management Using One Primary Technology 3.2.1 Simulation Scenario Results Using One Primary Technology This section of the report presents and explains the results of the waste management simulation scenarios introduced in Section 2.2. These simulation scenarios correspond to the Group 2 scenarios under Table 2.2.1 and Figure 2.2.2. They are hypothetical, meaning that they are not a representation of the waste management situation in any of the cities in this analysis, but are tailored to meet the objectives of this study. Special emphasis is made on detailing these results since they will aid understanding the optimization scenario results in Section 3.3. Each simulation scenario entails collecting and sending all MSW to one waste management process: • Recycling Using Manual Sorting • Recycling Using Mechanical Sorting • Mixed Waste Composting Using Manual Turning • Mixed Waste Composting Using Mechanical Windrow Turner • Incineration Without Energy Recovery • Incineration With Energy Recovery • Landfill With Gas Venting • Landfill With Gas Collection And Flaring • Landfill With Gas Collection And Energy Recovery To the extent possible, the results are presented in graphical format with the cities organized according to their present income level from lowest to highest. As discussed in Section 2, energy consumption is used as a general indicator for key air pollutants including CO, NOx, PM, and SOx. Carbon emissions are also presented due to their contribution to the greenhouse effect. When comparing the behavior of the simulation results across cities, it is usually helpful to determine the process having the largest impact on the net total results and the parameters governing the results for that process. For this purpose, Table 3.2.1 presents the key data inputs by waste management process and summarizes their effects on the cost, energy and emissions results. For example, according to Table 3.2.1 a recyclable price is a key input parameter for its impact in the cost results. This parameter does not affect the cost results from collection, composting, or landfill disposal, but it does play an important role in the 3-5 Solid Waste Management Holistic Decision Modeling Final Report recycling and incineration results. High recyclable prices usually result in low net total costs from recycling and incineration (ferrous material is recovered from incineration). 3-6 Table 3.2-1 Summary of the Significance of Key Input Parameters to the Scenario Results by Process* Holistic Decision Modeling Solid Waste Management Composting Using Recycling Manual and Mechanical Incineration Landfill Disposal Using Windrow Turning Key Data Inputs Collection Manual and With Gas With Gas Without With Mechanical Mechanical With Energy Collection Collection Manual Energy Gas Sorting Windrow Recovery and and Energy Recovery Venting Flaring Recovery Net Total Cost yes, Recyclables prices no no yes (ferrous), High→Low no High→Low Compost prices no no yes, High→Low no no yes, Electricity price no no no no no High→Low yes, yes, Electricity cost no yes, High→High yes, High→High yes, High→High High→High High→High yes, yes, Diesel fuel cost no no no no High→High High→High Wage rates for yes, yes, Model default values were yes, High→High yes, High→High labor High→High High→High used for all cities Collection vehicle yes, no no no no usable capacity High→Low 3-7 Multifamily yes, no no no no collection locations High→High Incineration facility capital and O & M no no no yes, High→High no cost Ash/LF capital and no no no no yes, High→High O & M cost Net Total Energy Consumption Percentage of natural gas and yes, yes, yes, yes, no yes, High→High yes, High→High distillate oil in High→High High→High High→Low High→Low electricity grid mix Collection vehicle yes, no no no no usable capacity High→Low Final Report Multifamily yes, no no no no collection locations High→High Percentage of remanufactured yes, no no yes, High ferrous→High no aluminum and High→Low ferrous materials yes, yes, Waste heating value no no no no no High→Low High→Low Holistic Decision Modeling Solid Waste Management Composting Using Recycling Manual and Mechanical Incineration Landfill Disposal Using Windrow Turning Key Data Inputs Collection Manual and With Gas With Gas Without With Mechanical Mechanical With Energy Collection Collection Manual Energy Gas Sorting Windrow Recovery and and Energy Recovery Venting Flaring Recovery Net Total Emissions Percentage of residual oil and coal yes, yes, yes, yes, no yes, High→High yes, High→High in electricity grid High→High High→High High→Low High→Low mix Collection vehicle yes, no no no no usable capacity High→Low Multifamily yes, no no no no collection locations High→High Percentage of yes, remanufactured no no yes, High ferrous→Low no High→Low metals and plastics Compost residence time and turning no no yes, High→High no no frequency Percentage of 3-8 landfill disposed no no no no yes, High→High food waste and grass Percentage of incinerated and no no no yes, High→High no metals *yes/no: whether the input parameter contributes to the LCI Results (cost, energy, or emissions) from the corresponding process, High/Low: refers to the relative magnitude of the input parameter, High/Low: refers to the relative magnitude of the LCI result as a results of the relative magnitude of the input parameter. Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.2.2 Recycling Using Manual and Mechanical Sorting There are a wide range of designs for MRFs. In this study, we focus on two basis design variations: manual and mechanical sorting. The scenarios modeled assume mixed waste is collected in a single-compartment vehicle and sent to a mixed waste MRF that uses either manual sorting or one that uses mechanical sorting. Both the manual and mechanical sort design options assume a materials separation efficiency2 of 55%. Results from these scenarios are recorded in Figure 3.2-1 Net Total Cost by City (with Land Price) Figure 3.2-4 Net Total Cost by City (without Land Price), Figure 3.2-7 Net Total Energy Consumption by City, and Figure 3.2-10 Net Total Carbon Emissions by City. Each figure shows the results for the nine cities under analysis on a per ton basis (i.e., the net total cost/energy/emissions divided by the amount of waste generated in each city). To aid the analysis, the recycling results were further broken down by processes (i.e., collection, MRF, remanufacturing, landfill disposal, and transportation) dividing the net totals from each process by the amount of waste managed by that process as shown in Figures 3.2-2, 3.2-3, 3.2-5, 3.2-6, 3.2-8, 3.2-9, 3.2-11 and 3.2-12. It should be noted that the values in the last figures cannot be summed to obtain the Net Totals by City in Figures 3.2-1, 3.2-4, 3.2-7 and 3.2-10, since the result of this sum will be on a per ton of waste as generated and not as managed by a mix of different processes. Table 3.2-2 indicates how much waste is available for recycling and how much waste goes to each of the management processes. This table is sorted in descending order by percentage of material sent to the remanufacturing process. Having a low recyclables recovery rate most of the waste in the cities goes to the landfill rather than remanufacturing. The same recovery rate applies to the manual and the mechanical MRFs. Therefore, there are not differences in the percentage of waste going to the different processes between the manual and the mechanical designs. Table 3.2-2 Percentage of Available Recyclables* and Flow to the Different Processes Percentage of Available Waste Flow Cities Recyclables (a) Material Recovered (a x 55%) Residuals Disposed Kawasaki 36.55% 20.10% 79.90% Atlanta 32.39% 17.81% 82.19% Buenos Aires 31.07% 17.08% 82.92% Amman 29.17% 16.03% 83.97% Shanghai 22.84% 12.56% 87.44% Conakry 19.80% 10.89% 89.11% Kathmandu 15.72% 8.65% 91.35% Sarajevo 12.76% 7.02% 92.98% Lahore 12.38% 6.80% 93.20% 2 The separation efficiency defines the amount of recyclables that get separated from the mixed waste and sent to a remanufacturing facility. At both, the manual and the mechanical sorting MRF, corrugated cardboard and newspaper are the only paper categories separated and sent to remanufacturing. 3-9 Solid Waste Management Holistic Decision Modeling Final Report *Not including non-paper compostable items. In general the cost results do not show a clear correlation with the cities socio-economic groups defined by their income level (e.g. Kathmandu has the lowest income level and Atlanta has the highest income level). Cost data inputs (e.g., capital, O&M, and energy costs, recyclables prices and wage rates for labor at the MRF) were not available for all the cities. Therefore, cost variations observed in the results among cities in the same socio-economic group may not accurately reflect reality. Cost data availability varies among the different cities (see Table 2.52). For example, Lahore is one a city that can be characterized as having poor data availability, while the city of Atlanta can be characterized as having good data availability. Energy results are highly dependent on the waste composition and electricity grid mix of each city. For example, city with a higher percentage of metals available in its waste for recycling can also have large energy savings according to Table 2.5-5. Similarly, a city that relies on a fossil-fuel based electrical energy grid can have higher energy requirements than a city that relies on renewable or alternative fuel based electrical energy (see Table 2.5-3). Similar to the energy results, carbon emission results mainly depend on the waste composition and the electricity grid mix of each city. For example, a large percentage of aluminum and plastic recyclables will produce large emission savings according to Table 2.5-6 and a mostly residual oil electricity grid mix will have the largest carbon emissions (see Table 2.5-4). The waste composition of the residuals going to a landfill is also very important and greater amounts of food waste and grass can produce greater amounts of landfill gas (see Table 2.5-7). Cost variation by process design Figures 3.2-1 and 3.2-4 show that there are very small differences in the cost per ton between a manual and a mechanical design and any differences are due to costs at the MRF (see the corresponding MRF values shown in Figures 3.2-2, 3.2-3, 3.2-5 and 3.2-6). MRFs using manual sorting are consistently more expensive due to additional labor requirements. Cost variation by city Figures 3.2-2, 3.2-3, 3.2-5 and 3.2-6 show that the key drivers behind the differences across cities are (1) the total cost per ton for landfill disposal using gas flaring, (2) the revenues obtained from recyclables sales, and (3) the costs incurred at the MRF. Kawasaki is the city with the largest collection cost and this can be attributed to having the highest wage rates for drivers and collectors along with a low usable capacity in its collection vehicles. Kawasaki is also the city with the highest overall cost (see Figure 3.2-1) mainly from having the highest landfill capital (e.g., very high land prices) and electricity costs, very low recyclables prices, and the highest wage rates for labor at the MRF. On the other hand Buenos Aires and Atlanta are the cities with the lowest overall cost from having the highest recyclable prices. Cost variation 3-10 Solid Waste Management Holistic Decision Modeling Final Report among the rest of the cities can be explained by looking at the total cost of landfill disposal and the revenues from recyclables sale (see Figures 3.2-2, 3.2-3, 3.2-5 and 3.2-6). 3-11 with land price f or landf ill cost 350.0 298.1 295.8 Solid Waste Management Holistic Decision Modeling 300.0 250.0 200.0 150.0 110.6 118.0 114.9 107.4 86.6 88.0 3-12 100.0 83.6 84.8 66.9 74.9 72.0 71.4 66.7 66.0 64.7 Net Total Cost ($)/ metric ton 47.4 50.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Recycling Using Manual Sorting Recycling Using Mechanical Sorting Final Report Figure 3.2-1 Net Total Cost by City (with Land Price) Recycling Using Manual Sorting (with Land Price) 250.0 200.0 150.0 Solid Waste Management Holistic Decision Modeling 100.0 50.0 0.0 -50.0 -100.0 -150.0 3-13 Unit Cost ($)/ metric ton f or each process -200.0 -250.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 33.819 33.841 29.868 27.000 27.581 27.743 27.839 56.109 35.721 MRF 28.796 13.610 34.043 15.284 19.328 29.194 48.036 71.621 34.789 Remanufacturing -65.755 -106.338 -180.510 -132.130 -124.925 -108.895 -83.413 -115.130 -196.669 LF 44.086 44.719 27.133 26.668 45.665 31.522 44.032 200.592 27.788 Transportation 1.785 1.741 1.821 1.816 1.640 1.620 1.708 1.561 1.606 Final Report Figure 3.2-2 Unit Cost by City and Process (with Land Price: Manual MRF Design) Recycling Using Mechanical Sorting (with Land Price) 250.0 200.0 150.0 Solid Waste Management Holistic Decision Modeling 100.0 50.0 0.0 -50.0 -100.0 3-14 -150.0 Unit Cost ($)/ metric ton f or each process -200.0 -250.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 33.819 33.841 29.868 27.000 27.581 27.743 27.839 56.109 35.721 MRF 25.937 10.973 31.132 14.531 16.737 26.779 45.321 69.579 32.990 Remanufacturing -65.755 -106.338 -180.510 -132.130 -124.925 -108.895 -83.413 -115.130 -196.669 LF 44.086 44.719 27.133 26.668 45.665 31.522 44.032 200.592 27.788 Transportation 1.785 1.741 1.821 1.816 1.640 1.620 1.708 1.561 1.606 Final Report Figure 3.2-3 Unit Cost by City and Process (with Land Price: Mechanican MRF Design) without land price f or landf ill cost 350.0 300.0 Solid Waste Management Holistic Decision Modeling 250.0 200.0 148.3 146.0 150.0 110.1 105.8 106.9 85.3 102.8 81.9 74.0 100.0 82.3 78.7 65.1 65.9 64.2 54.2 71.3 63.9 Net Total Cost ($)/ metric ton 3-15 51.3 50.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Recycling Using Manual Sorting Recycling Using Mechanical Sorting Figure 3.2-4 Net Total Cost by City (without Land Price) Final Report Recycling Using Manual Sorting (without land price) 250.0 200.0 150.0 Solid Waste Management Holistic Decision Modeling 100.0 50.0 0.0 -50.0 -100.0 -150.0 Unit Cost ($)/ metric ton f or each process 3-16 -200.0 -250.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 33.819 33.841 29.868 27.000 27.581 27.743 27.839 56.109 35.721 MRF 28.796 13.610 34.043 15.284 19.328 29.194 48.036 71.621 34.789 Remanufacturing -65.755 -106.338 -180.510 -132.130 -124.925 -108.895 -83.413 -115.130 -196.669 LF 43.516 43.433 21.306 24.910 23.674 31.466 31.596 32.987 26.927 Transportation 1.785 1.741 1.821 1.816 1.640 1.620 1.708 1.561 1.606 Final Report Figure 3.2-5 Unit Cost by City and Process (without Land Price: Manual MRF Design) Recycling Using Mechanical Sorting (without land price) 250.0 200.0 150.0 Solid Waste Management Holistic Decision Modeling 100.0 50.0 0.0 -50.0 -100.0 -150.0 3-17 Unit Cost ($)/ metric ton f or each process -200.0 -250.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 33.819 33.841 29.868 27.000 27.581 27.743 27.839 56.109 35.721 MRF 25.937 10.973 31.132 14.531 16.737 26.779 45.321 69.579 32.990 Remanufacturing -65.755 -106.338 -180.510 -132.130 -124.925 -108.895 -83.413 -115.130 -196.669 LF 43.516 43.433 21.306 24.910 23.674 31.466 31.596 32.987 26.927 Transportation 1.785 1.741 1.821 1.816 1.640 1.620 1.708 1.561 1.606 Final Report Figure 3.2-6 Unit Cost by City and Process (without Land Price: Mechanican MRF Design) Solid Waste Management Holistic Decision Modeling Final Report Energy variation by process design Consistent with the cost results, Figure 3.2-7 shows that there are very small differences in the energy results between a manual and a mechanical design on a per ton basis and any differences are due to the energy requirements at the MRF (see the corresponding MRF values shown in Figures 3.2-8 and 3.2-9). As expected, MRFs using mechanical sorting have consistently higher energy requirements from the use of equipment. Energy variation by city Figures 3.2-8 and 3.2-9 show that the key driver behind the differences across cities is the energy savings from remanufacturing. Kawasaki is the city with the largest collection energy, mostly due to the low usable capacity (half of the usable capacity of other cities’) in its collection vehicles, which increases the energy requirements associated with the vehicles fuel production. Atlanta and Kawasaki are the cities with the highest energy savings (see Figure 3.2-7) consistent with having the highest amounts of recyclables sent to remanufacturing (see Table 3.2-2). The net energy variation among the cities is due to specific energy savings from individual recyclables. Table 3.2-3 shows the percentages of recyclables categories sent to remanufacturing in each of the cities. This table is sorted by percentage of metals since these are the recyclables that produce the highest energy savings. Then, Table 3.2-3 explains why Atlanta has higher energy savings than Kawasaki despite of having lower percentage of recyclables going to remanufacturing. Table 3.2-3 Percentage of Recyclable Items Recovered for Remanufacturing by City (Sorted by Percentage of Total Metals) Organics Inorganics Metals Cities Paper Plastics Glass Ferrous Aluminum Total materials materials Atlanta 11.24% 1.37% 2.04% 2.39% 0.77% 3.16% Kawasaki 9.45% 6.05% 2.68% 1.32% 0.60% 1.92% Conakry 6.24% 2.62% 0.53% 1.50% 0.00% 1.50% Buenos Aires 6.34% 6.67% 2.81% 0.73% 0.53% 1.26% Lahore 1.30% 4.34% 0.67% 0.19% 0.29% 0.49% Shanghai 2.29% 9.31% 0.76% 0.08% 0.12% 0.20% Kathmandu 3.51% 3.78% 1.16% 0.19% 0.00% 0.19% Amman 7.32% 7.65% 1.06% 0.00% 0.00% 0.00% Sarajevo 4.11% 2.90% 0.00% 0.00% 0.00% 0.00% 3-18 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.2 -0.15 Aires -0.26 -0.17 -0.4 -0.27 -0.37 Solid Waste Management Holistic Decision Modeling -0.40 -0.6 -0.52 -0.53 -0.55 -0.56 -0.8 -0.86 -1.0 -0.88 -1.09 -1.2 -1.11 -1.4 3-19 -1.6 -1.52 -1.55 -1.8 Energy Consumption (MKcal)/ metric ton -1.87 -2.0 -1.88 Recycling Using Manual Sorting Recycling Using Mechanical Sorting Figure 3.2-7 Net Total Energy Consumption by City Final Report Recycling Using Manual Sorting 2.0 0.0 Solid Waste Management Holistic Decision Modeling -2.0 -4.0 -6.0 -8.0 3-20 -10.0 Energy Consumption (MKcal)/ metric ton f or each process -12.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.042 0.042 0.036 0.033 0.033 0.034 0.034 0.072 0.045 MRF 0.018 0.026 0.028 0.022 0.037 0.030 0.035 0.033 0.025 Remanufacturing -4.654 -9.045 -7.610 -4.363 -4.018 -6.711 -5.390 -7.928 -10.499 LF 0.103 0.137 0.098 0.104 0.095 0.108 0.114 0.127 0.124 Transportation 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 Final Report Figure 3.2-8 Energy Consumption by City and Process (Manual MRF Design) Recycling Using Mechanical Sorting 4.000 2.000 0.000 Solid Waste Management Holistic Decision Modeling -2.000 -4.000 -6.000 3-21 -8.000 -10.000 Energy Consumption (MKcal)/ metric ton f or each process -12.000 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.042 0.042 0.036 0.033 0.033 0.034 0.034 0.072 0.045 MRF 0.026 0.047 0.053 0.035 0.067 0.050 0.061 0.055 0.041 Remanufacturing -4.654 -9.045 -7.610 -4.363 -4.018 -6.711 -5.390 -7.928 -10.499 LF 0.103 0.137 0.098 0.104 0.095 0.108 0.114 0.127 0.124 Transportation 1.785 1.741 1.821 1.816 1.640 1.620 1.708 1.561 1.606 Final Report Figure 3.2-9 Energy Consumption by City and Process (Mechanical MRF Design) Solid Waste Management Holistic Decision Modeling Final Report Carbon emissions variation by process design Of the three parameters reported (i.e., cost, energy, and carbon emissions), carbon emissions exhibits the largest variation between the manual and mechanical design options. Cities such Amman, Shanghai and Lahore show an order of magnitude difference between the emissions from a manual and a mechanical design (see the corresponding MRF values shown in Figures 3.2-11 and 3.2-12), with the mechanical design having consistently higher values. The magnitude of the difference can be explained by the emissions associated to the electricity grid mix in each of the cities. For example, Kathmandu exhibits an almost zero difference in the emissions since most of the electricity used at the MRF is hydro-electricity, is assumed to not have any associated GHG emissions. Cities with hydro-electricity in their grid mix exhibit a smaller variation in the emissions between the manual and the mechanical MRF than cities such Amman, Shanghai, and Lahore that rely on more fossil-fuel based electricity grids. Carbon emissions variation by city Figures 3.2-11 and 3.2-12 show that the key drivers behind the differences across cities are (1) the emission savings from remanufacturing and (2) the emissions at the landfill. Buenos Aires is the city with the lowest overall emissions followed by Kawasaki, which is the city with the highest percentage of remanufactured material. This behavior is consistent with the amount of plastic and aluminum material remanufactured in each city. For example, according to Table 3.2-4 Buenos Aires has a higher percentage of plastic material than Kawasaki. In addition, Buenos Aires has a lower percentage of fossil fuels in its electricity grid mix when compared to Kawasaki, so its pre-combustion emissions at the MRF are less than Kawasaki’s. Table 3.2-4 Percentages of Remanufactured Recyclables by City (Sorted by Total Aluminum and Plastic) Organics Inorganics Cities Total Ferrous Aluminum Paper Plastics Glass Aluminum and materials materials Plastics Shanghai 2.29% 9.31% 0.76% 0.08% 0.12% 9.43% Amman 7.32% 7.65% 1.06% 0.00% 0.00% 7.65% Buenos Aires 6.34% 6.67% 2.81% 0.73% 0.53% 7.20% Kawasaki 9.45% 6.05% 2.68% 1.32% 0.60% 6.65% Lahore 1.30% 4.34% 0.67% 0.19% 0.29% 4.63% Kathmandu 3.51% 3.78% 1.16% 0.19% 0.00% 3.78% Sarajevo 4.11% 2.90% 0.00% 0.00% 0.00% 2.90% Conakry 6.24% 2.62% 0.53% 1.50% 0.00% 2.62% Atlanta 11.24% 1.37% 2.04% 2.39% 0.77% 2.14% 3-22 0.08 0.06 0.07 0.07 0.06 0.06 0.06 Solid Waste Management 0.05 Holistic Decision Modeling 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.01 3-23 0.01 0.00 Carbon Emissions (MTCE)/ metric ton 0.00 -0.001 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.01 Aires Recycling Using Manual Sorting Recycling Using Mechanical Sorting Figure 3.2-10 Net Total Carbon Emissions by City Final Report Recycling Using Manual Sorting 0.2 0.1 Solid Waste Management Holistic Decision Modeling 0.0 -0.1 -0.2 -0.3 3-24 -0.4 Carbon Emissions (MTCE)/ metric ton f or each process -0.5 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 MRF 0.001 0.005 0.006 0.003 0.009 0.004 0.009 0.006 0.003 Remanufacturing -0.224 -0.154 -0.421 -0.239 -0.201 -0.346 -0.348 -0.324 -0.238 LF 0.081 0.077 0.047 0.061 0.072 0.064 0.071 0.088 0.086 Transportation 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Final Report Figure 3.2-11 Carbon Emissions by City and Process (Manual MRF Design) Recycling Using Mechanical Sorting 0.2 0.1 Solid Waste Management Holistic Decision Modeling 0.0 -0.1 -0.2 -0.3 3-25 -0.4 Carbon Emissions (MTCE)/ metric ton f or each process -0.5 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 MRF 0.001 0.010 0.013 0.005 0.018 0.007 0.018 0.010 0.006 Remanufacturing -0.224 -0.154 -0.421 -0.239 -0.201 -0.346 -0.348 -0.324 -0.238 LF 0.081 0.077 0.047 0.061 0.072 0.064 0.071 0.088 0.086 Transportation 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Final Report Figure 3.2-12 Carbon Emissions by City and Process (Mechanical MRF Design) Solid Waste Management Holistic Decision Modeling Final Report On the other hand, Conakry is the city with the highest overall emissions and even higher than Lahore, which is the city with the lowest percentage of recyclables being sent to remanufacturing. From looking at Table 3.2-4 Conakry has smaller amounts of aluminum and plastic material than Lahore, which begins to explain the differences in their overall emissions. Furthermore, Conakry has a larger percentage of fossil fuels (primarily oil) in its electricity grid mix. Kathmandu has higher emissions than Sarajevo despite of having a higher percentage of aluminum and plastics and a hydro electricity grid mix. In this case, the emissions behavior is explained by looking at the percentage and composition of the residuals being sent to the landfill. Kathmandu has a higher percentage of landfill disposed residuals than Sarajevo and most of them are food waste, which have the highest landfill gas generation rate. 3-26 Solid Waste Management Holistic Decision Modeling Final Report 3.2.3 Mixed Waste Composting Using Manual Turning and Mechanical Windrow Turner Two types of designs are considered for the composting scenarios: composting using manual pile turning and composting using a mechanical pile turner (i.e., a windrow turner). For these composting scenarios, mixed waste collected and sent to either a manual turning or a mechanical turner design. The overall results are recorded in Figure 3.2-13 Net Total Cost by City (with Land Price), Figure 3.2-16 Net Total Cost by City (without Land Price), Figure 3.219 Net Total Energy Consumption by City, and Figure 3.2-22 Net Total Carbon Emissions by City. Each figure shows the results for the nine cities under analysis on a per ton basis (i.e., the net total cost/energy/emissions divided by the amount of waste generated in each city). To aid the analysis, the composting results were further broken down by processes (i.e., collection, composting, landfill disposal and transportation) dividing the net totals from each process by the amount of waste managed by that process as presented in Figures 3.2-14, 3.2-15, 3.2-17, 3.2-18, 3.2-20, 3.2-21 and 3.2-22. It should be noted that the values in the last figures cannot be summed to obtain the Net Totals by City in Figures 3.2-13, 3.2-16, 3.2-19 and 3.2-22, since the result of this sum will be on a per ton of waste as generated and not as managed by a mix of different processes. For the composting scenarios, there is no product (e.g., fertilizer) that the compost product is assumed to displace because it is difficult to determine what exactly the compost product displaces, if anything. If the compost product can be shown to reduce the consumption of another product, then there would be an added environmental benefit associated with composting. Table 3.2-5 indicates how much waste is available for composting and how much waste goes to each of the management processes. By design these scenarios aim to composting all the available organics. Therefore, the pre-screening inefficiencies of a conventional composting process were avoided by setting the pre-trommel efficiencies to 100% (i.e., all the available organics are separated and composted under these scenarios). Table 3.2-5 is sorted in descending order by percentage of material being composted. There are not differences in the percentage of waste going to the different processes between the manual and the mechanical windrow turner design. 3-27 Solid Waste Management Holistic Decision Modeling Final Report Table 3.2-5 Percentage of Compostable Organics and Waste Flow by City (Sorted by Percent of Organics Composted) Percentage of Waste Flow Cities Organics Available for Composting (a) Composting (a x 100%) Disposal Kathmandu 81.68% 81.68% 18.32% Kawasaki 71.70% 71.70% 28.30% Shanghai 69.72% 69.77% 30.23% Conakry 64.87% 64.87% 35.13% Amman 62.06% 62.04% 37.96% Buenos Aires 60.35% 60.35% 39.65% Sarajevo 54.40% 54.44% 45.56% Lahore 51.09% 51.11% 48.89% Atlanta 47.98% 47.93% 52.07% In general the composting cost results do not show a clear correlation with the cities socio- economic groups defined by their income level (e.g. Kathmandu has the lowest income level and Atlanta has the highest income level). Cost data inputs (e.g., landfill capital, O&M, and energy costs) were not consistently found for all the cities. Therefore, cost variations observed in the results among cities in the same socio-economic group may not accurately reflect reality. Cost variation by process design In Figures 3.2-14 and 3.2-15 (see the corresponding composting values) the costs for the mechanical windrow turner design are consistently 20-30% higher than those for the manual design. This range of variation mostly depends on each city’s diesel fuel price and electricity cost affecting the results of the mechanical windrow turner design. Cost variation by city Differences in the net total cost across cities can be explained by (1) the total cost per ton for disposal at a landfill using gas flaring and (2) net costs at the composting facility, which include the revenues from compost sale. Kawasaki is the city with the largest collection cost due to having the highest wage rates for drivers and collectors along with a low usable capacity in its collection vehicles. When looking at costs across cities, Kawasaki, Conakry, and Sarajevo have the highest overall cost. This is primarily due to having the highest landfill capital (e.g., very high land price) and electricity costs in the case of Kawasaki and Sarajevo and having the highest equipment and maintenance costs in the case of Conakry. The overall costs for the other cities are very similar and the net costs at the composting facility play a very important role. Differences in the net costs from composting facilities across cities are due to differences in the overall cost of energy (i.e., diesel and electricity) and the revenues from compost sale. 3-28 with land price f or landf ill cost 200.0 185.6 180.0 Solid Waste Management Holistic Decision Modeling 160.0 147.1 140.0 115.5 120.0 101.6 99.3 102.3 100.8 101.1 92.7 94.6 100.0 81.7 76.9 80.0 72.2 71.0 63.6 63.3 65.6 66.3 60.0 3-29 Net Total Cost ($)/ metric ton 40.0 20.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Composting Using Manual Turning Composting Using Windrow Turner Figure 3.2-13 Net Total Cost by City (with Land Price) Final Report Composting Using Manual Turning (with Land Price) 250.0 200.0 Solid Waste Management Holistic Decision Modeling 150.0 100.0 3-30 50.0 Unit Cost ($)/ metric ton f or each process 0.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 34.362 34.384 30.347 27.434 28.024 28.188 28.286 57.009 36.294 Composting 13.875 23.556 21.368 17.079 18.224 17.966 18.076 17.866 17.943 LF 44.794 45.437 27.568 27.096 46.397 32.028 44.738 203.812 28.234 Transportation 0.453 0.618 0.494 0.491 0.561 0.661 0.413 1.159 1.348 Final Report Figure 3.2-14 Unit Cost by City and Process (with Land Price: Manual Composting Design) Composting Using Windrow Turner (with Land Price) 250.0 200.0 Solid Waste Management Holistic Decision Modeling 150.0 100.0 3-31 50.0 Unit Cost ($)/ metric ton f or each process 0.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 34.362 34.384 30.347 27.434 28.024 28.188 28.286 57.009 36.294 Compost 48.456 54.219 45.993 43.708 46.583 44.295 49.591 52.841 40.034 LF 44.794 45.437 27.568 27.096 46.397 32.028 44.738 203.812 28.234 Transportation 0.452 0.618 0.494 0.491 0.561 0.661 0.411 1.159 1.348 Final Report Figure 3.2-15 Unit Cost by City and Process (with Land Price: Mechanical Composting Design) without land price f or landf ill cost 200.0 180.0 160.0 Solid Waste Management Holistic Decision Modeling 140.0 132.6 115.0 120.0 101.4 100.6 96.1 92.9 94.6 96.6 100.0 91.8 94.0 81.2 76.4 80.0 69.0 63.4 62.4 61.6 65.6 62.1 60.0 3-32 Net Total Cost ($)/ metric ton 40.0 20.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Composting Using Manual Turning Composting Using Windrow Turner Figure 3.2-16 Net Total Cost by City (without Land Price) Final Report Composting Using Manual Sorting (without land price) 250.0 200.0 Solid Waste Management Holistic Decision Modeling 150.0 100.0 3-33 Unit Cost ($)/ metric ton f or each process 50.0 0.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 34.36 34.38 30.35 27.43 28.02 28.19 28.29 57.01 36.29 Composting 13.88 23.56 21.37 17.08 18.22 17.97 18.08 17.87 17.94 LF 44.21 44.13 21.65 25.31 24.05 31.97 32.10 33.52 27.36 Transportation 0.45 0.62 0.49 0.49 0.56 0.66 0.41 1.16 1.35 Final Report Figure 3.2-17 Unit Cost by City and Process (without Land Price: Manual Composting Design) Composting Using Windrow Turner (without land price) 250.0 200.0 Solid Waste Management Holistic Decision Modeling 150.0 100.0 3-34 50.0 Unit Cost ($)/ metric ton f or each process 0.0 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 34.36 34.38 30.35 27.43 28.02 28.19 28.29 57.01 36.29 Compost 48.46 54.22 45.99 43.71 46.58 44.29 49.59 52.84 40.03 LF 44.21 44.13 21.65 25.31 24.05 31.97 32.10 33.52 27.36 Transportation 0.45 0.62 0.49 0.49 0.56 0.66 0.41 1.16 1.35 Final Report Figure 3.2-18 Unit Cost by City and Process (without Land Price: Mechanical Composting Design) Solid Waste Management Holistic Decision Modeling Final Report Energy variation by process design Figures 3.2-20 and 3.2-21 (see the corresponding composting values) shows energy requirements for the mechanical windrow turner design that are consistently 6-9% higher than those for the manual design. This difference can be attributed to the avoided energy requirements from equipment fuel (i.e., mostly diesel) consumption at the manual composting facility. Energy variation by city Composting energy consumption among cities does not exhibit as much variation as it did for the recycling scenarios. From Figures 3.2-20 and 3.2-21, the energy requirements at the composting facility are the main drivers behind the differences across cities. Kawasaki is the city with the largest collection energy mostly due to the low usable capacity (half of the usable capacity of other cities’) in its collection vehicles, which increases the energy requirements associated with vehicles fuel production. Consistent with the cost results Kawasaki is the city with the highest overall energy consumption followed closely by Lahore and Conakry. Kawasaki’s energy results can be explained by having the highest collection energy demand, a moderately high composting energy demand, and the second highest (after Atlanta) landfill energy demand. By having one of the highest percentages of organics Kawasaki’s energy requirements at the composting facility will be high and the combustion and pre-combustion energy from an electricity grid mix with a large amount of natural gas will also be high (i.e., according to Table 2.5-3, natural gas is the fuel with the highest ratio BTU per kWh of electricity delivered). 3-35 0.9 0.817 0.8 0.732 0.765 0.708 0.709 0.704 0.678 0.679 0.7 Solid Waste Management 0.653 0.653 0.641 0.657 Holistic Decision Modeling 0.624 0.591 0.6 0.549 0.503 0.5 0.445 0.407 0.4 0.3 0.2 3-36 0.1 Energy Consumption (MKcal)/ metric ton 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Composting Using Manual Turning Composting Using Windrow Turner Figure 3.2-19 Net Total Energy Comsumption by City Final Report Composting Using Manual Turning 0.14 0.12 Solid Waste Management Holistic Decision Modeling 0.10 0.08 0.06 3-37 0.04 0.02 Energy Consumption (MKcal)/ metric ton f or each process 0.00 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.042 0.042 0.037 0.033 0.034 0.034 0.034 0.073 0.046 Compost 0.026 0.066 0.062 0.028 0.070 0.053 0.071 0.062 0.031 LF 0.109 0.108 0.108 0.111 0.111 0.113 0.115 0.123 0.131 Transportation 0.002 0.003 0.002 0.002 0.002 0.003 0.002 0.005 0.006 Final Report Figure 3.2-20 Energy Comsumption by City and Process (Manual Composting Design) Composting Using Windrow Turner 0.14 0.12 Solid Waste Management Holistic Decision Modeling 0.10 0.08 0.06 0.04 3-38 0.02 Energy Consumption (MKcal)/ metric ton f or each process 0.00 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.042 0.042 0.037 0.033 0.034 0.034 0.034 0.073 0.046 Compost 0.035 0.078 0.074 0.039 0.083 0.064 0.084 0.073 0.041 LF 0.109 0.108 0.108 0.111 0.111 0.113 0.115 0.123 0.131 Transportation 0.002 0.003 0.002 0.002 0.002 0.003 0.002 0.005 0.006 Final Report Figure 3.2-21 Energy Comsumption by City and Process (Mechanical Composting Design) Solid Waste Management Holistic Decision Modeling Final Report Carbon emissions variation by process design Figures 3.2-22, 3.2-23 and 3.2-24 show that in general the differences in the carbon emissions between the manual and the mechanical windrow design are very small ranging from approximately 10E-6 to 10E-3. Carbon emissions variation by city Figures 3.2-23 and 3.2-24 show that the key drivers behind the differences across cities are: (1) landfill emissions and (2) composting facility emissions. Atlanta has the highest overall emissions from having the highest amount of material disposed at a landfill (see Table 3.2-5) and using an electricity grid mix that includes a high percentage of fossil fuels. 3-39 0.04 0.034 0.04 0.033 Solid Waste Management Holistic Decision Modeling 0.03 0.024 0.03 0.022 0.02 0.017 0.016 0.015 0.02 0.013 0.010 0.009 0.009 0.010 0.01 0.008 0.008 0.008 3-40 0.006 0.007 0.005 0.01 Carbon Emissions (MTCE)/ metric ton 0.00 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Composting Using Manual Turning Composting Using Windrow Turner Figure 3.2-22 Net Total Carbon Emissions by City Final Report Composting Using Manual Turning 0.06 0.05 Solid Waste Management Holistic Decision Modeling 0.04 0.03 0.02 3-41 0.01 Carbon Emissions (MTCE)/ metric ton f or each process 0.00 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 Compost 0.000 0.005 0.005 0.002 0.005 0.003 0.006 0.004 0.002 LF 0.034 0.039 0.018 0.009 0.016 0.001 0.001 0.001 0.051 Transportation 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Final Report Figure 3.2-23 Carbon Emissions by City and Process (Manual Composting Design) Composting Using Windrow Turner 0.06 0.05 Solid Waste Management Holistic Decision Modeling 0.04 0.03 0.02 3-42 0.01 Carbon Emissions (MTCE)/ metric ton f or each process 0.00 Buenos Katmandu Conakry Lahore Sarajevo Amman Shanghai Kawasaki Atlanta Aires Collection 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.001 Compost 0.001 0.007 0.006 0.003 0.006 0.004 0.007 0.005 0.003 LF 0.034 0.039 0.018 0.009 0.016 0.001 0.002 0.001 0.051 Transportation 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Final Report Figure 3.2-24 Carbon Emissions by City and Process (Mechanical Composting Design) Solid Waste Management Holistic Decision Modeling Final Report 3.2.4 Incineration Without and With Energy Recovery The modeled incineration facility is assumed to be modern mass burn combustor with an assumed plant heat rate of 17,500 BTU/kWh, 70% ferrous recovery rate from ash, and conforms to EU incinerator emission standards. The incineration scenarios consist of sending all the mixed waste collected to an incineration facility. Two variations are analyzed: (1) without energy recovery and (2) with energy recovery, both with ferrous recovery. The overall results are recorded in Figure 3.2-25 Net Total Cost by City (with Land Price), Figure 3.2-26 Net Total Cost by City (without Land Price), Figure 3.2-27 Net Total Energy Consumption by City, and Figure 3.2 Net Total Carbon Emissions by City. Because most of the cost, energy, and emissions are related to the combustion process (see percentages of waste going to the combustion process vs. other processes in Table 3.2-6), the results were not further broken down by process. Table 3.2-6 presents the total heating value of the waste in each of the cities, which in combination with the percentage of ferrous material recovered (i.e., waste sent to remanufacturing) from the waste stream are going to define the energy life cycle assessment results. Table 3.2-6 Percentage of Waste Going to the Different Processes (Sorted by Average Heat Content) Heating Content Cities Combustion Remanufacturing Disposal of Waste Stream (kcal/kg, dry-base) Atlanta 79.33% 3.04% 17.63% 7,643 Kawasaki 82.22% 1.68% 16.10% 6,289 Amman 89.11% 0.00% 10.89% 6,007 Buenos Aires 85.90% 0.93% 13.17% 5,862 Shanghai 76.44% 0.10% 10.88% 5,834 Lahore 80.96% 0.25% 18.80% 5,080 Sarajevo 71.52% 0.00% 28.48% 4,787 Conakry 83.08% 1.91% 15.01% 4,356 Kathmandu 90.66% 0.25% 8.91% 874 For incineration without energy recovery the costs results will depend on the capital, O&M, and energy costs for the combustion process in each city. Because this data were not consistently found, then the costs variations observed in the results among cities may not accurately reflect reality. For incineration with energy recovery each city receives revenues from electricity sales, which will vary according to the sale price, and revenues from ferrous recovery that will depend on the percentage of ferrous in the system and the ferrous material sale price. The energy results for incineration without energy recovery will vary according to the energy savings from ferrous recovery and in general, according to the combustion and pre-combustion energy associated with the production of the fuels in each city’s electricity grid mix. For 3-43 Solid Waste Management Holistic Decision Modeling Final Report incineration with energy recovery the initial energy requirements are offset by the energy produced and the energy savings from ferrous recovery. The carbon emission results from the incineration scenario without energy recovery will mainly depend on (1) the fuels in electricity grid mix of each city and their associated combustion and pre-combustion emissions (e.g., fossil fuels generated energy has higher emissions than nuclear generated energy) and (2) the percentage of plastics in the waste being burned. For the scenario with energy recovery, there will be emissions offsets from the energy produced and the ferrous material recovered. Cost variation by city Figures 3.2-25 and 3.2-26 shows that Kawasaki is the city with the highest overall cost resulting from having the highest capital, O&M, and energy costs at the incineration facility. On the other hand Shanghai has the lowest costs from having the lowest capital and O&M costs. 3-44 with land price f or landf ill cost 900.0 813.5 800.0 754.5 Solid Waste Management Holistic Decision Modeling 700.0 600.0 500.0 400.0 300.0 3-45 148.5 149.8 Net Total Cost ($)/ metric ton 146.4 145.1 143.4 143.0 151.6 200.0 131.2 144.7 131.8 125.2 131.4 124.6 129.4 110.4 86.5 100.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Incineration Incineration With Energy Recovery Figure 3.2-25 Net Total Cost by City (with Land price) Final Report without land price f or landf ill cost 900.0 802.0 800.0 742.9 Solid Waste Management Holistic Decision Modeling 700.0 600.0 500.0 400.0 300.0 148.4 149.7 145.9 144.9 143.0 3-46 Net Total Cost ($)/ metric ton 200.0 144.7 142.4 151.6 131.1 131.3 125.0 131.4 124.0 129.3 109.4 86.3 100.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Incineration Incineration With Energy Recovery Figure 3.2-26 Net Total Cost by City (without Land price) Final Report Solid Waste Management Holistic Decision Modeling Final Report Energy variation by city In Figure 3.2-27 the key drivers behind the differences among the cities are (1) the heating value of the waste in each of the cities, (2) the electricity grid mix displaced by the energy recovered and (3) the energy savings from ferrous recovery. For example, Atlanta is the city with the highest waste heating value and amount of ferrous materials in the waste stream, which explain why it has the highest energy savings from the incineration scenarios. On the other hand, Sarajevo and Amman do not have any ferrous material in their waste stream and they are not showing any energy savings from the incineration scenario without energy recovery. 3-47 0.5 0.06 0.05 0.00 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.06 -0.06 Aires Solid Waste Management Holistic Decision Modeling -0.5 -0.39 -0.50 -0.70 -0.84 -1.0 -0.98 -1.5 -1.37 -1.75 -2.0 -2.05 -2.11 3-48 -2.26 -2.5 -2.51 -2.61 Energy Consumption (MKcal)/ metric ton -3.0 -3.00 -3.5 Incineration Incineration With Energy Recovery Figure 3.2-27 Net Total Energy Comsumption by City Final Report Solid Waste Management Holistic Decision Modeling Final Report Carbon emissions variation by city Figure 3.2-28 offers an interesting picture of the carbon emissions from the incineration scenarios in all the cities. Different from previous figures there is much more variation in the emissions across cities. In the case of incineration with energy recovery this variation depends on each city’s potential to offset electricity generation emissions from the utility sector, the amount of plastics burned, and the emission savings from ferrous material recovery and recycling. For example, Kathmandu does not have overall carbon emission offsets from incineration with energy recovery since 90% of its electricity is hydroelectricity with assumed zero carbon emissions. Contrary, Conakry has the highest emissions savings from having the lowest amount of plastics, the highest amounts of ferrous, and offsetting energy from an electricity grid with ~75% residual oil, which has the highest combustion and pre-combustion emissions. Shanghai, which has the highest carbon emissions from incineration without energy recovery, also has the highest amount of plastics and among the smallest amounts of ferrous material. This city’s percentage of plastics is very similar to Atlanta, which also has high carbon emissions from incineration without energy recovery. For the scenario with energy recovery Shanghai perceives higher emission offsets than Atlanta from having an electricity grid mix richer in fossil fuels. Table 3.2-7 presents the percentage of each waste category being incinerated. Table 3.2-7 Percentage of Each Waste Category Incinerated by City (Sorted by Percentage of Plastics) Organics Inorganics Miscella Cities Yard Food Plastics Ferrous Aluminum Paper Total Glass Total neous waste waste * materials materials Shanghai 1.37% 57.49% 6.11% 64.97% 15.48% 0.03% 0.10% 0.11% 15.72% 8.43% Atlanta 4.67% 11.16% 27.81% 43.64% 13.21% 0.08% 3.05% 0.06% 16.39% 22.34% Amman 1.84% 41.18% 15.57% 58.59% 13.06% 0.04% 0.00% 0.00% 13.10% 17.42% Buenos Aires 4.49% 34.16% 18.26% 56.91% 11.39% 0.11% 0.93% 0.04% 12.47% 17.46% Kawasaki 3.13% 34.35% 29.24% 66.72% 10.33% 0.11% 1.68% 0.04% 12.16% 5.03% Lahore 16.29% 28.77% 10.51% 55.57% 7.41% 0.03% 0.25% 0.02% 7.70% 17.93% Kathmandu 2.50% 65.81% 7.71% 76.03% 6.45% 0.05% 0.25% 0.00% 6.74% 8.31% Sarajevo 0.83% 37.14% 13.48% 51.46% 4.96% 0.00% 0.00% 0.00% 4.96% 15.10% Conakry 7.35% 39.64% 14.66% 61.65% 4.47% 0.02% 1.91% 0.00% 6.40% 16.94% *The plastics non-recyclables were added to the plastics here. 3-49 0.20 0.14 0.15 0.12 0.12 0.10 Solid Waste Management Holistic Decision Modeling 0.10 0.07 0.06 0.06 0.07 0.04 0.04 0.05 0.01 0.00 0.00 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires -0.05 -0.05 3-50 -0.06 -0.06 -0.07 -0.08 Carbon Emissions (MTCE)/ metric ton -0.10 -0.09 -0.15 Incineration Incineration With Energy Recovery Figure 3.2-28 Net Total Carbon Emissions by City Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.2.5 Landfill with Gas Venting, Gas Collection and Flaring, and Gas Collection and Energy Recovery The modeled landfill is assumed to be a modern U.S. EPA Subtitle D type landfill with a liner and leachate collection and treatment system. The scenarios modeled consist of collecting and sending all waste to a landfill, with three types of landfill gas management practices being analyzed: (1) venting, (2) flaring, and (3) energy recovery. For scenarios that include gas management a 70% gas collection efficiency is assumed. For all scenarios, a 100-year time period is used for calculating landfill gas generation and emissions. The overall results were recorded in Figure 3.2-29 Net Total Cost by City (with Land Price), Figure 3.2-30 Net Total Cost by City (without Land Price), Figure 3.2-31 Net Total Energy Consumption by City, and Figure 3.2-32 Net Total Carbon Emissions by City. The same was done sending all the mixed waste to a landfill with gas collection and flaring and to one with gas collection and energy recovery. Cost variation by landfill type and city The cost variations across different landfills observed in Figures 3.2-29 and 3.2-30 are due to the additional equipment cost for gas collection and flaring and for gas collection and energy recovery and the revenues from energy sale in the energy recovery scenario. Across cities the differences are due (1) the capital, O&M, and energy costs and (2) the revenues from energy sale, which are going to depend on the composition of the waste (i.e., food waste and grass have the highest decay rate- methane gas yield and consequently the highest potential for energy recovery) and the energy sale price. In some cases the collection costs play an important role (e.g., Kathmandu, Buenos Aires, Atlanta, and Lahore) and this can be observed in the cost break downs by processes presented in Table 3.2-8. It should be noted that all the three landfill types have the same cost breakdown by process. Table 3.2-8 Percentage of Cost Attributed to the Different Landfill Scenario Processes (Sorted by Disposal Cost) Cities Collection Disposal Sarajevo 18% 82% Conakry 19% 81% Kawasaki 22% 78% Amman 38% 62% Shanghai 39% 61% Kathmandu 44% 56% Buenos Aires 47% 53% Atlanta 57% 43% Lahore 58% 42% 3-51 Solid Waste Management Holistic Decision Modeling Final Report Kawasaki has the highest costs as a result of having the highest electricity cost and landfill land price. This city and Shanghai also present the highest revenues from energy recovery due to having the highest energy sale prices and very high percent of methane/energy generating materials (i.e., most of Shanghai’s waste is food waste). Amman and Shanghai have almost the same cost for landfill venting and flaring and this is due to very similar capital, O&M, and energy costs. For example, Amman has higher land prices but a lower utility rate than Shanghai. Even thought Shanghai has much more organics in its waste stream than Amman, the revenues perceived from electricity sale are very similar since Amman is offsetting a mostly natural gas electricity grid mix, which has the highest ratio BTU per kWh of electricity delivered. Kathmandu has similar landfill venting and flaring costs to Amman and Shanghai even thought its landfill capital, O&M, and energy costs are much lower than either of these two countries. However, according to Table 3.2-8 collection constitutes a much higher percentage of the net total costs and Kathmandu’s diesel fuel prices are much higher than Amman and Shanghai’s. On the other hand, Lahore has the lowest overall costs and most of them can be attributed to collection. Then, Lahore’s low diesel fuel prices explain its low overall costs. 3-52 with land price f or landf ill cost 350.0 293.0 300.0 287.2 265.1 Solid Waste Management Holistic Decision Modeling 250.0 200.0 150.0 88.8 89.6 83.7 81.9 72.1 100.0 87.1 87.8 87.8 85.2 82.1 67.8 3-53 65.0 61.4 80.3 Net Total Cost ($)/ metric ton 63.7 66.4 64.2 58.8 70.7 65.4 54.5 60.2 49.9 59.0 50.0 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Landfill With Gas Venting Landfill With Gas Collection And Flaring Landfill With Gas Collection And Energy Recovery Figure 3.2-29 Net Total Cost by City (with Land Price) Final Report without land price f or landf ill cost 350.0 300.0 Solid Waste Management Holistic Decision Modeling 250.0 200.0 150.0 101.7 88.2 88.1 99.7 100.0 86.4 87.2 86.4 67.7 71.1 83.7 67.7 3-54 58.3 59.4 58.6 66.4 64.2 66.4 73.7 69.7 Net Total Cost ($)/ metric ton 57.2 58.2 57.4 64.4 47.9 47.9 44.6 50.0 33.8 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Landfill With Gas Venting Landfill With Gas Collection And Flaring Landfill With Gas Collection And Energy Recovery Figure 3.2-30 Net Total Cost by City (without Land Price) Final Report Solid Waste Management Holistic Decision Modeling Final Report Energy variation by landfill type and city The energy requirements for the landfill with gas venting and the landfill with gas flaring exhibit very little variation across cities (see Figure 3.2-31). Kawasaki has the highest energy requirements mostly attributed to collection (see Table 3.2-9). Then, Kawasaki’s low vehicle usable capacity (half of the usable capacity of other cities’) in combination with a moderately high BTU per kWh electricity produced explains its high overall energy requirements. Variation in the energy offsets from the landfill with energy recovery can be explained by the electricity grid mix in each city and the corresponding energy requirements that are being offset. For example, Amman, the city with the highest energy offsets, has an electricity grid mix with about 95% natural gas, which is the fuel with the highest ratio BTUs consumed per kWh of electricity produced. Therefore, for every BTU of energy recovered the energy savings from Amman’s electricity grid mix are much higher than other cities with a different grid mix. Contrary, 90% of Kathmandu’s electricity is hydro-electricity, which has the lowest ratio BTU per kWh of electricity produced. Table 3.2-9 Percentage of Energy Attributed to the Different Landfill Scenario Processes (Sorted by Disposal Energy Consumption) Cities Collection Disposal Shanghai 22% 78% Conakry 23% 77% Buenos Aires 23% 77% Sarajevo 23% 77% Amman 25% 75% Lahore 26% 74% Atlanta 26% 74% Kathmandu 28% 72% Kawasaki 36% 64% 3-55 0.3 0.23 0.21 0.23 0.19 0.16 0.21 0.17 0.19 0.2 0.15 0.16 0.15 0.16 0.16 0.15 0.16 0.16 0.17 0.15 Solid Waste Management Holistic Decision Modeling 0.1 0.0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.02 Aires -0.1 -0.14 -0.2 -0.17 -0.20 -0.22 3-56 -0.3 -0.27 -0.33 -0.33 Energy Consumption (MKcal)/ metric ton -0.4 -0.46 -0.5 Landfill With Gas Venting Landfill With Gas Collection And Flaring Landfill With Gas Collection And Energy Recovery Figure 3.2-31 Net Total Energy Comsumption by City Final Report Solid Waste Management Holistic Decision Modeling Final Report Carbon emissions variation by landfill type and city The carbon emission variations across cities from the scenarios with gas venting and gas flaring that can be observed in Figure 3.2-32 are explained by (1) each city’s waste composition which governs the methane gas production and (2) the emissions associated with each city’s grid mix. For example, Kathmandu’s landfill venting scenario has the highest carbon emissions explained by 60% of its MSW stream consisting of food waste (this is the waste category with the highest methane generation rate). Atlanta’s landfill flaring scenario has the highest carbon emissions even though its energy requirements are lower than other cities such Kawasaki. However, when compared with Kawasaki, Atlanta has a larger amount of fossil fuels in its electricity grid mix. Emission variations in the results from the energy recovery scenario are due to both the waste composition governing the methane gas production and the electricity grid mix governing the emission offsets. For example, Kathmandu’s has the highest emissions from this scenario because it has the largest amount of food waste and the lowest carbon offsets from a 90% hydroelectricity grid. Shanghai has the lowest emissions by having the highest carbon offsets in an electricity grid mix rich in fossil fuels. 3-57 0.35 0.29 0.30 0.28 0.28 0.28 0.26 Solid Waste Management Holistic Decision Modeling 0.25 0.24 0.22 0.21 0.20 0.19 0.15 0.09 0.10 0.10 0.10 0.09 0.09 0.08 0.07 0.07 0.08 3-58 0.07 0.06 0.06 0.05 0.04 0.04 0.05 Carbon Emissions (MTCE)/ metric ton 0.03 0.03 0.03 0.00 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires Landfill With Gas Venting Landfill With Gas Collection And Flaring Landfill With Gas Collection And Energy Recovery Figure 3.2-32 Net Total Carbon Emissions by City Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.3 Optimization Scenario Results In addition to the simulation-type scenarios analyzed in Section 3.2, a number of optimization-type scenarios were analyzed. Under the optimization scenarios RTI’s MSW DST identifies and selects the waste management strategy that best meets the defined optimization goal. For example, in solving for an optimization goal of minimizing energy consumption, the MSW DST will identify the waste management strategy that achieves the lowest net energy consumption, which is a function of the total process energy consumption less energy production from waste. Four optimization scenarios are analyzed for each city including: • Group 3—maximizing materials recovery (via recycling and composting) • Group 4—maximizing energy recovery • Group 5—minimize carbon (global warming) emissions and, minimize PM (global dimming) emissions. For each optimization scenario, different variations for collection and process design and operations were considered to assess their impact on the results. The variations analyzed include: • Daily and biweekly collection. • Low and high percent capture of recyclables: o low capture is defined by 50% participation factor3 and 50% capture rate4 o high capture is defined by 75% participation factor and 75% capture rate. • Recycling and composting using manual sorting and pile turning, respectively. • Recycling and composting using mechanical sorting and pile turning, respectively. Table 3.3.1 presents the waste management processes that were selected according to each scenario’s optimization goal. As shown in the table, similar strategies were generally selected by scenario for each city. The lessons learned from Section 3.2 where the simulation results were presented will be very useful understanding the optimization results. For example, Table 3.2.1 Summary Contribution of Key Input Parameters to Results by Process can be used to understand the optimization results once the main (those managing most of the waste in the system) waste management processes have been identified. 3 The participation factor indicates the average percentage of households within a region in a source segregated recyclables collection program. 4 The capture rate is the fraction of recyclable material removed by households from the waste and put in the recyclables collection bin. 3-59 Holistic Decision Modeling Solid Waste Management Table 3.3-1 Waste Management Processes Selected by the MSW DST for Each Optimization Scenario Non Scenario Segregated Landfill Scenario Segregated Recycling3 Composting4 Incineration5 Variations 1 Collection2 Disposal6 Collection OPTIMIZATION SCENARIOS Daily - High X X X X Capture Daily - Low X X X X Capture Manual Sorting MRF Biweekly and Manual Turning Collection Composting X X X X - High Capture Biweekly Collection X X X X - Low Group 3- maximizing Capture materials recovery (via recycling and Daily - composting) 3-60 High X X X X Capture Daily - Low X X X X Mechanical Sorting Capture MRF and Biweekly Mechanical/ Collection Windrow Turner X X X X - High Composting Capture Biweekly Collection X X X X - Low Capture Final Report Holistic Decision Modeling Solid Waste Management Non Scenario Segregated Landfill Scenario Segregated Recycling3 Composting4 Incineration5 Variations Collection2 Disposal6 Collection1 Daily - 8 High X X X X X Capture Daily - 8 Low X X X X X Capture 7 Group 4- maximizing energy recovery Biweekly X8 Collection X X X X - High Capture Biweekly X8 Collection X X X X - Low Capture Daily - X8 9 High X X X X 3-61 Capture 8 Daily - X Low X9 X X X Capture Minimize carbon X8 (global warming) Biweekly X8 emissions Collection X9 X X X - High Group 5- Optimize Capture Reduction of Global Biweekly X8 Warming and Dimming Collection 9 Emissions7 X X X X - Low Capture Daily - High X X X X Minimize Particulate Capture Final Report Material (PM- global Daily - dimming) emissions Low X X X X Capture Holistic Decision Modeling Solid Waste Management Non Scenario Segregated Landfill Scenario Segregated Recycling3 Composting4 Incineration5 Variations Collection2 Disposal6 Collection1 Biweekly Collection X X X X - High Capture Biweekly Collection X X X X - Low Capture 1 Non- segregated collection: mixed waste collection only. 2 Segregated collection: separate recyclables, organics, and residuals collection. 3 Recycling: Group 3 scenarios use a commingled MRF (Material Recovery Facility) and Group 4 and 5 scenarios use a mixed waste MRF with 55% separation efficiency. 4 Composting: includes a mixed waste and a yard waste composting facility. 5 Incineration: includes a modern mass burn facility with 17,500 BTU/kWh heat rate and 70% ferrous recovery rate. 6 Landfill disposal: The default landfill (LF) is a conventional, modern (EPA Subtitle D type) LF, with 70% gas collection efficiency and gas flaring. 7 The selection of the waste management processes will vary from city to city. For example in some cities all the waste is collected through segregated collection. 8 Ash LF 9 3-62 Conakry is the only city with non-segregated collection. Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.3.1 Cost Results Cost results are shown in Table 3.3-2 and illustrated in Figures 3.3-1 to 3.3-10. In general, we found the most significant parameters affecting the cost results to include: • MRF/ Composting/ Incineration/Landfill disposal costs • Recyclables revenues • Energy prices • Labor prices In addition, the optimization scenario cost results were found to exhibit the following general trends: • The combination manual MRF and composting is less expensive than mechanical MRF and composting. • Daily waste collection is typically more expensive than biweekly waste collection. High recyclables capture is typically more expensive than low recyclables capture (due to cost of recyclables collection being higher than mixed waste collection). Variation by process design in the scenarios maximizing materials recovery Figures 3.3-1, 3.3-2, 3.3-3 and 3.3-4 show the cost variation by MRF and composting process design (i.e., manual and mechanical). Consistently in all the scenarios and settings, the net cost for the manual design is lower than for the mechanical design. This is due to composting using manual turning being significantly less expensive than using a windrow turner. If labor wages at the composting facility increase, the margin of difference will decrease. Variation by collection frequency The waste collection frequency (daily vs. biweekly) does not change the overall amount or composition of waste managed under each of the scenarios. Therefore, it is expected that daily collection will be more costly than a biweekly collection and this is observed in the results for all the cities (see Figures 3.3-1 to 3.3-10). 3-63 Holistic Decision Modeling Solid Waste Management Table 3.3-2 Summary of Cost Variation by Scenario and Scenario Settings Maximize Materials Recovery Minimize Carbon (Global Minimize PM (Global Dimming ) (via recycling and Maximize Energy Recovery Warming) Emissions Emissions composting) Criteri Manual and Mechanical a Daily Daily Biweekl Biweekl Daily Daily Biweekl Biweekl Daily Daily Biweekly Biweekl Daily Daily Biweek Biweek High Low y High y Low High Low y High y Low High Low High y Low High Low ly High ly Low Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cap. Cost Variation By Collection Frequenc More Less More Less More Less y (See More expensive Less expensive Figures expensive expensive expensive expensive expensive expensive 3.3.1 to 3.3.5) By Percent Cap. of Recyclable s (See More* Less* More* Less* More* Less* More* Less* More* Less* More* Less* More* Less* More* Less* Figures 3.3.1 to 3.3.5) 3-64 Kawasaki: highest net cost due to: Kawasaki: highest net cost due to: - Highest electricity cost By city - Highest incineration cost (Highest capital and O & M costs) - Among the lowest recycling (See revenues Tables 3.3.3 to Shanghai: lowest net cost due Atlanta: lowest net cost due to: 3.3.6) Atlanta and Buenos Aires: to: - High incineration revenues (High waste heating content) lowest net cost due to: - Highest recycling revenues - Highest recycling revenues - Highest recycling revenues (sale prices for all recyclables ) * More: More expensive, Less: Less expensive Final Repor 300 251.2 250 218.9 218.7 Solid Waste Management Holistic Decision Modeling 200 181.2 143.9 150 137.1 112.8 112.7 111.9 110.2 107.9 106.2 105.4 105.3 102.6 102.4 101.0 99.7 98.5 97.2 96.4 95.1 91.1 90.0 89.3 88.7 87.6 85.8 83.3 100 82.3 Cost ($)/ metric ton 75.5 75.2 73.7 71.0 55.3 3-65 50 11.9 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-1 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Final Report Cost by City (with Land Price) 300 265.7 233.2 250 224.5 Solid Waste Management Holistic Decision Modeling 186.8 200 162.1 145.6 144.0 143.3 136.4 150 124.1 118.6 124.9 111.1 115.1 108.7 107.4 107.1 105.7 104.4 101.0 101.0 100.1 98.4 97.7 97.0 95.8 93.4 89.8 88.4 88.2 88.0 86.6 100 80.4 75.5 74.9 Cost ($)/ metric ton 70.8 3-66 50 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-2 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Final Report Cost by City (without Land Price) 300 280.6 254.7 248.1 250 217.0 Solid Waste Management Holistic Decision Modeling 200 176.4 169.5 150.3 146.9 143.9 142.6 142.5 142.4 141.4 140.1 139.1 137.5 134.6 134.2 150 131.2 131.2 130.6 129.0 127.8 126.4 126.1 124.3 120.9 119.5 119.0 117.8 116.4 115.9 110.9 105.7 103.9 96.9 100 Cost ($)/ metric ton 3-67 50 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-3 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Final Report Cost by City (with Land Price) 350 295.1 300 262.6 260.3 Solid Waste Management Holistic Decision Modeling 250 222.6 191.8 184.2 200 175.7 175.3 168.9 165.1 163.6 158.1 149.6 147.2 144.3 143.2 141.9 140.7 138.9 137.4 133.9 150 128.6 127.5 127.4 127.2 124.9 122.5 121.3 119.7 118.2 116.9 115.7 113.5 112.7 105.5 96.7 Cost ($)/ metric ton 100 3-68 50 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-4 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Final Report Cost by City (without Land Price) 1,800 1,585.9 1,600 Solid Waste Management Holistic Decision Modeling 1,400 1,200 1,000 694.6 800 658.0 625.5 600 Cost ($)/ metric ton 400 3-69 166.8 152.0 159.2 145.2 142.5 142.5 141.9 142.3 141.1 141.2 139.7 138.8 132.4 132.0 131.1 130.3 118.2 110.5 108.1 108.2 107.5 107.3 107.3 107.3 107.0 106.7 105.1 104.6 103.8 100.6 99.2 200 52.8 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-5 Group 4: Maximizing Energy Recovery- Net Total Cost by City (with Land Price) 1,800 1,585.9 1,600 1,400 Solid Waste Management Holistic Decision Modeling 1,200 1,000 694.6 800 658.0 625.5 600 Cost ($)/ metric ton 400 3-70 166.8 159.2 152.0 145.2 142.5 142.5 142.3 141.9 141.2 141.1 139.7 138.8 132.4 132.0 131.1 130.3 118.2 110.5 108.1 108.2 107.5 107.3 107.3 107.3 107.0 106.7 105.1 104.6 103.8 100.6 99.2 200 52.8 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-6 Group 4: Maximizing Energy Recovery- Net Total Cost by City (without Land Price) 800 748.6 748.2 715.7 710.8 700 Solid Waste Management 600 Holistic Decision Modeling 500 400 300 Cost ($)/ metric ton 205.7 198.8 191.6 182.4 178.1 175.2 174.1 169.8 164.4 160.0 158.5 158.1 156.5 154.9 200 153.0 151.7 150.3 149.6 149.4 149.1 148.4 148.0 143.2 141.7 139.6 132.1 127.0 126.0 124.7 122.7 120.3 116.9 3-71 100 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-7 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Cost by City (with Land Price) 800 748.6 748.2 715.7 710.8 700 600 Solid Waste Management Holistic Decision Modeling 500 400 300 Cost ($)/ metric ton 205.7 198.8 191.6 182.4 178.1 175.2 174.1 169.8 164.4 160.0 158.5 158.1 156.5 200 154.9 153.0 151.7 150.3 149.6 149.4 149.1 148.4 148.0 143.2 141.7 139.6 132.1 127.0 3-72 126.0 124.7 122.7 120.3 116.9 100 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-8 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Cost by City (without Land Price) 800 708.9 697.4 671.2 664.9 700 600 Solid Waste Management Holistic Decision Modeling 500 400 300 204.6 197.8 Cost ($)/ metric ton 180.7 170.6 167.4 162.9 157.9 157.6 155.7 154.9 200 146.9 145.6 141.2 141.1 140.1 138.8 134.8 133.4 132.0 126.5 123.0 115.5 112.1 111.6 110.1 109.9 107.6 105.4 103.9 104.0 96.9 3-73 89.4 100 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-9 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Cost by Final Report City(with Land Price) 800 708.9 697.4 671.2 664.9 700 600 Solid Waste Management Holistic Decision Modeling 500 400 300 204.6 197.8 Cost ($)/ metric ton 180.7 170.6 167.4 162.9 157.9 157.6 155.7 154.9 200 146.9 145.6 141.1 141.2 140.1 138.8 134.8 133.4 132.0 126.5 123.0 115.5 112.1 111.6 110.1 109.9 107.6 105.4 104.0 103.9 96.9 3-74 89.4 100 0 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-10 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Cost by Final Report City(without Land Price) Solid Waste Management Holistic Decision Modeling Final Report Variation by percent capture of recyclables Scenarios with high or low capture will vary in cost depending on the amount of waste going to each of the selected management process (See Appendix 3.3.1. Optimization Scenarios Mass Flows by City and Management Process). In general, we found that the higher recyclables capture rate case is generally less expensive since it increases materials recovery (and associated revenues) and reduces landfill disposal costs. However, this behavior can be reversed by the lack of revenues/markets for recycling and/or a very low landfill disposal cost. For example, the Group 3 low capture scenario results would be less expensive than the high capture scenarios if composting and landfill disposal of additional material were cheaper than recycling that material (i.e., higher MRF cost and lack of significant revenues from the sale of recyclables). A scenario with an optimization objective of minimizing energy will recover as much energy as possible from either recycling and/or incineration. A tradeoff exists between energy saved via recycling vs. incineration. For example, a city with low waste heating content and a high amount of recyclables (e.g., metals and plastics) will have more energy recovered from recycling. Therefore, for this city a scenario with high capture of recyclables will be less expensive since the additional revenues from increased recycling will offset the additional collection and MRF costs. Cost variations among cities can be explained according to the process having the largest effect on the overall results (e.g., recycling vs. incineration) and the input parameters governing the results for that process. Tables 3.3-3 to 3.3-6 can be used to define the most influential processes. The lessons learned from the analysis of the simulation scenarios under Section 3.2 will aid determining the input parameters governing the results for those processes. Cities with the highest and lowest cost results were chosen under Table 3.3-2 to illustrate the cost variation. This table also provides explanations for the cost behavior of these cities. For example, Kawasaki is the city with the highest costs for all the optimization scenarios. For the Group 3 scenarios maximizing material recovery, the highest net costs are mainly due to (1) having the highest electricity cost, which affects the cost for all processes except collection and transportation, and (2) having the lowest recycling revenues. For the other optimization scenarios the most influential process is incineration with energy recovery, whose high net total cost is due to Kawasaki’s highest capital and O&M costs. 3-75 Holistic Decision Modeling Solid Waste Management Table 3.3-3 Group 3 Scenarios- Percentage of Net Total Cost Attributed to the Different Processes Group 3: Maximizing Materials Recovery (Via Manual and Mechanical Recycling and Composting) Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation City Commingled Commingled Commingled Commingled Composting Composting Composting Composting Collection Collection Collection Collection Recycling Recycling Recycling Recycling Kathmandu 50.3% 4.7% 31.5% 5.9% 0.3% 7.4% 52.8% 2.1% 34.5% 6.8% 0.4% 3.3% 44.5% 5.2% 35.1% 6.6% 0.3% 8.2% 40.5% 2.7% 43.6% 8.6% 0.5% 4.1% 50.4% 4.7% 31.5% 5.8% 0.3% 7.4% 52.9% 2.1% 34.6% 6.8% 0.4% 3.3% 44.6% 5.2% 35.1% 6.5% 0.3% 8.2% 40.5% 2.7% 43.6% 8.5% 0.5% 4.1% 45.7% 3.3% 32.2% 10.3% 0.3% 41.6% 1.5% 44.4% 8.3% 0.5% 39.9% 3.6% 35.6% 11.4% 0.4% 33.8% 1.7% 50.4% 9.4% 0.6% Conakry 8.2% 3.7% 9.1% 4.1% 45.9% 3.3% 32.3% 10.0% 0.3% 8.2% 41.7% 1.5% 44.5% 8.1% 0.5% 3.7% 40.1% 3.6% 35.8% 11.1% 0.4% 9.1% 33.9% 1.7% 50.5% 9.2% 0.6% 4.2% 41.1% 10.0% 31.3% 9.1% 0.4% 8.2% 39.3% 5.2% 40.4% 10.6% 0.5% 4.1% 37.3% 10.6% 33.3% 9.7% 0.4% 8.7% 33.9% 5.6% 43.9% 11.5% 0.5% 4.5% Lahore 41.9% 10.2% 31.9% 7.3% 0.4% 8.4% 40.2% 5.3% 41.3% 8.5% 0.5% 4.2% 38.0% 10.8% 34.0% 7.8% 0.4% 8.9% 34.7% 5.8% 45.1% 9.3% 0.5% 4.6% 34.8% 16.1% 37.8% 2.5% 0.6% 33.6% 8.8% 48.9% 3.6% 0.8% 32.7% 16.7% 39.0% 2.5% 0.6% 30.5% 9.2% 51.1% 3.7% 0.9% Sarajevo 8.2% 4.3% 8.5% 4.5% 3-76 34.8% 16.2% 37.9% 2.3% 0.6% 8.2% 33.7% 8.8% 49.0% 3.3% 0.8% 4.4% 32.7% 16.7% 39.1% 2.4% 0.6% 8.5% 30.6% 9.2% 51.3% 3.5% 0.9% 4.6% 47.2% 6.4% 29.9% 4.7% 0.4% 11.31% 42.3% 3.7% 36.3% 10.5% 0.5% 6.6% 45.3% 6.6% 31.0% 4.9% 0.4% 11.7% 39.2% 3.9% 38.2% 11.1% 0.5% 7.0% Amman 48.3% 6.6% 30.6% 2.5% 0.4% 11.57% 44.6% 3.9% 38.2% 5.8% 0.5% 7.0% 46.4% 6.8% 31.7% 2.6% 0.4% 12.0% 41.4% 4.2% 40.4% 6.1% 0.6% 7.4% 40.2% 9.4% 20.8% 4.9% 0.3% 24.4% 37.9% 5.7% 35.6% 6.6% 0.5% 13.7% 38.3% 9.7% 21.4% 5.1% 0.3% 25.1% 34.8% 5.9% 37.4% 7.0% 0.5% 14.4% Buenos Aires 41.0% 9.6% 21.1% 3.2% 0.3% 24.8% 38.9% 5.8% 36.5% 4.3% 0.5% 14.0% 39.1% 9.9% 21.8% 3.3% 0.3% 25.6% 35.7% 6.1% 38.4% 4.5% 0.5% 14.7% Shanghai 46.2% 16.1% 27.6% 4.8% 0.3% 5.0% 41.5% 9.3% 38.7% 7.2% 0.4% 2.9% 44.3% 16.7% 28.6% 4.9% 0.3% 5.2% 38.4% 9.9% 40.6% 7.5% 0.5% 3.1% 46.9% 16.3% 28.0% 3.5% 0.3% 5.1% 42.4% 9.5% 39.5% 5.2% 0.5% 2.9% 44.9% 16.9% 29.0% 3.6% 0.3% 5.3% 39.2% 10.1% 41.5% 5.5% 0.5% 3.2% Kawasaki 50.2% 14.8% 13.8% 12.5% 0.3% 8.5% 47.9% 8.0% 19.8% 19.7% 0.4% 4.3% 44.9% 16.3% 15.2% 13.8% 0.3% 9.4% 39.7% 9.2% 22.9% 22.8% 0.5% 4.9% 56.0% 16.5% 15.4% 2.3% 0.3% 9.5% 57.3% 9.5% 23.7% 3.9% 0.5% 5.1% 50.8% 18.5% 17.2% 2.6% 0.3% 10.6% 49.0% 11.4% 28.3% 4.6% 0.6% 6.1% Final Report 41.1% 9.4% 23.4% 3.1% 0.6% 22.3% 43.6% 5.2% 33.2% 4.8% 0.9% 12.3% 35.3% 10.4% 25.7% 3.4% 0.7% 24.5% 35.2% 6.0% 38.2% 5.5% 1.1% 14.1% Atlanta 41.2% 9.5% 23.4% 3.0% 0.6% 22.3% 43.7% 5.2% 33.3% 4.6% 0.9% 12.3% 35.3% 10.4% 25.7% 3.3% 0.7% 24.6% 35.2% 6.0% 38.3% 5.3% 1.1% 14.1% * Upper percentage includeo land price for landfill disposal cost. Lower are is without land price * Net negative contributor to the net total cost. Remanufacturing includes recycling revenues. Holistic Decision Modeling Solid Waste Management Table 3.3-4 Group 4 Scenarios- Percentage of Net Total Cost Attributed to the Different Processes Group 4: Maximizing Energy Recovery Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Separation Separation Separation Separation Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation City Collection Collection Collection Collection Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Kathmandu 30.0% 15.3% 7.0% 36.4% 0.7% 1.2% 9.4% 27.1% 14.1% 1.6% 47.7% 0.4% 1.4% 7.6% 27.3% 15.9% 7.3% 37.8% 0.7% 1.3% 9.7% 23.8% 14.7% 1.6% 50.0% 0.6% 1.5% 7.9% 30.0% 15.3% 7.0% 36.4% 0.7% 1.2% 9.4% 27.1% 14.1% 1.6% 47.7% 0.4% 1.4% 7.6% 27.3% 15.9% 7.3% 37.8% 0.7% 1.3% 9.7% 23.8% 14.7% 1.6% 50.0% 0.6% 1.5% 7.9% 27.8% 5.7% 1.6% 53.2% 1.1% 1.5% 25.5% 6.2% 0.7% 56.9% 1.2% 1.7% 24.4% 6.0% 1.7% 55.7% 1.2% 1.6% 21.9% 6.5% 0.8% 59.7% 1.3% 1.7% Conakry 9.0% 7.7% 9.5% 8.1% 27.8% 5.7% 1.6% 53.2% 1.1% 1.5% 9.0% 25.5% 6.2% 0.7% 56.9% 1.2% 1.7% 7.7% 24.4% 6.0% 1.7% 55.8% 1.1% 1.6% 9.5% 21.9% 6.5% 0.8% 59.7% 1.2% 1.7% 8.1% 23.3% 12.7% 2.3% 50.2% 1.7% 1.6% 8.2% 22.3% 12.8% 1.2% 53.2% 1.8% 1.7% 7.0% 22.9% 12.4% 2.6% 50.6% 1.7% 1.7% 8.1% 22.1% 12.7% 1.3% 53.3% 1.8% 1.8% 7.1% Lahore 23.4% 12.7% 2.3% 50.3% 1.4% 1.6% 8.2% 22.4% 12.8% 1.2% 53.3% 1.5% 1.8% 7.1% 23.0% 12.5% 2.6% 50.7% 1.4% 1.7% 8.2% 22.1% 12.8% 1.3% 53.5% 1.5% 1.8% 7.1% 20.5% 14.1% 7.3% 45.4% 2.3% 1.6% 20.0% 14.2% 3.6% 50.8% 2.5% 1.8% 20.4% 13.7% 8.1% 45.1% 2.3% 1.6% 18.8% 14.4% 3.6% 51.6% 2.6% 1.9% Sarajevo 8.8% 7.0% 8.9% 7.1% 3-77 20.6% 14.1% 7.3% 45.4% 2.2% 1.6% 8.8% 20.1% 14.2% 3.6% 50.9% 2.4% 1.8% 7.0% 20.4% 13.7% 8.1% 45.1% 2.2% 1.6% 8.9% 18.8% 14.4% 3.6% 51.7% 2.4% 1.9% 7.2% 27.0% 0.0% 0.9% 69.1% 2.2% 0.2% 0.7% 28.5% 0.0% 0.4% 68.3% 2.2% 0.2% 0.3% 27.0% 0.0% 0.9% 69.1% 2.2% 0.2% 0.7% 26.6% 0.0% 0.4% 70.2% 2.3% 0.2% 0.3% Amman 27.2% 0.0% 1.0% 69.7% 1.3% 0.2% 0.7% 28.8% 0.0% 0.4% 68.9% 1.4% 0.2% 0.3% 27.2% 0.0% 1.0% 69.7% 1.3% 0.2% 0.7% 26.8% 0.0% 0.4% 70.9% 1.4% 0.2% 0.3% 21.7% 11.2% 3.7% 37.3% 0.5% 1.1% 24.5% 18.5% 11.2% 1.6% 44.5% 0.6% 1.3% 22.2% 19.1% 11.6% 3.8% 38.5% 0.5% 1.1% 25.3% 18.4% 11.1% 1.8% 44.4% 0.6% 1.3% 22.3% Buenos Aires 21.7% 11.2% 3.7% 37.3% 0.4% 1.1% 24.5% 18.5% 11.2% 1.6% 44.5% 0.6% 1.3% 22.2% 19.1% 11.6% 3.8% 38.5% 0.5% 1.1% 25.3% 18.4% 11.1% 1.8% 44.4% 0.6% 1.3% 22.3% Shanghai 27.4% 20.5% 2.1% 41.0% 1.2% 2.2% 5.5% 28.1% 19.4% 1.2% 43.1% 1.3% 2.3% 4.5% 26.8% 20.4% 2.4% 41.3% 1.2% 2.2% 5.8% 26.1% 20.0% 1.3% 44.3% 1.3% 2.4% 4.6% 27.5% 20.5% 2.1% 41.2% 0.8% 2.2% 5.6% 28.3% 19.5% 1.2% 43.3% 0.9% 2.3% 4.5% 26.9% 20.4% 2.4% 41.5% 0.8% 2.2% 5.8% 26.3% 20.1% 1.3% 44.5% 0.9% 2.4% 4.6% Kawasaki 12.0% 2.7% 1.6% 78.9% 0.9% 0.2% 3.6% 11.1% 3.5% 0.7% 81.0% 0.9% 0.3% 2.5% 8.9% 2.8% 1.7% 81.7% 0.9% 0.3% 3.8% 7.5% 3.6% 0.7% 84.3% 1.0% 0.3% 2.6% 12.1% 2.7% 1.7% 79.5% 0.1% 0.2% 3.7% 11.2% 3.5% 0.7% 81.7% 0.2% 0.3% 2.5% 9.0% 2.8% 1.7% 82.3% 0.2% 0.3% 3.8% 7.6% 3.7% 0.7% 85.0% 0.2% 0.3% 2.6% 29.5% 13.3% 5.6% 23.5% 0.6% 0.8% 26.6% 30.6% 13.7% 3.0% 29.5% 0.8% 1.0% 21.4% 23.8% 14.4% 6.0% 25.4% 0.6% 0.9% 28.8% 23.2% 15.2% 3.3% 32.6% 0.9% 1.2% 23.7% Atlanta 29.5% 13.3% 5.6% 23.5% 0.6% 0.8% 26.6% 30.6% 13.7% 3.0% 29.5% 0.8% 1.0% 21.4% 23.8% 14.4% 6.0% 25.4% 0.6% 0.9% 28.8% 23.2% 15.2% 3.3% 32.6% 0.8% 1.2% 23.7% Final Report * Upper percentage includeo land price for landfill disposal cost. Lower are is without land price * Net negative contributor to the net total cost. Remanufacturing includes recycling revenues and incineration with energy recovery includes energy sale revenues Due to rounding zero values may actually be very small values. Holistic Decision Modeling Solid Waste Management Table 3.3-5 Group 5 (Carbon) Scenarios- Percentage of Net Total Cost Attributed to the Different Processes Group 5: Minimizing Carbon (Global Warming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Separation Separation Separation Separation Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Transportation Transportation Transportation Transportation City Ash-Landfill Ash-Landfill Ash-Landfill Ash-Landfill Collection Collection Collection Collection Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Kathmandu 28.5% 8.8% 6.6% 28.1% 0.3% 0.8% 26.9% 25.1% 10.8% 3.6% 36.4% 0.5% 1.1% 22.5% 26.7% 9.0% 6.8% 28.8% 0.3% 0.8% 27.5% 22.5% 11.2% 3.7% 37.7% 0.5% 1.6% 10.3% 28.5% 8.8% 6.6% 28.1% 0.3% 0.8% 26.9% 25.1% 10.8% 3.6% 36.4% 0.5% 1.1% 22.5% 26.7% 9.0% 6.8% 28.8% 0.3% 0.8% 27.5% 22.5% 11.2% 3.7% 37.7% 0.5% 1.6% 10.3% 28.5% 6.1% 1.8% 52.8% 1.1% 1.5% 26.1% 6.7% 0.8% 57.9% 1.2% 1.7% 27.8% 6.0% 2.0% 52.9% 1.1% 1.5% 25.7% 6.6% 0.9% 58.0% 1.2% 1.4% Conakry 8.2% 5.6% 8.7% 17.5% 28.5% 6.1% 1.8% 52.8% 1.1% 1.5% 8.2% 26.1% 6.7% 0.8% 57.9% 1.2% 1.7% 5.6% 27.8% 6.0% 2.0% 53.0% 1.1% 1.5% 8.7% 25.7% 6.6% 0.9% 58.0% 1.2% 1.4% 17.5% 30.6% 13.1% 7.2% 35.9% 1.2% 1.1% 10.9% 28.0% 14.9% 3.6% 42.1% 1.4% 1.3% 8.7% 28.1% 13.6% 7.5% 37.3% 1.2% 1.2% 11.3% 23.6% 15.8% 3.8% 44.6% 1.5% 1.9% 4.8% Lahore 30.6% 13.2% 7.2% 36.0% 1.0% 1.1% 10.9% 28.0% 14.9% 3.6% 42.2% 1.2% 1.3% 8.7% 28.1% 13.6% 7.5% 37.3% 1.1% 1.2% 11.3% 23.7% 15.8% 3.8% 44.7% 1.3% 1.9% 4.8% 3-78 26.1% 12.4% 5.5% 45.8% 2.2% 1.6% 24.5% 13.4% 2.7% 50.4% 2.4% 1.8% 23.9% 12.7% 5.7% 47.1% 2.3% 1.7% 21.7% 13.9% 2.8% 52.3% 2.5% 1.1% Sarajevo 6.4% 4.7% 6.6% 23.3% 26.1% 12.4% 5.5% 45.8% 2.1% 1.6% 6.4% 24.6% 13.4% 2.7% 50.5% 2.3% 1.8% 4.7% 24.0% 12.7% 5.7% 47.2% 2.2% 1.7% 6.6% 21.7% 13.9% 2.8% 52.4% 2.4% 1.1% 23.3% 36.9% 7.5% 4.7% 35.7% 0.9% 1.2% 13.1% 32.4% 9.1% 2.5% 43.4% 1.2% 1.5% 9.8% 34.9% 7.7% 4.8% 36.9% 1.0% 1.2% 13.5% 29.4% 9.5% 2.6% 45.4% 1.3% 1.7% 5.8% Amman 37.0% 7.5% 4.7% 35.9% 0.6% 1.2% 13.2% 32.6% 9.1% 2.5% 43.6% 0.8% 1.5% 9.9% 35.0% 7.7% 4.9% 37.0% 0.6% 1.2% 13.6% 29.5% 9.5% 2.6% 45.6% 0.8% 1.7% 5.8% 28.5% 8.8% 6.6% 28.1% 0.3% 0.8% 26.9% 25.1% 10.8% 3.6% 36.4% 0.5% 1.1% 22.5% 26.7% 9.0% 6.8% 28.8% 0.4% 0.8% 27.5% 22.5% 11.2% 3.7% 37.7% 0.5% 1.4% 9.2% Buenos Aires 28.5% 8.8% 6.6% 28.1% 0.3% 0.8% 26.9% 25.1% 10.8% 3.6% 36.4% 0.5% 1.1% 22.5% 26.7% 9.0% 6.8% 28.8% 0.3% 0.8% 27.5% 22.5% 11.2% 3.7% 37.7% 0.5% 1.4% 9.3% Shanghai 36.8% 17.6% 12.8% 24.8% 0.6% 1.0% 6.5% 32.4% 21.8% 7.2% 30.3% 0.8% 1.4% 6.2% 34.9% 18.2% 13.2% 25.5% 0.6% 1.1% 6.6% 29.5% 22.7% 7.5% 31.7% 0.8% 0.2% 2.5% 36.8% 17.7% 12.8% 24.8% 0.4% 1.0% 6.5% 32.5% 21.8% 7.2% 30.4% 0.5% 1.4% 6.2% 34.9% 18.2% 13.2% 25.5% 0.4% 1.1% 6.7% 29.5% 22.8% 7.5% 31.7% 0.6% 0.2% 2.6% Kawasaki 14.7% 3.9% 4.2% 73.1% 0.8% 0.2% 3.0% 12.1% 5.8% 1.9% 76.6% 0.9% 0.2% 2.5% 12.0% 4.0% 4.3% 75.5% 0.8% 0.2% 3.1% 8.8% 6.0% 1.9% 79.5% 0.9% 1.4% 6.5% 14.8% 3.9% 4.2% 73.6% 0.1% 0.2% 3.0% 12.2% 5.8% 1.9% 77.2% 0.2% 0.2% 2.5% 12.1% 4.1% 4.4% 76.1% 0.1% 0.2% 3.1% 8.9% 6.0% 1.9% 80.2% 0.2% 1.4% 6.5% 29.5% 12.3% 5.6% 31.3% 0.7% 1.1% 19.5% 30.6% 13.5% 2.7% 35.4% 0.9% 1.2% 15.7% 23.2% 13.3% 6.1% 34.0% 0.8% 1.2% 21.3% 22.7% 15.0% 3.0% 39.4% 1.0% 1.1% 23.3% Atlanta Final Report 29.5% 12.3% 5.6% 31.3% 0.7% 1.1% 19.5% 30.7% 13.5% 2.7% 35.4% 0.9% 1.2% 15.7% 23.2% 13.3% 6.1% 34.0% 0.8% 1.2% 21.3% 22.7% 15.0% 3.0% 39.4% 1.0% 1.1% 23.3% * Upper percentage includeo land price for landfill disposal cost. Lower are is without land price * Net negative contributor to the net total cost. It includes recycling revenues. Remanufacturing includes recycling revenues and incineration with energy recovery includes energy sale revenues. Holistic Decision Modeling Solid Waste Management Table 3.3-6 Group 5 (PM) Scenarios- Percentage of Net Total Cost Attributed to the Different Processes Group 5: Minimizing PM (Global Dimming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Separation Disposal Separation Disposal Separation Disposal Separation Disposal Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Transportation Transportation Transportation Transportation City Collection Collection Collection Collection Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Ash-landfill Ash-landfill Ash-landfill Ash-landfill Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Landfill Landfill Landfill Landfill Kathmandu 29.6% 8.0% 6.9% 23.6% 0.0% 0.3% 0.7% 30.9% 25.3% 10.2% 3.8% 32.6% 0.0% 0.5% 1.0% 26.7% 27.9% 8.2% 7.1% 24.2% 0.0% 0.3% 0.7% 31.6% 22.8% 10.5% 3.9% 33.7% 0.0% 0.5% 1.0% 27.6% 29.6% 8.0% 6.9% 23.6% 0.0% 0.3% 0.7% 30.9% 25.3% 10.2% 3.8% 32.6% 0.0% 0.5% 1.0% 26.7% 27.9% 8.2% 7.1% 24.2% 0.0% 0.3% 0.7% 31.6% 22.8% 10.5% 3.9% 33.7% 0.0% 0.5% 1.0% 27.6% 37.7% 4.8% 2.7% 40.8% 0.0% 0.9% 1.2% 34.8% 5.6% 1.3% 46.7% 0.0% 1.0% 1.4% 32.4% 5.2% 2.9% 44.4% 0.0% 0.9% 1.3% 27.7% 6.3% 1.4% 51.8% 0.0% 1.1% 1.5% Conakry 11.9% 9.2% 12.9% 10.2% 37.8% 4.8% 2.7% 40.9% 0.0% 0.8% 1.2% 11.9% 34.9% 5.6% 1.3% 46.7% 0.0% 1.0% 1.4% 9.2% 32.4% 5.2% 2.9% 44.4% 0.0% 0.9% 1.3% 12.9% 27.7% 6.3% 1.4% 51.8% 0.0% 1.1% 1.5% 10.2% 31.1% 12.9% 7.4% 34.5% 0.0% 1.2% 1.1% 11.8% 28.1% 14.7% 3.8% 40.9% 0.0% 1.4% 1.3% 9.8% 28.0% 13.5% 7.8% 36.1% 0.0% 1.2% 1.1% 12.3% 23.8% 15.6% 4.0% 43.4% 0.0% 1.5% 1.4% 10.4% Lahore 31.1% 12.9% 7.5% 34.6% 0.0% 1.0% 1.1% 11.8% 28.1% 14.7% 3.8% 41.0% 0.0% 1.2% 1.3% 9.8% 28.0% 13.5% 7.8% 36.1% 0.0% 1.0% 1.1% 12.4% 23.8% 15.6% 4.0% 43.5% 0.0% 1.2% 1.4% 10.4% 24.3% 10.1% 8.4% 46.8% 0.0% 2.4% 1.7% 23.9% 11.8% 4.0% 51.1% 0.0% 2.5% 1.9% 21.9% 10.4% 8.7% 48.2% 0.0% 2.4% 1.8% 20.9% 12.2% 4.1% 53.1% 0.0% 2.6% 2.0% Sarajevo 6.4% 4.9% 6.6% 5.1% 24.3% 10.1% 8.4% 46.8% 0.0% 2.3% 1.7% 6.4% 23.9% 11.8% 4.0% 51.1% 0.0% 2.4% 1.9% 4.9% 21.9% 10.4% 8.7% 48.3% 0.0% 2.3% 1.8% 6.6% 20.9% 12.2% 4.1% 53.1% 0.0% 2.5% 2.0% 5.1% 3-79 46.0% 7.6% 6.3% 0.0% 18.9% 0.0% 0.8% 20.3% 40.1% 10.2% 3.5% 0.0% 26.5% 0.0% 1.1% 18.5% 44.5% 7.8% 6.5% 0.0% 19.4% 0.0% 0.8% 20.9% 37.1% 10.7% 3.7% 0.0% 27.9% 0.0% 1.2% 19.4% Amman 47.4% 7.9% 6.5% 0.0% 0.0% 19.5% 0.8% 17.9% 41.2% 10.5% 3.6% 0.0% 0.0% 27.2% 1.2% 16.3% 45.9% 8.1% 6.7% 0.0% 0.0% 20.0% 0.9% 18.5% 38.1% 11.0% 3.8% 0.0% 0.0% 28.7% 1.2% 17.1% 29.6% 8.0% 6.9% 23.6% 0.0% 0.3% 0.7% 30.9% 25.3% 10.2% 3.8% 32.6% 0.0% 0.5% 1.0% 26.7% 27.9% 8.2% 7.1% 24.2% 0.0% 0.3% 0.7% 31.6% 22.8% 10.5% 3.9% 33.7% 0.0% 0.5% 1.0% 27.6% Buenos Aires 29.6% 8.0% 6.9% 23.6% 0.0% 0.3% 0.7% 30.9% 25.3% 10.2% 3.8% 32.6% 0.0% 0.5% 1.0% 26.7% 27.9% 8.2% 7.1% 24.2% 0.0% 0.3% 0.7% 31.6% 22.8% 10.5% 3.9% 33.7% 0.0% 0.5% 1.0% 27.6% Shanghai 37.3% 16.9% 13.0% 23.0% 0.0% 0.5% 0.9% 8.3% 32.7% 21.3% 7.4% 28.9% 0.0% 0.7% 1.3% 7.7% 35.5% 17.4% 13.4% 23.6% 0.0% 0.5% 0.9% 8.6% 29.9% 22.3% 7.7% 30.1% 0.0% 0.8% 1.3% 8.0% 37.4% 17.0% 13.0% 23.0% 0.0% 0.3% 0.9% 8.3% 32.8% 21.4% 7.4% 28.9% 0.0% 0.5% 1.3% 7.7% 35.6% 17.5% 13.4% 23.7% 0.0% 0.4% 0.9% 8.6% 29.9% 22.3% 7.7% 30.2% 0.0% 0.5% 1.3% 8.0% Kawasaki 16.6% 4.1% 4.8% 69.1% 0.0% 0.8% 0.2% 4.4% 13.1% 6.1% 2.1% 74.1% 0.0% 0.9% 0.2% 3.4% 13.8% 4.2% 5.0% 71.4% 0.0% 0.8% 0.2% 4.6% 9.7% 6.4% 2.2% 77.0% 0.0% 0.9% 0.2% 3.5% 16.7% 4.1% 4.8% 69.5% 0.0% 0.1% 0.2% 4.4% 13.2% 6.2% 2.1% 74.6% 0.0% 0.2% 0.2% 3.4% 13.9% 4.3% 5.0% 71.9% 0.0% 0.1% 0.2% 4.6% 9.8% 6.4% 2.2% 77.6% 0.0% 0.2% 0.2% 3.6% 31.4% 11.1% 7.0% 22.7% 0.0% 0.6% 0.8% 26.4% 31.2% 13.1% 3.5% 29.0% 0.0% 0.8% 1.0% 21.4% 26.0% 11.9% 7.5% 24.5% 0.0% 0.6% 0.9% 28.5% 23.9% 14.5% 3.9% 32.0% 0.0% 0.9% 1.1% 23.7% Atlanta 31.4% 11.1% 7.0% 22.7% 0.0% 0.6% 0.8% 26.4% 31.2% 13.1% 3.5% 29.0% 0.0% 0.8% 1.0% 21.5% 26.0% 11.9% 7.5% 24.5% 0.0% 0.6% 0.9% 28.5% 23.9% 14.5% 3.9% 32.0% 0.0% 0.8% 1.1% 23.7% * Upper percentage includeo land price for landfill disposal cost. Lower are is without land price * Net negative contributor to the net total cost. It includes recycling revenues. Remanufacturing includes recycling revenues and incineration with energy recovery includes energy sale revenues. Final Report Due to rounding zero values may actually be very small values. Solid Waste Management Holistic Decision Modeling Final Report 3.3.2 Energy Results Energy in the form of fuel and/or electricity is consumed by all processes. Some processes produce energy (e.g., incineration and landfill disposal with gas collection and energy recovery). Other processes, such as recycling, may avoid (or offset) energy use. The most significant parameters affecting the energy consumption results include: • Quantity and composition of recyclable materials in the waste stream. • Waste heating value. • Electricity grid mix In addition, the optimization scenario energy results were found to exhibit the following general trends: • The combination manual MRF and composting consumes less energy than mechanical MRF and composting. • Daily collection typically consumes more energy than biweekly • High capture typically has less net energy requirements than low capture Additional details about the energy results can be found in Table 3.3-7 and Figures 3.3-11 to 3.3- 15. Variation by process design in the scenarios maximizing materials recovery Figures 3.3-11 and 3.3-12 show the energy variation by MRF and composting process design. Consistently in all the scenarios and settings, the net energy consumed by the manual design is lower than by the mechanical design. In both cases there are net energy savings due to recycling related offsets. Variation by collection frequency It is expected than daily collection consumes more energy than biweekly collection as is observed in the results for all the cities (see Figures 3.3-11 and 3.3-12). This is due to the difference between daily and biweekly energy requirements since collection trucks going to the same number of stops daily vs. biweekly will consume more fuel. 3-80 Table 3.3-7 Summary of Energy Variation by Scenario and Scenario Settings Holistic Decision Modeling Solid Waste Management Maximize Materials Recovery (via Minimize Carbon (Global Warming) Minimize PM (Global Dimming ) Maximize Energy Recovery recycling and composting) Emissions Emissions Manual and Mechanical Criteria Daily Biweekly Biweekly Daily Biweekly Biweekly Daily Biweekly Biweekly Daily Daily Low Daily Low Daily Low Daily Biweekly Biweekly High High Low High High Low High High Low High Capture Capture Capture Low High Low Capture Capture Capture Capture Capture Capture Capture Capture Capture Captur Capture Capture Capture e Energy Consumption Variation By Collection Frequency More energy Less energy More energy Less energy More energy Less energy More energy Less energy (See consumption consumption consumption consumption consumption consumption consumption consumption Figures 3.3.6 to 3.3.10) By Percent Capture of Recyclable Less More Less More Less More Less More Less More Less More Less More Less More s (See energ energy energy energy energy energy energy energy energy energy energy energy energy energy energy energy Figures y 3.3.6 to 3-81 3.3.10) Kathmandu: highest net energy consumption due to: Sarajevo: highest net energy Sarajevo and Kathmandu: highest net energy consumption due to: - Lowest energy savings from consumption due to: - Lowest energy savings from incineration and recycling (Lowest waste heating incineration and recycling (Lowest waste - Lowest energy savings from value, lowest energy requirements for electricity production due to the large heating value, lowest energy recycling (Lowest amounts of percent of hydro in their grid mix, and low amounts of ferrous material for requirements for electricity production By city recyclables and metals among them). recycling). due to the large percent of hydro in their (See grid mix, and low amounts of ferrous Tables material for recycling). 3.3.8 to 3.3.11) Atlanta: lowest net energy consumption due to: Atlanta: lowest net energy consumption due to: - Highest energy savings from - Highest energy savings from recycling and incineration (Highest waste heating value and amounts of metals for recycling). recycling (Highest amounts of recyclables and metals among them). Final Repor Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 -0.05 -0.06 -0.07 -0.07 -0.09 -0.10 -0.15 -0.15 -0.16 -0.17 Solid Waste Management -0.5 -0.29 -0.29 -0.30 Holistic Decision Modeling -0.32 -0.34 -0.37 -0.40 -0.41 -0.41 -0.42 -0.51 -0.52 -0.54 -0.55 -0.55 -0.60 -1.0 -0.79 -0.82 -0.93 -0.95 -1.13 -1.14 -1.5 -1.56 3-82 -1.61 -2.0 -2.01 Energy Consumption (MKcal)/ metric ton -2.03 -2.5 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-11 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Energy Consumption by City Final Report Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 0.00 0.00 0.00 0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.02 -0.03 -0.03 -0.03 -0.05 -0.07 -0.08 -0.10 -0.10 -0.11 -0.11 -0.13 -0.16 -0.18 -0.20 -0.5 -0.28 Solid Waste Management Holistic Decision Modeling -0.37 -0.60 -0.76 -1.0 -0.78 -1.00 -1.19 -1.5 -1.33 -1.37 -1.39 3-83 -2.0 Energy Consumption (MKcal)/ metric ton -2.11 -2.14 -2.5 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-12 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Total Energy Consumption by City Final Report Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 -0.5 Solid Waste Management Holistic Decision Modeling -1.0 -0.8 -0.8 -1.0 -1.0 -1.1 -1.5 -1.06 -1.3 -1.31 -2.0 -2.0 -2.0 -2.5 -2.2 -2.2 -2.2 -2.3 -2.28 -2.15 -2.5 -2.5 -2.5 -2.5 -2.5 -2.6 -2.6 -3.0 -2.6 -2.53 -2.63 -2.8 -2.80 -3.5 3-84 -3.3 -3.4 -3.6 -4.0 -3.52 -4.5 -4.1 -4.2 Energy Consumption (MKcal)/ metric ton -4.6 -5.0 -4.57 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-13 Group 4: Maximizing Energy Recovery- Net Total Energy Consumption by City Final Report Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 -0.5 Solid Waste Management Holistic Decision Modeling -1.0 -0.7 -0.7 -0.9 -0.9 -1.0 -1.0 -1.5 -1.2 -1.2 -2.0 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -2.0 -2.0 -2.0 -2.0 -2.1 -2.1 -2.5 -2.4 -2.4 -2.5 -2.5 -2.5 -2.5 -3.0 -2.7 -3.5 -2.7 -3.2 -3.3 -3.4 -3.4 3-85 -4.0 -4.0 -4.0 -4.5 Energy Consumption (MKcal/ metrric ton) -4.3 -4.3 -5.0 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-14 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Energy Consumption by City Final Report Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 -0.5 -0.3 -0.4 Solid Waste Management -0.5 -0.5 Holistic Decision Modeling -0.6 -0.7 -1.0 -0.9 -0.9 -1.0 -1.0 -1.0 -1.1 -1.5 -2.0 -1.9 -1.9 -1.9 -1.9 -2.0 -2.0 -2.0 -2.0 -2.5 -2.4 -2.4 -2.5 -2.5 -2.5 -2.5 -2.6 -2.6 -3.0 -3.5 -3.3 -3.3 3-86 -3.4 -3.5 -4.0 Energy Consumption (MKcal)/ metric ton -4.1 -4.5 -4.2 -4.6 -4.6 -5.0 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-15 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Cost by City Final Report Solid Waste Management Holistic Decision Modeling Final Report Variation by percent capture of recyclables In general, the energy variation by percent capture of recyclables depends on: • Segregated vs. non-segregated collection energy requirements • MRF energy requirements • Energy savings associated with materials recycling • Energy recovery from incineration • Energy requirements for residuals landfill disposal It is expected that high capture scenarios will have less net energy consumption than low capture scenarios due to energy savings associated with recycling. However, this trend could be reversed in a city where most of the recyclables are paper and/or glass, which generally produce less energy savings than metals. Energy consumption variation among cities can be explained according to the process (e.g., recycling vs. incineration) having the largest effect on the overall results and the input parameters driving the results for that process. Tables 3.3-8 to 3.3-11 can be used to define the most influential processes. The lessons learned from the analysis of the simulation scenarios under Section 3.2 will aid determining the input parameters governing the results for those processes. Cities with the highest and lowest energy consumption results were chosen under Table 3.3-7 to illustrate the energy consumption variation. This table also provides explanations for the energy consumption behavior of these cities. For example, Atlanta is the city with the lowest net energy consumption for all the optimization scenarios. For the Group 3 scenarios (maximizing material recovery) having the lowest energy consumption is mainly due to Atlanta’s highest amount of recyclables and metals in particular, which produce the highest energy offsets from recycling. For the other optimization scenarios Atlanta’s highest waste heating value explains its high energy offsets from incineration with energy recovery and having the highest amounts of metals explains the offsets from recycling. 3-87 Holistic Decision Modeling Solid Waste Management Table 3.3-8 Group 3 Scenarios- Percentage of Net Total Energy Attributed to the Different Group 3: Maximizing Materials Recovery (Via Manual and Mechanical Recycling and Composting) Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation Commingled Commingled Commingled Commingled Composting Composting Composting Composting Collection Collection Collection Collection Recycling Recycling Recycling Recycling City Sarajevo Lahore Conakry Kathmandu 15.0 1.0 5.2 3.4 0.3 75.0 28.5 0.7 9.1 6.5 0.6 54.6 12.0 1.0 5.4 3.5 0.3 77.7 17.4 0.8 10.5 7.5 0.7 63.1 7.0 1.2 5.2 3.9 0.2 82.4 11.7 1.0 12.7 6.0 0.5 68.0 5.5 1.2 5.3 4.0 0.2 83.8 8.3 1.0 13.2 6.3 0.5 70.6 10.2 1.7 8.7 6.5 0.3 72.6 14.9 1.3 16.5 11.4 0.6 55.3 8.7 1.7 8.8 6.6 0.3 73.8 11.7 1.3 17.1 11.8 0.6 57.4 3-88 12.7 1.7 9.2 2.8 0.8 72.7 17.7 1.4 17.0 6.2 1.5 56.3 11.6 1.8 9.3 2.9 0.8 73.6 15.4 1.4 17.4 6.3 1.5 57.9 Amman 10.8 3.4 9.7 1.9 0.4 73.86 13.6 2.8 16.1 6.0 0.6 60.9 10.0 3.4 9.8 1.9 0.4 74.5 11.9 2.9 16.4 6.1 0.6 62.1 Buenos Aires 6.8 1.8 3.6 2.3 0.2 85.3 9.6 1.5 9.1 4.8 0.5 74.5 6.3 1.8 3.7 2.3 0.2 85.8 8.3 1.6 9.2 4.9 0.5 75.6 Atlanta Kawasaki Shanghai 10.8 2.7 8.7 2.4 0.3 75.1 13.9 2.2 17.0 5.2 0.5 61.2 10.0 2.7 8.8 2.5 0.3 75.8 12.0 2.3 17.1 5.3 0.5 62.9 9.9 1.6 2.8 1.2 0.2 84.3 15.6 1.4 6.4 3.2 0.5 72.9 8.0 1.6 2.9 1.3 0.2 86.1 11.3 1.4 6.7 3.4 0.5 76.6 4.6 0.9 2.0 1.2 0.2 91.0 8.3 0.8 4.7 3.2 0.6 82.4 3.6 0.9 2.0 1.3 0.3 92.0 5.8 0.8 4.9 3.3 0.6 84.7 Final Report *Net negative contributor to the net total energy. Remanufacturing includes recycling energy offsets Holistic Decision Modeling Solid Waste Management Table 3.3-9 Group 4 Scenarios- Percentage of Net Total Energy Attributed to the Different Processes Group 4: Maximizing Energy Recovery Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation Separation Separation Separation Separation Collection Collection Collection Collection City d Recycling d Recycling d Recycling d Recycling Commingle Commingle Commingle Commingle Recycling Recycling Recycling Recycling Waste Waste Waste Waste Mixed Mixed Mixed Mixed Amman Sarajevo Lahore Conakry Kathmandu 4.9 1.7 0.4 43.0 0.2 0.4 49.5 6.2 2.8 0.2 29.6 0.2 0.6 60.4 4.3 1.7 0.4 43.2 0.2 0.4 49.9 5.2 2.9 0.3 29.9 0.2 0.6 61.0 2.0 1.6 0.3 38.5 0.1 0.2 57.3 1.9 1.7 0.1 43.2 0.1 0.2 52.7 1.7 1.6 0.3 38.6 0.1 0.2 57.5 1.5 1.7 0.1 43.4 0.1 0.2 52.8 2.0 2.3 0.3 59.0 0.2 0.3 35.9 2.0 2.5 0.2 65.4 0.2 0.3 29.5 2.0 2.2 0.3 58.7 0.2 0.3 36.3 2.0 2.4 0.2 65.0 0.2 0.3 29.9 3-89 3.7 3.0 0.4 50.7 0.6 0.5 41.1 3.4 3.1 0.2 62.2 0.6 0.6 29.9 3.7 2.9 0.4 50.0 0.6 0.5 41.9 3.2 3.1 0.2 62.4 0.6 0.6 30.0 1.4 0.0 0.1 96.6 0.1 0.0 1.9 1.5 0.0 0.0 97.5 0.1 0.0 0.8 1.4 0.0 0.1 96.6 0.1 0.0 1.9 1.3 0.0 0.0 97.7 0.1 0.0 0.8 Buenos Aires 1.9 1.6 0.4 39.7 0.1 0.2 56.2 1.5 1.8 0.2 47.5 0.1 0.2 48.7 1.6 1.6 0.4 39.8 0.1 0.2 56.4 1.5 1.8 0.2 47.2 0.1 0.2 49.1 Atlanta Kawasaki Shanghai 1.6 2.4 0.1 78.7 0.1 0.2 16.8 1.7 2.4 0.1 81.5 0.1 0.2 14.0 1.6 2.4 0.1 78.5 0.1 0.2 17.0 1.5 2.4 0.1 81.7 0.1 0.2 14.0 3.8 1.2 0.5 37.7 0.1 0.1 56.7 3.9 1.4 0.2 42.0 0.1 0.1 52.3 2.7 1.2 0.5 38.2 0.1 0.1 57.3 2.5 1.4 0.2 42.6 0.1 0.2 53.1 2.0 0.8 0.3 23.7 0.1 0.1 73.0 2.1 0.9 0.2 29.0 0.1 0.1 67.6 1.5 0.8 0.3 23.9 0.1 0.1 73.4 1.4 0.9 0.2 29.2 0.1 0.1 68.1 *Net negative contributor to the net total energy. Remanufacturing and incineration with energy recovery include energy offsets. Final Report Due to rounding zero values may actually be very small values. Holistic Decision Modeling Solid Waste Management Table 3.3-10 Group 5 (Carbon) Scenarios- Percentage of Net Total Energy Attributed to the Different Processes Group 5: Minimizing Carbon (Global Warming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Transportation Transportation Transportation Transportation Separation Separation Separation Separation Ash-Landfill Ash-Landfill Ash-Landfill Ash-Landfill City Collection Collection Collection Collection d Recycling d Recycling d Recycling d Recycling Commingle Commingle Commingle Commingle Recycling Recycling Recycling Recycling Waste Waste Waste Waste Mixed Mixed Mixed Mixed Amman Sarajevo Lahore Conakry Kathmandu 3.2 1.5 0.7 27.5 0.1 0.2 66.8 2.6 1.8 0.3 37.3 0.1 0.2 57.7 2.9 1.5 0.7 27.6 0.1 0.2 67.0 2.2 1.8 0.3 37.5 0.1 0.3 36.5 2.3 1.8 0.2 38.1 0.1 0.2 57.2 2.1 1.9 0.1 43.0 0.1 0.2 52.6 2.3 1.8 0.3 37.9 0.1 0.2 57.5 2.1 1.9 0.1 42.7 0.1 0.2 24.5 3.8 2.3 0.7 39.4 0.2 0.3 53.2 3.4 2.6 0.3 50.3 0.2 0.3 42.9 3.4 2.3 0.7 39.6 0.2 0.3 53.5 2.6 2.6 0.3 50.7 0.2 0.3 43.2 3-90 5.3 3.1 0.3 57.3 0.7 0.6 32.6 4.7 3.2 0.2 66.0 0.7 0.6 24.6 4.7 3.2 0.3 57.7 0.7 0.6 32.8 3.9 3.2 0.2 66.5 0.7 0.2 52.9 4.1 2.8 0.9 58.2 0.1 0.2 33.7 2.9 2.8 0.4 69.2 0.1 0.2 24.4 3.7 2.8 0.9 58.5 0.1 0.2 33.9 2.5 2.8 0.4 69.5 0.1 0.2 61.2 Buenos Aires 3.2 1.5 0.7 27.5 0.1 0.2 66.8 2.6 1.8 0.3 37.3 0.1 0.2 57.7 2.9 1.5 0.7 27.6 0.1 0.2 67.0 2.2 1.8 0.3 37.5 0.1 0.2 58.0 Kawasaki Shanghai 4.8 2.6 1.0 43.2 0.1 0.2 48.1 3.4 2.8 0.5 56.7 0.1 0.3 36.3 4.4 2.7 1.0 43.4 0.1 0.2 48.3 2.9 2.8 0.5 57.0 0.1 0.6 24.8 5.1 1.2 0.6 27.8 0.1 0.1 65.1 4.6 1.5 0.3 33.2 0.1 0.2 60.3 4.0 1.2 0.6 28.2 0.1 0.1 65.8 3.1 1.5 0.3 33.7 0.1 0.2 58.0 Atlanta 1.9 0.9 0.2 30.8 0.1 0.1 66.0 2.1 1.0 0.1 34.7 0.1 0.1 61.9 1.4 0.9 0.2 31.0 0.1 0.1 66.4 1.3 1.0 0.1 34.9 0.1 0.1 62.4 Final Report *Net negative contributor to the net total energy. Remanufacturing and incineration with energy recovery include energy offsets. Holistic Decision Modeling Solid Waste Management Table 3.3-11 Group 5 (PM) Scenarios- Percentage of Net Total Energy Attributed to the Different Processes Group 5: Minimizing PM (Global Dimming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Separatio Separatio Separatio Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Separation Disposal Disposal Disposal Disposal Transportation Transportation Transportation Transportation n n n City Collection Collection Collection Collection Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Ash-landfill Ash-landfill Ash-landfill Ash-landfill Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Landfill Landfill Landfill Landfill Aires Amman Sarajevo Lahore Conakry Kathmandu 3.6 1.4 0.9 22.0 0.0 0.0 0.2 71.9 2.7 1.8 0.4 33.7 0.0 0.1 0.2 61.1 3.3 1.4 0.9 22.1 0.0 0.0 0.2 72.1 2.3 1.8 0.4 33.8 0.0 0.1 0.2 61.3 3.5 1.6 0.6 26.1 0.0 0.1 0.2 67.9 3.3 1.8 0.3 34.0 0.0 0.1 0.2 60.4 2.7 1.6 0.6 26.3 0.0 0.1 0.2 68.5 2.2 1.8 0.3 34.4 0.0 0.1 0.2 61.0 4.0 2.3 0.8 36.6 0.0 0.2 0.3 55.8 3.4 2.6 0.4 48.4 0.0 0.2 0.3 44.7 3.4 2.3 0.8 36.9 0.0 0.2 0.3 56.2 2.6 2.6 0.4 48.8 0.0 0.2 0.3 45.1 3-91 4.3 2.7 0.4 64.7 0.0 0.6 0.5 26.7 4.2 2.9 0.2 72.8 0.0 0.6 0.6 18.7 3.7 2.7 0.4 65.1 0.0 0.6 0.5 26.9 3.5 3.0 0.2 73.3 0.0 0.6 0.6 18.8 7.4 4.0 2.4 0.0 4.8 0.0 0.3 81.1 6.8 5.8 1.4 0.0 7.3 0.0 0.4 78.2 7.0 4.0 2.4 0.0 4.8 0.0 0.3 81.5 5.9 5.9 1.4 0.0 7.4 0.0 0.4 79.0 Buenos 3.6 1.4 0.9 22.0 0.0 0.0 0.2 71.9 2.7 1.8 0.4 33.7 0.0 0.1 0.2 61.1 3.3 1.4 0.9 22.1 0.0 0.0 0.2 72.1 2.3 1.8 0.4 33.8 0.0 0.1 0.2 61.3 Atlanta Kawasaki Shanghai 5.0 2.5 1.3 35.9 0.0 0.1 0.2 55.1 3.5 2.7 0.6 51.6 0.0 0.1 0.2 41.3 4.6 2.5 1.3 36.0 0.0 0.1 0.2 55.3 3.0 2.7 0.6 51.9 0.0 0.1 0.2 41.5 5.3 1.1 0.8 22.3 0.0 0.1 0.1 70.4 4.7 1.4 0.4 28.7 0.0 0.1 0.1 64.6 4.3 1.1 0.8 22.5 0.0 0.1 0.1 71.2 3.2 1.5 0.4 29.1 0.0 0.1 0.1 65.6 2.2 0.7 0.4 23.3 0.0 0.1 0.1 73.2 2.2 0.9 0.2 28.7 0.0 0.1 0.1 67.8 1.6 0.7 0.4 23.5 0.0 0.1 0.1 73.6 1.5 0.9 0.2 28.9 0.0 0.1 0.1 68.3 Final Report *Net negative contributor to the net total energy. Remanufacturing and incineration with energy recovery include energy offsets. Due to rounding zero values may actually be very small values. Solid Waste Management Holistic Decision Modeling Final Report 3.3.3 Emissions Results In the optimization scenario analysis, carbon and PM emissions were selected for their contribution to global warming and global dimming, respectively. PM emissions from solid waste systems result from any handling, loading, sorting, and turning operations. When operations occur within buildings, or are part of processes such incineration, air pollution control systems are used to reduce emissions from the buildings or process equipment. Carbon and PM emissions result from the combustion of fossil fuels or products (e.g., plastic). Carbon emissions in the form of methane are also produced via anaerobic decomposition in landfills. Carbon emissions are analyzed for all the optimization scenarios and both carbon and PM emissions are analyzed for Group 5—minimizing PM emissions. We found the key parameters that affect the carbon and PM emissions results to include: Quantity and composition of recyclables (metals and plastics recycling produce higher carbon emission offsets and aluminum and corrugated cardboard recycling produces higher PM emissions offsets). Residuals amount and composition being disposed at a LF (food and yard waste generate the highest amounts of methane at a LF). The electricity grid mix (cities with a high percent of hydro in their grid mix will not have as high emission offsets from recycling and incineration with energy recovery as cities with a mostly fossil based grid mix) In addition, the carbon and PM optimization scenario results were found to exhibit the following general trends: The combination manual MRF and composting produces less carbon emissions than mechanical MRF and composting. Daily collection typically produces more carbon emissions than biweekly. It also produces more carbon and PM emissions than biweekly collection for the scenario minimizing PM emissions. High capture of recyclables typically results in less carbon emissions than low capture of recyclables. This is consistent with the lower energy consumption trend and also based on less waste going to the landfill where it can produce methane. Additional details about the energy results can be found in Table 3.3-12 and Figures 3.3-16 to 3.3-21. Variation by process design in the scenarios maximizing materials recovery Figures 3.3-16 and 3.3-17 show the carbon emissions variation by MRF and composting process design. Consistently in all the scenarios and settings, the net carbon emissions 3-92 Solid Waste Management Holistic Decision Modeling Final Report from the manual design are lower than from the mechanical design. This is due to emissions from fuel combustion or electricity consumption by equipment in the mechanical design. Variation by collection frequency It is expected that daily collection produces more carbon and PM emissions than biweekly collection as observed in the results for all the cities (see Figures 3.3-16 and 3.3-17). This is due to much higher fuel consumption by collection trucks in the daily collection system. Variation by percent capture of recyclables In general, the carbon and PM emissions variation by percent capture of recyclables depends on: • Segregated vs. non-segregated collection vehicle emissions • MRF emissions • Emission offsets from recycling • Emissions savings from incineration energy recovery • Emissions from residuals LF disposal Consistent with the energy results, it is expected that high capture scenarios will have less net carbon and PM emissions than low capture scenarios. Different behavior for carbon emissions could be observed in cities where most of the recyclables are paper and/or glass, which produce less carbon emissions offsets from recycling than other recyclables categories such metals and plastics. Aluminum recycling also produces the highest PM emissions offsets followed by corrugated cardboard and ferrous materials recycling. Carbon and PM emissions variation among cities can be explained according to the process (e.g., recycling vs. incineration) having the largest effect on the overall results and the input parameters driving the results for that process. Tables 3.3-13 to 3.3-17 can be used to define the most influential processes. The lessons learned from the analysis of the simulation scenarios under Section 3.2 will aid determining the input parameters governing the results for those processes. Cities with the highest and lowest cost results were chosen under Table 3.3-12 to illustrate the emissions variation. This table also provides explanations for the emissions behavior of these cities. For example, for Group 3 (maximizing material recovery) scenarios, Conakry is the city with the highest carbon emissions mainly due to (1) the lack of recyclables, in particular aluminum and plastics, which are responsible for high carbon emission offsets and (2) having high amounts of methane producing waste going to the landfill. For Group 4—maximizing energy recovery and Group 5—minimizing carbon emissions and minimizing PM emissions, Kathmandu has the lowest waste heating content and an electricity grid mix with 90% hydro, which explain why this city has the lowest carbon emission offsets and consequently the highest carbon emissions. For Group 5—minimizing PM emissions Lahore is the city with the highest PM emissions due to having the lowest amounts of recyclables and consequently the lowest emission offsets. 3-93 Holistic Decision Modeling Solid Waste Management Table 3.3-12 Summary of Carbon and PM Emissions Variation by Scenario and Scenario Settings Maximize Materials Recovery (via Minimize Carbon (Global Warming) Minimize PM (Global Dimming ) Maximize Energy Recovery recycling and composting) Emissions Emissions Manual and Mechanical Criteria Biweekly Biweekly Biweekly Biweekly Biweekly Biweekly Daily Biweekly Biweekly Daily High Daily Low Daily High Daily Low Daily High Daily Low Daily Low High Low High Low High Low High High Low Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Capture Carbon and PM Emissions Variation By Collection Frequency (See More carbon Less carbon More carbon Less carbon More carbon Less carbon More carbon and Less carbon and Figures 3.3.11 emissions emissions emissions emissions emissions emissions PM emissions PM emissions to 3.3.16) By Percent Capture of Less More Less More Less More Less More Less More Less More Less More Less More Recyclables carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon carbon (See Figures and PM and PM and PM and PM emission. emissions emission. emissions emission. emissions emission. emissions emission. emissions emission. emissions 3.3.11 to emissions emissions emissions emissions 3.3.16) Conakry: highest net carbon emissions Kathmandu: highest net carbon emissions due to: due to: - Lowest carbon emission offsets from - Lowest carbon emission offsets from recycling (Among the lowest amounts of recycling and incineration (Lowest waste Kathmandu: highest net carbon emissions due to: recyclables and the lowest amount of heating content and an electricity grid mix - Lowest carbon emission offsets from recycling and incineration (Lowest waste heating aluminum and plastic materials among with 90% hydroelectricity). content and an electricity grid mix with 90% hydroelectricity). them). - High carbon emissions from LF (Among Lahore: highest net PM emissions due to: 3-94 the highest amounts of material going to - Lowest PM emission offsets from recycling the LF). (Lowest amount of recyclables). Kawasaki: lowest net carbon emissions due By city (See to: Tables 3.3.13 to - Highest carbon emissions offsets from 3.3.17) Kawasaki: lowest net carbon emissions due recycling and incineration (Among the Buenos Aires and Kawasaki: lowest net to: highest amounts of metals for recycling, the Sarajevo: lowest net carbon emissions due carbon emissions due to: - Highest carbon emissions offsets from second highest waste heating content and to: - Highest carbon emission offsets from recycling and incineration (Among the not as many plastics as other cities with - Lowest carbon emissions from incineration recycling (Highest amounts of recyclables highest amounts of metals for recycling, the similar waste heating content). (Lowest amounts of plastics and aluminum and aluminum and plastic materials second highest waste heating content and Atlanta: lowest net PM emissions due to: material). among them). not as many plastics as other cities with - Highest PM emissions offsets from similar waste heating content). recycling and incineration (The highest amounts of metals and corrugated cardboard for recycling, the highest waste heating content). Final Report 0.04 0.027 0.027 0.03 0.021 0.021 0.021 0.021 0.020 0.019 0.019 0.018 Solid Waste Management 0.016 0.016 0.02 Holistic Decision Modeling 0.014 0.013 0.007 0.006 0.01 0.004 0.003 0.003 0.003 0.002 0.002 0.00 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.01 Aires -0.006 -0.006 -0.001 -0.001 -0.0004 -0.02 -0.0003 -0.013 3-95 -0.014 -0.020 -0.020 -0.03 -0.04 Carbon Emissions (MTCE)/ metric ton -0.038 -0.038 -0.05 -0.040 -0.041 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-16 Group 3: Maximizing Materials Recovery (Via Manual Recycling and Composting)- Net Total Carbon Emissions by City 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 Solid Waste Management 0.02 0.01 Holistic Decision Modeling 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta -0.01 Aires 0.00 -0.01 -0.01 -0.02 -0.01 -0.01 3-96 -0.03 Carbon Emissions (MTCE)/ metric ton -0.04 -0.03 -0.04 -0.04 -0.04 -0.05 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-17 Group 3: Maximizing Materials Recovery (Via Mechanical Recycling and Composting)- Net Final Report Total Carbon Emissions by City Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.00 0.00 -0.01 Solid Waste Management Holistic Decision Modeling -0.02 -0.02 -0.02 -0.03 -0.03 -0.03 -0.04 -0.03 -0.03 -0.04 -0.04 -0.05 3-97 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.07 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 Carbon Emissions (MTCE)/ metric ton -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.08 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Final Report Figure 3.3-18 Group 4: Maximizing Energy Recovery- Net Total Carbon Emissions by City Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.00 0.00 0.00 -0.02 Solid Waste Management Holistic Decision Modeling -0.02 -0.02 -0.04 -0.06 -0.05 -0.05 -0.07 -0.08 -0.07 -0.10 -0.09 -0.09 -0.09 -0.09 3-98 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.12 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.12 -0.12 -0.12 -0.12 -0.12 -0.13 Carbon Emissions (MTCE)/ metric ton -0.14 -0.13 -0.13 -0.14 -0.14 -0.16 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-19 Group 5: Minimize Carbon (Global Warming) Emissions- Net Total Carbon Emissions by Final Report City 0.10 0.09 0.08 0.06 0.06 0.05 0.04 0.05 0.03 0.03 Solid Waste Management Holistic Decision Modeling 0.00 -0.05 -0.03 -0.03 -0.04 -0.04 -0.05 -0.05 -0.06 -0.06 3-99 -0.07 -0.08 -0.10 -0.08 -0.08 -0.09 -0.09 -0.09 -0.09 Carbon Emissions (MTCE)/ metric ton -0.10 -0.10 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.12 -0.12 -0.12 -0.13 -0.15 Katmandu Conakry Lahore Sarajevo Amman Buenos Shanghai Kawasaki Atlanta Aires DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-20 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total Final Report Carbon Emissions by City Buenos Katmandu Conakry Lahore Sarajevo Amman Aires Shanghai Kawasaki Atlanta 0.0 -0.5 Solid Waste Management Holistic Decision Modeling -0.4 -0.4 -0.5 -0.5 -0.5 -0.5 -0.6 -0.6 -0.6 -0.6 -0.7 -0.7 -0.8 -0.8 -1.0 -0.8 -0.8 -0.9 -0.9 -0.9 -0.9 -1.1 -1.1 -1.1 -1.1 -1.2 -1.2 -1.5 -1.4 -1.4 -1.5 -1.5 -1.7 -1.7 -2.0 3-100 -2.0 -2.0 PM Emissions (kg)/ metric ton -2.5 -2.5 -2.5 -3.0 DHC: Daily- High Capture Scenarios DLC: Daily- Low Capture Scenarios BHC: Biweekly- High Capture Scenarios BLC: Biweekly- Low Capture Scenarios Figure 3.3-21 Group 5: Minimize Particulate Material (PM- Global Dimming) Emissions- Net Total PM Final Report Emissions by City Holistic Decision Modeling Solid Waste Management Table 3.3-13 Group 3 Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes Group 3: Maximizing Materials Recovery (Via Manual and Mechanical Recycling and Composting) Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Remanufacturing Remanufacturing Remanufacturing Remanufacturing Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation Commingled Commingled Commingled Commingled Composting Composting Composting Composting Collection Collection Collection Collection Recycling Recycling Recycling Recycling City * * * * Kathmandu 6.1 0.4 2.0 41.5 0.5 49.5 8.1 0.2 2.7 60.5 0.7 27.8 5.1 0.4 2.0 42.0 0.5 50.0 5.1 0.2 2.8 62.5 0.7 28.7 Amman Sarajevo Lahore Conakry 4.2 6.4 9.9 49.1 0.4 30.1 4.7 3.8 17.3 55.8 0.7 17.8 3.5 6.4 10.0 49.5 0.4 30.3 3.6 3.8 17.5 56.4 0.7 18.0 3.3 5.6 8.1 36.9 0.3 45.8 3.7 3.4 12.5 51.7 0.5 28.2 2.9 5.6 8.2 37.1 0.3 45.9 3.1 3.4 12.6 52.0 0.5 28.4 3-101 5.3 4.2 12.2 34.0 1.1 43.2 5.0 2.3 15.5 52.8 1.4 23.1 5.0 4.2 12.3 34.1 1.1 43.3 4.5 2.3 15.6 53.1 1.4 23.2 4.1 13.6 9.5 22.7 0.4 49.71 3.8 8.4 11.7 47.6 0.5 28.0 3.9 13.6 9.5 22.7 0.4 49.8 3.4 8.4 11.8 47.7 0.5 28.1 Buenos Aires 3.0 4.4 3.5 21.4 0.2 67.4 3.4 3.1 7.2 37.3 0.5 48.4 2.8 4.4 3.5 21.5 0.2 67.6 3.0 3.2 7.2 37.5 0.5 48.6 Kawasaki Shanghai 3.7 10.8 9.1 18.5 0.3 57.7 4.0 7.8 15.5 31.6 0.5 40.6 3.5 10.8 9.1 18.6 0.3 57.8 3.6 8.0 15.6 31.6 0.5 40.6 5.2 6.3 3.8 18.1 0.3 66.3 6.1 4.3 6.9 36.7 0.7 45.4 4.4 6.4 3.8 18.2 0.3 66.8 4.7 4.4 7.0 37.3 0.7 46.0 Atlanta 3.5 4.1 4.5 31.0 0.6 56.3 3.9 2.4 6.9 52.8 1.0 33.1 2.9 4.2 4.6 31.2 0.6 56.6 2.9 2.5 7.0 53.3 1.0 33.4 Final Report *Net negative contributor to the net total carbon emissions. Remanufacturing includes emission offsets. Holistic Decision Modeling Solid Waste Management Table 3.3-14 Group 4 Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes Group 4: Maximizing Energy Recovery Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Landfill Disposal Landfill Disposal Landfill Disposal Landfill Disposal Transportation Transportation Transportation Transportation Separation Separation Separation Separation City Collection Collection Collection Collection d Recycling d Recycling d Recycling d Recycling Commingle Commingle Commingle Commingle Recycling Recycling Recycling Recycling Waste Waste Waste Waste Mixed Mixed Mixed Mixed Kathmandu 4.4 2.9 0.3 19.7 0.2 1.1 71.5 3.2 2.9 0.1 43.0 0.1 1.0 49.8 4.0 2.9 0.3 19.7 0.2 1.1 71.8 2.8 2.9 0.1 43.1 0.1 1.0 50.0 Amman Sarajevo Lahore Conakry 1.9 11.1 2.4 70.6 0.1 0.6 13.2 1.5 11.0 1.0 74.6 0.1 0.6 11.2 1.6 11.2 2.4 70.8 0.1 0.6 13.3 1.3 11.0 1.0 74.8 0.1 0.6 11.2 1.4 13.3 2.0 44.5 0.1 0.5 38.2 1.3 14.3 1.1 52.4 0.1 0.6 30.2 1.3 13.2 2.2 44.0 0.1 0.5 38.7 1.3 14.2 1.2 51.9 0.1 0.6 30.7 3-102 1.6 6.5 0.9 65.6 0.2 0.7 24.4 1.4 6.3 0.4 72.9 0.2 0.7 18.1 1.6 6.5 1.0 65.3 0.2 0.7 24.6 1.3 6.3 0.4 73.0 0.2 0.7 18.1 1.6 0.0 0.9 91.9 0.1 0.1 5.5 1.7 0.0 0.4 95.2 0.1 0.1 2.5 1.6 0.0 0.9 91.9 0.1 0.1 5.5 1.6 0.0 0.4 95.3 0.1 0.1 2.5 Buenos Aires 1.6 7.4 1.8 12.0 0.0 0.5 76.7 1.6 9.8 0.9 13.7 0.1 0.7 73.2 1.4 7.4 1.8 12.0 0.0 0.5 76.8 1.6 9.6 1.0 13.8 0.1 0.7 73.3 Atlanta Kawasaki Shanghai 1.3 19.4 1.0 60.7 0.0 0.6 16.9 1.3 19.2 0.8 63.9 0.1 0.6 14.2 1.2 19.4 1.2 60.5 0.0 0.6 17.1 1.2 19.2 0.8 64.0 0.1 0.6 14.2 4.0 8.8 3.8 18.8 0.1 0.5 64.0 3.9 10.6 1.8 25.7 0.1 0.6 57.3 3.1 8.9 3.8 18.9 0.1 0.5 64.6 2.8 10.8 1.8 26.0 0.1 0.6 58.0 2.2 5.0 2.3 24.7 0.1 0.4 65.4 2.5 6.8 1.3 24.5 0.1 0.5 64.2 1.7 5.0 2.3 24.8 0.1 0.4 65.7 1.8 6.9 1.3 24.7 0.1 0.5 64.6 Final Report *Net negative contributor to the net total carbon emissions. Remanufacturing and incineration with energy recovery include emission offsets. Due to rounding zero values may actually be very small values. Holistic Decision Modeling Solid Waste Management Table 3.3-15 Group 5 (Carbon) Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes Group 5: Minimizing Carbon (Global Warming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Separation Separation Separation Separation Transportation Transportation Transportation Transportation Ash-Landfill Ash-Landfill Ash-Landfill Ash-Landfill Collection Collection Collection Collection City Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Conakry Kathmandu 1.7 4.4 2.1 21.5 0.0 0.3 70.0 1.7 6.4 1.2 19.7 0.0 0.4 70.5 1.6 4.4 2.1 21.5 0.0 0.3 70.0 1.5 6.4 1.2 19.8 0.0 0.4 70.6 1.3 7.5 1.1 65.3 0.1 0.4 24.3 1.2 8.0 0.5 69.3 0.1 0.4 20.4 1.3 7.3 1.2 65.2 0.1 0.4 24.5 1.2 8.0 0.6 69.2 0.1 0.3 58.4 Lahore 1.6 8.6 3.1 39.9 0.1 0.3 46.5 1.4 10.4 1.6 46.3 0.1 0.4 39.8 1.4 8.5 3.1 40.0 0.1 0.3 46.6 1.2 10.4 1.6 46.4 0.1 0.4 31.4 3-103 Amman Sarajevo 1.5 4.8 0.6 65.0 0.2 0.5 27.3 1.4 5.1 0.3 70.9 0.2 0.6 21.5 1.4 4.8 0.6 65.1 0.2 0.5 27.4 1.2 5.1 0.3 71.0 0.2 0.4 20.6 1.6 10.3 3.8 48.0 0.0 0.3 36.1 1.3 12.4 1.9 52.6 0.0 0.4 31.3 1.5 10.3 3.8 48.0 0.0 0.3 36.1 1.2 12.4 1.9 52.7 0.0 0.6 21.6 Buenos Aires 1.7 4.4 2.1 21.5 0.0 0.3 70.0 1.7 6.4 1.2 19.7 0.0 0.4 70.5 1.6 4.4 2.1 21.5 0.0 0.3 70.0 1.5 6.4 1.2 19.8 0.0 0.4 70.6 Atlanta Kawasaki Shanghai 1.7 9.7 4.4 42.0 0.0 0.3 41.9 1.3 12.0 2.3 47.7 0.0 0.3 36.4 1.6 9.8 4.4 42.1 0.0 0.3 41.9 1.2 12.0 2.3 47.7 0.0 0.4 39.9 2.8 4.8 2.6 28.4 0.0 0.3 61.2 2.6 6.3 1.3 31.3 0.0 0.3 58.1 2.4 4.8 2.6 28.5 0.0 0.3 61.5 1.9 6.4 1.3 31.6 0.1 0.7 79.3 2.2 5.9 1.5 7.0 0.1 0.5 82.8 2.6 7.7 0.8 9.4 0.1 0.7 78.7 1.7 5.9 1.5 7.0 0.1 0.5 83.3 1.9 7.7 0.8 9.5 0.1 0.3 36.5 Final Report *Net negative contributor to the net total carbon emissions. Remanufacturing and incineration with energy recovery include emission offsets. Due to rounding zero values may actually be very small values. Holistic Decision Modeling Solid Waste Management Table 3.3-16 Group 5 (PM) Scenarios- Percentage of Net Total Carbon Emissions Attributed to the Different Processes Group 5: Minimizing PM (Global Dimming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Transportation Transportation Transportation Transportation Separation Disposal Separation Disposal Separation Disposal Separation Disposal City Collection Collection Collection Collection Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Ash-landfill Ash-landfill Ash-landfill Ash-landfill Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Landfill Landfill Landfill Landfill Kathmandu 2.1 4.4 3.0 16.7 0.0 0.0 0.3 73.6 1.9 6.6 1.8 16.0 0.0 0.0 0.4 73.2 2.0 4.4 3.0 16.7 0.0 0.0 0.3 73.7 1.7 6.6 1.8 16.1 0.0 0.0 0.4 73.4 Sarajevo Lahore Conakry 2.4 8.5 3.7 56.6 0.0 0.1 0.5 28.3 2.0 9.2 1.6 64.1 0.0 0.1 0.5 22.5 2.0 8.5 3.7 56.8 0.0 0.1 0.5 28.5 1.5 9.2 1.6 64.4 0.0 0.1 0.5 22.7 1.7 8.8 3.6 38.1 0.0 0.1 0.3 47.5 1.5 10.6 1.8 45.2 0.0 0.1 0.4 40.4 1.5 8.8 3.6 38.1 0.0 0.1 0.3 47.6 1.2 10.7 1.8 45.3 0.0 0.1 0.4 40.5 3-104 2.2 7.2 1.2 82.0 0.0 0.3 0.9 6.3 1.8 6.8 0.5 84.7 0.0 0.3 0.8 5.1 2.0 7.2 1.2 82.2 0.0 0.3 0.9 6.3 1.6 6.8 0.5 84.9 0.0 0.3 0.8 5.1 Atlanta Kawasaki Shanghai Aires Amman 2.2 11.5 7.6 0.0 38.1 0.0 0.2 40.4 1.6 13.7 3.6 0.0 48.0 0.0 0.3 32.8 2.1 11.5 7.6 0.0 38.1 0.0 0.2 40.5 1.5 13.7 3.6 0.0 48.0 0.0 0.3 32.9 Buenos 2.1 4.4 3.0 16.7 0.0 0.0 0.3 73.6 1.9 6.6 1.8 16.0 0.0 0.0 0.4 73.2 2.0 4.4 3.0 16.7 0.0 0.0 0.3 73.7 1.7 6.6 1.8 16.1 0.0 0.0 0.4 73.4 1.9 10.1 5.7 36.2 0.0 0.0 0.3 45.8 1.5 12.5 2.9 43.4 0.0 0.0 0.3 39.4 1.8 10.1 5.7 36.3 0.0 0.0 0.3 45.9 1.3 12.5 2.9 43.5 0.0 0.0 0.3 39.4 3.3 4.9 3.7 23.6 0.0 0.0 0.3 64.2 2.9 6.7 1.8 27.6 0.0 0.1 0.3 60.6 2.8 4.9 3.7 23.8 0.0 0.0 0.3 64.5 2.2 6.8 1.8 27.8 0.0 0.1 0.3 61.0 2.4 4.8 2.6 23.3 0.0 0.1 0.4 66.5 2.6 6.7 1.4 23.5 0.0 0.1 0.5 65.1 1.9 4.8 2.6 23.4 0.0 0.1 0.4 66.8 1.9 6.8 1.5 23.7 0.0 0.1 0.5 65.6 Final Report *Net negative contributor to the net total carbon emissions. Remanufacturing and incineration with energy recovery include emission offsets. Due to rounding zero values may actually be very small values. Holistic Decision Modeling Solid Waste Management Table 3.3-17 Group 5 (PM) Scenarios- Percentage of Net Total PM Emissions Attributed to the Different Processes Group 5: Minimizing PM (Global Dimming) Emissions Daily- High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture Incineration w/ER* Incineration w/ER* Incineration w/ER* Incineration w/ER* Remanufacturing* Remanufacturing* Remanufacturing* Remanufacturing* Separation Disposal Separation Disposal Separation Disposal Separation Disposal Transportation Transportation Transportation Transportation Collection Collection Collection Collection City Mixed Waste Mixed Waste Mixed Waste Mixed Waste Commingled Commingled Commingled Commingled Ash-landfill Ash-landfill Ash-landfill Ash-landfill Recycling Recycling Recycling Recycling Recycling Recycling Recycling Recycling Landfill Landfill Landfill Landfill Kathmand 0.4 0.4 0.2 1.7 0.0 0.0 0.1 97.2 0.4 0.6 0.1 2.4 0.0 0.0 0.2 96.3 0.4 0.4 0.2 1.7 0.0 0.0 0.1 97.2 0.3 0.6 0.1 2.4 0.0 0.0 0.2 96.3 u Sarajevo Lahore Conakry 0.3 0.5 0.2 0.4 0.0 0.0 0.2 98.4 0.4 0.7 0.1 0.2 0.0 0.0 0.2 98.3 0.3 0.5 0.2 0.4 0.0 0.0 0.2 98.5 0.3 0.7 0.1 0.2 0.0 0.0 0.2 98.4 0.7 1.6 0.6 1.0 0.0 0.1 0.3 95.6 0.7 2.3 0.3 2.6 0.0 0.1 0.5 93.5 0.6 1.6 0.6 1.0 0.0 0.1 0.3 95.7 0.6 2.3 0.3 2.6 0.0 0.1 0.5 93.6 0.3 1.8 0.3 38.0 0.0 0.1 0.3 59.3 0.3 2.2 0.2 48.0 0.0 0.1 0.3 49.0 0.2 1.8 0.3 38.0 0.0 0.1 0.3 59.3 0.2 2.2 0.2 48.0 0.0 0.1 0.3 49.0 3-105 Amman 0.3 0.3 0.1 0.0 1.6 0.0 0.1 97.6 0.3 0.4 0.1 0.0 2.4 0.0 0.1 96.7 0.3 0.3 0.1 0.0 1.6 0.0 0.1 97.6 0.3 0.4 0.1 0.0 2.4 0.0 0.1 96.7 Buenos Aires 0.4 0.4 0.2 1.7 0.0 0.0 0.1 97.2 0.4 0.6 0.1 2.4 0.0 0.0 0.2 96.3 0.4 0.4 0.2 1.7 0.0 0.0 0.1 97.2 0.3 0.6 0.1 2.4 0.0 0.0 0.2 96.3 Atlanta Kawasaki Shanghai 0.8 1.2 0.6 0.5 0.0 0.0 0.3 96.6 0.7 1.7 0.3 2.5 0.0 0.0 0.4 94.4 0.7 1.2 0.6 0.5 0.0 0.0 0.3 96.7 0.6 1.7 0.3 2.5 0.0 0.0 0.4 94.5 0.5 1.0 0.8 5.2 0.0 0.0 0.1 92.4 0.5 1.5 0.4 7.6 0.0 0.0 0.1 89.8 0.5 1.0 0.8 5.2 0.0 0.0 0.1 92.4 0.4 1.5 0.4 7.6 0.0 0.0 0.1 89.9 0.2 0.5 0.3 6.1 0.0 0.0 0.1 92.8 0.2 0.7 0.2 8.8 0.0 0.0 0.1 90.0 0.1 0.5 0.3 6.1 0.0 0.0 0.1 92.9 0.1 0.7 0.2 8.8 0.0 0.0 0.1 90.0 * Net negative contributor to the net total PM emissions. Remanufacturing and incineration with energy recovery include emission offsets. Final Report Due to rounding zero values may actually be very small values Solid Waste Management Holistic Decision Modeling Final Report 3.4 Option Comparison per City This section shows summary charts that compare the technology options per city so that each target city may consider which option would be better to be adopted from the viewpoints of cost, energy consumption and carbon emissions. However, since the comparison charts include various assumptions and default data, it should be noted that the results in this report should be seen only as a reference for each city to gain a rough idea of adoption of a suitable waste treatment option. 3.4.1 Amman Summary of Simulation Scenarios Costs Results Figures 3.4-1 and 3.4-2 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration only is most expensive than that for composting with windrow turner, and incineration with energy recovery follows. The cheapest option is landfill with energy recovery in both case with and without land price for landfill cost. Energy Recovery Results Figure 3.4-3 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Energy recovery by collecting the landfill gas can be also expected as well as saving energy through the use of recovered material by recycling. Carbon Emission Results As Figure 3.4-4 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. The second worst option is incineration only. Incineration with energy recovery is the best option because of offsetting electricity generation emissions from the utility sector. Composting with both manual turning and windrow turner is a better option with less carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-5 and 3.4-6 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning, than other options. On the 3-106 Solid Waste Management Holistic Decision Modeling Final Report other hand, the cost for the scenario for minimizing carbon emissions is rather more expensive than others. Energy Recovery Results Figure 3.4-7 shows the energy recovery results. Needless to say, the energy optimization scenario achieves the lowest energy consumption, then the scenario for minimizing carbon emissions follows. The scenario for maximizing material recovery but with a low capture rate contributes much less for reduction of energy consumption. Carbon Emission Results As Figure 3.4-8 shows, other than the scenario for minimizing carbon emissions, only the scenario for energy optimization will contribute to the reduction of carbon emissions. The other scenarios, maximizing the material recovery and minimizing PM emissions, still show positive carbon emissions except the material recovery case with manual operation and high capture rate. 3-107 160 Composting with manual turning and 140 landfill with energy recovery are the least options, in case the land price is considered for the landfill cost. 120 100 Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-108 -20 -40 Recycling - Composting - Composting - Incineration - Landfill- Recycling - Landfil - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.84 1.84 0.56 0.56 0.24 0.24 0.00 0.00 0.00 Disposal 42.97 42.97 19.42 19.42 2.58 2.58 51.17 51.99 39.63 Treatment 0.00 0.00 20.10 51.38 109.65 76.66 0.00 0.00 0.00 Separation 21.66 18.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 30.91 30.91 30.91 30.91 30.91 30.91 30.91 30.91 30.91 Remanufacturing -22.44 -22.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-1 Cost Results of Simulation Scenarios (Amman: with Land Price) Final Report 160 Without considering land price for the landfill cost, the option for landfill with 140 energy recovery is the least expensive. 120 100 Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-109 -20 -40 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.84 1.84 0.56 0.56 0.24 0.24 0.00 0.00 0.00 Disposal 22.28 22.28 10.07 10.07 1.59 1.59 26.53 27.10 14.73 Treatment 0.00 0.00 20.10 51.38 109.65 76.66 0.00 0.00 0.00 Separation 21.66 18.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 30.91 30.91 30.91 30.91 30.91 30.91 30.91 30.91 30.91 Remanufacturing -22.44 -22.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-2 Cost Results of Simulation Scenarios (Amman: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 -6.0 Incineration with energy recovery  contributes greatly by saving energy.  -8.0 Energy recovery by collecting the  landfill gas can be also expected as well  as saving energy through the use of  -10.0 recovered material by recycling. Energy Consumption (MJ)/metric ton 3-110 -12.0 Recycling - Composting - Composting - Incineration - Landfill- Recycling - Landfil - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.018 0.018 0.010 0.010 0.002 0.002 0.000 0.000 0.000 Disposal 0.377 0.377 0.195 0.195 0.012 0.012 0.475 0.475 -2.096 Treatment 0.000 0.000 0.326 0.385 0.043 -10.756 0.000 0.000 0.000 Separation 0.177 0.316 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Transfer 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Collection 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 0.158 Remanufacturing -3.046 -3.046 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Figure 3.4-3 Energy Recovery Results of Simulation Scenarios (Amman) Final Report 0.30 The worst option is landfill with gas venting. The second  0.25 worst option is incineration only.  Incineration with energy recovery is the best option  because of offsetting electricity generation emissions from  0.20 the utility sector.  Composting with both manual turning and windrow turner  Solid Waste Management Holistic Decision Modeling 0.15 is a better option with less carbon emissions. 0.10 0.05 0.00 3-111 Carbon Emissions (MTCE)/metric ton -0.05 -0.10 Recycling - Composting - Composting - Incineration - Landfill- Recycling - Landfil - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Disposal 0.068 0.068 0.007 0.007 0.000 0.000 0.259 0.082 0.040 Treatment 0.000 0.000 0.005 0.007 0.116 -0.063 0.000 0.000 0.000 Separation 0.010 0.020 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Transfer 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Collection 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 Remanufacturing -0.036 -0.036 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Figure 3.4-4 Carbon Emissions Results of Simulation Scenarios (Amman) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 180.0 Maximizing material The scenario for Minimizing PM recovery with manual minimizing carbon emissions is also 160.0 MRF and composting emissions is rather 154.9 less expensive is less expensive more expensive than 148.4 option. 143.9 Solid Waste Management Holistic Decision Modeling option. others. 139.6 Low capture rate is 137.5 140.0 Low capture rate is rather cheaper than rather cheaper than 132.1 126.4 high capture rate. high capture rate. 119.0 120.0 115.5 115.7 111.9 110.5 105.4 107.3 107.3 107.5 95.1 96.9 100.0 87.6 89.4 80.0 Cost ($)/metric ton 3-112 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Amman) Figure 3.4-5 Cost Results of Optimizations Scenarios (Amman: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 180.0 Due to the small land price, cost without land price is not so different than that with land price for any scenarios. 160.0 154.1 147.7 Solid Waste Management Holistic Decision Modeling 139.7 138.7 140.0 133.3 131.3 119.0 121.1 120.0 115.7 111.6 109.6 107.6 106.4 106.4 106.6 101.2 100.9 100.0 93.5 87.7 80.2 80.0 Cost ($)/metric ton 3-113 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Amman) Figure 3.4-6 Cost Results of Optimizations Scenarios (Amman: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.7 -0.6 -0.6 -0.5 Solid Waste Management -2.0 Holistic Decision Modeling -2.1 -2.0 -2.1 -2.2 -2.7 -2.8 -4.0 The scenario for minimizing carbon emissions follows the energy optimization -3.9 -4.0 scenario. The scenario for maximizing material recovery but with a low capture rate -6.0 contributes much less for reduction of energy consumption. 3-114 -8.0 -8.4 -8.4 Energy Consumption (MJ)/metric ton -8.9 -9.0 -10.0 -10.7 -10.6 -10.7 -10.6 -12.0 Optimization Scenarios (Amman) Figure 3.4-7 Energy Recovery Results of Optimizations Results (Amman) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.06 0.05 0.04 0.04 0.02 0.03 0.03 0.02 Solid Waste Management 0.02 0.02 Holistic Decision Modeling 0.02 0.00 0.00 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM -0.01 Compost Compost Emissions Emissions -0.02 -0.01 Other than the scenario for minimizing -0.04 carbon emissions, only the scenario for energy optimization will contribute to the reduction of carbon emissions. 3-115 -0.06 The other scenarios, maximizing the material recovery and minimizing PM -0.06 -0.06 -0.06 -0.06 emissions, still show positive carbon -0.08 emissions except the material recovery Carbon Emissions (MTCE)/metric ton case with manual operation and high capture rate. -0.10 -0.10 -0.10 -0.12 -0.12 -0.12 -0.14 Optimization Scenarios (Amman) Figure 3.4-8 Carbon Emissions Results of Optimizations Results (Amman) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.2 Buenos Aires Summary of Simulation Scenarios Costs Results Figures 3.4-9 and 3.4-10 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration with and without energy recovery is rather expensive. Composting with manual turning and landfill with any gas treatment options are less expensive options. Energy Recovery Results Figure 3.4-11 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation. Carbon Emission Results As Figure 3.4-12 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. Composting and incineration with energy recovery produce less carbon emissions. Material recycling can also contribute greater reduction of carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-13 and 3.4-14 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning, than other options. Energy Recovery Results Figure 3.4-15 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. Carbon Emission Results As Figure 3.4-16 shows, as well as the scenario for minimizing carbon emissions, the scenario for minimizing PM emissions can also largely reduces carbon emissions. Scenarios for energy optimization and maximizing the material recovery with high capture rate will contribute to the reduction of carbon emissions but less than previous mentioned scenarios. 3-116 160 Incineration with and without energy recovery is 140 rather expensive. Composting with manual turning and landfill with any gas treatment options are less expensive 120 options, in case with land price for landfill cost. 100 Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-117 -20 -40 Recycling- Composting- Composting- Incineration- e Landfill- Recycling- Mechanical manual windrow Incineration nergy Landfill- vent Landfill- flare energy manual sort sort turning turner recovery recovery Transportation 1.82 1.82 0.66 0.66 0.29 0.29 0.00 0.00 0.00 Disposal 29.30 29.30 14.01 14.01 1.93 1.93 35.33 36.00 34.23 Treatment 0.00 0.00 19.82 48.86 109.68 98.05 0.00 0.00 0.00 Separation 32.72 30.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 Remanufacturing -20.85 -20.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-9 Cost Results of Simulation Scenarios (Buenos Aires: with Land Price) Final Report 160 140 120 Due to the small land price, cost without land price is not so 100 decreased than that with land Solid Waste Management price. Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-118 -20 -40 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.82 1.82 0.66 0.66 0.29 0.29 0.00 0.00 0.00 Disposal 29.24 29.24 13.99 13.99 1.93 1.93 35.27 35.93 34.17 Treatment 0.00 0.00 19.82 48.86 109.68 98.05 0.00 0.00 0.00 Separation 32.72 30.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 Remanufacturing -20.85 -20.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-10 Cost Results of Simulation Scenarios (Buenos Aires: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 -6.0 Incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by -8.0 material recovery scenarios with both manual and mechanical operation. 3-119 Energy Consumption (MJ)/metric ton -10.0 Recycling- Composting- Composting- Incineration- e Recycling- Landfill- energ Mechanical manual windrow Incineration nergy Landfill- vent Landfill- flare manual sort y recovery sort turning turner recovery Transportation 0.0177 0.0177 0.0122 0.0122 0.0027 0.0027 0.0000 0.0000 0.0000 Disposal 0.4239 0.4239 0.2084 0.2084 0.0156 0.0156 0.5320 0.5320 -0.8705 Treatment 0.0000 0.0000 0.2445 0.2972 0.0412 -6.9674 0.0000 0.0000 0.0000 Separation 0.1437 0.2370 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.1588 0.1588 0.1588 0.1588 0.1588 0.1588 0.1588 0.1588 0.1588 Remanufacturing -5.4223 -5.4223 0.0000 0.0000 -1.8605 -1.8605 0.0000 0.0000 0.0000 Figure 3.4-11 Energy Recovery Results of Simulation Scenarios (Buenos Aires) Final Report 0.25 The worst option is the landfill with gas venting. Composting and incineration with energy recovery 0.20 produce less carbon emissions. Material recycling can also contribute greater reduction of carbon emissions. 0.15 Solid Waste Management Holistic Decision Modeling 0.10 0.05 0.00 -0.05 3-120 Carbon Emissions (MTCE)/metric ton -0.10 Recycling- Composting- Composting- Incineration- e Recycling- Landfill- energ Mechanical manual windrow Incineration nergy Landfill- vent Landfill- flare manual sort y recovery sort turning turner recovery Transportation 0.0004 0.0004 0.0003 0.0003 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0592 0.0592 0.0005 0.0005 0.0001 0.0001 0.2130 0.0679 0.0496 Treatment 0.0000 0.0000 0.0030 0.0045 0.0992 0.0076 0.0000 0.0000 0.0000 Separation 0.0047 0.0083 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 Remanufacturing -0.0662 -0.0662 0.0000 0.0000 -0.0029 -0.0029 0.0000 0.0000 0.0000 Figure 3.4-12 Carbon Emissions Results of Simulation Scenarios (Buenos Aires) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 160.0 Maximizing material recovery with manual 140.0 MRF and composting is less expensive Solid Waste Management 127.0 Holistic Decision Modeling 124.7 option. 120.3 120.0 116.4 116.9 110.6 112.1 111.6 108.6 107.3 103.9 105.1 104.6 105.4 103.9 100.6 100.0 85.8 83.3 79.1 80.0 75.5 Cost ($)/metric ton 60.0 3-121 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Buenos Aires) Figure 3.4-13 Cost Results of Optimizations Scenarios (Buenos Aires: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 160.0 Due to the small land price, cost without land price is not so different than that with land price for any scenarios. 140.0 127.0 Solid Waste Management 124.6 Holistic Decision Modeling 120.3 120.0 116.4 116.9 110.6 112.1 111.6 108.6 107.3 103.9 105.1 104.6 105.4 103.8 100.6 100.0 85.8 83.3 79.1 80.0 75.5 60.0 3-122 Cost ($)/metric ton 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Buenos Aires) Figure 3.4-14 Cost Results of Optimizations Scenarios (Buenos Aires: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -2.0 -1.7 -1.8 -1.6 -1.7 Solid Waste Management Holistic Decision Modeling -4.0 -4.8 -4.8 -4.7 -4.7 -6.0 Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy -8.0 consumption into the same level as the energy optimization scenario. 3-123 The scenario for maximizing material recovery but with a low capture rate contributes much less for reduction of energy consumption. Energy Consumption (MJ)/metric ton -10.0 -10.6 -10.6 -10.6 -10.6 -10.9 -11.0 -11.1 -11.2 -11.3 -11.1 -12.0 -11.8 -11.8 -14.0 Optimization Scenarios (Buenos Aires) Figure 3.4-15 Energy Recovery Results of Optimizations Results (Buenos Aires) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.02 0.00 0.00 0.00 0.00 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Solid Waste Management Holistic Decision Modeling Compost Compost Emissions Emissions -0.02 -0.04 -0.03 -0.04 -0.03 -0.04 -0.04 -0.04 -0.05 -0.05 -0.06 As well as the scenario for minimizing carbon emissions, the scenario for minimizing PM emissions can also largely reduces carbon emissions. 3-124 -0.08 Scenarios for energy optimization and maximizing the material recovery with high capture rate will contribute to the reduction of -0.09 -0.09 -0.09 -0.09 Carbon Emissions (MTCE)/metric ton -0.10 carbon emissions but less than previous mentioned scenarios. -0.12 -0.12 -0.12 -0.13 -0.13 -0.14 Optimization Scenarios (Buenos Aires) Figure 3.4-16 Carbon Emissions Results of Optimizations Results (Buenos Aires) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.3 Conakry Summary of Simulation Scenarios Costs Results Figures 3.4-17 and 3.4-18 show the cost results of the simulation scenarios using one primary technology. Since the total unit cost per tonne-waste for incineration with and without energy recovery is rather expensive. Other options except composting with windrow turner are ranked as less expensive options. Energy Recovery Results Figure 3.4-19 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation. Carbon Emission Results As Figure 3.4-20 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. Scenarios for composting with both manual and mechanical turning produce less carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-21 and 3.4-22 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning under the condition of biweekly collection and low capture rate. Other options are almost same level of the cost. Energy Recovery Results Figure 3.4-23 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. Carbon Emission Results As Figure 3.4-24 shows, as well as the scenario for minimizing carbon emissions, the scenarios for minimizing PM emissions and energy optimization can also reduces carbon emissions. Scenarios for maximizing the material recovery will still discharge carbon emissions through its treatment processes. 3-125 250 Incineration with and without energy recovery is rather expensive. 200 Other options except composting with windrow turner are ranked as less expensive options, in case the land price is considered for the landfill cost. 150 Solid Waste Management Holistic Decision Modeling 100 50 Cost ($)/metric ton 0 3-126 -50 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.95 1.95 0.61 0.61 0.33 0.33 0.00 0.00 0.00 Disposal 44.54 44.54 17.38 17.38 2.27 2.27 49.98 50.86 48.68 Treatment 0.00 0.00 25.91 59.64 109.36 104.31 0.00 0.00 0.00 Separation 15.21 12.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 37.82 37.82 37.82 37.82 37.82 37.82 37.82 37.82 37.82 Remanufacturing -12.95 -12.95 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-17 Cost Results of Simulation Scenarios (Conakry: with Land Price) Final Report 250 Due to the small land price, cost 200 without land price is not so decreased than that with land price. 150 Solid Waste Management Holistic Decision Modeling 100 Cost ($)/metric ton 50 0 3-127 -50 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.95 1.95 0.61 0.61 0.33 0.33 0.00 0.00 0.00 Disposal 43.26 43.26 16.88 16.88 2.19 2.19 48.54 49.41 47.23 Treatment 0.00 0.00 25.91 59.64 109.36 104.31 0.00 0.00 0.00 Separation 15.21 12.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 37.82 37.82 37.82 37.82 37.82 37.82 37.82 37.82 37.82 Remanufacturing -12.95 -12.95 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-18 Cost Results of Simulation Scenarios (Conakry: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 Incineration with energy recovery contributes greatly -6.0 by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation. -8.0 In addition to those, incineration scenarios contribute energy recovery from the remanufacturing process. -10.0 3-128 Energy Consumption (MJ)/metric ton -12.0 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0185 0.0185 0.0113 0.0113 0.0031 0.0031 0.0000 0.0000 0.0000 Disposal 0.5761 0.5761 0.1746 0.1746 0.0165 0.0165 0.6715 0.6715 -1.5808 Treatment 0.0000 0.0000 0.3063 0.3643 0.0419 -5.9305 0.0000 0.0000 0.0000 Separation 0.1232 0.2200 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.1964 0.1964 0.1964 0.1964 0.1964 0.1964 0.1964 0.1964 0.1964 Remanufacturing -4.6463 -4.6463 0.0000 0.0000 -3.8051 -3.8051 0.0000 0.0000 0.0000 Figure 3.4-19 Energy Recovery Results of Simulation Scenarios (Conakry) Final Report 0.3 The worst option is landfill with gas venting. Incineration with energy recovery is the best option because of offsetting 0.3 electricity generation emissions from the utility sector. Scenarios for composting with both manual and mechanical turning produce less carbon emissions. 0.2 Solid Waste Management 0.2 Holistic Decision Modeling 0.1 0.1 0.0 -0.1 3-129 -0.1 Carbon Emissions (MTCE)/metric ton -0.2 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0002 0.0002 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0770 0.0770 0.0150 0.0150 0.0001 0.0001 0.2765 0.0889 0.0406 Treatment 0.0000 0.0000 0.0057 0.0073 0.0416 -0.0867 0.0000 0.0000 0.0000 Separation 0.0051 0.0109 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0012 0.0012 0.0012 0.0012 0.0012 0.0012 0.0012 0.0012 0.0012 Remanufacturing -0.0188 -0.0188 0.0000 0.0000 -0.0060 -0.0060 0.0000 0.0000 0.0000 Figure 3.4-20 Carbon Emissions Results of Simulation Scenarios (Conakry) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 250.0 Maximizing material recovery with manual MRF and composting is less Solid Waste Management expensive option. Holistic Decision Modeling 200.0 In which, biweekly collection and low capture rate 157.9 scenario is the least 151.7 149.4 157.6 expensive option. 150.3 150.3 149.1 150.0 142.6 138.8 139.7 132.0 141.1 138.8 131.2 131.1 126.1 112.8 110.2 100.0 96.4 91.1 Cost ($)/metric ton 3-130 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Conakry) Figure 3.4-21 Cost Results of Optimizations Scenarios (Conakry: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 250.0 Due to the small land price, cost without land price is not so different than that with land price for any scenarios. Solid Waste Management Holistic Decision Modeling 200.0 157.9 150.3 149.0 157.5 149.9 139.6 151.6 149.3 150.0 142.1 138.8 131.9 141.1 138.8 130.8 131.1 125.6 112.3 109.8 95.9 100.0 90.7 Cost ($)/metric ton 3-131 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Conakry) Figure 3.4-22 Cost Results of Optimizations Scenarios (Conakry: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -1.2 -1.1 -1.3 -1.3 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 -3.8 -3.9 -4.0 -3.9 Scenarios for minimizing carbon and for -6.0 minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. The scenario for maximizing material recovery 3-132 but with a low capture rate contributes much -8.0 less for reduction of energy consumption. Energy Consumption (MJ)/metric ton -10.0 -10.2 -10.2 -10.2 -10.3 -10.5 -10.5 -10.4 -10.5 -10.6 -10.7 -11.1 -11.1 -12.0 Optimization Scenarios (Conakry) Figure 3.4-23 Energy Recovery Results of Optimizations Results (Conakry) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.04 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 Solid Waste Management Holistic Decision Modeling 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.02 -0.04 3-133 The scenarios for minimizing PM emissions -0.06 and energy optimization can also reduces -0.06 -0.06 carbon emissions. -0.06 -0.06 Scenarios for maximizing the material recovery will still discharge carbon emissions Carbon Emissions (MTCE)/metric ton -0.08 -0.07 -0.08 through its treatment processes. -0.08 -0.08 -0.10 -0.10 -0.10 -0.11 -0.11 -0.12 Optimization Scenarios (Conakry) Figure 3.4-24 Carbon Emissions Results of Optimizations Results (Conakry) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.4 Kathmandu Summary of Simulation Scenarios Costs Results Figures 3.4-25 and 3.4-26 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration is rather expensive than other options. The cheapest option is composting with manual turner because of the low treatment cost and reduction of waste to be buried at the landfill. Energy Recovery Results Figure 3.4-27 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. In addition, material recovery by recycling can also contribute large saving of energy consumption. Carbon Emission Results As Figure 3.4-28 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. Composting, irrespective of its operation method, can produce much less carbon emissions than others. Summary of Optimization Scenarios Costs Results Figures 3.4-29 and 3.4-30 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning, than other options. On the other hand, the cost for the scenario for minimizing carbon emissions is rather more expensive than others. Energy Recovery Results Figure 3.4-31 shows the energy recovery results. Needless to say, the energy optimization scenario achieves the lowest energy consumption, then the scenario for minimizing carbon emissions follows. It is interesting in Kathmandu that there is a big gap of energy consumption for all scenarios between the capture rate, high and low. Carbon Emission Results As Figure 3.4-32 shows, other than the scenario for minimizing carbon emissions, only the scenario for energy optimization will contribute to the reduction of carbon emissions. However, carbon emissions by the scenario for minimizing PM emissions will be much higher than others. 3-134 160 The cheapest option is composting with manual Incineration with and without 140 sorting because of the low treatment cost and energy recovery is rather reduction of waste to be buried at the landfill. expensive. 120 Solid Waste Management 100 Holistic Decision Modeling 80 60 Cost ($)/metric ton 40 20 3-135 0 -20 Recycling - Composting - Composting- Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.99 1.99 0.44 0.44 0.19 0.19 0.00 0.00 0.00 Disposal 45.01 45.01 10.07 10.15 1.40 1.40 49.27 50.14 49.64 Treatment 0.00 0.00 15.26 53.20 109.15 91.83 0.00 0.00 0.00 Separation 32.18 28.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 37.80 37.80 37.80 37.80 37.80 37.80 37.80 37.80 37.80 Remanufacturing -6.36 -6.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-25 Cost Results of Simulation Scenarios (Kathmandu: with Land Price ) Final Report 160 140 Due to the small land price, cost without land price is not so decreased than that with land 120 price. Solid Waste Management 100 Holistic Decision Modeling 80 60 Cost ($)/metric ton 40 20 3-136 0 -20 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.99 1.99 0.44 0.44 0.19 0.19 0.00 0.00 0.00 Disposal 44.43 44.43 9.94 10.01 1.30 1.30 48.64 49.50 49.00 Treatment 0.00 0.00 15.26 53.20 109.15 91.83 0.00 0.00 0.00 Separation 32.18 28.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 37.80 37.80 37.80 37.80 37.80 37.80 37.80 37.80 37.80 Remanufacturing -6.36 -6.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-26 Cost Results of Simulation Scenarios (Kathmandu: without Land Price ) Final Report 1.0 0.5 0.0 Solid Waste Management Holistic Decision Modeling -0.5 Incineration with energy recovery contributes greatly by saving energy. -1.0 In addition, material recovery by recycling can also contribute large saving of energy consumption. -1.5 Energy recovery is also expected from the remanufacturing process at the incineration scenarios. -2.0 3-137 Energy Consumption (MJ)/metric ton -2.5 Composting Recycling - Composting- Incineration - Landfill - Recycling - - Landfill - Landfill - Mechanical windrow Incineration energy energy manual sort manual vent flare sort turner recovery recovery turning Transportation 0.01885 0.01885 0.00806 0.00803 0.00179 0.00179 0.00000 0.00000 0.00000 Disposal 0.44362 0.44362 0.10305 0.10380 0.01045 0.01045 0.50010 0.50010 -0.28944 Treatment 0.00000 0.00000 0.12238 0.16179 0.03896 -1.84395 0.00000 0.00000 0.00000 Separation 0.08606 0.12312 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Transfer 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Collection 0.19609 0.19609 0.19609 0.19609 0.19609 0.19609 0.19609 0.19609 0.19609 Remanufacturing -1.89819 -1.89819 0.00000 0.00000 -0.49024 -0.49024 0.00000 0.00000 0.00000 Figure 3.4-27 Energy Recovery Results of Simulation Scenarios (Kathmandu) Final Report 0.4 0.3 0.3 Solid Waste Management Holistic Decision Modeling The worst option is landfill with gas venting. 0.2 Composting, irrespective of its operation method, can produce much less carbon emissions than others. 0.2 0.1 0.1 Carbon Emissions (MTCE)/metric ton 3-138 0.0 -0.1 Recycling - Composting - Composting- Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.00039 0.00039 0.00017 0.00017 0.00004 0.00004 0.00000 0.00000 0.00000 Disposal 0.08255 0.08255 0.00760 0.00775 0.00062 0.00023 0.29240 0.08855 0.08855 Treatment 0.00000 0.00000 0.00001 0.00097 0.05864 0.05864 0.00000 0.00000 0.00000 Separation 0.00152 0.00152 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Transfer 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Collection 0.00122 0.00122 0.00122 0.00122 0.00122 0.00122 0.00122 0.00122 0.00122 Remanufacturing -0.02167 -0.02167 0.00000 0.00000 -0.00077 -0.00077 0.00000 0.00000 0.00000 Figure 3.4-28 Carbon Emissions Results of Simulation Scenarios (Kathmandu) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 250.0 Maximizing material recovery with manual MRF and composting is less expensive option. On the other hand, the cost for the scenario for minimizing carbon emissions is rather more expensive than others. Solid Waste Management 200.0 191.6 Holistic Decision Modeling 178.1 175.2 166.8 159.2 160.0 152.0 145.2 146.9 150.0 142.4 134.2 133.4 126.5 117.8 110.9 105.3 107.6 99.7 100.0 Cost ($)/metric ton 83.3 3-139 73.7 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Kathmandu) Figure 3.4-29 Cost Results of Optimizations Scenarios (Kathmandu: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 250.0 ## Due to the small land price, cost without land price is not so different than that with land price for any scenarios. Solid Waste Management Holistic Decision Modeling 200.0 191.6 178.1 175.2 166.8 159.1 160.0 152.0 145.1 146.9 150.0 142.3 134.1 133.4 126.5 117.7 110.7 107.6 105.1 99.6 100.0 Cost ($)/metric ton 83.2 3-140 73.5 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Kathmandu) Figure 3.4-30 Cost Results of Optimizations Scenarios (Kathmandu: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.4 -0.2 -0.4 -0.2 Solid Waste Management -1.0 Holistic Decision Modeling -1.5 -1.4 -1.4 -1.5 -1.5 -1.5 -2.0 -2.1 -2.2 -3.0 -2.9 Scenarios for minimizing can reduce -2.8 energy consumption into the same level as -3.4 -3.3 3-141 the energy optimization scenario. It is observed that a big gap of energy -4.0 consumption for all scenarios between the capture rate, high and low. Energy Consumption (MJ)/metric ton -5.0 -4.9 -5.0 -5.5 -5.6 -6.0 Optimization Scenarios (Kathmandu) Figure 3.4-31 Energy Recovery Results of Optimizations Results (Kathmandu) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.10 0.085 0.085 0.08 Solid Waste Management Holistic Decision Modeling Other than the scenario for minimizing carbon 0.061 emissions, only the scenario for energy optimization will 0.058 0.06 contribute to the reduction of carbon emissions. However, carbon emissions by the scenario for minimizing PM emissions will be much higher than others. 0.04 0.02 0.015 0.014 0.013 0.014 3-142 0.000 0.000 0.000 0.00 0.000 -0.001 0.000 Carbon Emissions (MTCE)/metric ton Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions -0.002 Emissions -0.002 -0.02 -0.020 -0.020 -0.023 -0.024 -0.04 Optimization Scenarios (Kathmandu) Figure 3.4-32 Carbon Emissions Results of Optimizations Results (Kathmandu) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.5 Lahore Summary of Simulation Scenarios Costs Results Figures 3.4-33 and 3.4-34 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration only is most expensive than that for incineration with energy recovery follows. Direct landfill is the least expensive options than any other options with intermediate treatment. Energy Recovery Results Figure 3.4-35 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation. Carbon Emission Results As Figure 3.4-36 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. Incineration with energy recovery produces least carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-37 and 3.4-38 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning, than other options. On the other hand, the cost for the scenario for minimizing carbon emissions and PM emissions is rather more expensive than others. Energy Recovery Results Figure 3.4-39 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. Carbon Emission Results As Figure 3.4-40 shows, as well as the scenario for minimizing carbon emissions, the scenario for minimizing PM emissions can also largely reduces carbon emissions. Scenarios for energy optimization will also contribute to the reduction of carbon emissions but those for maximizing material recovery still show the positive carbon emissions. 3-143 160 Incineration with and without 140 energy recovery is rather expensive. Direct landfill is the least 120 expensive options than any other options with intermediate 100 treatment. Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-144 -20 -40 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 2.03 2.03 0.50 0.50 0.41 0.41 0.00 0.00 0.00 Disposal 28.26 28.26 14.83 14.83 3.20 3.20 30.32 30.96 25.74 Treatment 0.00 0.00 23.50 50.59 109.36 94.78 0.00 0.00 0.00 Separation 38.05 34.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 33.38 33.38 33.38 33.38 33.38 33.38 33.38 33.38 33.38 Remanufacturing -13.72 -13.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-33 Cost Results of Simulation Scenarios (Lahore: with Land Price) Final Report 160 140 120 Landfill cost without land price 100 shows a small decrease of the total cost than that with land price. Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 0 3-145 -20 -40 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 2.03 2.03 0.50 0.50 0.41 0.41 0.00 0.00 0.00 Disposal 22.19 22.19 11.64 11.64 2.74 2.74 23.81 24.38 19.16 Treatment 0.00 0.00 23.50 50.59 109.36 94.78 0.00 0.00 0.00 Separation 38.05 34.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 33.38 33.38 33.38 33.38 33.38 33.38 33.38 33.38 33.38 Remanufacturing -13.72 -13.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-34 Cost Results of Simulation Scenarios (Lahore: without Land Price) Final Report 2.0 1.0 0.0 -1.0 Solid Waste Management Holistic Decision Modeling -2.0 -3.0 Incineration with energy recovery contributes greatly by saving energy. -4.0 Greater energy recovery can be also expected by material recovery -5.0 scenarios with both manual and mechanical operation. -6.0 Energy Consumption (MJ)/metric ton -7.0 3-146 -8.0 -9.0 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0191 0.0191 0.0092 0.0092 0.0038 0.0038 0.0000 0.0000 0.0000 Disposal 0.4308 0.4308 0.2451 0.2451 0.0206 0.0206 0.4817 0.4817 -1.1142 Treatment 0.0000 0.0000 0.2894 0.3457 0.0424 -7.0997 0.0000 0.0000 0.0000 Separation 0.1343 0.2522 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.1717 0.1717 0.1717 0.1717 0.1717 0.1717 0.1717 0.1717 0.1717 Remanufacturing -2.4411 -2.4411 0.0000 0.0000 -0.4938 -0.4938 0.0000 0.0000 0.0000 Figure 3.4-35 Energy Recovery Results of Simulation Scenarios (Lahore) Final Report 0.3 The worst option is landfill with gas venting. 0.2 Incineration with energy recovery produces least carbon emissions. Composting, irrespective of its operation method, can produce much less carbon 0.2 emissions than others. Solid Waste Management Holistic Decision Modeling 0.1 0.1 0.0 Carbon Emissions (MTCE)/metric ton -0.1 3-147 -0.1 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0002 0.0002 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0488 0.0488 0.0095 0.0095 0.0001 0.0001 0.1875 0.0585 0.0293 Treatment 0.0000 0.0000 0.0051 0.0066 0.0655 -0.0651 0.0000 0.0000 0.0000 Separation 0.0065 0.0147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 Remanufacturing -0.0320 -0.0320 0.0000 0.0000 -0.0008 -0.0008 0.0000 0.0000 0.0000 Figure 3.4-36 Carbon Emissions Results of Simulation Scenarios (Lahore) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 200.0 Maximizing material recovery with manual MRF and composting is 182.4 180.7 180.0 less expensive option. 174.1 On the other hand, the cost for the scenario for minimizing carbon 169.8 170.6 167.4 Solid Waste Management Holistic Decision Modeling emissions and PM emissionsis rather more expensive than others. 158.1 155.7 160.0 142.5 141.1 141.9 141.2 139.1 140.0 131.2 129.0 119.5 120.0 112.7 101.0 102.6 100.0 89.3 Cost ($)/metric ton 80.0 3-148 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Lahore) Figure 3.4-37 Cost Results of Optimizations Scenarios (Lahore: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 200.0 ## Land price does not affect the tendency of any optimization 182.0 180.3 180.0 scenarios. 173.7 169.4 170.2 167.0 157.7 Solid Waste Management 155.3 Holistic Decision Modeling 160.0 142.1 141.5 140.7 140.8 140.0 135.8 128.0 125.7 120.0 116.3 109.5 97.7 99.4 100.0 86.0 80.0 Cost ($)/metric ton 3-149 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Lahore) Figure 3.4-38 Cost Results of Optimizations Scenarios (Lahore: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost -0.4 Compost Emissions Emissions -0.3 -0.2 -0.3 -1.0 -1.6 Solid Waste Management -1.7 -1.7 -1.7 Holistic Decision Modeling -2.0 -3.0 -4.0 -5.0 -6.0 3-150 Scenarios for minimizing carbon and for -7.0 minimizing PM emissions can reduce energy consumption into the same level as Energy Consumption (MJ)/metric ton the energy optimization scenario. -8.0 -8.0 -8.1 -8.0 -8.1 -8.3 -8.4 -8.3 -8.4 -8.6 -8.6 -9.0 -9.1 -9.1 -10.0 Optimization Scenarios (Lahore) Figure 3.4-39 Energy Recovery Results of Optimizations Results (Lahore) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.04 0.022 0.022 0.020 0.019 0.02 Solid Waste Management Holistic Decision Modeling 0.006 0.006 0.002 0.002 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.02 -0.04 As well as the scenario for minimizing carbon emissions, the scenario for minimizing PM emissions can also largely 3-151 -0.06 reduces carbon emissions. Scenarios for energy optimization will also -0.062 -0.062 -0.066 -0.066 contribute to the reduction of carbon -0.08 emissions but those for maximizing material Carbon Emissions (MTCE)/metric ton recovery still show the positive carbon emissions. -0.094 -0.094 -0.10 -0.098 -0.098 -0.107 -0.107 -0.113 -0.113 -0.12 Optimization Scenarios (Lahore) Figure 3.4-40 Carbon Emissions Results of Optimizations Results (Lahore) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.6 Sarajevo Summary of Simulation Scenarios Costs Results Figures 3.4-41 and 3.4-42 show the cost results of the simulation scenarios using one primary technology. Since the total unit cost per tonne-waste for incineration with and without energy recovery is rather expensive. Other options except composting with windrow turner are ranked as less expensive options. The cheapest option is landfill with energy recovery. Energy Recovery Results Figure 3.4-43 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation and by landfill with energy recovery. Carbon Emission Results As Figure 3.4-44 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting as well as most of other cities. Scenarios for composting with both manual and mechanical turning produce less carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-45 and 3.4-46 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual MRF and composting with manual turning, than other options. Energy Recovery Results Figure 3.4-47 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the almost same level as the energy optimization scenario. Material recovery with high capture rate can also save energy consumption more than low capture rate. Carbon Emission Results As Figure 3.4-48 shows, as well as the scenario for minimizing carbon emissions, the scenarios for minimizing PM emissions and energy optimization can also reduces carbon emissions. Scenarios for maximizing the material recovery will still discharge carbon emissions through its treatment processes. 3-152 200 Incineration with and without energy recovery is rather expensive. Other options except composting with windrow turner are ranked as less expensive options. The cheapest option is landfill with energy recovery. 150 Solid Waste Management Holistic Decision Modeling 100 50 Cost ($)/metric ton 0 3-153 -50 Recycling - Recycling - Composting - Composting - Incineration - Landfill - Landfill - Landfill - manual Mechanical manual windrow Incineration energy energy vent flare sort sort turning turner recovery recovery Transportation 2.04 2.04 0.50 0.50 0.62 0.62 0.00 0.00 0.00 Disposal 27.82 27.82 13.63 13.63 4.37 4.37 29.92 30.52 24.76 Treatment 0.00 0.00 18.86 48.26 109.78 89.95 0.00 0.00 0.00 Separation 17.15 16.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 30.29 30.29 30.29 30.29 30.29 30.29 30.29 30.29 30.29 Remanufacturing -10.40 -10.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-41 Cost Results of Simulation Scenarios (Sarajevo: with Land Price) Final Report 200 150 Landfill cost without land price Solid Waste Management shows a small decrease of the Holistic Decision Modeling 100 total cost than that with land price. 50 Cost ($)/metric ton 0 3-154 -50 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 2.04 2.04 0.50 0.50 0.62 0.62 0.00 0.00 0.00 Disposal 25.99 25.99 12.73 12.73 4.17 4.17 27.95 28.53 22.77 Treatment 0.00 0.00 18.86 48.26 109.78 89.95 0.00 0.00 0.00 Separation 17.15 16.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 30.29 30.29 30.29 30.29 30.29 30.29 30.29 30.29 30.29 Remanufacturing -10.40 -10.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-42 Cost Results of Simulation Scenarios (Sarajevo: without Land Price) Final Report 2.0 1.0 0.0 Solid Waste Management Holistic Decision Modeling -1.0 Incineration with energy recovery contributes greatly by saving energy. -2.0 Greater energy recovery can be also expected by material recovery scenarios with both manual and -3.0 mechanical operation and by landfill with energy recovery. -4.0 3-155 Energy Consumption (MJ)/metric ton -5.0 Recycling - Recycling - Composting - Composting - Incineration - Landfill - Landfill - Landfill - manual Mechanical manual windrow Incineration energy energy vent flare sort sort turning turner recovery recovery Transportation 0.0192 0.0192 0.0091 0.0091 0.0057 0.0057 0.0000 0.0000 0.0000 Disposal 0.4586 0.4586 0.2364 0.2364 0.0336 0.0336 0.5031 0.5031 -0.9854 Treatment 0.0000 0.0000 0.1310 0.1794 0.0405 -4.3262 0.0000 0.0000 0.0000 Separation 0.1025 0.1647 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.1543 0.1543 0.1543 0.1543 0.1543 0.1543 0.1543 0.1543 0.1543 Remanufacturing -1.4495 -1.4495 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Figure 3.4-43 Energy Recovery Results of Simulation Scenarios (Sarajevo) Final Report 0.3 0.2 The worst option is landfill with gas venting. Scenarios for composting with both manual and mechanical turning produce less carbon 0.2 emissions. Solid Waste Management Holistic Decision Modeling 0.1 0.1 0.0 Carbon Emissions (MTCE)/metric ton -0.1 3-156 -0.1 Recycling - Recycling - Composting - Composting - Incineration - Landfill - Landfill - Landfill - manual Mechanical manual windrow Incineration energy energy vent flare sort sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0002 0.0002 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0641 0.0641 0.0043 0.0043 0.0002 0.0002 0.2227 0.0709 0.0301 Treatment 0.0000 0.0000 0.0026 0.0038 0.0397 -0.0799 0.0000 0.0000 0.0000 Separation 0.0031 0.0057 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 Remanufacturing -0.0188 -0.0188 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Figure 3.4-44 Carbon Emissions Results of Simulation Scenarios (Sarajevo) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 180.0 Maximizing material 169.3 163.8 recovery with 160.4 160.0 156.4 manual MRF and 153.1 153.3 154.0 152.0 150.9 composting is less 146.9 145.8 145.6 Solid Waste Management 143.2 Holistic Decision Modeling expensive option. 141.4 140.0 130.6 124.3 120.0 107.9 102.4 100.0 88.7 82.3 80.0 Cost ($)/metric ton 3-157 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Sarajevo) Figure 3.4-45 Cost Results of Optimizations Scenarios (Sarajevo: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture ## 180.0 169.1 Land price does not affect the tendency of 163.6 any optimization scenarios. 160.2 160.0 156.2 152.9 153.1 153.8 151.8 150.7 146.6 145.6 145.4 Solid Waste Management 143.0 Holistic Decision Modeling 141.1 140.0 130.3 123.9 120.0 107.6 102.1 100.0 88.4 82.0 80.0 Cost ($)/metric ton 3-158 60.0 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Sarajevo) Figure 3.4-46 Cost Results of Optimizations Scenarios (Sarajevo: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost -0.22 Emissions Emissions -0.29 -0.5 -0.18 -0.25 Solid Waste Management Holistic Decision Modeling -1.0 -1.14 -1.17 -1.22 -1.25 -1.5 -2.0 -2.5 3-159 -3.0 Scenarios for minimizing carbon and for minimizing PM emissions can reduce -3.5 energy consumption into the same level as the energy optimization scenario. Energy Consumption (MJ)/metric ton Material recovery with high capture rate can also save energy consumption more -4.0 than low capture rate. -4.01 -4.00 -4.04 -4.04 -4.26 -4.30 -4.5 -4.32 -4.34 -4.47 -4.41 -4.44 -4.49 -5.0 Optimization Scenarios (Sarajevo) Figure 3.4-47 Energy Recovery Results of Optimizations Results (Sarajevo) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.04 0.018 0.018 0.02 0.016 0.016 Solid Waste Management Holistic Decision Modeling 0.005 0.005 0.003 0.003 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.02 -0.04 3-160 -0.052 -0.052 -0.06 As well as the scenario for minimizing carbon emissions, the scenario for -0.060 -0.060 minimizing PM emissions can also largely -0.067 -0.066 -0.071 -0.071 reduces carbon emissions. Carbon Emissions (MTCE)/metric ton -0.08 Scenarios for energy optimization will also contribute to the reduction of carbon emissions but those for maximizing material -0.090 -0.090 -0.10 recovery still show the positive carbon -0.095 -0.095 emissions. -0.12 Optimization Scenarios (Sarajevo) Figure 3.4-48 Carbon Emissions Results of Optimizations Results (Sarajevo) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.7 Shanghai Summary of Simulation Scenarios Costs Results Figures 3.4-49 and 3.4-50 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for recycling at MRF and incineration without energy recovery is more expensive than others. Composting with manual turning is less expensive option as well as the option for landfill with energy recovery. Energy Recovery Results Figure 3.4-51 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also expected by material recovery scenarios with both manual and mechanical operation. Carbon Emission Results As Figure 3.4-52 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. The second worst option is incineration without energy recovery. However, incineration with energy recovery can contribute to reduce the carbon emissions greatly. Composting with both manual turning and windrow turner is a better option with less carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-53 and 3.4-54 show the cost results of the optimization scenarios. It clearly shows that the unit cost per tonne-waste is less expensive for the scenario which is energy optimization, than other options except the scenario for maximizing material recovery with low capture rate. Energy Recovery Results Figure 3.4-55 shows the energy recovery results. Needless to say, the energy optimization scenario achieves the lowest energy consumption, then the scenarios for minimizing carbon emissions and PM emissions follow. The scenario for maximizing material recovery contributes much less for reduction of energy consumption. There are more energy savings for recycling options with high capture rate than low capture rate. Carbon Emission Results As Figure 3.4-56 shows, other than the scenario for minimizing carbon emissions, scenarios for energy optimization and minimizing PM emissions will contribute to the reduction of carbon emissions. Recycling options with high capture rate can contribute to reduce the carbon emissions more than low capture rate. 3-161 140 Recycling at MRF and incineration without energy recovery is more expensive than others. 120 Composting with manual turning is the least expensive option than the option for landfill with energy recovery in case the land price is 100 considered for the landfill cost. Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 3-162 0 -20 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.91 1.91 0.41 0.40 0.20 0.20 0.00 0.00 0.00 Disposal 43.01 43.01 14.87 14.96 1.91 1.91 49.19 49.99 38.43 Treatment 0.00 0.00 19.87 54.38 91.37 53.33 0.00 0.00 0.00 Separation 53.66 50.63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 Remanufacturing -11.70 -11.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-49 Cost Results of Simulation Scenarios (Shanghai: with Land Price) Final Report 140 120 In case without land price for landfill cost, the option for landfill 100 with energy recovery is replaced to the cheapest option. Solid Waste Management Holistic Decision Modeling 80 60 40 Cost ($)/metric ton 20 3-163 0 -20 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.91 1.91 0.41 0.40 0.20 0.31 0.00 0.00 0.00 Disposal 30.86 30.86 10.67 10.73 1.32 1.59 35.30 35.96 24.40 Treatment 0.00 0.00 19.87 54.38 91.37 53.33 0.00 0.00 0.00 Separation 53.66 50.63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 31.10 Remanufacturing -11.70 -11.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-50 Cost Results of Simulation Scenarios (Shanghai: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 Incineration with energy recovery contributes greatly by saving energy. Greater energy recovery can be also -6.0 expected by material recovery scenarios with both manual and mechanical operation. -8.0 3-164 Energy Consumption (MJ)/metric ton -10.0 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0183 0.0183 0.0075 0.0074 0.0018 0.0028 0.0000 0.0000 0.0000 Disposal 0.4688 0.4688 0.1606 0.1616 0.0100 0.0264 0.5487 0.5487 -1.5476 Treatment 0.0000 0.0000 0.3316 0.3882 0.0432 -8.8808 0.0000 0.0000 0.0000 Separation 0.1628 0.2898 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.1590 0.1590 0.1590 0.1590 0.1590 0.2651 0.1590 0.1590 0.1590 Remanufacturing -3.1911 -3.1911 0.0000 0.0000 -0.2064 -0.2003 0.0000 0.0000 0.0000 Figure 3.4-51 Energy Recovery Results of Simulation Scenarios (Shanghai) Final Report 0.3 The worst option is landfill with gas venting. The second worst option is incineration without energy recovery. 0.3 Incineration with energy recovery can contribute to reduce the carbon emissions greatly. Composting with both manual turning and windrow turner is a better 0.2 option with less carbon emissions. Solid Waste Management Holistic Decision Modeling 0.2 0.1 0.1 0.0 Carbon Emissions (MTCE)/metric ton -0.1 3-165 -0.1 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0002 0.0002 0.0000 0.0001 0.0000 0.0000 0.0000 Disposal 0.0694 0.0694 0.0004 0.0006 0.0001 0.0020 0.2390 0.0735 0.0258 Treatment 0.0000 0.0000 0.0066 0.0081 0.1412 -0.0660 0.0000 0.0000 0.0000 Separation 0.0099 0.0206 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0011 0.0011 0.0011 0.0011 0.0011 0.0015 0.0011 0.0011 0.0011 Remanufacturing -0.0488 -0.0488 0.0000 0.0000 -0.0003 -0.0003 0.0000 0.0000 0.0000 Figure 3.4-52 Carbon Emissions Results of Simulation Scenarios (Shanghai) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 250.0 The scenario for energy optimization is less expensive than other options except the scenarios for maximizing material recovery with low capture rate. 205.7 204.6 Solid Waste Management 198.8 Holistic Decision Modeling 197.8 200.0 176.4 169.5 164.4 162.9 156.5 154.9 143.9 142.5 150.0 137.1 134.6 106.2 108.1 107.0 106.7 103.8 98.5 100.0 3-166 Cost ($)/metric ton 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Shanghai) Figure 3.4-53 Cost Results of Optimizations Scenarios (Shanghai: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture ## 250.0 Land price does not affect the tendency of any optimization scenarios. 205.3 204.2 Solid Waste Management 198.4 197.4 Holistic Decision Modeling 200.0 173.7 166.9 163.9 162.4 156.0 154.5 150.0 141.3 139.4 134.5 131.5 107.6 106.3 106.5 103.2 103.3 100.0 95.4 Cost ($)/metric ton 3-167 50.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Shanghai) Figure 3.4-54 Cost Results of Optimizations Scenarios (Shanghai: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.70 -0.61 -0.62 -0.54 Solid Waste Management Holistic Decision Modeling -2.0 -2.29 -2.18 -2.22 -2.33 -4.0 The energy optimization scenario achieves -6.0 the lowest energy consumption, then the scenarios for minimizing carbon emissions and PM emissions follow. 3-168 The scenario for maximizing material recovery contributes much less for -8.0 reduction of energy consumption. There -7.85 -7.81 -7.88 -7.92 are more energy savings for recycling -8.16 -8.20 -8.23 -8.28 options with high capture rate than low Energy Consumption (MJ)/metric ton capture rate. -9.41 -9.43 -10.0 -9.61 -9.63 -12.0 Optimization Scenarios (Shanghai) Figure 3.4-55 Energy Recovery Results of Optimizations Results (Shanghai) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.02 0.010 0.010 0.007 0.006 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Solid Waste Management Holistic Decision Modeling -0.013 -0.013 -0.02 -0.020 -0.020 -0.04 -0.051 -0.050 -0.06 -0.051 -0.051 Other than the scenario for minimizing carbon emissions, scenarios for energy optimization and minimizing PM emissions -0.08 will contribute to the reduction of carbon 3-169 emissions. Recycling options with high capture rate can contribute to reduce the carbon emissions -0.10 more than low capture rate. -0.098 -0.098 Carbon Emissions (MTCE)/metric ton -0.106 -0.107 -0.110 -0.110 -0.12 -0.124 -0.124 -0.14 Optimization Scenarios (Shanghai) Figure 3.4-56 Carbon Emissions Results of Optimizations Results (Shanghai) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.8 Kawasaki Summary of Simulation Scenarios Costs Results Figures 3.4-57 and 3.4-58 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration is most expensive than other options. Energy Recovery Results Figure 3.4-59 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. In addition, recycling can also save energy consumption. Carbon Emission Results As Figure 3.4-60 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. On the other hand, scenarios for recycling, composting and incineration with energy recovery produce less carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-61 and 3.4-62 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery, than other options. Energy Recovery Results Figure 3.4-63 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. Carbon Emission Results As Figure 3.4-64 shows, as well as the scenario for minimizing carbon emissions, the scenarios for minimizing PM emissions and energy optimization can also reduces carbon emissions. The other scenario for maximizing the material recovery with low capture rate still shows little positive carbon emissions. 3-170 1,200 The total unit cost for incineration is most 1,000 expensive than other options. 800 Solid Waste Management Holistic Decision Modeling 600 400 Cost ($)/metric ton 200 0 3-171 -200 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.75 1.75 1.14 1.14 0.35 0.35 0.00 0.00 0.00 Disposal 179.32 179.32 63.51 63.51 13.87 13.87 224.44 227.31 213.38 Treatment 0.00 0.00 19.67 58.19 736.51 677.46 0.00 0.00 0.00 Separation 80.13 77.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 62.78 62.78 62.78 62.78 62.78 62.78 62.78 62.78 62.78 Remanufacturing -25.90 -25.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-57 Cost Results of Simulation Scenarios (Kawasaki: with Land Price) Final Report 1,200 1,000 In case without land price for landfill cost, total cost for any scenarios except 800 incineration scenarios will be largely decreased. Solid Waste Management Holistic Decision Modeling 600 400 Cost ($)/metric ton 200 0 3-172 -200 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.75 1.75 1.14 1.14 0.35 0.35 0.00 0.00 0.00 Disposal 29.49 29.49 10.44 10.44 2.35 2.35 36.91 37.90 23.94 Treatment 0.00 0.00 19.67 58.19 736.51 677.46 0.00 0.00 0.00 Separation 80.13 77.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 62.78 62.78 62.78 62.78 62.78 62.78 62.78 62.78 62.78 Remanufacturing -25.90 -25.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-58 Cost Results of Simulation Scenarios (Kawasaki: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 -6.0 -8.0 Incineration with energy recovery contributes greatly by saving energy. -10.0 Energy recovery also expected from the remanufacturing process in the incineration scenarios. In addition, recycling can also save energy 3-173 -12.0 Energy Consumption (MJ)/metric ton consumption. -14.0 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0173 0.0173 0.0209 0.0209 0.0033 0.0033 0.0000 0.0000 0.0000 Disposal 0.4777 0.4777 0.1614 0.1614 0.0193 0.0193 0.6131 0.6131 -1.4572 Treatment 0.0000 0.0000 0.2863 0.3408 0.0422 -8.0302 0.0000 0.0000 0.0000 Separation 0.1580 0.2619 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.3384 0.3384 0.3384 0.3384 0.3384 0.3384 0.3384 0.3384 0.3384 Remanufacturing -7.5236 -7.5236 0.0000 0.0000 -3.3512 -3.3512 0.0000 0.0000 0.0000 Figure 3.4-59 Energy Recovery Results of Simulation Scenarios (Kawasaki) Final Report 0.4 The worst option is landfill with gas venting. 0.3 On the other hand, scenarios for recycling, composting and incineration with energy recovery produce less carbon emissions. 0.3 0.2 Solid Waste Management Holistic Decision Modeling 0.2 0.1 0.1 0.0 Carbon Emissions (MTCE)/metric ton 3-174 -0.1 -0.1 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0004 0.0004 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0789 0.0789 0.0004 0.0004 0.0001 0.0001 0.2819 0.0930 0.0628 Treatment 0.0000 0.0000 0.0041 0.0054 0.0758 -0.0422 0.0000 0.0000 0.0000 Separation 0.0062 0.0114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0020 0.0020 0.0020 0.0020 0.0020 0.0020 0.0020 0.0020 0.0020 Remanufacturing -0.0730 -0.0730 0.0000 0.0000 -0.0053 -0.0053 0.0000 0.0000 0.0000 Figure 3.4-60 Carbon Emissions Results of Simulation Scenarios (Kawasaki) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 1200.0 984.3 972.1 946.6 1000.0 Solid Waste Management 939.6 Holistic Decision Modeling 934.4 927.5 884.6 896.7 898.1 889.8 852.1 865.6 800.0 Maximizing material recovery scenarios with both manual and mechanical MRF 600.0 and compost are much less expensive than other options. Cost ($)/metric ton 3-175 400.0 280.6 251.2 218.7 254.7 248.1 218.9 217.0 181.2 200.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Kawasaki) Figure 3.4-61 Cost Results of Optimizations Scenarios (Kawasaki: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture ## 1,200.0 1,000.0 965.1 976.5 Solid Waste Management Holistic Decision Modeling 926.7 932.6 938.8 920.1 877.8 889.0 891.7 882.4 845.3 859.2 800.0 Land price does not affect the tendency of any optimization scenarios. 600.0 3-176 Cost ($)/metric ton 400.0 245.3 215.9 208.9 212.8 200.0 173.1 183.4 171.1 135.3 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Kawasaki) Figure 3.4-62 Cost Results of Optimizations Scenarios (Kawasaki: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -2.0 -2.2 Solid Waste Management -2.3 Holistic Decision Modeling -2.5 -2.5 -4.0 -6.0 -6.6 -6.5 -6.7 -6.8 -8.0 Scenarios for minimizing carbon and for 3-177 -10.0 minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. There are more energy savings for -12.0 Energy Consumption (MJ)/metric ton recycling options with high capture rate than low capture rate. -14.0 -13.6 -13.8 -13.8 -14.1 -14.1 -14.0 -14.3 -14.3 -14.4 -14.6 -14.8 -15.0 -16.0 Optimization Scenarios (Kawasaki) Figure 3.4-63 Energy Recovery Results of Optimizations Results (Kawasaki) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.02 0.007 0.006 0.004 0.003 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Solid Waste Management Holistic Decision Modeling -0.02 -0.04 -0.036 -0.037 -0.040 -0.041 -0.06 -0.058 -0.059 -0.061 Other than the scenario for minimizing -0.062 carbon emissions, scenarios for energy -0.08 optimization and minimizing PM emissions will contribute to the reduction of carbon 3-178 emissions. -0.10 The other scenario for maximizing the material recovery with low capture rate still shows little positive carbon emissions. -0.112 -0.12 -0.113 Carbon Emissions (MTCE)/metric ton -0.124 -0.125 -0.125 -0.126 -0.14 -0.142 -0.143 -0.16 Optimization Scenarios (Kawasaki) Figure 3.4-64 Carbon Emissions Results of Optimizations Results (Kawasaki) Final Report Solid Waste Management Holistic Decision Modeling Final Report 3.4.9 Atlanta Summary of Simulation Scenarios Costs Results Figures 3.4-65 and 3.4-66 show the cost results of the simulation scenarios using one primary technology. The total unit cost per tonne-waste including collection cost (which is same for all options) for incineration is most expensive than others. The less expensive option is still direct landfill. Considering the revenue from sales of collected recyclables, recycling options are also the less expensive alternatives. Energy Recovery Results Figure 3.4-67 shows the energy recovery results. As is easily understood, adoption of incineration with energy recovery contributes greatly by saving energy. In addition, recycling can also save energy consumption. Carbon Emission Results As Figure 3.4-68 shows, the carbon emission results show that the worst option can be said to be landfill with gas venting. On the other hand, scenarios for recycling, composting and incineration with energy recovery produce less carbon emissions. In which, incineration with energy recovery is least option for carbon emissions. Summary of Optimization Scenarios Costs Results Figures 3.4-69 and 3.4-70 show the cost results of the optimization scenarios. It shows that the unit cost per tonne-waste is less expensive for the scenario which is maximizing material recovery with manual operation and biweekly collection, than other options. Energy Recovery Results Figure 3.4-71 shows the energy recovery results. Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as the energy optimization scenario. Carbon Emission Results As Figure 3.4-72 shows, as well as the scenario for minimizing carbon emissions, the scenarios for minimizing PM emissions and energy optimization can also reduces carbon emissions. The other scenario for maximizing the material recovery with low capture rate still shows little positive carbon emissions. 3-179 200 The total unit cost for incineration is most expensive than other options. The less expensive option is still 150 direct landfill. Considering the revenue from sales of collected recyclables, recycling options are also the less expensive Solid Waste Management alternatives. Holistic Decision Modeling 100 50 Cost ($)/metric ton 0 3-180 -50 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.79 1.79 1.37 1.37 0.38 0.38 0.00 0.00 0.00 Disposal 25.41 25.41 16.10 16.10 2.62 2.62 30.92 31.62 28.30 Treatment 0.00 0.00 19.65 43.84 108.87 86.62 0.00 0.00 0.00 Separation 38.71 36.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 39.74 39.74 39.74 39.74 39.74 39.74 39.74 39.74 39.74 Remanufacturing -38.97 -38.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-65 Cost Results of Simulation Scenarios (Atlanta: with Land Price) Final Report 200 Due to the small land price, cost 150 without land price is not so decreased than that with land price. Solid Waste Management Holistic Decision Modeling 100 50 Cost ($)/metric ton 0 3-181 -50 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 1.79 1.79 1.37 1.37 0.38 0.38 0.00 0.00 0.00 Disposal 24.62 24.62 15.60 15.60 2.56 2.56 29.96 30.66 27.33 Treatment 0.00 0.00 19.65 43.84 108.87 86.62 0.00 0.00 0.00 Separation 38.71 36.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Transfer 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Collection 39.74 39.74 39.74 39.74 39.74 39.74 39.74 39.74 39.74 Remanufacturing -38.97 -38.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Figure 3.4-66 Cost Results of Simulation Scenarios (Atlanta: without Land Price) Final Report 2.0 0.0 -2.0 Solid Waste Management Holistic Decision Modeling -4.0 -6.0 -8.0 -10.0 Incineration with energy recovery contributes greatly by saving energy. Energy recovery also expected from the remanufacturing process in the incineration scenarios. 3-182 Energy Consumption (MJ)/metric ton -12.0 In addition, recycling can also save energy consumption. -14.0 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.017 0.017 0.025 0.025 0.004 0.004 0.000 0.000 0.000 Disposal 0.480 0.480 0.315 0.315 0.022 0.022 0.594 0.594 -0.813 Treatment 0.000 0.000 0.141 0.191 0.042 -6.838 0.000 0.000 0.000 Separation 0.120 0.195 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Transfer 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Collection 0.212 0.212 0.212 0.212 0.212 0.212 0.212 0.212 0.212 Remanufacturing -8.778 -8.778 0.000 0.000 -6.040 -6.040 0.000 0.000 0.000 Figure 3.4-67 Energy Recovery Results of Simulation Scenarios (Atlanta) Final Report 0.3 The worst option is landfill with gas venting. On the other hand, scenarios for recycling, composting and 0.3 incineration with energy recovery produce less carbon emissions. In which, incineration with energy recovery is least option 0.2 for carbon emissions. Solid Waste Management Holistic Decision Modeling 0.2 0.1 0.1 0.0 Carbon Emissions (MTCE)/metric ton 3-183 -0.1 -0.1 Recycling - Composting - Composting - Incineration - Landfill - Recycling - Landfill - Landfill - Mechanical manual windrow Incineration energy energy manual sort vent flare sort turning turner recovery recovery Transportation 0.0004 0.0004 0.0005 0.0005 0.0001 0.0001 0.0000 0.0000 0.0000 Disposal 0.0787 0.0787 0.0291 0.0291 0.0001 0.0001 0.2789 0.0986 0.0749 Treatment 0.0000 0.0000 0.0023 0.0035 0.1258 0.0096 0.0000 0.0000 0.0000 Separation 0.0038 0.0069 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Transfer 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Collection 0.0013 0.0013 0.0013 0.0013 0.0013 0.0013 0.0013 0.0013 0.0013 Remanufacturing -0.0471 -0.0471 0.0000 0.0000 -0.0095 -0.0095 0.0000 0.0000 0.0000 Figure 3.4-68 Carbon Emissions Results of Simulation Scenarios (Atlanta) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 160.0 Maximizing material 148.0 recovery scenarios with 141.7 140.0 manual operation and biweekly collection are 130.3 132.0 Solid Waste Management 127.8 Holistic Decision Modeling less expensive than 126.0 122.7 123.0 other options. 118.2 120.0 115.9 108.2 109.9 105.7 104.0 97.2 96.9 99.2 100.0 90.0 75.2 80.0 71.0 Cost ($)/metric ton 60.0 3-184 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Atlanta) Figure 3.4-69 Cost Results of Optimizations Scenarios (Atlanta: with Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 160.0 Land price does not affect the tendency of 148.0 any optimization scenarios. 141.7 140.0 130.2 132.0 127.5 Solid Waste Management 125.9 Holistic Decision Modeling 122.6 122.9 118.2 120.0 115.7 108.2 109.9 105.4 103.9 99.2 100.0 97.0 96.7 89.8 80.0 74.9 70.8 Cost ($)/metric ton 60.0 3-185 40.0 20.0 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions Optimization Scenarios (Atlanta) Figure 3.4-70 Cost Results of Optimizations Scenarios (Atlanta: without Land Price) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.0 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -3.3 -3.2 -3.4 Solid Waste Management -3.5 Holistic Decision Modeling -5.0 -8.5 -8.6 -8.4 -8.5 -10.0 Scenarios for minimizing carbon and for minimizing PM emissions can reduce energy consumption into the same level as -15.0 the energy optimization scenario. 3-186 There are more energy savings for recycling options with high capture rate than low capture rate. -16.7 -16.8 -17.5 -17.6 -17.5 -17.6 Energy Consumption (MJ)/metric ton -18.2 -18.3 -20.0 -19.3 -19.4 -19.3 -19.4 -25.0 Optimization Scenarios (Atlanta) Figure 3.4-71 Energy Recovery Results of Optimizations Results (Atlanta) Final Report Daily - High Capture Daily - Low Capture Biweekly Collection - High Capture Biweekly Collection - Low Capture 0.03 0.021 0.021 0.019 0.018 0.02 Solid Waste Management Holistic Decision Modeling 0.01 0.00 Group 3: Manual MRF and Group 3: Mechanical MRF and Group 4: Energy Optimization Group 5: Mininimize Carbon-eq Group 5: Mininimize PM Compost Compost Emissions Emissions -0.01 -0.010 -0.011 -0.013 -0.014 -0.02 Other than the scenario for minimizing -0.026 -0.026 -0.03 carbon emissions, scenarios for energy -0.027 -0.028 optimization and minimizing PM emissions 3-187 will contribute to the reduction of carbon -0.035 -0.04 emissions. -0.037 -0.038 The other scenario for maximizing the -0.05 material recovery with low capture rate still Carbon Emissions (MTCE)/metric ton -0.046 -0.047 shows little positive carbon emissions. -0.06 -0.065 -0.066 -0.07 -0.08 Optimization Scenarios (Atlanta) Figure 3.4-72 Carbon Emissions Results of Optimizations Results (Atlanta) Final Report Solid Waste Management Holistic Decision Modeling Final Report CHAPTER 4 CONCLUSIONS 4.1 Key Findings of the Scenario Modeling Exercise As discussed in Section 2.5, there is a considerable amount of data and assumptions that were used to model the MSW management scenarios for each city in this study. Key data and assumptions used include a mixture of city-specific data that was collected through site visits and default data and assumptions that are built into the MSW DST. Table 2.5-1 presented a summary of the most important input parameters and whether they represent actual city data or MSW DST defaults. Cost data in particular were lacking in availability. Therefore, cost results may not be an accurate reflection of actual costs in a given city. The city of Lahore is one of the cities with least data availability, including cost and energy related data. The city of Atlanta is one of the cities with the best data availability. These city-specific data are carefully reviewed and some of them which are considered to be unreliable are modified into the model. For example, the cost for equipment and maintenance for landfill disposal in Conakry is not applied for the model because its value collected at the field is quite expensive than others. Operation and maintenance cost for the incineration plant in Shanghai is replaced to the MSW DST default because its value collected is much less than usual cost for incineration. In addition, we compare the landfill cost with and without the land price in the cost analysis because usually the government does not pay for land in case the land is the government land. As a modeling exercise, we were able to successfully input the city-specific data into the MSW DST and run both simulation and optimization type scenarios as presented in Section 3 of this report. In general, the following trends were observed through the scenario modeling exercise and examination of scenario results: Cost: • The lowest cost MSW management strategy appears to be landfill disposal. The highest cost MSW management strategy appears to be incineration without energy recovery. • High land prices in some cities (e.g., Kawasaki) can significantly increase the cost of MSW management operations. • Capital, labor and energy prices are keys in determining cost tradeoffs between manual and mechanical operations. For example, composting manual operations are more cost-effective than mechanical operations since those do not require any equipment, and labor is cheaper than energy in most cities. Should labor prices increase, this tradeoff can be minimized or reversed. Manual MRF operations are 4-1 Solid Waste Management Holistic Decision Modeling Final Report less cost effective than mechanical MRF operations (Manual operations are 2- 4% more expensive), which seems to contradict previous statements. However, the cost difference is very small and due to manual operations being very labor intensive and still requiring equipment. • Potential cost savings associated with materials and energy recovery are large and can significantly reduce the total cost of MSW management. Modeled revenues from the sale of recyclables ranged from US$66- 197 per metric ton of recyclables. Modeled revenues from the sale of electrical energy, for example in case of incineration with energy recovery, ranged from US$3- 59 per ton of waste incinerated. • Markets for and market prices obtained for recyclables varies by city and causes significant variation in recycling costs among cities. For recycling, the revenue obtained from the sale of recyclables is dependent on available markets for recyclables. This is important because the revenue stream from the sale of recyclables can significantly lower the net cost of the recycling scenarios. If price data were not found a US$0 market value was used for the recyclables that has no market in the surveyed area, and defaults or prices for similar materials are used for the recyclables which have the market. Conakry is the city with the least recyclable price data available. • Scenarios with high or low capture of recyclables will vary in cost depending on the amount of waste going to each of the selected management scenarios. In general, scenarios with higher capture of recyclables are less expensive since this increases the revenues from material recovery and reduces landfill costs. However, this behavior can be reversed by the lack of revenues/markets from recycling and/or very low landfill disposal costs. Energy: • The strategies that appeared to be more effective minimizing net total energy consumption included recycling of key materials (e.g., metals) and incineration with energy recovery. • The quantity and composition of recyclables in the MSW stream is key in determining energy savings associated with recycling. For example, metals production is very energy intensive so recycling metals achieves large energy savings. A city that has a larger percentage of metals, and other energy intensive materials, in its MSW stream can thus achieve higher levels of energy savings from recycling. • As expected, manual labor-based operations (MRF and compost) generally consume less energy than mechanical-based operations. • The amount of energy that can be recovered via incineration depends on the MSW composition and characteristics, and higher income countries and major urban areas within countries of higher income have more plastic, paper, cardboard, and textile wastes that drive up calorific values of the wastes. For many of the Bank member 4-2 Solid Waste Management Holistic Decision Modeling Final Report cities (i.e., excluding Atlanta and Kawasaki), the MSW stream generally has a low average BTU/ton value due to high levels of food waste and other wet organics waste, as well as inerts (ash and soil) in some cases. In general, incineration needs to be more than 1500 kcal/kg of lower calorific value to sustain combustion, and few cities in developing countries have wet, as received, waste that reaches this calorific value. • Energy offsets by virtue of incineration with energy recovery can be significant and are directly dependent on the electricity grid mix used for each city. A city that relies on fossil-based electricity production will achieve higher levels of energy savings than a city with hydroelectricity or other renewable electricity production systems. Emissions: • The waste management strategies that appeared to be most effective minimizing emissions included recycling of key materials (e.g., metals) and incineration with energy recovery. • The life cycle environmental burdens associated with electricity consumption are highly variable between the cities studied and dependent on city-specific electricity grid mixes of fuels. For example, a city that relies on fossil-fuel based electricity production will have higher emissions associated with electricity use than a city that relies on renewable electricity production (e.g., hydroelectricity). • Energy consumption (fuels and electricity) is a key indicator for criteria type air emissions and emission savings or offsets by virtue of materials and/or energy recovery. Some cities (e.g., Katmandu) have high percentages of hydroelectricity production and thus in these cities, electricity-related emissions are zero. • Cities with high amounts of plastics in their waste stream will have higher GHG emissions from any waste management strategy involving combustion. • Landfill gas management can greatly reduce landfill-related GHG emissions. This can be observed by comparing the results of landfill disposal with gas venting to the results of landfill disposal with gas flaring or gas-to-energy. • Landfill diversion (via recycling, composting, incineration) of organics can greatly reduce net GHG emissions by avoiding landfill disposal and subsequent production of landfill gas. 4-3 Solid Waste Management Holistic Decision Modeling Final Report 4.2 Discussion of Sensitivity for Parameters of Interest Up-front in the scoping phase of the study, several parameters were identified as parameters of interest in regards to their overall impact on the total MSW management system results. The sensitivity of the total results to these parameters were evaluated using the scenario results and are discussed below. • Landfill gas collection efficiency: For this study a landfill gas collection efficiency of 70% was used. Landfill gas emissions (for cases where landfill gas is managed via flaring or energy recovery systems) are highly dependent on gas collection efficiency. A lower efficiency would directly increase landfill gas emissions and a higher efficiency would directly reduce landfill gas emissions. • Fuel Price: Fuel prices will primarily affect the cost for waste collection and other operations that utilize fuel-burning equipment (e.g., compost windrow turner). • Electricity cost: Electricity prices will primarily affect the cost for operations that use power-based equipment such as MRFs that are equipment (balers, screens, magnet, conveyor belts, etc.) intensive. Other operations, such as waste collection, will not be significantly impacted by electricity costs. • Price at which electricity can be sold back to the grid: The price for electricity sale directly affects the net cost for landfill with gas-to-energy systems and incineration with energy recovery operations. The cost of these operations can be significantly reduced if a good sale price for electricity produced can be negotiated. In general, the results showed a 34- 43% decrease in cost for landfills and 15- 47% decrease in cost for incineration due to electricity sale revenues. • Labor costs: Labor cost will primarily affect labor intensive operations such as waste collection and manual recycling/composting. In the cities analyzed, labor wages ranged from 0.08- 18$US/hour. • Carbon Finance pricing: The prices obtained for reduction of carbon emissions can significantly impact total costs for the waste treatment options. The results of this modeling exercise did not consider revenues from carbon pricing, which would be considered if comparing a given management strategy against a baseline strategy. For example, a simple exercise comparing the emissions from a worst case scenario in which all the waste is sent to a LF with gas venting vs. a best case carbon minimization scenario indicate that revenues from carbon pricing may range from US$516,000- 2,323,000 among the studied cities using a carbon price of 12 $US/MTCE. • Recyclables market price: The availability of markets and prices obtained for the sale of recyclable varies by city and by the composition of recyclables in the MSW stream. For some cities, there are well established markets and good prices. In other cities, there are poor markets and low (or no) prices. Revenues obtained from 4-4 Solid Waste Management Holistic Decision Modeling Final Report the sale of recyclables ranged from US$66- 197 per metric ton of recyclables. • Compost market price: The availability of markets and prices obtained for the sale of compost product varies by city. For some cities, there are well established markets and good prices. In other cities, there are poor markets and low prices. Revenues obtained from the sale of compost product ranged from US$15- 60 per metric ton of the compost products. • Land price: The land price impacts significantly on the overall cost of landfill disposal. For example, Kawasaki, which is the city with the highest land costs, has also the highest landfill overall cost even though its O&M costs are relatively low compared to other cities. Land price are usually not paid when the landfill is constructed at the government land. Therefore, we calculated the net total cost in all scenarios without land price for the landfill cost. 4-5 Solid Waste Management Holistic Decision Modeling Final Report 4.3 Appropriateness of Future Holistic analysis of Waste Management in Bank Member Countries This study was a groundbreaking challenge that has tried to confirm whether or not the MSW-DST developed jointly by the US EPA and RTI and developed based on the SWM experiences and conditions in the US could be useful if applied to the cities in the developing countries. In summary, the study confirmed that the MSW DST can play an important role for the decision making of the municipal solid waste management in those cities. However, it should be said that the existing MSW DST needs to be more tailored so as to conform with the actual conditions of those cities when preparing an individual detailed solid waste management plan. In the development of this kind of simulation model, , there are often many gaps in the baseline data that a developer of the model does not know about the fields of solid waste management, and in these instances the developer has no option but to use the default settings, which can often be unsuitable to the city in question. In other words, the developer should understand the actual situation of the SWM practices at the target city, in addition to key aspects affecting cost and disposal techniques, such as waste composition, moisture content, fuel prices and so forth. Furthermore, users of the model, such as decision makers and SWM staff should similarly understand the essence of the model and the effects of using or overriding the default values contained within the model. The result of the study should therefore be widely released to the concerned people, as the latest frontiers of SWM decision making, thus encouraging further additions to and verifications of default data. Good baseline data to override the US-based defaults is key to the accurate functioning of the model. Needless to say, when planning the waste management system of a developing country, the information that is the most necessary for the decision-maker is cost. However the cheapest options such as open dumping or open burning cause severe environmental pollution and unsanitary conditions, and adoption of such scenarios is definitely not better choice for the decision makers who need to have the backing of their people because secure of the hygiene circumstances is one of the most important public services that is often committed by the decision makers. Therefore, the present conditions in cities of many developing countries are such that the landfill option is the cheapest realistic alternative to open burning or dumping. When preparing the solid waste disposal plan, in general, several disposal options are compared In order to assess the feasibility using cost benefit analysis for the final decision. Because it is usually difficult to estimate such benefits, especially when putting the currency 4-6 Solid Waste Management Holistic Decision Modeling Final Report value on it, the proposed project might be given the green light to go ahead when the project is seen to be feasible by an economic analysis that considers some external cost as the benefit, even though it is not feasible financially. In this regard, MSW DST can surely bring much considerable information, such as cost, energy consumption, carbon emissions and PM emissions by different disposal alternatives such as landfill, incineration, composting and material recovery, to the decision makers. Three points that the decision makers who could take such information may be facing are; Firstly, alternatives showing better results from the views of energy consumption, carbon emissions and PM emissions are not always the cheapest options. It is hard to the decision makers to adopt such costly options unless some financial policy will be put on such efforts to reduce of environmental impacts. Secondly, sufficient capacity for the introduction and operation of the selected disposal options are needed. For example, in order to adopt the incineration with energy recovery option with the expectation of achieving a reduction of total net cost and total energy consumption via sales of the energy produced, a large amount of investment and advanced skills for the operation of the plant should be available. Lastly, expected offsets by energy recovery or material recovery are not usually a direct benefit for the decision makers of the target city. In addition, a certain judgment for the economic perspectives should be also required because the oil price and sales price of recycled materials will always fluctuate. It is also stressed that the reliability of SWM and associated data which are inputted to the model should be secured. Default values should be also tailored to reflect the actual situation of the city in the developing countries. The consulting team collected as much data as possible from the field visits in about two weeks per city, with some follow-up communications, but these inputs were in practice insufficient to optimally fulfill the model run. A considerable limitation of data availability at the selected cities was encountered even though those cities are considered to be representative cities with better SWM management than others in the regions. Therefore, other data sources such as JICA, PAHO and METAP were also reviewed. Historical data availability is commonly difficult to obtain in developing cities, as the management authorities frequently have little capacity to gather and store accurate data, and so any further application of the model should initiate a data collection programme well in advance. This study relied on available data in the target cities. While the level of detail of analysis is adequate to indicate how technologies and scenarios compare, more detail would normally be required for deciding on the most cost-effective technology that addresses local preferences to optimize energy security, land use minimization, carbon reduction, particulate reduction, materials recovery, etc. For future use of the model beyond this study, when a city would like to compare the disposal options of the waste in detail using MSW DST, more detailed data will be recommended.. For example, as clearly understood from the 4-7 Solid Waste Management Holistic Decision Modeling Final Report experiences of JICA development studies, a certain amount of input of human resources, time and money are necessary to conduct the waste quantity and composition survey. Though it is clear that even the basic surveys required for the detailed planning of solid waste disposal needs a large sum of investment, the general guidance utilizing the result of this study can be considered a useful tool for many cities to grasp the general perspectives of appropriate waste disposal options. 4-8