Analytical report on URBAN CREDITING METHODOLOGY © 2020 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions, including from the Partnership for Market Readiness. The Partnership for Market Readiness supports collective innovation and action by providing a platform for countries to share lessons and work together to shape the future of cost- effective greenhouse gas mitigation. It also funds capacity building to scale up climate change mitigation efforts. The findings, interpretations, and conclusions expressed by World Bank Staff or external contributors in this work do not reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Acknowledgements This report is prepared as part of the Jordan Partnership for Market Readiness initiative. The World Bank team includes, Monali Ranade, Harikumar Gadde and Sara Mills-Knapp. The main authors of this report, from Ricardo Energy and Environment, are Marianna Budaragina, Rose Bailey, Florianne de Boer, Sina Wartmann, Eleanor Kilroy, Dominic Sheldon, Ancelin Coulon and Mark Johnson. The team would like to thank the peer reviewers Claire Markgraf (C40), Johan Nylander (independent expert), Massamba Thioye (UNFCCC) and, Alexandrina Platonova-Oquab and Neha Mukhi (WB) for their advise and inputs. A special thank you to the C40 Cities program for supporting the emissions data collection and inventory in the city of Amman that was integral to this report. The team appreciates the leadership and efforts of the staff at the Ministry of Environment and the Greater Amman Municipality of the Hashemite Kingdom of Jordan, which has made this innovative effort possible. Design and layout: Clarity Global Strategic Communications (www.clarityglobal.net) Cover image: Shutterstock Analytical report on URBAN CREDITING METHODOLOGY This Urban Carbon Crediting methodology is being published as a contribution towards the continuing global efforts to help cities undertaken urgent climate actions. This report seeks to serve three broad agendas: To further the conversation around market-based instruments under the forthcoming rules of the Paris Agreement. To encourage cities to produce robust city-wide emission inventories that can inform long-term planning. To identify new mechanisms to fill the financing gap faced by cities across the globe face as they seek to implement low-carbon actions. This methodology report builds on past global efforts to create city-level accounting for carbon emissions, including the city-wide Programme of Activities (POA) created under the CDM and the Global Protocol for Community-Scale GHG emissions (GPC). This report seeks to address many of the challenges observed in implementing, monitoring and verifying climate action in Cities and, the issues related to GHG accounting between national and sub-national actors. The WB team and authors expect this proposed methodology to reinvigorate these discussions and invite colleagues to take this work further through additional analysis, modelling and piloting. The quest is to identify a simple yet robust framework for urban carbon crediting and help cities achieve their potential to lead climate mitigation. contents Table of Acronyms iv Executive Summary 1 1. Introduction 6 1.1. Urban GHG Emission Reduction Context 7 1.2. Role of Carbon Crediting in Urban Emission Reduction 8 1.3. Carbon Partnership Facility Objectives and Role in Piloting Urban Carbon Crediting Approaches 8 1.4. Report Objective and Structure 10 2. Designing an Urban Carbon Crediting Program 11 2.1. Urban Actions for Meeting NDC Targets: Current Situation and Challenges 12 2.1.1. City Actions in NDC Roadmaps 13 2.1.2. City Target Alignment with National Emission Reduction Targets 14 2.1.3. Barriers to Urban Emission Reductions 16 2.1.4. Summary 19 2.2. Carbon Crediting and Application to Cities: Current Situation and Challenges 20 2.2.1. Key Features of Carbon Crediting 20 2.2.2. Scaled-up Crediting Mechanisms 22 2.2.3. Carbon Crediting Application in an Urban Environment 24 2.2.4. Alignment with NDC Targets 26 2.2.5. Summary 27 2.3. Urban Carbon Crediting: Methodology Development 28 2.3.1. Considering Options for Up-scaled Carbon Crediting Programs 28 2.3.2. NDC-aligned Carbon Crediting: Program Development 29 2.3.3. Non-NDC Aligned Carbon Crediting Program Development 41 2.4. Addressing Non-methodological Barriers 48 2.4.1. Carbon Leakage Considerations 49 2.5. Summary 51 3. City GHG Inventories: A Tool to Measure Urban Mitigation Progress 54 3.1. Current Situation and Challenges 55 3.1.1. Key Features of City GHG Inventory Methodology Relevant to Urban Crediting 55 3.1.2. Calculation Methods 60 3.1.3. Challenges 65 3.1.4. Summary 69 3.2. City GHG Inventory Enhancement for Urban Crediting 72 3.2.1. GHG Inventory Review and Alignment with Urban Crediting 81 3.2.2. Summary 86 4. Application to the city of Amman, Jordan 88 4.1. Context 89 4.1.1. Jordan: Specific Country Context 89 4.1.2. Amman’s Inventory Analysis and Suitability for Crediting Program 91 4.2. Development of an Urban Crediting Program in Amman 96 4.2.1. Inventory Enhancement Steps 96 4.2.2. Development of Carbon Crediting Approach 100 4.3. Implementation of the Pilot Urban Crediting Scheme 114 4.4. Summary 117 Appendix 1. GPC Emission Sources, Sectors, Sub-sectors and Scopes 119 Appendix 2. GPC-IPCC Source Category Mapping 122 Appendix 3. References 124 i Boxes Box 2.1: Examples of Country and City/Sub-national Target Alignment 15 Box 2.2: Identified Barriers 19 Box 2.3: Types of Mitigation Targets in NDCs 26 Box 2.4: Identified Barriers 27 Box 2.5: Steps for Developing a Crediting Baseline 32 Box 3.1: GPC Scope Definition 56 Box 3.2: Definitions of Data Quality 78 Figures Figure ES.1: Urban Crediting Methodology Steps 3 Figure ES.2: Emission Inventory Development Steps 4 Figure 2.1: Urban Content from the NDCs 13 Figure 2.2 Baseline Ranges in Relation to NDC Pathways 31 Figure 2.3: Overview of Available Methodologies for Emission Baseline Projections in an Urban Context 43 Figure 2.4: Baseline Ranges in Relation to NDC Pathways 46 Figure 2.5: Urban Carbon Crediting Program: Development Steps 51 Figure 2.6: Overview of Steps to Establish an Urban Carbon Crediting Scheme (with a number of possible options) 53 Figure 3.1: GPC Scope Boundaries 57 Figure 3.2: Basic Equation for Calculating GHG Emissions 60 Figure 3.3: GHG Inventory Review and Alignment Steps 87 Figure 4.1: Jordan’s Net GHG emissions by Sector (%) 90 Figure 4.2: Geographic Scope of Amman’s Inventory 91 Figure 4.3: Overview of Inventory Results 92 Figure 4.4: Possible BAU Scenarios for Amman 102 Figure 4.5: Amman’s Projected Emissions and National Energy Supply Actions (in BUR1) 103 Figure 4.6: BAU Emissions for Amman (based on BUR1 sectoral growth rates) 105 Figure 4.7: Options for Updating the Baseline with Improved Data 109 Figure 4.8: Comparison of Amman’s 2014 and Actual versus Projected BAU 2016 Emissions 111 Figure 4.9: Potential Pilot Implementation Timeline 114 ii Tables Table 2.1: Example of City Targets 16 Table 2.2: Assessment of Baseline Development Options 34 Table 2.3: High-level Discounting Approach 36 Table 2.4: Activity Data Assessment 37 Table 2.5: Emission Factor Assessment 39 Table 2.6: Cross-cutting Assessment 40 Table 2.7: Assessment of Discounting Options 41 Table 2.8: Assessment of Options to Avoid Over-transfer of Credits 47 Table 2.9: Non-methodological Barriers to Urban Crediting 49 Table 3.1: Typical Methods, Data Sources and Relevance for Urban Crediting 61 Table 3.2: Boundary Issues and Solutions for Urban Crediting 73 Table 3.3: Suggested Requirements for Boundary of Sources and Scopes 75 Table 3.4: General Data Quality Assessment and Examples 77 Table 3.5: Emission Factor Data Quality Assessment and Examples 78 Table 3.6: Prioritization of Sources for Crediting Boundary 79 Table 4.1: Sample of Third National Communication Scenario Energy Projects (now part of the BUR1 baseline scenario) 90 Table 4.2: Emissions by Sector and Scope (2014) 92 Table 4.3: Emissions by Sector and Scope (2016) 92 Table 4.4: Data/Approach and Improvements for the Stationary Energy Sub-sector 93 Table 4.5: Data/Approach and Improvements for the Transport Sub-Sector (road transport only) 94 Table 4.6: Data/Approach and Improvements for Each Waste Sub-sector 95 Table 4.7: Defining the Inventory Boundary 96 Table 4.8: Amman’s 2014 GHG Emissions by Sub-sector 97 Table 4.9: Amman’s Inventory Data: Quality and Completeness 98 Table 4.10: Inventory Crediting Boundary for Amman 100 Table 4.11: Application of CURB Growth Drivers by Sub-sector 102 Table 4.12: BUR1 Specified and Calculated Growth Rates 103 Table 4.13: Projected Business-as- Usual Emissions Scenario for Amman 104 Table 4.14: Projected Business-as-Usual Emissions for Amman by Sector 105 Table 4.15: Crediting Baseline for Amman 106 Table 4.16: Projected Target Emissions for Amman by Sector 107 Table 4.17: Project Emissions Available for Credit Generation 108 Table 4.18: Comparison of Actual versus Projected Emissions for 2016 (target emissions and actual savings achieved) 111 Table 4.19: Inventory: Weighted Discounts for Amman 112 Table 4.20: Overarching Data Quality Discount for Amman 112 Table 4.21: Indicative Discounted Emission Volumes Eligible for Credits 113 Table 4.22: Amman: Assessment of Data Quality for Discounting 113 Table 4.23: Potential Aggregate Emission Savings Eligible for Credit Generation during Pilot Periods (2017-2023) 116 Table 4.24: Potential Discounted Aggregate Emission Savings Eligible for Credit Generation during Pilot Periods (2017-2023) 116 iii Acronyms AFOLU Agriculture, Forestry and Other Land Use FAO Food and Agriculture Organization BAU Business-as-Usual FOD First Order Decay BEI/MEI Baseline Emissions Inventory/Monitoring GAM Greater Amman Municipality Emissions Inventory GDP Gross Domestic Product BUR Biennial Update Report GHG Greenhouse Gas CDM Clean Development Mechanism GHGI Greenhouse Gas Inventory CGE Computable General Equilibrium GPC Global Protocol for Community-Scale CH4 Methane Greenhouse Gas Emission Inventories CIRIS City Inventory Reporting and GWP Global Warming Potential Information System ICLEI International Council for Local CRGE Climate Resilient Green Economy Environmental Initiatives CO2 Carbon Dioxide IDA Index Decomposition Analysis COP Conference of Parties IEA International Energy Agency CPF Carbon Partnership Facility IEAP International Local Government GHG CURB Climate Action for Urban Sustainability Emissions Analysis Protocol DOC Degradable Organic Carbon INDC Intended Nationally Determined EF Emission Factor Contribution ETS Emissions Trading System IPCC Intergovernmental Panel on Climate EU European Union Change iv IPPU Industrial Processes and Product Use OECD Organisation for Economic Co-operation ISDGC International Standard for Determining and Development ITMO Internationally Transferred Mitigation PoA Program of Activities Outcomes QA Quality Assurance LEAP Long-range Energy Alternatives Planning QC Quality Control LMDA Logarithmic Mean Divisia Index RBCF Results-Based Climate Finance LNG Liquified Natural Gas SCM Sectoral Crediting Mechanism LRT Light Rail Transit SDM Sustainable Development Mechanism MC Methane Commitment TCCCA Transparency, Completeness, MEMR Ministry of Energy and Mineral Resources Consistency, Comparability or Accuracy MMCFD Million Cubic Feet per Day TNC Third National Communication MOU Memorandum of Understanding TOD Transit-oriented Development MRV Monitoring, Review and Verification UN United Nations MSW Municipal Solid Waste UNEP United Nations Environment Programme MW Megawatt UNFCCC United Nations Framework Convention on NAP National Allocation Plans Climate Change NAZCA Non-State Actor Zone for Climate Action VKM Vehicle Kilometers Travelled NDC Nationally Determined Contribution ZEV Zero Emission Electric Vehicle NGO Non-governmental Organization v Executive summary Countries around the world are looking for more Carbon crediting methodology efficient ways to reduce their greenhouse gas To increase support, the Carbon Partnership (GHG) emissions and fulfil their Paris Agreement Facility (CPF) has supported the development of pledges. Urbanization is steadily increasing, and an upscaled urban carbon crediting methodology it is expected that the focus on urban emission tailored to support city-wide mitigation actions. reductions will intensify. This would help cities This methodology is similar to the widely applied to reduce their carbon footprint, which currently carbon crediting approaches in that it relies on equals 70 percent of global emissions. In addition, a baseline-and-credit technique to quantify the the effect of carbon “lock-in”, whereby the GHG emission reductions/avoidance resulting from infrastructure constructed in cities today will define mitigation actions. However, a key difference is urban emissions for decades to come, further a shift in focus away from the project or program increases the need for immediate action at the basis, to a city-wide emission performance urban level. approach as measured through the city GHG inventory. This significant methodological change Carbon crediting in cities allows for the capturing of wider benefits of Carbon crediting is a mechanism that turns certified city level mitigation actions. It directly rewards emission reductions into tradable commodities. municipalities that are proactive in their mitigation However, to date, its use in cities has been limited, efforts, and can grant them access to finance due to the focus of carbon crediting methodologies which they often cannot gain in other ways. on project-based emission reductions. These appear to be less suitable for cities that often The credits generated from an urban carbon implement mitigation policies of a wider scope. crediting approach may be used for a range of The emission reduction effects of city-wide purposes. The two key options are: (1) for use policies are less suitable to be captured through toward the unconditional or conditional targets traditional monitoring, review and verification (MRV) established through Nationally Determined approaches used in past carbon crediting schemes. Contributions (NDC); or (2) use as seed financing to However, carbon crediting has the potential to drive engagement with the international investment reduce city emissions and increase financing for community or private sector investors. The design infrastructure and should be supported in order to requirements of the crediting mechanism will differ accelerate urban emission reduction. depending on its primary purpose. As such option 1 will require NDC alignment to ensure that carbon credits generated through urban city programs are recognized internationally and that no double counting takes place. 1 Implementation steps The complexity and time frame for the development • Once the expected city-level performance is of an urban crediting methodology will vary established, either based on the NDC pathway depending on the type of the national NDC or a BAU trajectory, it is then necessary to commitment, the availability of projections of the establish a crediting baseline against which city emission pathway, and the quality of the city emission reductions will be measured GHG inventory. As such, a sequence of steps will (Step 4). The crediting baseline can be fully need to be followed to develop a comprehensive aligned with the NDC pathway or a BAU urban crediting methodology. These steps are listed trajectory. Alternatively, a more conservative below (Figure ES1), with the first three steps only approach can also be applied to ensure the applicable to the NDC aligned carbon crediting environmental integrity of the mechanism. approaches. • It will also be necessary to decide how often • Should a city wish to take advantage of the the baseline should be updated, specifically demand for offsets created by the Paris by defining the crediting baseline dynamics Agreement, it would first need to ensure that (Step 5). The choice will depend on multiple its national NDC is expressed in emission factors. Ultimately, it will need to represent a reduction units (Step 1). This will allow it to balanced approach to maintaining both the reflect international credit sales in the national environmental integrity of the mechanism by NDC performance of the selling country, thereby ensuring accurate baselining, as well as by ensuring the environmental integrity of the global investment certainty by offering investors a NDC performance. reliable basis upon which they can forecast their future revenues from credit sales. • It will then be necessary to ensure that the country’s NDC commitment has a yearly • The final design element to be defined for an emission reduction pathway (Step 2) to urban crediting mechanism is the discounting enable credit sales in each year of the NDC approach (Step 6). The discounting approach commitment period. plays a crucial role in urban crediting. It addresses concerns related to the quality • The national NDC commitment will then need to of data by reducing the number of credits be disaggregated to derive the city level NDC- generated for each ton of emission reduction based pathway (Step 3). However, if the crediting if emissions data relies on lower quality data. mechanism is intended for use solely as a way Given the uncertainty associated with the city to attract investment — and sold credits would level inventories which are often based on not be counted toward another country’s NDC extensive assumptions and data extrapolations, performance — then a simpler approach relying the discounting mechanism can serve to ensure on a business-as-usual (BAU) scenario for city environmental integrity. This also provides an emissions can be applied. incentive for cities to improve the quality of their GHG emission accounting approaches. 2 EXEC SUMMARY Figure ES1: Urban Crediting Methodology Steps Step 1 NDC TARGET METRIC ALIGNMENT Step 2 NDC aligned carbon Non-NDC aligned ESTABLISHING A NDC crediting programmes carbon crediting PATHWAY programmes Step 3 Step 3 DEFINING A CITY NDC DEFINING A CITY BAU PATHWAY TRAJECTORY Step 4 Step 4a Step 4 SETTING A CREDITING CONSIDERING FUTURE SETTING A CREDITING BASELINE ALIGNMENT WITH NDC BASELINE Step 5 DEFINING THE CREDITING BASELINE DYNAMICS Step 6 ESTABLISHING A DISCOUNTING APPROACH Source: Developed by Ricardo Energy & Environment. Note: BAU= Business-as-Usual; NDC= Nationally Determined Contribution. City GHG inventory as a tool for (Figure ES2). These steps will apply to NDC-aligned carbon crediting and non-NDC aligned approaches. However, the The urban crediting mechanism is based on the city first two steps will be the same for both approaches. level GHG inventory. The inventory is used as a tool They will include defining the boundary for the GHG to set the baseline, define the crediting threshold, inventory and aligning it with the crediting approach and track emission reduction progress. An inventory (Step A) and assessing the quality of available data with clear boundaries, a consistent application which will become the basis for the discounting of methods and strong data can accurately method (Step B). Regarding the non-NDC aligned demonstrate real progress toward targets, thereby approach, one additional step will be needed, which providing investors with increased confidence about would require the establishment of a crediting the reliability of the data. boundary based on the data quality and availability (Step C). For the NDC-aligned approach, further steps To ensure the necessary quality of the city GHG (Step C – Step E) will focus on the alignment of the inventory, a series of steps similar to the development city inventory with the national inventory, as reported of the credit mechanism will need to be followed for the NDC performance tracking. 3 Figure ES2: Emission Inventory Development Steps Step A DEFINE BOUNDARY OF THE GHG INVENTORY AND ALIGN WITH CREDITING APPROACH Full coverage Partial coverage Identify suitable sectors Step B ASSESS THE QUALITY OF THE AVAILABLE DATA AND METHODS EMPLOYED IN CALCULATING THE INVENTORY Non-NDC aligned NDC aligned Step C (i) Step C (ii) ESTABLISH INVENTORY CREDITING IDENTIFY INVENTORY CATEGORIES BOUNDARY WHERE URBAN/NATIONAL ALIGNMENT IS DESIRED PRIORITIZE SOURCES FOR CREDITING BOUNDARY Step C (iii) IDENTIFY GENERAL GHG INVENTORY ALIGNMENT REQUIREMENTS Step D IDENTIFY NECESSARY ALIGNMENT AT THE CATEGORY-LEVEL Step E Piloting of urban carbon crediting PLAN, IMPLEMENT AND MAINTAIN ALIGNMENT in the city of Amman In order to test the application with real data, the city Source: Developed by Ricardo Energy & Environment Note: GHG= greenhouse gases. of Amman, Jordan was chosen for piloting the urban crediting methodology, and a detailed assessment of the Amman city inventory was conducted. The crediting approach in Amman was based on and aligned with the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC). The GPC is currently being applied by the city of Amman applied to the Amman emission reductions is quite in its inventory compilation. Due to limitations with high. However, small improvements to inventory Jordan’s NDC, the approach suggested for piloting quality can impact this score significantly, thereby is the “not aligned with the national inventory” path. enabling the city to receive higher benefits for its Also, due to data limitations, the overall discount carbon abatement policies. 4 1 introduction 6 INTRODUCTION 1.1 URBAN GHG EMISSION City and regional policy makers are well REDUCTION CONTEXT placed to affect urban emission pathways through city-wide actions. They can have a major influence on the city’s emissions through the Following the Paris Agreement, various elements of urban design. These elements countries around the world include a vast array of measures, with the most important typically being development and planning are looking for new and policies (for example, building and land use zoning, more efficient ways of transportation networks) and service delivery and reducing Greenhouse Gas procurement (for example, public transit operations, (GHG) emissions. waste management, urban forestry and green space). The involvement of municipal authorities in With an increasing share of populations these sectors is particularly important due to their dwelling in cities, urban emission reduction is access to local stakeholders, including utilities, gaining priority at the national level. Indeed, over service providers, and developers, as well as their two-thirds of countries party to the Paris Agreement ability to identify urban mitigation projects with mention urban mitigation actions as part of their high co-benefits (OECD 2010). For all these Nationally Determined Contributions (NDC) (World reasons, it is crucial to engage city and local Bank and Carbon Partnership Facility 2018). With authorities in climate action. In doing so, countries intensifying urbanization, the need to develop city- will be better able to meet their national and specific approaches and urban-scale measures international climate targets. will continue to grow. A high share of GHG emissions in the global footprint is ultimately attributable to urban areas; as such, the effect of carbon “lock- in” further increases the need for immediate action at the urban level. The infrastructure being built in cities today — particularly those rapidly growing in the global south — will largely define their GHG emissions in the upcoming decades. Therefore, any delay in implementation of the urban low-carbon agenda would result in cities being unable to effectively reduce their emissions in the short to medium term, thereby “locking them in” on a high carbon pathway (World Bank and Carbon Partnership Facility 2018). Thus, according to the latest projections, in order to meet the global 2050 targets, all city-level actions should be initiated 2050 by 2032. Any further delay would result in an inability to make a timely switch to a low carbon scenario (C40 and ARUP 2016). 7 1.2. ROLE OF CARBON such as strategic city-wide planning and low carbon CREDITING IN URBAN development programs such as ‘Transit-Oriented Development’ initiatives. To address these issues EMISSION REDUCTION and facilitate cities’ access to climate financing through carbon crediting, a more integrated The Paris Agreement also approach is needed. Such an approach should envisages a number of consider broader policy objectives and carbon reductions at the city level. instruments designed to support member countries. The need for wider scaled-up approaches to carbon crediting has been widely recognized In essence, Article 6 of the Paris Agreement (World Bank 2017); however, there are encourages international collaboration in the a significant number of barriers to their implementation of NDCs by allowing cooperative implementation at the city level. These barriers approaches. These approaches enable countries to include the lack of alignment of urban mitigation meet a part of their NDCs by using internationally actions with the national climate targets; the transferred mitigation outcomes (ITMOs). quantification of city-level emission reduction Specifically, they help countries to internationally achievements; and the use of city inventories, not transfer GHG mitigation, thereby allowing them to project-by-project accounting, as a main tool to use emission reductions achieved in one country measure a city’s mitigation progress. All of these to meet the NDC target of another. Article 6 does areas need to be carefully considered, and rigorous not explicitly refer to market mechanisms as part methodological approaches to address them should of this voluntary cooperation. However, crediting be developed, thereby enabling the implementation approaches are understood to be one of the of urban carbon crediting programs. instruments that can be applied for this purpose. Globally, carbon crediting is becoming a new 1.3 CARBON PARTNERSHIP source of finance for low-carbon developments; FACILITY OBJECTIVES however, it has not yet gained much attention AND ROLE IN PILOTING as a means of supporting emission reductions URBAN CARBON CREDITING in cities. Municipal authorities face a number APPROACHES of barriers in implementing carbon crediting projects, such as limitations on their autonomy and institutional capacity, as well as limited budgets The Carbon Partnership Facility and access to start-up capital (OECD 2010). Some (CPF) is one of the major carbon of these barriers are directly or indirectly linked finance instruments being to the fact that carbon crediting is now primarily focused on specific projects. When considered at implemented by the World Bank to the city level, the project-level approach aggregates support climate change measures an extensive number of projects with emission following the first Kyoto Protocol’s savings. As such, they are often hard to quantify and may be a burdensome task for city authorities commitment period. to manage. Moreover, it fails to account for the The CPF brings together various players of the effect of environmental policies, which may have emission reductions market, such as industrial a significant emission reduction effect overall. country buyers and developing country sellers, However, it cannot be presented in a project format, 8 INTRODUCTION as well as the host countries and donor aggregated form. However, they may altogether governments. By benefiting from shared decision- represent a very significant emissions reduction. making power and experience, this partnership Therefore, to support countries in meeting their was designed to facilitate the development of climate reduction commitments, carbon crediting low carbon programs through climate finance approaches may need to be expanded to include upscaling. The CPF’s governance structure is these emission reductions. This would be in based on the balanced participation of sellers addition to the large carbon crediting projects. and buyers of carbon credits. The project host countries and international donors are The CPF is supporting a pilot of a city-wide participating in an advisory capacity. carbon finance program to test this approach. This study was developed to provide the To reach its goals, the CPF is working in methodological underpinnings for this project, two directions: first, by supporting emission which is planned to be implemented in the city of reductions in developing countries; and, Amman. The development of the methodology will second, by facilitating purchase of emission be followed by a pilot phase during which the CPF reduction credits through the funds available. is intending to become a primary buyer of carbon By approaching the climate finance upscaling from credits generated by the Amman municipality. different perspectives, the CPF aims at addressing Through support to this pilot project, the CPF areas which were not effectively reached by the will create a real financial stimulus for emissions Clean Development Mechanism (CDM)1. The CDM is reduction in Amman. As such, it will enable a currently working on a project basis only. The CPF demonstration of the application of urban crediting differs in that one of its major initiatives is intended approaches for cities around the world. to bring carbon crediting to the city level and capture smaller emission reductions. These smaller reductions are not currently being credited in an INDUSTRIAL COUNTRY BUYERS DECOU LER RN OR S / S V E NT S NT VE ON IE G O D NTR T SE S LO RY ME CO H O L PI N U G 9 1.4. REPORT OBJECTIVE To do so, the report will first consider the current AND STRUCTURE situation and barriers to urban crediting, and then suggest a series of steps to develop such programs. The report will then look at urban GHG The key objective of this report inventories as the main tool to assess mitigation is to develop and test the progress in cities, as well as steps to adjust it to support urban crediting programs. feasibility of a city-level crediting program, as well as explore the The situation in Amman and its specific issues will application as a tool to support be outlined in Chapter 4, including a discussion of city governments to incentivize how the suggested urban crediting approach can be applied. their mitigation policies and eventually meet country NDC The main body of work described in this report targets. was developed during November 2018- June 2019 and subsequently peer reviewed during January – This report aims to provide methodological February 2020. The report incorporates responses guidelines describing how such city-level crediting to peer reviewer comments. programs can be designed, but this is just a start and additional analysis and exploration is needed. 10 2 design Designing an Urban Carbon Crediting Program 11 DESIGN 2.1 URBAN ACTIONS FOR MEETING NDC TARGETS: CURRENT SITUATION AND CHALLENGES Cities are responsible for more than 70 percent of global GHG emissions (IPCC 2014). Therefore, they should play a substantial role in achieving national GHG emission reduction targets as defined by the Nationally Determined Contributions. There has been growing global CITIES ARE recognition of the role of cities, RESPONSIBLE with numerous networks, events, OF MORE THAN 70% conferences and initiatives. However, for the most part, the spheres of climate governance have remained largely separate with little consistent engagement OF GLOBAL between national and municipal GHG EMISSIONS governments. City actions, including those led or supported internationally, have largely focussed on those areas where cities have the greatest power, These initiatives have undoubtedly helped to raise control or leverage, such as improving waste the profile of local-scale action and the climate management, upgrading transport infrastructure mitigation imperative. They have also helped to (for example, through bike lanes, cycle hire and galvanize support for city-level action. However, bus rapid transit), or increasing community there are still challenges remaining for cities to access to clean energy or grid connections. Other realize their low carbon ambitions, strengthen city-led action has taken the form of climate their role in global climate action, and contribute advocacy, including networks such as the C40 to national NDCs. Cities should take actions in a Cities Climate Leadership Group, the International clear and organized manner, recognizing their key Council for Local Environmental Initiatives (ICLEI), contributions as they drive forward more ambitious and initiatives such as ‘We’re Still In’, the Global low carbon development. In this way, cities can Covenant of Mayors for Climate and Energy, and support transformative policies and actions at the Under2 Memorandum of Understanding (MOU). local level. 12 DESIGN 2.1.1 City Actions in NDC the urban content in the NDCs, concluded that it can mainly be found in fast urbanizing geographic Roadmaps areas, such as Africa and Asia (UN HABITAT 2016) To date, NDCs are not adequate to limit (Figure 2.1). warming to within 2 degrees (UNEP 2015); as such, the global community is increasingly looking to cities to help fill the gap and scale Whereas the national GHG emissions up action to deliver the transformational inventories have to be disaggregated by change required. The crucial role of cities is sectors to justify action plans, the NDCs did defined by a number of unique features, such not require targets at the subnational levels. as their large populations and attendant global Some country NDCs previously defined specific emissions, as well as the closeness of municipal mitigation actions to achieve their emissions governments to key climate action stakeholders. reductions targets. Some actions specifically target urban areas, such as “Increasing the Of the 164 NDCs submitted to the United number of commuters using public transport as a Nations Framework Convention on Climate percentage of the total number to 25% by 2025” Change (UNFCCC) as of the end of 2016, (Jordan’s NDC). However, the estimated increase 110 (around 70 percent) mentioned planned is based on national GHG emissions reductions actions in the cities. This can vary from urban target and is not linked to city mitigation actions. mentions within text headers (26 countries), thereby dedicating whole sections to urban actions, to only Some countries have recognized the relevant mentioning cities in the text body (84 countries). influence of cities in their GHG national The United Nations (UN) Human Settlements emission inventories and are specifically Programme, which provided the first analysis of targeting emissions at the city level to achieve Figure 2.1: Urban Content from the NDCs Source: UN HABITAT (2016). 13 their national emissions reductions targets, in the developing world, targets were largely see examples in Box 2.1. This is the case for derived from a process of ‘bottom-up’ assessment Mexico. In the beginning of 2018, it held a series of current and planned actions. It combined of four workshops, gathering national and local business-as-usual trends and likely developments authorities, to emphasize the importance of (unconditional targets), as well as potential participation by cities and regional authorities additional actions possible with international in the NDCs. However, as yet, no quantitative support (conditional targets). Some NDCs were emissions reductions targets at the city level have developed with more rigor and analysis than been identified to help achieve the national target. others. However, most are cautious and wary of In a recent report, the UN Habitat recognizes that the need for continued growth. In this context, the NDCs are a strong tool in integrating climate most developing country targets are framed as policies on a national level. However, it also reductions against a Business-as-Usual scenario. recommends including local and mainly urban areas in the upcoming discussions (UN HABITAT 2016). Although they are often not a part of the initial NDC discussion, many cities have decided to define emissions reductions targets based on the Paris Agreement obligations. For example, many American cities have pledged their commitment and set ambitious targets despite the withdrawal of the United States (US) from the Paris Agreement. The C40 Cities Climate Leadership Group (C40)2 has been encouraging its member cities (including Amman) to commit to GHG emission reduction targets that are consistent with those defined by the Paris Agreement. This was done through an analysis of the data submitted by member cities. The analysis helped to identify the In order to further support cities’ involvement ‘carbon budget’ for all C40 in NDC actions, during the 2015 UN Conference cities combined, thereby of Parties (COP) in Paris, cities were registered defining the limit to the 2.9t 2050 2030 at the Non-State Actor Zone for Climate Action cities’ aggregated (NAZCA) platform (UNFCCC 2016). Launched emissions until 2030. carbon by UN Climate Change in France and Peru in Each member city / year 2014, this online portal is a place where non-party was assigned a / capita stakeholders can display their engagements to take pathway based on action on climate change. In 2018, this platform its development gathered 9,367 cities aiming to align their emissions status and current reductions targets to global commitments. emissions profile. This was followed 0 carbon by a ‘contraction and / year 2.1.2 City Target Alignment with convergence’ model to / capita National Emission Reduction achieve a 2.9 tCO2e per Targets year per capita by 2030 National governments mostly defined their (C40 and ARUP 2016) and net NDCs without prior discussions with cities zero carbon by 2050. and urban regions. In general, and particularly 14 DESIGN BOX 2.1: EXAMPLES OF COUNTRY Some examples of typical city targets are shown AND CITY/SUB-NATIONAL TARGET in Table 2.1. They indicate the range of ambition, ALIGNMENT target type, base year and target year currently employed. City targets are generally more ambitious ETHIOPIA and represent absolute targets. Targets have Whether through coincidence or design, the largely been set on the basis of political motivation Ethiopian national GHG reduction target in or commitment to global initiatives, adopting a the country’s Intended Nationally Determined ‘top-down’ approach. In general, cities have been setting more ambitious targets than those of Contribution (INDC) and Climate Resilient Green their respective national governments. However, Economy (CRGE) Strategy is to ensure net zero to deliver their ambitious targets, such as net emissions growth, with emissions of 3 tons of zero carbon emissions by 2050, they are now carbon dioxide equivalent (tCO2e) per capita by conducting studies and analyses to develop plans 2030 — and an ultimate goal of achieving net zero and policies to realize these ambitions. There carbon emissions. This is aligned with Addis Ababa’s are exceptions to this observation, for example, commitment as a member of the C40 network to where there is alignment between national and also limit emissions growth to 3 tCO2e per capita local/regional targets, or alignment in processes by 2030 and net zero carbon by 2050. In contrast to deliver these targets. Some examples can be to many other countries and cities, the CRGE is found in Table 2.1. also applicable to Addis Ababa. There is, at least on paper, a good alignment of policy and planning Although city targets often go far beyond between the tiers of government. national level targets, achieving national targets will not occur evenly across a country. VIETNAM As noted, cities account for approximately 70 percent of global carbon emissions. Thus, in Vietnam’s governance structure is comprised of a most cases, cities should shoulder the burden system of Provincial People’s Committees (PPC). Each of much of the national target. This assumes govern one of the provinces in the country. National- that rural areas are much lower emitters with level policies are also expected to be implemented lower reduction potential. However, it does not at the local level. Likewise, activities flow upwards deal well with large point sources outside of the and are aggregated nationally. All 63 provincial scope of any local control. Cities that align their governments have received financial support from the emissions reductions targets with NDCs could be implicitly assuming that non-urban regions national government to prepare Climate Action Plans. will observe at least an equivalent reduction rate Hanoi and Ho Chi Minh City, Vietnam’s largest cities, as cities. This may be true in some geographies, plus the secondary cities of Da Nang and Hoi An, have where agriculture or land use change makes up also been undertaking studies and projects in support a significant share of emissions and reduction of the national ‘Green Growth Strategy’ program. As potential. Yet, from an energy perspective, cities such they have developed their own PPC-level Green undoubtedly bear the burden of responsibility. Growth Strategies. However, Hanoi and Ho Chi Minh City have also committed to C40’s ambitious targets The lack of alignment in developing, setting as outlined in ‘Deadline 2020’, which go far beyond and meeting targets raises the question of the unconditional 8 percent reduction on Business as how far city (or sub-national) and national Usual (BAU) by 2030 in Vietnam’s NDC. targets should align or be disaggregated, 15 Table 2.1: Example of City Targets TARGET CITY TARGET TYPE LINK YEAR Melbourne, Fixed level / absolute Net Zero Emission 100% 2020 Australia (neutrality) Strategy Boston, Fixed level / absolute How we work to reduce 100% 2050 USA (neutrality) GHG emissions Oslo, 50% 2020 Base year Climate and Energy Norway 95% 2050 (1990 baseline) Strategy for Oslo Moshi Municipality Business as usual Local ‘Cleanest municipality 60% 2025 Council, Government Operations in Tanzania’ Tanzania 13% 2022 Business as Usual Cape Town, City Climate Change 29% 2030 (2012 baseline); Energy-related South Africa policy 37% 2040 emissions Kampala, Business as Usual Kapala Climate Change 22% 2030 Uganda (2012 baseline); All sectors Action Plan Emissions Intensity (2005 baseline) Hong Kong 65% 2030 - equivalent to 26% to 36% Climate Ready Plan absolute, and 3.3-3.8 t/capita Source: The data are derived from selected submissions to CDP Cities (2017) and the Carbon Climate Registry. and on what basis national targets and associated carbon budgets (if applicable) are allocated and progress toward these goals tracked. 2.1.3 Barriers to Urban Emission Reductions Although discrete city-level actions and city-led advocacy on climate change have been valuable, the climate imperative requires a scaling-up of action and a level of transformational change that is beyond the realm of usual urban policy making. Cities can make valuable contributions to national governments, but importantly, national governments can provide valuable support to cities to realize those contributions. Currently, there remains a gap between the national and city scale that has not been adequately bridged in certain areas. In addition, cities face many challenges in delivering climate action at the scale and pace needed. The following four key areas will be critical to the success or failure for any urban crediting initiative. In this context, it will also be important to assess the impact of the methodological approach presented in this report against these challenges. 16 1 2 DESIGN • Monitoring Reporting and • Finance: In general terms, those cities Verification: Most cities currently with the greatest GHG emissions need to have low-quality systems for monitoring, access climate (or other) finance to implement reporting and verifying emissions and climate-relevant actions. However, they climate actions. These systems are also are often those with the lowest capacity to not aligned with activities at the national access such funds. In addition, issues of level. A lack of data available to cities — creditworthiness make cities less appealing whether simply not collected or not made for financiers, and most international funds available by national bodies — has limited are channelled through national treasuries. the quality and resolution of climate- The flow of finance for city-level action relevant information that cities can share is usually part of the standard approved with national governments. In addition, to municipal government financing plan. In this date, many cities have been focussed on context, it is linked to long-term development the compilation of some data for a first strategies and allocated to specific city GHG inventory as they seek to meet their functions and budgets. Alternatively, it obligations as members of various networks may be connected with specific programs and initiatives. However, cities have varying and projects, Again, this often means that or even non-existent processes for verifying ‘projects’ at the city level are implemented, the quality and accuracy of the results. It but bigger city-wide programs, infrastructure, will take time before many developing cities and strategic planning decisions suffer can improve the quality of the data or move from the need to either tap into multiple to methods that ensure it is truly fit for departmental budgets (with varying buy-in decision-making purposes. and success) or require additional funds. 17 3 • Power, authority and autonomy: Cities are well recognized as the location of climate actions (C40 and ARUP 2016) and have a key role as implementers. However, many climate actions that have the biggest 4 • Capacity and resources: consistent problem for most global cities is a lack of capacity. Many climate change teams are small, A impact (for decarbonizing the electricity grid) lie outside under-resourced and have high staff the scope of control by the cities. Cities can influence turn-over. As such, they struggle consumption, and local-level supply where they have to keep up with the day-to-day the power and ability to do so, but ultimately, they are demands of core service delivery in the recipients of larger regional or national decisions on a city. Budgets are often limited, and the supply-side over which they have no control. environmental elements can be the first to be restricted when finances The same could be said for other actions that are are tight. Many climate change teams typically considered more local in scale. Electric and are also located within environmental hybrid vehicles, for example, require investment in departments, which can limit their supportive infrastructure, emission or parking zones ability to influence and communicate and local campaigns — all of which lie within the at a strategic level, which is often local authorities’ remit. However, their uptake also situated in a cross-cutting division of often requires a wider enabling environment including the government (for example, within the availability of acceptable technology, fuel price the ‘city office’, ‘strategic planning’ disincentives, tax or import duty incentives/reductions, or ‘mayor’s office’). The location systems of subsidies or penalties, and infrastructure within environmental departments that go beyond the city limits. As such, a joint approach may give the teams greater access between multiple levels of government and departments to technical expertise and closer would facilitate the greatest outcome in this case. Cities working relationships with related are also sometimes tied into procurement procedures teams working on areas such as air that restrict their ability to favor lower carbon options as quality or energy. However, this does they may be linked to national budget disbursements3. not generally make it easier to deliver city-wide transformational, cross- Finally, given the nature of more transformational departmental climate actions and programmatic actions that lead to city-wide changes programs. This will be an important across a number of sectors, it can be daunting to issue to consider in the implementation untangle and quantify the impacts of a program on of any urban crediting initiative. different sectors, as well as the power and influence of different stakeholders and their role in delivery. This complex web of power and influence may be one reason why specific city actions, where they do occur within NDCs, are generally discrete projects rather than programmatic. Thus, cities are simply more widely recognized for their ‘enabling and implementing’ role. 18 DESIGN 2.1.4 Summary There is an overall trend toward increased climate mitigation ambitions among cities, as well as a recognition of the importance of urban action for meeting the national climate change targets. However, there are still some barriers related to the alignment of city actions with the countries’ NDC commitments, as well as the capacity of cities to contribute to meeting the NDC targets. These barriers are summarized in Box 2.2. BOX 2.2: IDENTIFIED BARRIERS Alignment of City Targets City Capacities to Contribute with NDC Targets to NDCs • Target alignment: NDC targets were • MRV: Cities may have low-quality systems for largely developed without consideration of monitoring, reporting and verifying emissions the potential for cities to mitigate emissions. and climate actions in cities, which are not They also do not include city-level emission aligned with activities at the national level. reduction targets. • Finance: Cities with the greatest GHG • Target ambition: City targets are generally emissions and the greatest need to access more ambitious than those set by their climate finance often have the lowest capacity respective national government NDCs. to access such funds. International finance is • Defining cities’ expected contributions: often channelled through national entities, and Given the complexities of separating emission city budgets are allocated either to specific responsibilities between cities and non-urban projects or routine service delivery. regions, as well as the cross-cutting nature of • Power, authority and autonomy: Many sectors present in cities, it may be challenging of those climate actions that have the biggest to define city-level targets and align them with mitigation impact lie outside the scope of NDC targets. control by the cities. • Capacity and resources: Many cities experience a lack of capacity and resources to address climate issues. 19 2.2 CARBON CREDITING AND APPLICATION TO CITIES: CURRENT SITUATION AND CHALLENGES Key Features of Carbon Crediting 2.2.1 Carbon crediting approaches rely on a Baselines can be set in three different ways, baseline-and-credit technique to quantify the depending on the application and objective GHG emission reductions/avoidance resulting of the crediting scheme (PMR 2013). Firstly, from mitigation actions; these can be applied business-as-usual (BAU) scenarios can describe a to support sectoral programs and policies situation that would occur if no emission reduction that have a demonstrable mitigation impact. activities had taken place, including those that Crediting approaches can be used both in the could have been incentivized by the crediting international carbon markets in the form of market scheme. The BAU baselines can be defined by mechanisms. They can also be used as a modality using simple modelling approaches to project to disburse results-based climate finance (RBCF) historical emission trends into the future. These when the GHG emission reduction metric (typically, baselines are currently most commonly used in tCO2e) is used to demonstrate the achieved project-based crediting approaches to ensure outcomes of the activities supported by RBCF. environmental integrity. Secondly, a baseline can be set based on performance standards. This Under the crediting mechanisms, in order means that it represents a certain rate of emission for GHG emissions savings to be calculated, reductions that is expected from particular mitigation activities need to be compared to activities or technologies. an assessment referred to as the “without activity” baseline. The difference between the These types of baselines are usually based on baseline emissions and the activity emissions historical data and are typically set at a lower range will then represent the emission reductions than BAU baselines. Lastly, baselines can be set achieved through the activity and for which through the net mitigation method, which means credits could be issued. Calculating the baseline they are intentionally set below a BAU scenario to is often considered the most challenging aspect account for uncertainties in ensuring environmental of calculating net GHG emissions. Further, it integrity. This method requires a detailed bottom-up becomes even more challenging when looking at analysis of the city’s mitigation costs and potential, projected activity implementation into the future. as well as possible modelling of these. Alternatively, they could also be based on apportioning a certain Baselines used for crediting can be either part of national targets to the city. static/fixed or dynamic, with each type being more suitable for different purposes. Emissions Baseline projection development and emission in a static baseline do not change over the measurement are often linked to a high level assessment period time frame, whereas those in of uncertainty; therefore, a discounting a dynamic baseline may change over the time mechanism can be applied to address these frame of the emission reduction activity. issues. Discounting is the process whereby an Therefore, they will require a more complex activity that would reduce one metric ton of GHG calculation. Dynamic baseline scenarios are emissions is multiplied with a discount factor that generally considered more appropriate for activities is between 0 and 1, so that the activity is credited that are expected to undergo major changes4 only as reducing less than one metric ton of GHG during the assessment period. emissions to reflect a variety of uncertainties. 20 DESIGN Most crediting mechanisms use additional For compatibility reasons, countries now aim criteria beyond environmental integrity to to improve their carbon pricing methodologies select projects that are eligible for earning (measuring and verifying emissions) and credits. Many of these selection criteria include robustness in terms of accounting (registries the social and environmental co-benefits for allowances, offsets and exchanges). This that emission reduction activities can deliver will ensure they are ready to integrate into a (ADB 2017). Carbon credits have the potential possible future system. This also means that to promote sustainability through direct and there is a general trend for crediting mechanisms indirect economic, environmental and social to harmonize their methodologies and MRV benefits. These can be achieved through job for emission reductions across sectors and creation, improving air quality, and increasing geographies. access to energy. There is also the capacity for negative impacts, such as the displacement of Article 6 of the Paris Agreement also communities, increased emissions and habitat encourages nations to cooperate on mitigating destruction. Although these factors are important emissions through the use of Internationally during the assessment of emission reduction Transferred Mitigation Outcomes (ITMOs) activities, these criteria are not core to the (IETA 2016). An ITMO is any scheme whereby mechanism of carbon crediting. either carbon pricing mechanisms, climate finance or technologies are transferred from one country’s Crediting mechanisms create a financial NDC to another country’s NDC. When an ITMO is incentive for emission reductions, but they used, both countries are required to adjust their work in a voluntary way. This means that the performance against NDC targets to avoid double success of the scheme depends on the presence counting. Thus, the use of credits in one country of sufficient demand for credits to ensure that to count toward the NDC achievement of another there is enough finance to support additional country would be characterised as an ITMO. As activities to be carried out to deliver emission such, one requirement for this is that countries reductions (PMR 2011). Generally, crediting harmonize their carbon accounting standards mechanisms are more successful when they are and methods. directly linked to carbon pricing instruments.   In such cases, crediting often provides lower The Paris Agreement has also established cost opportunities for entities to comply with Sustainable Development Mechanisms (SDMs), the carbon pricing scheme, thereby providing which are processes to move countries sufficient demand for credits. to reduce emissions beyond their NDC   commitments (IETA 2016). Crediting mechanisms Article 6 of the Paris Agreement offers would provide an opportunity for these SDMs to opportunities to link emission reduction be realized. systems, thereby creating sufficient demand for credits. By allowing the linkage of To date, crediting approaches have been emission reduction systems, Article 6 creates used mostly in market mechanisms, such the opportunity for crediting mechanisms to as the Kyoto Protocol, and were focused on substantially contribute to the achievement of the specific emission reduction projects. The Clean overall goals of the Paris Agreement. One aspect Development Mechanism (CDM), introduced under concerns the future possibility of linking carbon the Kyoto Protocol, is one of the most widely used markets between countries to create a larger, crediting schemes. The CDM allows countries consistent carbon market. without emission reduction targets to earn carbon credits (Certified Emission Reductions) 21 through emission reduction projects, programs or activities. These are then sold to countries with emission reduction targets. A fundamental feature of the CDM and Article 6 is the aim to achieve environmental integrity by ensuring that emission reductions generated are real, measurable, verifiable and permanent. Additionality is an essential criterion for crediting approaches under CDM. A credit is considered additional if the emission reductions that underpin the credit would not have occurred as part of the BAU scenario. If emission reductions would have occurred in the absence of the incentive of a crediting scheme, then their certification and later use as offsets (compensation for emissions elsewhere) would lead to a net increase in emissions — and a loss of environmental integrity. Moreover, a non- additional unit generated and used as an offset implies a redistribution of the social cost of emission reductions, with a transfer or attribution of value to the issuer of the non-additional units (World Bank 2016). Therefore, additionality is core to environmental integrity. However, the scope of the latter goes beyond the additionality element. 2.2.2 Scaled-up Crediting Mechanisms Crediting mechanisms can be applied in various ways; they can apply to emission reductions achieved on different levels in order to support mitigation programs or policies. CDM provides an example of an approach that works on a project-by-project basis or through a collection of projects, that is, using a program of activities (PoA) approach. An alternative to the project approach is a scaled-up crediting program. Up-scaled crediting programs involve credits issued for emission reductions across whole sectors, large groups of GHG sources or a country’s entire economy. In these approaches, baselines are calculated collectively for a large group of GHG emitters. 22 DESIGN Credits are then issued based on all emission developing countries to earn credits through the reduction activities realized across this entire voluntary nature of the scheme, as well as for group. This means that emission reduction implementing specific policies and measures activities could take various forms. They would also as these activities will all earn credits. This is be undertaken by a wide variety of entities. For different under the CDM, where there might example, scaled-up crediting programs could apply be a perverse incentive for governments not to to a whole sector, or to specific policy programs or implement these programs or policies because a specific urban environment. they may undermine the potential of the country to earn CDM credits for specific projects. One example of an approach Lastly, a SCM approach helps a using the scaled-up country to focus on those sectors crediting program is carbon / year that are of the highest priority in 2050 a Sectoral Crediting / capita achieving emission reductions. Mechanism (SCM), whereby all GHG emission reductions in a certain sector can be credited (Öko- Institut 2009). Under such a mechanism, a baseline needs to be defined so that for each unit of emission reductions within the sector, credits can be issued (PMR 2011). Through this mechanism, a voluntary incentive for sector emission reductions can be set. The SCM approach is, however, just a concept at this stage. It has not yet been implemented on either a piloting basis or an operational mode. The benefit of the SCM is that the scope of the mitigation effort could be much greater than the CDM project-by-project approach (PMR 2011). In addition, within the SCM, there is no need for individual projects to be assessed for their additionality. This can greatly reduce uncertainty in the system. At the same time, it can increase the administrative feasibility. A SCM approach also allows 23 Apart from these numerous benefits, there are The Organisation for Economic Co-operation also several drawbacks to the SCM approach. and Development (OECD) has published a list of Firstly, a challenge associated with the SCM commonly experienced barriers that illustrate why approach is related to the collection of reliable and it is particularly challenging for cities to make use consistent data. Under this approach, there may be of project-based carbon crediting mechanisms uncertainty around boundaries of the sector and (Clapp and others 2010). Although these barriers GHG emission data. It may also be challenging to and challenges may apply to all types of contexts, estimate future BAU emissions, thereby making it most are more pronounced in the urban context difficult to assess what the mitigation potential will due to the limited budgets, knowledge and be in the future. Such an assessment could inform capacity available in city governments around the the choice of the crediting baseline. In addition, world. Typical challenges include the following: there may be uncertainty around the responsibility • Lack of clarity around the responsibility for of different entities for certain emission reductions, GHG emissions, including the frequent overlap for example, between the public and private sector. of urban entities responsible for emissions; • Lack of knowledge among city governments 2.2.3. Carbon Crediting around the world in identifying feasible Application in an Urban carbon reduction projects or in connecting to Environment international carbon markets; In theory, carbon crediting can be a powerful tool • Lack of capacity among city governments to enable urban emission reduction; however, in developing and tracking suitable carbon to date, its application in the urban environment projects, especially because typical urban has been limited. This is primarily due to a limited projects are hard to quantify in terms of ability of project-based crediting to support carbon benefits, for example, low-carbon urban mitigation policies, particularly because of transport projects; the complexity and regulatory uncertainty of the crediting mechanism, its focus on technology-based • High transaction costs associated with interventions, as well as the marginal abatement complicated administrative procedures in urban perspective as the main rationale behind crediting. environments, especially because typical urban Moreover, its ex-post payments were not effectively projects are smaller than non-urban projects solving the problem of overcoming financial barriers and have higher proportionate transaction costs; (World Bank and CPF 2019). • Financial barriers due to city government budgetary constraints, especially in overcoming By their nature, most of the urban level high upfront costs that are often associated mitigation actions would not fit the project- with urban mitigation projects; based carbon crediting requirements. The • Risks of projects not delivering the intended scale and complexity of urban mitigation policies carbon reductions; and results in high uncertainties related to both the development of policy scenarios over time, as • Difficult political contexts in which to well as quantifying their outcomes. Since there encourage carbon reduction projects. are a great multitude of factors which can affect sector- or city-level emissions, accounting for Some of these barriers are not dependent on the them appears to be particularly challenging. Also, methodology. Rather, they are inherent to crediting the lower level of data accuracy and its limitations in the urban environment. They are also closely do not support the consideration of urban policies aligned with barriers to urban emission reductions and programs as part of the project-based highlighted in section 2.1.3. crediting mechanisms. 24 DESIGN As such, they mainly focus on complexities of the implementation of emission reduction collecting accurate emissions data at the city level, activities. Examples of other mechanisms as well as the uncertainty of urban policy outcomes include the identification of emission reduction and the limited capacity of city administrations to options, supportive deployment activities, and participate in crediting programs. performance tracking of emission reduction activities. Thus, it is crucial for crediting In order for a carbon crediting mechanism to mechanisms and technical assistance funds succeed in an urban environment, certain pre- to be in place. Together, they can support the conditions are necessary to overcome the barriers other relevant stages. identified above (World Bank Group 2018). These • Risks should be mitigated and distributed pre-conditions include the following: across actors so that they can be minimized. As such, they will not form a • The existence of appropriate incentives barrier to implementing entities in realizing their and price signals at the city level to facilitate emission reduction activities. the efficient allocation of financial resources to where they are most needed, for example, • The urban crediting mechanism should be for emission reduction activities in urban consistent with the wider climate goals environments; and policy instruments within the country. Crediting mechanisms can become important • Ensure that policy-based emission first steps in the development of wider reduction activities are eligible to earn climate policies. In this context, they initiate credits as opposed to only project-based the development of data collection systems, technology interventions. Policy and regulation baseline calculations, assessment of mitigation are crucial measures for cities around the options and institutional set-up. world, helping them to realize their mitigation potential. Therefore, it is necessary to design Therefore, in order to ensure the long-term success the mechanism in such a way that policy of a carbon crediting mechanism, it is important initiatives — independent of technology to scale it up to the city level. This will provide interventions — can earn credits. urban governments with a mechanism which • To ensure the crediting mechanism has can effectively support their mitigation planning a transformational impact, it will need to and policy implementation. However, together be integrated into the urban planning and with overcoming certain barriers of the project- financing processes. City action on climate based carbon crediting, it faces a high number of is generally linked to wider policy objectives, challenges related to urban crediting. These include such as social development goals. It is also emission baseline development, accurate sector embedded in operational and institutional and/or city-level data, as well as ensuring that any processes within the city. Emission reduction achieved savings are linked to mitigation action. activities should be prioritized. Therefore, it is crucial that the crediting mechanism be aligned Another concern related to the implementation of with the longer-term policy objectives and upscaled urban crediting approaches is related to priorities of the city. the fact that many factors which define the city level emissions lie beyond the geographical boundary • The urban crediting mechanism should be or direct control of the city. This applies to many complemented by other mechanisms that important aspects including energy generation, support the relevant stages in the lifecycle transport and waste management. of emission reduction activities. Crediting mechanisms often provide only finance for 25 Therefore, for successful application urban crediting approaches should recognise these BOX 2.3: TYPES OF MITIGATION factors in crediting methodologies (e.g. via TARGETS IN NDCS correct reflection of changes in the electricity mix which occur outside of the city boundary). GHG-RELATED TARGETS: However, city authorities should also seek to • Economy-wide emission reduction targets; exert greater influence on emissions within their geographical boundary through partnerships or • Non-economy-wide emission reduction targets, that is, those that cover only certain sectors; other incentives to influence development. For example, through awareness campaigns, planning • Emission reduction targets that are economy- and/or development approvals or local transport wide and relate to a business-as-usual (BAU) baseline; pricing policies. The advantage of this approach is that it would create a framework in which this • Emission reduction targets that are not extended influence can be rewarded. economy-wide and relate to a BAU baseline • Peak emissions in a given year; and 2.2.4. Alignment with NDC • Emissions intensity goals related to gross Targets domestic product (GDP) or (kilogram of carbon dioxide equivalent [kgCO2-eq] per unit of GDP) Up-scaled urban crediting programs can be or emissions per capita goals (tCO2-eq per used for incentivizing mitigation actions at cap). the city level. Since many urban policies are designed to produce emission savings across an NON-GHG RELATED TARGETS: extended period of time, it is crucial to ensure that • Non-GHG targets, for example, renewable the crediting program is not affected by political energy targets, energy efficiency or forestry changes or any other factors that may have an targets; and impact on urban priorities. • Implementation of qualitative policies and measures (for example, Egypt, Nepal). One of the ways to address this issue and ensure a long-term commitment from the Source: Briner and Moarif (2017). urban government is to link the urban crediting program to the country’s NDC. This would in turn lead to increased support of the national government and ensure demand for the generated GHG reduction during the commitment period credits on the international market. However, it and/or NDC target year (see Box 2.3). would also lead to some difficulties, chief among them the alignment of the crediting baseline with For a majority of countries, the issue of the NDC targets. elaborating and developing the NDC target is likely to become a major barrier for linking By their nature, different types of NDC targets urban carbon crediting programs to NDC will have varying capacity to support carbon commitments. This would require an extensive crediting mechanisms. Since carbon crediting amount of data and effort (for example, modelling). requires a baseline which can be used to define Most importantly, it would require the commitment creditable emission reductions in each crediting and support of the national government, which period (for example, annually), many mitigation at times might be hard to obtain. For this reason, targets which are included in NDCs will have although the connection to the NDCs has multiple limited applicability for carbon crediting in their benefits, it is likely that for most cities it will only be current form because they do not define targeted achievable in the longer term. 26 DESIGN 2.2.5 Summary The experience of carbon crediting in cities shows that the project approach has had limited success in the urban environment due to both its complexity and limited applicability to city mitigation actions. Some of these difficulties can be addressed by moving away from a project focus toward an upscaled carbon crediting program. However, other difficulties would not be related to the methodology. Rather, they would be inherent to carbon crediting in cities. Therefore, other solutions should be sought to address those challenges. The alignment of the crediting program to the NDC targets can become a beneficial development to the urban crediting program. Yet, it will face a set of challenges should a city decide to proceed with the alignment. These are summarized in Box 2.4. BOX 2.4: IDENTIFIED BARRIERS Crediting Methodology in an Alignment of City and NDC Urban Environment Targets • Accommodating city-level mitigation: • Target alignment: Not all NDC targets in The project-based carbon crediting approach their current form can support a crediting is not capable of effectively capturing wider program. sectoral or city-level policies. • Commitment of the national • Quantifying emissions savings: The government: The support of the national complexity of quantifying emission savings government will be required to ensure for each policy may be complicated due alignment of the urban carbon crediting to its scope, as well as a large number of policy with the national NDC performance. factors affecting the final sector/city-level emissions. 27 2.3. URBAN CARBON CREDITING: METHODOLOGY DEVELOPMENT 2.3.1. Considering Options for Up-scaled Carbon Crediting Programs Urban carbon crediting programs can used 1) Generated credits can be used toward the for a variety of purposes. The first issue to be unconditional or conditional NDC targets by taken into consideration when designing an urban setting a baseline and withholding credits. carbon crediting program is to decide whether it will In order to link an urban carbon crediting be used as a tool to support meeting the national program to the national NDC commitment, climate change target, primarily the NDC, or it is necessary to ensure that the country does whether it will serve solely as a means of attracting not overcommit to selling generated credits finance from non-NDC credit buyers and donors. internationally, thereby resulting in a situation where it is not able to meet its own NDC In order to link an urban carbon crediting target. Thus, credits can be withheld in the program to the national NDC and Paris country where the emission reductions Agreement commitments, it is necessary to are achieved. identify the purposes for which generated credits can be used. Many countries have set 2) a. Crediting can be used as a financing conditional targets for their NDCs under the Paris model to engage the international Agreement. This means that the achievement investment community. of these targets is dependent upon the country There are two possibilities to use credits as receiving additional international financial support a financing model to engage the investment and technical assistance to implement the community (PMR 2017). Firstly, generated necessary emission reductions. Some countries credits can be earmarked toward the have set both an unconditional and a conditional (conditional) NDC targets without transferring target, whereas others have only set a conditional emission reductions. In this case, the emission target (PMR 2017). reductions that are represented by the credits only count toward the host country’s NDC. When credits are issued for certain emission This means that the issuance of credits is a reduction activities, the credits can be used means of providing the finance upon which the to count toward a variety of purposes and NDC target is conditional. However, allowing targets. For example, when credits are used to the host country to only count the emission meet conditional targets, they could theoretically reductions toward its NDC and not the buyer also count toward providing the necessary country effectively removes the incentive for financing to meet the conditional target. However, buyer countries to purchase credits. Thus, this Article 6.5 of the Paris Agreement states that option can only work if the credits are bought emission reductions achieved under the Article by dedicated funds or from sovereign nations 6.4 mechanism (that is, ITMOs) “shall not be used seeking to provide climate finance under the to demonstrate achievement of the host Party’s Paris Agreement. In providing this international [NDC] if used by another Party to demonstrate finance to buy the non-transferrable credits, achievement of its [NDC]” (UNFCCC 2015). Based the finance used to buy the credits can count on this Article, there are a number of different ways toward the requirements upon which the in which generated credits can be used: conditional target is dependent. 28 DESIGN b. Crediting can be used as a financing 2.3.2. NDC-aligned Carbon model to engage the private sector investment community. Crediting: Program Development Crediting can also be used by a country Step 1: NDC target metric alignment as a financing model to engage the non- state actor investment community. This can If an NDC target is not expressed as be done without the objective of meeting a quantified GHG emission reduction, the requirements of the conditional target. it should be translated into quantified Similarly, emission reduction activities emission reductions through the will count toward the host country’s NDC. necessary calculations and/or modelling. Thus, it provides buyers of credits, such as private sector actors, with the opportunity to make claims about providing climate To enable urban crediting program to finance, but without transferring the actual contribute to the international commitments emission reductions to them. In theory, when of the host country, it is crucial to ensure the international non-state actors would finance alignment of the crediting approach with the credits using this model, it could be perceived country’s NDC. Such an alignment will help to as finance provided to meet the conditional ensure that carbon credits generated through NDC targets. Therefore, the difference urban city program are recognized internationally between options 2a and 2b might be difficult to and that no double counting takes place. determine. Examples of this model include the issuance of ‘certified statements’ by the Gold Since NDC targets are set in different ways Standard. These are statements that allow and expressed in different units (for example, the buyer of credits to claim responsibility for GHG emission units, energy units, mitigation unlocking climate finance without including the actions, and so on), in certain cases it may transfer of emission reductions. be challenging to calculate the targeted NDC emission reductions. To address this barrier, Although alignment with the NDC appears to it is important to note that in theory all types be beneficial from a variety of perspectives, of NDC targets can be translated into absolute it would require a much more sophisticated targeted emission reductions. They can also target alignment and may delay the be recalculated to define a maximum absolute implementation of the crediting program. amount of GHG emissions which can be emitted For this reason, cities may decide to start during the commitment period or year. This should implementing the program in line with their own be done through modelling, where possible. city-level targets, which may or may not be aligned with the national target. They may wish to The GHG-related NDC targets appear to be consider the NDC alignment at a later stage when better suited to accommodate urban crediting the necessary capacity has been built. approaches as they can be easier aligned with carbon crediting programs. Non-GHG Since the NDC-linked carbon crediting program NDC targets, in turn, would need to be translated requires a more sophisticated approach, this into a GHG emission level to serve as a basis for chapter will present the necessary steps for its carbon crediting. Although it can be a complex development. It will then separately lay out those and resource-intensive task, with the necessary which would be required for a non-NDC aligned data and calculation/modelling approaches, it carbon crediting program. should be possible to quantify the effects of 29 implementation of non-GHG targets. These of the Paris Agreement. From the perspective can then be expressed as a targeted amount of crediting programs, this may result in a high of emissions. demand for carbon credits in the target year — but also extremely low demand prior to that. A number of countries, particularly those in From the wider perspective, this arrangement the developing world, have selected an NDC may also have negative effects on the consistency mitigation target not related to GHG emissions. of ambition in relation to emission reduction Limited capacity to calculate targeted emission activities. As such, it might result in encouraging reductions and poor data quality and availability abnormal behaviors in the target year, that is, may be a constraint, particularly where the NDC behaviors aimed at artificially applying extreme target relates to the implementation of qualitative measures to reduce emissions. policies and measures. It is recognized that it might be extremely challenging for such countries To provide a consistent signal for emission to undertake steps to translate their targets into reduction policies and effectively support carbon quantifiable GHG reductions. This would in turn crediting programs, an alignment of the NDC result in their limited ability to implement NDC- pathway and the crediting program baseline aligned, up-scaled carbon crediting approaches. will be required. This can be done through an evaluation of the sectoral emission abatement Step 2: Establish an NDC pathway potential in order to define the contributions of each sector to the national emission reduction If an NDC target is not expressed as a target in each year of the target period. A sectoral series of annual emission reduction targets, breakdown of the NDC pathway will also facilitate the annual targets should be defined a tailored approach for each of the next steps to through calculation and/or modelling, each sector, which can then lead to an increase in potentially through evaluation of the the overall environmental integrity of the scheme. sectoral emission abatement potential. Step 3: Define a city NDC pathway as a minimum requirement for the urban Crediting programs should be able to incentivize crediting program baseline. the implementation of emission reduction activities during a specific period in order to align them In order to define the contribution expected with the NDC. As such, it is desirable for the NDC from a given city, the overall national NDC commitment to be translated into a pathway with targeted emission reduction should be annual emission reduction targets. Such a pathway disaggregated by sector. It would then be would be able to confirm the validity of crediting distributed based on the share of each savings in each crediting period by effectively sector represented by each city. creating a “national emission allowance”. An NDC pathway would also have the capacity Once the NDC pathway is established, it is to support crediting programs through ensuring necessary to distribute the expected emission regular demand for generated savings. This reduction among cities and non-urban areas is significant because, at the moment, most within the country. As noted, this can be countries relate their NDC GHG to a target year, complicated due to the different emission mostly to the year 2030. This effectively implies profiles of areas, including varying emission that only emission reductions achieved in the abatement potential. target year could be credited under Article 6 30 DESIGN A direct alignment of city targets and the national This calculation will, however, only establish a target is the simplest way to approach this step. minimum requirement for the city carbon crediting However, it carries a high risk because it assumes baseline. As noted, many cities around the world the same pace of emission reduction from non- have established targets which go far beyond the urban areas, which for most countries would be national targets. Therefore, in order to support almost impossible to achieve. their more ambitious mitigation targets, cities can establish a crediting baseline which goes beyond The sectoral breakdown approach mentioned in the NDC prescribed minimum requiring greater Step 2 can be further utilized for city-level target emission reductions at the city level. breakdowns. This implies that once the targeted emission reduction from each sector is calculated, Step 4: Set a crediting baseline it can then be distributed among cities based on the share of each sector which they represent. A crediting baseline should be set For example, if a residential sector is expected for a city carbon crediting program, to achieve 100,000 tCO2e in emission savings which would be aligned with — or more and a considered city is home to 20 percent of ambitious than — the NDC pathway. the country’s population, then this city would need to contribute 20 tCO2e from the residential In linking an urban carbon crediting program to sector toward the national target. This calculation the national NDC commitment, it is necessary to would then need to be repeated for all sectors ensure that a city does not overcommit to selling to define the city’s expected emission reduction generated credits internationally. This could lead contribution. Alternatively, gross domestic product the national government to a situation where it (GDP) or historic proportions of city versus national is not able to meet its national NDC target. This emissions could be used to appropriate a certain concern can be addressed by the way in which the part of the emission reduction target to a city. crediting baseline is set. Figure 2.2: Baseline Ranges in Relation to NDC Pathways BAU emissions Baseline range (1): Emissions withholding required NDC pathway emissions GHG emissions Baseline range (2): from sources Emissions can be covered by transferred the crediting program Actual emissions Start of the End of the baseline period baseline period Source: PMR (2013). Note: BAU= Business-as-Usual; GHG= greenhouse gases; NDC= Nationally Determined Contribution. 31 The main question to be considered is whether the Should a baseline for crediting programs be set crediting baseline is above or below the city NDC up in line or below the NDC emission pathway, pathway, as this will define whether all credits will then all credited amounts would be eligible for be eligible for international transfer. As shown international transfer. In this case, there will be no in Error! Reference source not found., in case a need for emissions withholding. baseline is set above the NDC pathway, there is a risk of over-transfer of credits. This risk and the The steps required to develop a baseline and ways in which it can be managed are explained in choose an approach that is suitable for the more detail in step 4a in section 2.3.3 (Non-NDC required application and objective of the urban aligned carbon crediting program development). crediting is outlined in Box 2.5 (PMR 2013; World Bank 2016). BOX 2.5: STEPS FOR DEVELOPING A CREDITING BASELINE The establishment of a baseline a BAU scenario is based on by major city networks such is important to ensuring the expected legal (regulatory), as C40 and the Compact of environmental integrity. economic and physical Mayors. Therefore, it is generally changes to the covered • Phase 5: Define how long a recommended to take a activities. Modelling may be baseline remains valid and conservative approach in setting required in this step. It should indicate a choice between an a business-as-usual forecast be based on assumptions ex-ante or ex-post dynamic so that all credits issued in around population growth, baseline. a scheme represent real and GDP projections and global credible emission reductions energy prices. • Phase 6: Choose one of the (PMR 2013). However, a baseline baselines developed in Phases • Phase 4: The previous step that is too ambitious could risk 3, 4 and 5. may need to be repeated in having an insufficient price or case a baseline is desired to In some cases, choices made in quantity to promote investing in be more stringent than the later steps require the previous GHG mitigation actions under BAU scenario. Additional data steps need to be repeated to the scheme, thereby causing about costs and the potential ensure the necessary data is some emission reductions to of mitigation activities is available. Throughout the process go unrealized. This box outlines necessary here. The Climate of developing the baseline, it is the different methodologies Action for Urban Sustainability also necessary to consider the and data necessary to set a fair (CURB) tool could support this following procedures: and realistic business-as-usual step. It is designed to support baseline for crediting (PMR 2013). • Identification of data and cities in obtaining insights into resources needed to develop • Phase 1: Clearly define the their emission profiles and the baseline and contacting type of activities to which the identifying emission reduction of relevant institutions with baseline applies, for example, activities by exploring and access to this data; and sectors, covered entities and evaluating costs, impacts time period. and feasibility. As the tool is • Ensure the baseline undergoes city-specific, it can fill data a public, domestic and • Phase 2: Based on the type of gaps by providing data from international stakeholder activities involved in Phase 1, comparable city contexts. review as well as an in-depth the appropriate metrics need This is particularly the case consultation with experts and to be identified. when using the CURB tool, affected entities. • Phase 3: The development of as the tool has been adopted 32 DESIGN Step 5: Defining the crediting The key purpose of the baseline emission scenario baseline dynamics is to separate the additional emission savings from those which would occur in the absence of the Policy makers need to determine whether credited emission reduction activities. Thus, this a static or dynamic baseline will be used approach bears considerable risks in terms and how often it will be revised. This of under- or over-crediting the results of the will require an assessment of a trade- credited activities. off between investment certainty and ensuring environmental integrity. 2) Dynamic ex-ante baseline The dynamic ex-ante baseline is similar to the In considering the urban context, aggregated previous option in that it assumes projection of emission levels can depend on a variety of the baseline GHG emission for the whole duration factors. Thus, it is crucial to establish a baseline of the crediting program. However, as part of this which would serve as a comparative reference approach, the crediting program can be broken point for achieved emission reductions. Different down into shorter periods, after which the baseline approaches can be used to develop the baseline. can be updated. 1) Static ex-ante baseline The baseline update can be approached from two perspectives. It can either be routinely done The first option to baseline development relies prior to the beginning of each crediting period. on the approach currently widely used in CDM, Alternatively, the need for an update can be namely: a static ex-ante baseline. This implies assessed based on certain triggers. The latter that the baseline is calculated on a one-time implies that the key external factors influencing basis for the crediting program duration. emissions (population dynamics, economic cycles, All reductions against this baseline will then and so on) can be reviewed. Should any of the become creditable units. variables or their combination change beyond a defined threshold, the baseline update would then Using a static baseline approach can maximize be triggered. In principle, this option allows for the predictability of the crediting program the baseline to be updated only when significant and enhance certainty for investors and other changes in the urban conditions are occurring. stakeholders. It also requires reduced effort Yet, its ability to reflect the realistic emission in from the city government as the baseline can the absence of the credited activities will largely be established once and then applied throughout depend on how often the baseline update is the whole crediting period. considered. Also, such an approach would allow for the alignment of the baseline updates with the The static ex-ante baseline approach also entails updates of the country’s NDC. risks. It will not be able to consider changes arising from factors that are not linked to emission This approach allows policy makers to develop reduction activities. With the scale of aggregation baseline scenarios that are more precise and and the complexity of urban GHG emission reflective of the situation in the urban area. dynamics, other factors can significantly impact However, in this case, an important trade-off emission levels. These factors include migration, between the baseline accuracy and the investment economic cycles and energy prices, among certainly needs to be considered. Whereas the others. A static baseline approach will fail to more frequent adjustments of the baseline would recognize the effect of these factors on emissions. help make it more accurate, it would also reduce 33 the certainty parameters for long-term investment. inventories to allow for the calculation of the These are needed to address the issues related to readjusted baseline in a timely manner. urban infrastructure development and other urban policy programs. This will also require a higher Option assessment. Overall, the three options in level of effort from the government in terms of Error! Reference source not found. demonstrate program management because regular baseline how the baseline development can, to varying reassessment will be needed. This will, in turn, degrees, ensure the provision of a reliable require the timely production of the latest GHG economic signal (that is, investment certainty), inventories, as well as the collection of all other crediting “real savings” (that is, environmental necessary information about many economic and integrity). It can also facilitate the crediting social factors affecting GHG emission dynamics. program management for the local government (that is, ease of administration). However, given 3) Dynamic ex-post baseline that none of the options can maximize all three of the criteria simultaneously, the selection of the Another option for the development of a crediting approach becomes a question of priorities. baseline is through the application of ex-post adjustments. As part of this approach, the initial In the event that crediting baselines are set baseline is developed for the entire duration of the separately for each sector, the baseline dynamics crediting program, taking into consideration the can also be tailored accordingly. This is important, key factors affecting emissions in the same way as the variability of estimates for some sectors as the first two cases. However, following the end might be higher than for others. For example, if the of each crediting period, such as a year or longer, national grid emission factors change drastically the baseline is retrospectively readjusted. Carbon within the crediting period, an ex-post adjustment credits are then issued against the revised baseline. of the baseline might be required for this sector. However, for other sectors this variability might This approach allows for the maximization of not be relevant. Therefore, to ensure investment accuracy in crediting “real savings”. However, certainty and reasonable administrative costs, a it will significantly reduce the certainty of the static or dynamic ex-ante baseline may be applied investment, as investors will not be able to reliably to these other sectors instead. It is important to predict the volume of credits that a given emission note that while baselines may be set on a sectoral reduction activity will generate. It will also require basis, credits will still be issued on a city-wide an increased effort from the municipal government level to capture the benefits from an urban in relation to the preparation of frequent GHG crediting approach as outlined in section 2.2.3. Table 2.2: Assessment of Baseline Development Options Investment Environmental Ease of Baseline Dynamics Options Certainty Integrity Administration 1. Static ex-ante baseline High Low High 2. Dynamic ex-ante baseline Medium Medium Medium 3. Dynamic ex-post baseline Low High Low 34 DESIGN 1| Step 6: Define the Discounting Approach Assessment of percentage uncertainties for activity data, emission factors and other Given the high uncertainty and often low- calculation factors, modelling approaches, quality data typical of city-level emissions and so on. data, a discounting approach will need to 2| Emission calculation for all categories be developed. included in urban crediting, applying the upper limit — for example, with an Discounting is an important instrument which can uncertainty of +-10 percent, this would be be applied to limit the risk that credits are earned +10 percent of the percentage uncertainty for business as usual emissions reductions, or to each calculation. This basically entails to divide the emissions benefits between host overestimating GHG emissions in line with the country and funding (credit purchasing) agent. identified uncertainty values to err on the side Application of discounting implies that one tonne of caution.5 of generated emission reduction earns less than 3| one credit. The effect of this approach will be two- Add all GHG emissions calculated under fold. Viewed from the perspective of mitigation step 2 and compare them with the baseline within the crediting mechanism, the price signal value to then calculate the discounted GHG per tonne of emission reduction is reduced, which reduction value. would result in a lower incentive for mitigation. Considered more broadly (for instance countries This approach requires considerable effort in the seeking to acquire mitigation outcomes to help assessment of uncertainties. Where data quality meet their international commitments), the supply is limited, relevant information about uncertainties of fewer credits to the market may elevate the is often not available. Thus, developing this carbon market price. While the final price signal information would require considerable resources. and the volume of mitigation incentivised by the mechanism will depend on the interaction of The selection of uncertainty value would multiple factors, the application of discounting in have to be undertaken with great care. The the urban environment is particularly important Intergovernmental Panel on Climate Change due to the uncertainty of the city-level emission (IPCC) 2006 Guidelines (Volume 1, Chapter 3) pathways given typically low data quality. provides advice about dealing with uncertainty Discounting can be used to address data quality for specific categories in the sectoral volumes. concerns and the baseline uncertainty that is This could provide input to the development of caused by multiple factors. the standardized uncertainty values. Furthermore, uncertainty assessments performed by Annex I Defining discounting rates. The data used to countries with similar situations relating to data calculate the city GHG inventory is often not of — potentially from earlier reporting years — might sufficiently high-level quality required for a carbon also provide relevant input. market approach, that is, it is not at a level of quality to confirm that “a ton is a ton”. Where A further simplification would be to establish the data uncertainty is known, it is possible to a discrete set of standardized uncertainty apply a discounting approach when calculating values for qualitative levels, for example, the amount of GHG reductions achieved. This “low – medium – high” (L, M, H). These values approach would be based on the following steps: would be specified with regard to the types of data, and ideally broken down by sector and subsector. Activity data from processes generally 35 subject to monitoring/metering, for example, in the This would then be applied after individual energy or industrial processes sectors, typically sectoral discounting factors have been have lower uncertainties than data related to aggregated, while also weighted by their biological processes, for example, data related to proportional contribution to city-wide landfilling, manure management or forestry. There emission levels. are of course exceptions in each sector that will challenge the validity of standardized values. The discounting type will then be applied accordingly. Table 2.3 shows the types of data that fall under the H, M, L classification. The following tables (Table 2.3, Table 2.4, Table 2.5, and Table 2.6) show the proposed data quality assessment approach to generate an objective data quality assessment against a number of key criteria, also following the H, M, L approach. This includes scores for emission factor accuracy/relevance, as well as for cross-cutting issues that provide greater or lesser confidence in the quality of the inventory. The aim is to calculate a discounting factor for each sector independently, as data quality and approach may vary significantly between sectors. Table 2.6 also highlights a list of cross-cutting issues from which a city-wide discounting factor can be derived. Table 2.3: High-level Discounting Approach Activity Discounting Example Justification Data Quality Level Highly likely to represent real consumption City-specific measured data, such as billed in the city, thereby changing over time; High energy consumption by sub-sector or by reflects actual change and can be used to Low fuel type, aligned with inventory boundary. inform policy and implement crediting with more confidence. Modelled data, that is, consumption data Likely representative of the city within an from <3 years scaled to city population/ acceptable range of uncertainty – if Medium boundary/building stock. Data for inventory Medium scaled based on recent trends and using year for part of the city, scaled to the justifiable factors. entire city. Does not represent consumption within the city boundary with any degree of accuracy; Low Scaled national- or regional-level data. High only general, likely trend/level. Cities should be encouraged to implement improvements. 36 DESIGN Table 2.4: Activity Data Assessment Data Score Score Score Quality Sector High Quality Medium Quality Low Quality Issue Scaled national data that is not deemed to be detailed or City-specific, measured accurate, for example, issues Applies data (for example, not Detailed bottom-up national data 1 0.9 pertaining to the national 0.7 to all using scaled national that has been scaled to the city. energy balance. Is there large inventory data). uncertainty about electricity consumption data? National-level fuel sales data City-specific data scaled to the city by a proxy (for example, not dataset, such as population, using scaled national would be considered low inventory data) or National data collected from, for quality. This approach would Stationary national-scale data example utility companies, that is 1 0.9 not account for different 0.7 energy collected from utility disaggregated to city-level using a types of fuels used or companies, if there is a proxy data set e.g. population. behavior changes in cities. way of disaggregating This approach would also this data to the city insufficiently reflect city level scale. policies and actions. If national transport data If national transport data is comprised of detailed is simply taken from the bottom-up data, this national fuel balance, and could be considered If national transport data is then scaled to the city scale, high quality — if there is comprised of detailed bottom-up this would be considered a way of disaggregating data, but is disaggregated using low quality because city Transport this data to the city 1 0.85 0.65 proxy data such as population, activity would be very Data scale. If the national this could be considered medium different than that of rural scaled data uses data from the quality. areas. Scaled fuel sales from a city aggregated with data for transport would not national data from other regions, reflect changes to policy or data the policy at the city- activity at the city level. set level may be reflected. National data collected National data collected through through detailed surveys detailed surveys of waste treatment of waste treatment facilities across the country, with National data that is largely facilities, with specific no specific way of understanding comprised of estimates Waste information about waste 1 the cities’ waste treatment facilities, 0.85 and then scaled to the city 0.7 treatment facilities may be considered as medium by population would be used by the city, may quality. However, it would more considered low quality. be considered as high useful to a city than a low-quality, quality. city-scale data set. Scaled national data relating to product use could be considered Industrial National data is used, as medium rather than low quality Process- If a city has no industry, but this national data because much of the product use is es and scaling national IPPU data reflects an industry that 1 likely to be in the city or attributed 0.8 0.6 Product to the city level could be is only present within to the city’s activity. If, for example, Use considered as low quality. the city. the city has the country’s only iron (IPPU) and steel works, this data would be considered as high quality. If a city has a large agriculture Agri- If a city has detailed industry, scaling national AFOLU culture, If a city has no agriculture, AFOLU data that can data to the city level could be Forestry scaling national AFOLU data be disaggregated to the 1 considered as medium quality. 0.8 0.4 and Other to the city scale could be city-level, this could be However, in actuality, a city will rarely Land Use considered as low quality. considered high quality. have an equal proportion of AFOLU (AFOLU) activity due to space constraints. 37 Score Score Score Data Quality Sector High Quality Medium Quality Low Quality Issue Data representing Data year within 4 years of the Applies Data for the inventory year Data that is more than 4 different year 1 inventory year, where the trend 0.8 0.6 to all is used. years out of date. than inventory shows small annual variability. year Detailed city-specific data Data for inventory which covers largely the same If the boundary of the Data boundary, or if detailed boundary, but with some areas data is considered very representing city-specific data is easily of the city either included or different from the city different Applies manipulated so that the excluded, could be considered boundary (for example, 1 0.85 0.75 boundary to all boundaries match (for as medium quality. If scaling the it is regional- or state- than inventory example, if certain areas of data involves using population or level data), this should boundary the city can be excluded/ another proxy data set, the data be treated as low included). should be considered medium quality. quality. Generally Data from a project applies that represents the only 1 to all emissions in the sector. Data from one project might be considered good Data that has been obtained Data scaled Data from a very small enough to use for the from a project and then scaled up from small IPPU 1 project being scaled for whole city, particularly for up (or maybe down) might be data sample, the whole city might be an industry that is only very detailed, but it might not 0.85 0.75 for example, considered low quality, domiciled in the city. be representative of a whole from project depending on the scale Data from one waste city. As such, it could be data and circumstances. survey/landfill project considered as medium quality. might be considered good Waste 1 enough to use for the whole city, and it could be considered high quality. Data scaled from a proxy city will generally be considered low Scaling data from a similar city quality, particularly if could be deemed as medium Proxy data the cities differ quite Applies No data from another city quality. However, for the data from another 1 0.8 drastically. Proxy data 0.6 to all used. to be deemed medium quality, city from another city should there should be some verification only be considered if with real city data. the two cities are similar in size, climate, region and economy. This will likely not lead to large In some cases, it may Uncertainty data quality issues as units be more difficult to in data can usually be identified in estimate the units. reporting, Applies some way, whether it be by If after verification, for example, All data reporting is clear. 1 0.9 0.8 to all comparison with data at the confidence in the units that are national scale or from different estimated unit is low, unclear or cities. In this case, data could be the data quality should inconsistent considered as medium quality. also be considered low. Lack of information about the Data is missing specific source source, of information (for example, reference Applies All data is transparently Source of the data is 1 weblink), but has been obtained 0.9 0.8 or other to all reported. unknown. from a credible source (for information example, a city government). to support transparency of data 38 DESIGN Table 2.5: Emission Factor Assessment Score Score Score High Quality Medium Quality Low Quality Comparable to national inventory Not in line with national Not in line with national inventory. emission factor (EF) inventory but can be justified. EF from different country/city, Regional or representing a City-specific EF. with little evidence of verification different boundary. between city and proxy. 1 Representing a year that is 0.9 Representing a year which is 0.8 more than 4 years apart/different within 4 years of the inventory Fuel/technology specific. from the inventory year; or the EF year, where there is likely to be represents a different year, but limited annual variability. annual variability is likely. Representing correct year (if national or city-scale) Application of discounting. Depending on 1) Discounting with pre-defined rates to how it is applied, the discounting approach address environmental integrity concerns can address the key issues raised in relation to the establishment of the baseline, ensuring the The pre-issuing discounting occurs prior to credit environmental integrity of the achieved emission issuance or at the time that offset credits are used savings. This is linked to the fact that the for compliance. This type of discounting is usually discounting mechanism turns a ton of emission built into the crediting methodology and is used reductions to a lower amount of credited emission to address uncertainties linked to specific types reductions. Therefore, it can be applied as a of emission reduction activities. This includes means of adjustment to the baseline allowing uncertainties in measurement techniques and for changes in key external factors (for example, activities related to emissions sources with a high population dynamics, economic cycles, and likelihood of over-crediting. so on). By taking account of the non-mitigation related factors, discounting would support the On the sectoral level, this type of discounting linkage of credited emission reductions to city- may help to address those sectors likely to level mitigation achievements. experience the significant effect of factors not directly related to emission reduction activities, Discounting can be applied through ex-post such as population dynamics or economic cycles. adjustments at the end of the crediting cycle, or However, in the case of urban crediting, the through ex-ante adjustments based on the data achieved emission reductions are not separated received for the previous assessment period. by sources. Therefore, this approach cannot Similar to ex-post baseline adjustments which be applied directly. Yet, it can help to inform maximize the environmental integrity at the the over-discounting rate to the city’s total expense of investor certainty, discounting will face emission savings. This will help to decrease the the same trade off. Given the nature of the crediting uncertainty at the city level and minimize the risk mechanism, this trade off will be present in any of international over-transferring of carbon credits. type of crediting methodology. There are two ways to use discounting to address these issues. 39 Table 2.6: Cross-cutting Assessment Score Score Score Data Quality High Quality Medium Quality Low Quality Issue If outliers are unexplained, but Time series If the time series is If outliers are unexplained, are not within a reasonable inconsistency inconsistent but such but within a reasonable range, data should be (for example, inconsistencies are 1 range, data should be 0.9 considered as low quality. 0.7 unexplained explained, data should be considered as medium If this check has not been ‘outliers’) considered as high quality. quality. undertaken, mark as low quality. If there are clear differences If data looks particularly If there are clear differences, but these are unexplained high or low compared to but these are unexplained and not within a reasonable Benchmarking data from similar cities and within a reasonable range, data should be against other but there are justifiable 1 0.9 0.8 range, data should be considered as low quality. cities reasons for this, data considered as medium If this check has not been should be considered as quality. undertaken, mark as low high quality. quality. Some basic checks have been undertaken by a person Calculations have been Calculations who was not involved in thoroughly checked by have been compiling the emission Calculations have not been a person who was not 1 0.9 0.8 checked estimates, or the person checked. involved in compiling the internally who compiled the emission emission estimates. estimates has thoroughly checked them. Calculations have been Calculations have been Data has not been externally verified externally verified. 1 verified. 1 - - externally A reduction of 10 percent should be applied to data uncertainly in the relevant scope, where uncertainly is defined as (1-discounting factor). * Note: * A 10 percent reduction in the calculated data uncertainty should be applied as shown in the following example. Example. Assessment of the discounting factors in each sector resulted in a discounting factor 0.7. The city GHG inventory was then externally verified, which makes it eligible for a 10 percent uncertainty reduction. This is calculated as follows: Uncertainty = 1 – 0.7 = 0.3 New discounting factor = Discounting factor – Uncertainty * (100% - 10%) = 1 – Uncertainty * 90% New discounting factor = 1 - 0.3 * 90% = 0.73 2) Retrospective discounting Option assessment. Table 2.7 illustrates how discounting approaches can be assessed against Discounting can also be applied at the stage when the criteria defined earlier. It is important to note the credits are being issued. Similar to ex-post that in step 3, discounting is used as a basis for baseline adjustments, this option can be designed withholding credits. This can also be applied both in to apply either at the end of each crediting period, a retrospective way and in a way in which discounting or only upon significant changes in relevant external rates are pre-defined. The options presented in this factors. This option still increases uncertainty chapter are not exhaustive. In this regard, the options for investors. However, by making it work in an could be combined in various ways in line with automated fashion, it can reduce the uncertainty if priorities regarding investor certainty, environmental the methodology it follows is transparent enough. integrity and ease of implementation. 40 DESIGN Table 2.7: Assessment of Discounting Options Investment Environmental Ease of Discounting Approach Certainty Integrity Administration 1. Discounting with pre-defined rates to address uncertainty High Low High 2. Retrospective discounting to address uncertainty Low High High 2.3.3. Non-NDC Aligned Carbon Step 3: Defining City’s BAU Emissions Crediting Program Development The first requirement is to define a The development of a non-NDC aligned carbon reliable BAU emission trajectory for the crediting program will require a reduced number city to ensure the environmental integrity of steps. Generally, it can be considered as of the crediting program. a simpler exercise as it will not necessarily require alignment with national targets and the Since carbon crediting programs rely on a involvement of the national government. However, baseline-and-credit technique, defining a city’s it will face a number of challenges, which it will BAU scenario below which emission savings can need to address. be credited is a first step to the development of the crediting program. In the case of an NDC- Another benefit of a non-NDC aligned approach aligned program, the NDC emission pathway is is that it can be adjusted to fit a city’s long tern likely to serve as a baseline. However, for the non- decarbonisation plan which may be much more NDC aligned program, the city government may ambitious than the country’s NDC. Also, should develop its BAU trajectory independently. the city desire achieve an alignment with the national strategy in a more ambitious way than Figure 2.3 below provides an overview of the that offered by the NDC, alignment with a national methodologies available to establish a business-as- Long-term Low Emissions and Development usual scenario for emissions in an urban context. Strategy (LT-LEDS) can be considered. However, The methodologies have been mapped from simple since LT-LEDs focus on the emission pathway to complex approaches. They are based on the to 2050, the uncertainties associated with this type of calculations that are used to predict future alignment are higher than in the case of NDC developments. From simple to complex, they alignment which only extends to 2030. To address include the following methodologies: these uncertainties, the methodology offers flexibility in relation to the baseline update which  Approaches that use linear projections, would allow adjustment of the baseline as new such as growth rates for population, GDP, fuel data emerges and yet still signal the long-term demand and others. For example, this could ambitious target of the mechanism. be a simple linear extrapolation, whereby a few key drivers — including historic emissions, GDP and population — are defined. It would be argued to be reasonably approximated by a linear relationship. These are then scaled up on a pro-rata basis to estimate the city-wide emission profile in the future. 41 • This approach includes the Climate Action used to derive the relationships between for Urban Sustainability (CURB) tool, which is these variables and the emissions. Then a World Bank tool to support cities in gaining forecasts can be applied for the variables insight intov their emission profiles. CURB under the same relationships to predict calculates baseline projects by using growth business-as-usual emissions. A limitation of rates for specific periods for one or more this methodology, however, is that variables of the following factors: population, GDP, need to be truly independent. Also, between emissions, and the use of various fuel types. them, they should cover all the determinants for emissions. This may not always be  Use of existing forecasts. In this approach, the case, for example, if there is a lack of forecasts done for other purposes, such as data available. Similar to the IDA/LMDA grid demand forecasts, waste management approach, this is a methodology generally volumes, or economic analysis related to the used for retrospective analysis, although it national budget, are used to calculate the can be used for forecasts as well. baseline. These forecasts can be used to scale baseline year emissions on a pro-rata  The Long-range Energy Alternatives Planning basis according to city activity levels. The (LEAP) System. This is a wide-spread applicability of this approach depends on the modelling approach used by governments, type of data and forecasts already carried out including city governments, around the for the city. It also depends on whether these world for mitigation assessments. LEAP can be linked to emissions data to provide a uses a scenario-based approach to predict consistent picture of future developments. energy production and consumption, as well • Index decomposition analysis/LMDA. The as resource extraction in all sectors of the approach of using existing forecasts can economy. The advantage of the LEAP model is be combined with the Index Decomposition that it is easy to use and provides projections Analysis (IDA) Tool for energy emissions, of emissions even in cases of low availability of that is, the Logarithmic Mean Divisia data. However, a disadvantage of this approach Index (LMDA). This is a tool used to derive is that it requires a set of assumptions of future how much of a change in energy use, or developments in a baseline scenario. Also, as it emissions, is attributable to changes in does not always match demand and supply, it activity, fuel mix, or efficiency in the use of may provide inconsistent price solutions. energy. This methodology is typically used as a retrospective analytical technique.  Computable General Equilibrium modelling, However, it could also be applied to such as the MARKAL/TIMES Model. The most forecasts of emissions, if combined with complex approach presented here to calculate future activity predictions. This would baseline projections is the Computable General require that assumptions are made that past Equilibrium (CGE) Model. These modelling changes in drivers for emissions continue to tools use economic input and output data to apply in the same way in the future. predict changes in a country’s economy based on changes in policy, technology or external • Regression analysis can also be used in factors. This type of modelling can also be combination with existing forecasts. In this used to forecast emissions. For example, approach, a set of independent variables this approach was used by European Union (population, GDP per capita, and so on) (EU) member states in the Emissions Trading are defined upon which the emissions System (ETS) Phases 1 and 2 to develop depend. A regression analysis can then be National Allocation Plans (NAPs) for allowance 42 DESIGN allocation. An example of a CGE model used However, data requirements and costs for in this context is the MARKAL/TIMES model 6. this approach may be high, and the model is Advantages of this approach are that it can generally applied at the national level, not at accurately predict future emission patterns. the city- level. Figure 2.3: Overview of Available Methodologies for Emission Baseline Projections in an Urban Context Non-modelling approaches Modelling approaches Simple Complex Use of exisiting Accounting CGE Linear projections forecasts framework modelling / of growth rates modelling Optimisation Simple linear Use of Index CURB existing Regression LEAP TIMES/ emissions decomposition analysis MARKAL projection forecasts analysis Practicability of approach Data Low Low Low Medium Medium Medium High requirements Costs Low Medium Low Low Medium High High Ease of use Simple Simple Medium Medium Medium Medium Complex Extent approach is representative Accuracy Low Medium Medium Medium Medium High High Pro-rate of Both City-level Both Both Both City-level Both city specific Accuracy City-wide Sectoral Sectoral Sectoral Sectoral Established Not for Not for No Yes No Yes Yes methodology forecast forecast 43 Step 4: Set a crediting baseline Step 4a: Consider future alignment with the NDC A crediting baseline should be set for a city carbon crediting program, which If alignment with the NDC is to be would be more ambitious than the BAU considered at some point in the future, it projection. It should be aligned with city may useful to consider alignment options carbon mitigation targets. and mechanisms which may be required for such future alignment. Once the BAU scenario is defined, the crediting baseline should be developed in alignment with The non-NDC aligned program does not city carbon mitigation targets. It is important to necessarily need to account for NDC targets. set the baseline for crediting in a conservative However, at some point in the future, when NDC way, that is, lower than expected. To ensure planning is more developed, the city might want environmental integrity, a too ambitious baseline to consider linking its crediting program to the can risk disincentivizing investment. Therefore, NDC. Similar to the discussion in Step 4 regarding it is important that the crediting baseline is set the NDC-aligned program, to enable the link of an based on rigorous research into the expected costs urban carbon crediting program to the national and potential for emission reductions in a city. NDC commitment, it would be necessary to As indicated in Box 2.5, cities can use the CURB ensure that a city does not overcommit to selling tool to evaluate impacts, feasibility and costs of generated credits internationally. various mitigation activities. Alternatively, any of the other methodologies presented in may also If the baseline is established above the NDC be used to assess the impact of various emission emission reduction pathway (for example, the BAU reduction activities. These can include the impact scenario), there may be a chance of over-transfer of policies, technological improvements and others (see Figure 2.4)7. This is because when credits on the overall emission profile. As described in are transferred from a host to a receiving country, the NDC aligned approach, a crediting baseline adjustments are required to those country’s independently tailored to each sector can improve determined emissions in order to avoid double the environmental integrity of the scheme by setting counting of emissions savings. It also helps to a conservative baseline in line with relevant sectoral ensure that the benefits of the mitigation that conditions. However, credits will still be issued on a generated the credits are accrued to the receiving city-wide level. country and not the host country. In practice this means an upward adjustment in the host country’s emissions by the crediting amount, and a decrease of the receiving country’s emissions by the same amount. This adjustment mechanism raises the prospect that the quantity of credits transferred is sufficiently high that after the necessary emission adjustments are made, the host country fails to meet its NDC targets. There are several possible ways in which that could be dealt with, and the options include: 44 DESIGN 1| 5| Accept the risk of failure to meet the NDC Crediting against a conservative baseline target Should a baseline for crediting programs be This would be politically very difficult. If established in line with or below the NDC there is no planning by the city or country for emission pathway, as illustrated in Figure 2.4, mechanisms to prevent this from happening, then all credited amounts would be eligible it would be seen as a significant shortcoming. for international transfer. As such, there will Therefore, this option will not be discussed be no need for emissions withholding. This any further. would increase investment certainty. This is the method discussed in the previous chapter where the baseline is set in line with 2| Unconstrained crediting with purchasing of ITMOs of third parties or below the NDC targets, which have been appropriated for cities. In this option, unconstrained crediting against an ex-ante baseline is permitted. If that leads to more crediting than is desired, the onus falls to the host country government 6| Withhold a proportion of credits to avoid over-transfer For this purpose, the emission reductions to acquire corresponding ITMOs from third necessary for meeting the NDC commitment parties in order to bridge any gap. The will need to be separated and withheld after revenues received for the credit transfers every crediting cycle. This can be done either could be used to fund such purchases. based on: (a) a fixed ex-ante defined share of The approach requires the ITMO market to emissions being withheld, or (b) an ex-post be sufficiently liquid so that such a strategy calculation of the withheld credits. These two is practical. options have similar implications in terms of environmental integrity and economic 3| Unconstrained crediting with additional domestic mitigation action signalling. In this regard, the ex-ante approach increases the investment certainty In this option, again, unconstrained crediting against an ex-ante baseline is permitted. However, if that is foreseen to lead to more at the expense of environmental integrity. The ex-post approach effects the program in the opposite way. A combination of these crediting than is desired. As such, the host approaches (c) applies ex-ante adjustments country should initiate additional mitigation at the beginning of the crediting program, action outside the scope of the crediting with an ex-post correction closer to the end instrument in order to bridge the gap. of the crediting period. 4| Unconstrained crediting with re- adjustment of the baseline for each crediting period In order to choose a suitable approach from the six options, it is important to determine the cause of the over-transfer, which can be either: In this option, there is unconstrained crediting against dynamic ex-ante baselines. However, the risk of over-crediting is • Over-crediting, that is, more credits are being sold than initially planned as an appropriate level for the host country to still be able to evaluated and factored into the baseline for achieve its NDC targets; or each subsequent crediting period to avoid over-transfer. This option requires sufficient • As a result of underachievement in emission ability to use future crediting periods to reduction activities elsewhere, that is, the NDC overcome a shortfall. targets in sectors and areas outside of the crediting scheme have not achieved the planned emission reductions to achieve the NDC. 45 Figure 2.4: Baseline Ranges in Relation to NDC Pathways BAU emissions Baseline range (1): Emissions withholding required NDC pathway emissions GHG emissions Baseline range (2): from sources Emissions can be covered by transferred the crediting program Actual emissions Start of the End of the baseline period baseline period Source: PMR (2013). Note: BAU= Business-as-Usual; GHG= greenhouse gases; NDC= Nationally Determined Contribution. For example, in the case of over-transfer is caused • Political risk, referring to the risk faced by a by over-crediting, option 2 would be a suitable county when failing to achieve its commitments approach, as the revenues from the crediting can under the Paris Agreement; be used to purchase ITMOs from other countries. • Market risk, referring to the reliability of the Likewise, option 3 can be used to compensate for existence of sufficient demand for credits; the over-transfer of credits. It could achieve more emission reductions elsewhere. As such, it is only • Financial risk, which includes the cost suitable if the over-transfer is caused by over- consequences of the option to the government crediting. If this is caused by underachievement or the crediting agency; and of emission reductions elsewhere, no further • Technical or feasibility risk, which refers to emission reductions can be expected to be realized whether it is technically possible to carry out the outside of the crediting scheme. Instead, if an option, for example, whether there are enough over-transfer occurs because of underachievement emission reduction possibilities available for in emission reduction activities elsewhere, option credits to be issued — or in the event there 4 would be a more suitable solution because is a possibility to adjust the baseline where the underachievement can be factored into the necessary. crediting baseline at an early stage. Table 2.8 provides an overview of the main risks Likewise, each option can be associated with associated with each of the options to deal with various risks, including: over-transfer and whether they are more suitable to deal with over-transfer caused by over-crediting or as a result of underachievement elsewhere. 46 DESIGN Table 2.8: Assessment of Options to Avoid Over-transfer of Credits Suitability based Option to Avoid Over- Technical/ on Cause of Over- Political Risk Market Risk Financial Risk transfer Feasibility Risk transfer 1. Accepting the risk of Both High Lo N/A w N/A N/A failure to meet NDC target 2. Unconstrained crediting with purchasing of ITMOs Over-crediting Medium High High Low of third parties 3. Unconstrained crediting with additional domestic Over-crediting Low Medium High High mitigation action Under- 4. Unconstrained crediting achievement of with re-adjusting baseline Medium High Medium High emission reduction for each crediting period elsewhere 5. Crediting against a Over-crediting Low Medium High High conservative baseline Under- achievement of 6. Withholding of credits Medium High Medium High emission reduction elsewhere Step 5: Defining the crediting Step 6: Defining discounting approach baseline dynamics Given the high uncertainty and often low Policy makers should determine whether quality of the data typical of city-level a static or dynamic baseline will be used emissions data, a discounting approach and how often it will be revised. This will need to be developed. will require an assessment of a trade- off between investment certainty and environmental integrity This step will be the same as for the NDC aligned program. This step will be the same as for the NDC aligned program. 47 2.4. ADDRESSING NON-METHODOLOGICAL BARRIERS The up-scaling of the urban crediting program This upfront effort would require significant time helps to address some fundamental difficulties and resources that may not be recovered through that the project-based approach was not a specific project but will require governmental able to accommodate. This could include the support, technical assistance from partners, or complexity of urban mitigation policies and the pooled contributions from multiple projects. difficulty of accounting for city-level actions in the form of projects. However, there are also some Urban crediting can also provide financing non-methodological barriers which cannot be for future mitigation actions and policies, addressed with this approach. These barriers which would become more attractive from are related to four key areas. To a certain extent, the perspective of the city administration. these barriers limit cities’ ability to operate carbon To a certain extent, this would address the problem crediting, as well as implement mitigation actions of limited climate financing available to cities. in a broader sense. These barriers primarily refer to However, crediting would not be able to solve the four areas, including: the lower quality of existing barrier of the upfront investment needed to initiate urban MRV systems and lack of capacity to update the crediting activity. Therefore, cities might need city inventories regularly; limited access to finance to seek initial funding from another source. for urban governments; the lack of power, authority and autonomy in relation to some major sources of The issue of limited power, authority and urban emissions; and the capacity and resources autonomy would be hard to solve, and crediting to implement mitigation actions, including programs would only have a minor effect on participation in crediting programs. this barrier. However, should the implementation of a NDC-aligned crediting program be possible, Although the design of the crediting program then the alignment with the national NDC could help cannot address these challenges, the financing align city mitigation plans with national targets and from urban crediting programs could facilitate priorities. If a city is implementing a more ambitious the overcoming of some of these barriers by climate change agenda than the country, then creating a financial incentive. As an example, crediting would not facilitate this situation. if a city has poor quality emissions data, a high discounting rate will be applied to credit its emission The effects of this barrier on urban crediting savings. This means that the city would not take full programs and vice versa are shown in benefit for achieving these savings. This would then Table 2.9 below. create an incentive to invest in improvement of the urban MRV system to increase the financial flow from urban crediting in the future. The same may apply to the city’s capacity and resources. However, it should be noted that the investment from the city level will be required upfront in order to start operating a carbon crediting system, while the payments for generated credits will only be received at a later stage. 48 DESIGN Table 2.9: Non-methodological Barriers to Urban Crediting Impact of Urban Crediting Challenge Impact of Barrier on Urban Crediting on Barrier Poor MRV quality would significantly reduce the ability of cities to prove the Urban crediting would create a achieved carbon savings under the financial incentive to create a Monitoring, Reporting crediting program. more robust MRV system and and Verification (MRV) build up capacity for regular Lack of capacity to develop inventories inventory preparation and could regularly can undermine cities’ ability to provide financing for it. participate in the crediting mechanism. The lack of upfront investment to establish Urban carbon crediting can a crediting program and implementing become a source of finance for the initial mitigation policies would have a cities. To certain extent, it would Finance negative impact on the city’s ability to take solve the issue of limitations in advantage of financing available through the availability of climate/carbon urban crediting. financing. Limited decision-making power would Urban crediting programs would restrict cities from implementing certain not directly address this barrier. mitigation policies which require the Power, authority and However, they can highlight the national support. However, cities should autonomy need for the alignment of the city still be able to implement a large number and national policies as part of of mitigation measures, which are within the NDC. their control. Through urban carbon crediting, Urban crediting will require additional cities can receive the financing resources from the city government. The necessary to increase their Capacity and resources lack of capacity to implement an urban capacity and resources to program may significantly delay the manage and administer the implementation of the program. crediting program, as well as implement mitigation policies. Source: Developed by Ricardo Energy and Environment 2.4.1. Carbon Leakage of emissions). Regarding the urban crediting Considerations mechanism, carbon leakage should be considered Carbon leakage is the displacement of economic from the perspective of carbon pricing and activities and/ or changes in investment patterns incentivized sequestration activities. that directly or indirectly cause GHG emission displacement from a jurisdiction with GHG 1| Carbon pricing-induced carbon leakage constraints to another jurisdiction with no or The risk of carbon price-induced leakage typically less GHG constraints (CEPS 2013). There are occurs in regions with high GHG constraints multiple definitions of carbon leakage and its (that is, high carbon prices), resulting in certain scope. Therefore, it is important to keep in mind production or investment activities becoming more that in order to inform the development of well- profitable outside of the carbon priced region. balanced climate change policies, carbon leakage This would typically occur with carbon pricing has to focus on the relocation of production instruments, such as carbon taxes or emission (that is, actual sources of emissions), as well as trading schemes, among others. These can result investment displacement (that is, future sources in additional costs to GHG-emitting businesses, 49 unless special provisions for businesses at risk of it may take place in cities. To address this concern, carbon leakage are in place. the scope of land protection and sequestration activities within a particular city should be A voluntary crediting mechanism at the city level considered on an annual basis to assess the scale does not create a carbon price by introducing of potential leakage impact. carbon costs to emitters; rather, it provides financial benefits for those entities that manage If the share of agriculture, forestry and other land to reduce their carbon impact. This results in low use (AFOLU) emissions within the city boundary is emission activities becoming more financially significant and leakage concern appears relevant, attractive without penalizing carbon-intensive the carbon leakage situation can be reviewed prior businesses. Therefore, urban crediting should not to granting credits. Where necessary, the sectoral trigger carbon price-related carbon leakage. discounting factor can be adjusted to reflect this. If a city decides to implement additional policies The logic of this approach is based on the net to incentivize emission reduction within the carbon sequestration methodology. It takes into carbon crediting scope to earn more credits, consideration the carbon emissions sequestered by these additional policies may have an effect the project and any emissions resulting from land on carbon leakage (depending on whether the use intensification elsewhere, which may occur as costs borne by businesses in relation to their a result of project implementation. The discounting emissions exceed any benefits they receive from factor adjustment can be based on expert the value of credits earned). This will also apply judgement. Closer project evaluation can if revenue-raising mechanisms such as carbon be considered for the largest sequestration taxes are implemented to fund the urban crediting projects if their impact is expected to be significant. mechanism (that is, to raise revenue for the Similar to the net carbon sequestration approach government to purchase the generated credits). applied by the United Kingdom’s Woodland Carbon Code (Forestry Commission 2018), the application The implementation of these policies is not a of a sensitivity threshold is recommended. If requirement for the urban crediting mechanism. the project-related emission increase is below Therefore, any carbon leakage risks should not be 5 percent of the sequestered emissions, then viewed as a direct consequence of urban crediting. the risk of carbon leakage can be considered negligible. Thus, it should not trigger adjustment 2 | Sequestration activities of the sectoral discounting factor. Apart from the carbon pricing aspects, the carbon leakage concerns typically associated with carbon Since the share of AFOLU emissions in cities is crediting are related to situations where a crediting typically very low, this type of carbon leakage is mechanism can incentivize sequestration activities considered of minimal concern. It is not expected in one region (for example, a forest or wetland to have a significant impact. protection), which may then deflect pressure to another area (IPCC 2001). This could result in land use changes outside the city boundary that increase emissions. This consideration is important because it could result in intra-country emission displacement, which would ultimately have an impact on the national emission performance. While sequestration activities and protection of certain areas is less relevant in the urban context, 50 DESIGN 2.5. SUMMARY When implementing its carbon crediting program, implementation of the city level crediting program. a city may decide whether to link it to the national For this reason, cities may decide to implement a NDC target. This choice will largely depend on non-NDC aligned crediting program. Cities may the stage of development of NDC targets, as well then decide whether to eventually align it with the as national NDC mitigation planning and support national NDC. from the national government. The NDC alignment may bring benefits from a variety of perspectives. The steps necessary for the development of NDC- However, given that it would require detailed target aligned and non-NDC aligned crediting programs and performance tracking alignment, it may delay are presented in Figure 2.5. Figure 2.5: Urban Carbon Crediting Program: Development Steps NDC aligned carbon crediting programs Non-NDC aligned carbon crediting programs Step 1 NDC TARGET METRIC ALIGNMENT Step 2 ESTABLISHING A NDC PATHWAY Step 3 Step 3 DEFINING A CITY NDC DEFINING A CITY BAU PATHWAY TRAJECTORY Step 4 Step 4a Step 4 SETTING A CREDITING CONSIDERING FUTURE SETTING A CREDITING BASELINE ALIGNMENT WITH NDC BASELINE Step 5 DEFINING THE CREDITING BASELINE DYNAMICS Step 6 ESTABLISHING A DISCOUNTING APPROACH Source: Developed by Ricardo Energy & Environment. Note: NDC= Nationally Determined Contribution. 51 Investment Certainty Ease of Implementation Environmental Integrity Although some steps will be more straightforward than others, a wide variety of options are available to develop the crediting program. These are based on the ways in which baselines are set and the discounting factors are applied. The choice of options will need to be based on balancing priorities pertaining to investment certainty, environmental integrity and the ease of implementation. Figure 2.6 provides an overview of how decision-makers prioritize design options for urban crediting. The figure highlights the trade-offs between options. These will be based on the priorities of the decision-maker regarding the purpose of using the generated credits, as well as the preference of prioritizing investor certainty or additionality. It is important to note that the options presented here are not exhaustive or mutually exclusive. One can imagine using some elements of one option combined with elements of another. For example, a static ex-ante baseline approach might be used in combination with a dynamic ex-post discounting. However, for the purpose of illustrating the main trade-offs at play between priorities, a limited number of distinct options have been illustrated here. 52 DESIGN Figure 2.6: Overview of Steps to Establish an Urban Carbon Crediting Scheme (with a number of possible options) NDC-aligned carbon Non-NDC aligned Step 1 crediting programs carbon crediting NDC TARGET METRIC programs ALIGNMENT Step 2 Source: Developed by Ricardo Energy and Environment ESTABLISHING A NDC Note: The list of options demonstrated in this figure is not exhaustive. PATHWAY Step 3 Step 3 DEFINING A CITY NDC DEFINING A CITY BAU PATHWAY TRAJECTORY Step 4a Step 4 CONSIDERING FUTURE Step 4 SETTING A CREDITING ALIGNMENT WITH NDC SETTING A CREDITING BASELINE BASELINE Withholding portion Attracting finance Baseline set Baseline set in of credits when with no NDC to ensure line with or below baseline is above compliance environmental NDC pathway NDC pathway for buyer integrity Choice for baseline Dynamic ex-post Dynamic ex-ante Static ex-ante dynamics baseline baseline baseline Choice for address uncertainty address uncertainty address uncertainty discount rates to discount rates to dicounting Combination of discounting to Retrospective Pre-defined approach OPTION 1 OPTION 2a OPTION 2b OPTION 2c OPTION 3 OPTION 4a OPTION 4b OPTION 4c Baseline Static Dynamic Dynamic Credits for Baseline Baseline Baseline below ex-ante ex-ante ex-post climate with pre- with retro- with either (un)- baseline for baseline for baseline for finance only defined spective combined conditional withholding withholding withholding (no transfer discounting discounting discounting NDC target credits credits credits abatement) approaches Investor certainty Environmental integrity Ease of implementation 53 3 measure City GHG Inventories: A Tool To Measure Urban Mitigation Progress 54 MEASURE 3.1. CURRENT SITUATION AND CHALLENGES Increasingly, cities around the world are looking greater consistency in GHG accounting; it is to take action on climate change, with compiling now considered the ‘global standard’ for city and reporting their GHG emissions inventory GHG emissions reporting. The GPC “offers a seen as a critical first step. An inventory enables robust and clear framework that builds on existing cities to understand the emissions contribution of methodologies for calculating and reporting city- different activities within the community. It is an wide GHG emissions” (World Resources Institute economic, city-wide accounting of all emissions 2014). It aligns with the Intergovernmental Panel generated by or as a result of city activities. When on Climate Change 2006 Guidelines for National undertaken regularly, it allows cities to understand Inventory Compilers (IPCC 2006 Guidelines). In emissions trends, including how these change over addition, it integrates the concept of emission time in response to various activities. Therefore, it ‘scopes’ from the GHG Protocol Corporate is considered the key tool for identifying areas for Reporting Standard (World Resources Institute mitigation action, tracking progress and reporting and World Business Council for Sustainable against targets. Development 2004), as well as best practices from other city GHG accounting standards. A city-level GHG inventory can become the main tool to measure the progress of cities’ Recently, the Global Covenant of Mayors for mitigation policies and actions, as required Climate and Energy, published their common by the urban crediting program. Given the reporting framework to be used under that importance of accurate, reliable and consistent initiative (Global Covenant of Mayors 2018). This performance measurement required for urban is based on the GPC and the Emission Inventory crediting and its direct link to climate finance flows, Guidance used by the European Covenant of it is crucial to establish an inventory methodology Mayors to form a new emissions accounting and and related procedures capable of accommodating reporting standard. However, this standard is highly a crediting mechanism. aligned with the GPC with no material differences between them, particularly in terms of reporting Multiple methods and standards for estimating and categorization of activities, transparency, and and reporting greenhouse gas emissions have calculation methods, which are also aligned with been used previously, including the International the IPCC 2006 Guidelines. Local Government GHG Emissions Analysis Protocol (IEAP), the International Standard for For the purposes of the analysis presented in Determining GHG Emissions for Cities (ISDGC), this report, the GPC is used as the main city the Baseline Emissions Inventory/Monitoring GHG inventory reporting standard, given its Emissions Inventory methodology (BEI/MEI), the established usage and widespread adoption. U.S. Community Protocol for Accounting and Reporting of GHG Emissions and the GHG Protocol 3.1.1. Key Features of City GHG Corporate Standard. However, many of these were Inventory Methodology Relevant superseded in 2014 by the publication of the Global to Urban Crediting Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC). Several key features of a city GHG inventory methodology are essential for best practice. The GPC was developed to allow for more Most are covered by the GPC, and can be credible and meaningful reporting, as well as categorized into: 55 • Boundary and emission source definitions: including the geographic boundary, emission BOX 3.1: GPC SCOPES DEFINITION ‘scopes’, and the different reporting frameworks. • Sectoral definitions: including the sectoral • Scope 1 emissions: Sources that lie directly classifications and inclusion under different within the boundary of the city, for example, emissions from fuel combustion in buildings frameworks, as well as alignment between and vehicles, livestock, waste disposal reporting standards. facilities, or industrial processes or products. Under the ‘territorial’ framework, this also Boundary and emission source includes any energy industries supplying definitions: power to the grid, as well as imported waste. Geographic boundary (Figure 3.1) The geographic boundary is simply the perimeter • Scope 2 emissions: Indirect emissions from the city chooses to define for the inventory. consumption of energy from grid-supplied There is no set guideline on where to draw this sources, typically electricity. Although the electricity is consumed within the city, it is boundary, and it varies considerably by city. For usually generated outside of the boundary. example, Sydney and Melbourne, Australia use Emissions of grid-supplied energy are the central business district (the jurisdiction of the reallocated to end users —and not to the point city government), with resulting high emissions of of generation. (Figure 3.1) commercial buildings together and low population • Scope 3 emissions: Sources outside of the — giving rise to very high per capita emissions. city boundary that occur as a result of city However, cities such as Hanoi, Vietnam report activities. For example, transportation that geographic boundaries consistent with the crosses the boundary, international shipping Province boundary, which includes large areas and aviation, or the disposal of the city’s waste outside the boundary. (Figure 3.1) of agricultural land and rural settlements. The boundary chosen largely aligns with administrative Source: GPC areas, but these do not often align with the physical urban area. This creates issues for strategic planning where actions cross boundaries national inventory, which is required to cover a (for example, a regional rail network or wider calendar year. However, some cities (such as metro area transport connections), as well as for Melbourne and Sydney) use a financial year. This boundaries of actions relevant to crediting. will need to be aligned either through an adjustment of data or an application of assumptions to scale to The use of administrative boundaries supports the calendar year. better aggregation and avoids the issue of double-counting. Sources are solely within those Emission sources and scopes boundaries; as such, this represents the preferred The GPC categorizes emissions by source (See approach. Box 3.1 GPC Scope Definitions), following a classification similar to the IPCC 2006 Guidelines8, Temporal boundary plus categorization by Scope which reflects the The duration covered by an inventory is fixed location of the emissions. (one year); however, there is flexibility over which 12-month period this can cover. It can The GPC’s clear demarcation of sources and be the calendar year, the financial year or another boundaries through the use of scopes (as seen period that is relevant and useful to the city. The in Figure 3.1) and reporting frameworks helps to crucial consideration in this context is ensuring reduce the risk of attributing the same emissions that the period chosen is aligned with that of the to more than one city, that is, ‘double-counting’. 56 MEASURE Figure 3.1: GPC Scope Boundaries Source: GPC SCOPE 1 SCOPE 3 In-boundary waste Out-of-boundary & wastewater waste & wastewater Other indirect Agriculture, emissions forestry & other land use SCOPE 2 Stationary fuel combustion Transmission & distribution Grid-supplied energy Industrial processes & In-boundary product use transportation Out-of-boundary transportation Inventory boundary Geographic city boundary Grid supplied energy from a (including scopes 1, 2 and 3) (including scope 1) regional grid (scope 2) Reporting Framework The “territorial” framework includes only Scope 1 The “city-induced” framework uses the same scope emissions. It requires the separate reporting distinctions, but it encompasses all sources and of emissions from sources not allocated to the scopes under two additional, distinct reporting city, but that occur in-boundary, including power levels. These are: generation to the grid, and waste disposal from • BASIC: Simpler reporting framework, covering outside the city. sources that are found in most cities (stationary energy, transport, waste). It includes only Scope One of the primary concerns when considering a 1 and Scope 29. BASIC is generally a more crediting mechanism is to avoid double counting. accessible methodologically. Regarding data, it The territorial approach of reporting only in- represents the ‘minimum’ emission reporting a boundary emissions allows for aggregation across city should undertake. several cities, which leads to double-counting. This is generally avoided through the strict use of • BASIC+: More comprehensive reporting, aimed geographic boundaries to define the sources of at covering all emission sources and scopes emissions. It is a simple accounting method, but it (including IPPU and AFOLU sectors). This comes creates significant challenges for the principle of with additional data complexity, boundary and completeness. Furthermore, it does not align well methodology considerations due to increased with the nature of the city, its economy, services, coverage of sources, as well as the inclusion of demands and associated mitigation actions. Scope 3 sources such as transboundary transport. 57 In order to gain a more complete picture of emissions generated from a city’s activities. emissions associated with all city activities, This aligns better with strategic policy making in the city-induced framework is preferred.This terms of accounting for the activities happening can be aggregated to a national total, but it will in the city. However, this can become complex require a combination of methodologies and careful from an accounting perspective, especially when categorization of emissions by scope. Therefore, there are a lot of transboundary movements. cities must ensure that they have transparently and It is necessary to draw a clear distinction and thoroughly reported the emissions. Both reporting categorization of sources within the city-induced frameworks are required under the GPC, and there inventory that are appropriate for an urban crediting is a clear distinction between the two reported approach. Furthermore, a city must decide within totals. For example, Scope 1 territorial sources this framework whether to report a BASIC or are excluded from the total in the city-induced BASIC+ inventory. Therefore, the city must also framework, so double counting can be avoided. decide whether the IPPU and AFOLU sectors (see below), plus Scope 3 emissions, are included. A The critical difference between the frameworks BASIC+ inventory with a large number of Scope is whether emissions are attributed to the point 3 sources will necessarily introduce additional of production or consumption. complexities for a crediting approach where • For the city-induced framework, “emissions the boundaries become more fluid and double- from grid-supplied energy are calculated at the counting a greater risk. point of energy consumption, and emissions from waste at the point of waste generation”. Sectoral definitions Cities must report on a variety of sources within • For the territorial approach under the scope different sectors, the completeness of which framework, “emissions from grid-supplied varies depending on the reporting framework. energy are calculated at the point of energy In addition, the emissions sources included in the generation, and emissions from waste at the different approaches may vary. They may also point of waste disposed.” (GPC) differ from the standard categorization of national emission sources stipulated by the IPCC guidelines The choice of reporting framework will affect (IPCC 2006). The full list of sectors and sub-sectors the crediting approach. The territorial framework as defined in the GPC is provided in Appendix 1. is intended to support alignment with the national The alignment with the IPCC 2006 Guidelines is level, but it does not cover key city activities, such provided in Appendix 2. as electricity consumption. Rather, it focuses solely on what is emitted within the boundary. This means The GPC includes 28 emissions categories. This is some cities would be unfairly penalized where a significantly lower number compared to the 2006 power stations are located within its boundary, IPCC Guidelines on Greenhouse Gas Emission or more commonly, the city would be responsible Inventories. The IPCC Guidelines are used for the for fewer emissions where the point of generation national GHG inventory compilation and define 70 is not actually present in the city. Transboundary categories of emissions. However, this is largely transport and exported waste are also excluded due to a requirement to differentiate emission and can be absolved of responsibility. Even if sources in greater detail in national approaches, power generation or other energy industries are including aggregated sub-sectors in the GPC10. operating within the boundary, the city often has This is because the GPC was based on the IPCC limited control over this activity. 2006 Guidelines, but the simplified approach makes the standard more accessible for a larger The city-induced approach is much more number of cities. If cities report their inventories comprehensive and aims to capture the 58 MEASURE accurately and transparently, it is possible to map • Waste (BASIC). Includes emissions generated emission sources between the GPC and IPCC and, through solid waste disposal (landfill), and theoretically, scale up and down between cities and biological treatment (anaerobic digestion and national inventories (for Scope 1 sources). composting), from incineration and open burning, as well as from wastewater treatment. If the There is a great deal of simplification and treatment occurs within the city, the emissions aggregation, but there are also some key points are accounted for under Scope 1. However, if the of disaggregation of city-relevant data points. waste is treated outside the city boundary, these Transportation, for example, is separated from other emissions are accounted for under Scope 3. combustive elements of energy included in stationary The GPC and IPCC sectors are aligned although energy. Also, residential buildings are separated from the IPCC 2006 Guidelines and do not recognize commercial and institutional buildings; this distinction methods to estimate ‘potential’ emissions (for is more important to understand at the city-level. example, methane commitment) which the GPC includes, and many cities use for simplicity. Further discussion of the nature of the GPC • Industrial Processes and Product Use (IPPU) sectors and alignment with the IPCC 2006 (BASIC+). Includes all non-energy related Guidelines is found in Appendix 1, but some key industrial activities and product uses that points are as follows: generate emissions which do not originate from • Stationary energy (BASIC). Includes emissions fuel combustion. These are all Scope 1, as Scope from the combustion of fuels in buildings 3 emissions from IPPU are not yet covered in the (Scope 1), fugitive emissions (Scope 1), the GPC. This sector is optional, reported only under consumption of grid-supplied electricity, steam, a BASIC+ inventory. However, it is encouraged heating or cooling (Scope 2), and emissions from where there are significant industries. GPC and the transmission and distribution of electricity IPCC sectors are aligned. (Scope 3). This sector is broadly similar to • Agriculture, Forestry and Other Land Use the national level energy sector in the IPCC (AFOLU) (BASIC+). Includes emissions from Guidelines, specifically, the sub-sectors for fuel land-use changes altering the composition combustion of 1A1 energy industries (GPC I.4.1 of the soil, for example, from the digestive and I.x.2 where allocated to end users), 1A2 processes and manure of livestock, and nutrient manufacturing industries and construction (GPC management. If these activities occur within I.3.1) and 1A4 other sectors (GPC I.1.1 & I.2.1). the city boundary, they are accounted for under • Transportation (BASIC). Includes emissions Scope 1. There are no Scope 3 emissions. As from vehicles that produce GHG emissions with IPPU, this sector is optional, and the GPC directly by combusting fuel (Scope 1 if from and IPCC 2006 sectors are aligned11. travel occurring within the city boundary, Scope 3 if from transboundary journeys) or indirectly Inventory base year by consuming grid-supplied energy (Scope A base year is used to assess progress over 2, Scope 3 for transmission and distribution time, to set a climate target, or as a starting losses), It is often one of the largest contributors point for projections. Cities are encouraged to to a city’s GHG emissions. At the city level, produce an inventory for their ‘base year’ and for emissions from transportation are reported as subsequent years thereafter. There is no prescription a specific sector (II), separate from stationary of a base year in the GPC, but most international energy (I). However, both are combined under reporting initiatives request that the most recent ‘Energy’ (1A) in national inventories. Emissions inventory submitted by a city be no more than 4 are classified by mode including road, rail, years prior to the reporting year (for example, in waterborne, aviation and off-road. 59 2019, any newly compiled and submitted inventory There are implications for implementing a crediting should be for a year no older than 2015). approach where alignment of base years is required, or where multiple years of inventory data As many cities are relatively new to the are available but are not consistent. Additionally, inventory reporting process, they must compile there are implications in cases where targets have and report an inventory for 2015 or later. This been developed from one year of inventory data applies to those countries whose current inventory without regard to previous or subsequent years. is older than 2015, or to those countries that do not have any existing inventory. This is part of what 3.1.2. Calculation Methods countries must do to meet the requirements of reporting initiatives, such as the Global Covenant The methods a city uses to calculate its GHG of Mayors for Climate and Energy. Cities can report emissions depend on the types of sources, emissions from a base year older than 4 years the Scope, the availability of data to undertake as long as their current inventory is more recent. different kinds of calculations, and the capacity There is no mandatory base year for undertaking of the inventory compiler(s). The methods chosen emission projections and developing an action will have a significant impact on how simple it is to plan. However, for C40 cities producing a ‘Deadline integrate the inventory with a crediting mechanism, 2020’ compatible action plan, the inventory must and the quality of the data will impact the results. be no older than 4 years from the date of the plan An inventory should aim to be as accurate as submission (in this case 2020, so it would be 2016). possible, but this accuracy has added importance when the data will be used for crediting. The Many more advanced cities have undertaken method of calculation chosen significantly affects emissions modelling based on older years; at the accuracy of an inventory. This section will present, the concept of ‘re-baselining’ is not well explore some of the methods used for compiling an developed and there is little available guidance. inventory and the implications for integrating that At this point, cities can largely choose whichever data into a crediting mechanism. Typical methods method and base year are most relevant and are different in each sector/sub-sector. Table 3.1 appropriate for them. It is recommended that concentrates on the sectors included in a BASIC inventories be updated annually to ensure the GPC inventory, as this is the reporting level that highest degree of accuracy and usefulness. most cities will use. Updating it annually is highly recommended, but it is not required. In this context, many cities are The basic equation for calculating GHG emissions implementing updates to align with the increased is to multiply activity data by an emission factor, as focus and move toward climate action planning. shown in Figure 3.2. Figure 3.2: Basic Equation for Calculating GHG Emissions ACTIVITY DATA A quantitative measure of a EMISSION FACTOR level if activity that results in GHG emissions per EMISSIONS GHG emissions taking place unit of activity during a given period of time 60 MEASURE Table 3.1: Typical Methods, Data Sources and Relevance for Urban Crediting Sector/Sub- Relevance to Crediting Typical Methods Used Typical Data Source sectors Approaches Activity data * Emission factor Activity data: Billing data from For urban crediting, detailed utility companies; surveys data about the consumption The best method is to use city-specific of energy users; scaled-up of each relevant fuel by all consumption data disaggregated by use and partial local statistics; and end-users is required, as fuel type, along with emission factors (EFs). scaled-down regional/ national well as city-specific emission The EFs would ideally be obtained from local or fuel consumption data from factors for each fuel. When national fuel suppliers (typically found in national national statistics. considering alignment with inventory reports). However, this data is often the national GHGI, the use difficult to obtain. Cities commonly use fuel Emission factors: national of emission factors that are sales data or scale their national energy balance. EFs from utility companies, more specific than the national Both of these approaches lack accuracy. Firstly, fuel suppliers or national level (for example, the city or sales do not mean the fuel is being used. statistics. International default regional level) may be complex. Secondly, the accuracy of scaling national data fuel EFs from IPCC Guidelines However, this is not a common is poor. In terms of emission factors, for a city- or the International Energy approach among developing scale greenhouse gas inventory (GHGI), it is Agency (IEA). City-specific countries. best to use locally-specific fuel emission factors. data are rarely available. Stationary These may be available from fuel suppliers, but Energy in practice cities rarely have access to better emission factors than country-specific factors. However, even these are often difficult to obtain; as such, cities commonly use international default EFs from the IPCC. City-specific EFs have been more commonly reported for electricity consumption, where cities calculate an ‘implied emission factor’ based on the local supply of power. Where there are multiple suppliers, it differs from the national average. Activity data: Amount of specified fuel type consumed Emission factor: EF of specified fuel type (either default, national or city-specific) 61 Sector/Sub- Relevance to Crediting Typical Methods Used Typical Data Source sectors Approaches Transport The calculation is the same as for stationary Activity data: For urban crediting, one of the energy (that is, Activity data * Emission factor) Top-down – Fuel consumption bottom-up methods would but the type of activity data can be more variable. data. Scale up partial local ideally be used to provide the statistics, scale down most detailed and accurate There are four main methods: (i) fuel sales regional/ national fuel emission estimates. However, approach (top-down); (ii) city-induced activity consumption data from the models and data that these (bottom-up); (iii) geographic/territorial (bottom- National statistics methods require would need up); and (iv) resident activity (bottom-up). Many Bottom-up – Surveys, to be frequently updated to cities (particularly when first starting) use the fuel vehicle registration records capture changes in emissions sales approach, as this is often easer. However, or transport/traffic demand as a result of policies. If a in general, it is not a good metric to use as it model data from local or policy is designed to simply fails to differentiate fuel combusted in or out of national transport/planning/ reduce overall fuel use, the city boundary, or by mode. Emissions should air quality departments to fuels sales method using preferably be based on consumption of the fuels understand the amount of detailed city-specific fuel by the users. Bottom-up methods are usually activity by vehicle/mode type consumption data would reflect more challenging and depend on having a good and/or by fuel. this. However, it is likely that road transport model (or in the case of resident road transport policies will go activity method, a robust and representative Emission factors: beyond this to improve vehicle Road survey). It is generally not recommended to create High quality - Disaggregated standards/efficiency, alter Transport a detailed model just for an inventory. However, by fuel type and technology- mode share, reduce private in working with transport, planning, infrastructure specific. Typically provided vehicle usage, and so on. To and air quality departments, it is recommended by national environmental reflect this, detailed information to use existing data sets to understand transport agencies or research as required by a bottom-up activity. The bottom-up methods all represent institutions. method would be required. ways of obtaining and allocating detailed activity Medium quality - Vehicle and Where modelling takes place, data. technology-specific factors, the alignment with the national for example, United Kingdom GHG inventory needs to be Activity data: Defra emission factors, considered and may be more Top down - Amount of specified type of fuel United States Environmental complicated. In addition, consumed. Protection Agency (EPA) where bottom-up methods are Bottom up - Amount of activity by vehicle/mode factors, and European used, careful delineation of type and/or by fuel. Environment Agency Emission boundaries and scopes may Inventory Guidebook. be needed to avoid potential Emission factor: Low quality - IPCC default double-counting. Additional Fuel Emission Factor - EF of specified fuel type emission factors can be alignment steps may also (either default, national or city-specific) found in Volume 2 Energy; be needed where national Implied Emission Factor an activity/mode and and Chapter 3 Mobile inventories report only total fuel fuel, e.g. per km by vehicle/mode type. Combustion. consumption. Activity data * Emission factor Activity data: Real fuel Use of real fuel/electricity consumption from rail consumption data by transport Non-road transport modes can be estimated operators/port authority/ mode/type will offer the most through real consumption data, scaled data from airlines by fuel type and by appropriate emission estimates national or regional statistics, or from international application (for example, for urban crediting. or regional default information. National/regional transit system, freight); survey data is easier to obtain, but city-specific detailed of rail/ferry/airports for real Careful delineation of scopes data provides better estimates of emissions. fuel consumption and amount and boundaries is needed. of goods/people transported; Non-Road Activity data: Amount of specified type of fuel scales-up partial local activity Transport consumed by transport mode (rail, waterborne, data; and scaled-down aviation, off-road). May include electricity regional/national transit system consumption. fuel consumption based on the share of population /transit Emission factor: EF of specified fuel type (either revenue service miles. default, national or city-specific) for each mode. Emission factors: Same applies as for road transport. 62 MEASURE Sector/Sub- Relevance to Crediting Typical Methods Used Typical Data Source sectors Approaches Methodologies to estimate emissions from waste are more complicated than those for stationary Waste energy and transport; however, lots of tools and guidance are available. There are two methods to quantify methane Activity data: Mass of solid As mentioned, the FOD model (CH4) emissions from landfill. The Methane waste from local statistics is the more detailed of the two Commitment (MC) method assumes landfill or scaled from national level solid waste approaches. This emissions based on the waste disposed in a combined with composition will provide more accurate given year, regardless of when the emissions data. estimates of annual emissions actually occur. The First Order Decay (FOD) than the MC method. Thus, it model assumes that degradable organic carbon Emission factors: Waste will likely be more appropriate (DOC) in waste decays slowly over a few characterization information for urban crediting. However, in decades, during which CH4 and carbon dioxide from local study/survey, terms of likelihood of usage, a (CO2) are released. The FOD method provides national data, or international city is more likely to use the MC more accurate estimates of annual emissions, defaults (to calculate DOC), method due to data availability, but it requires detailed historical waste disposal data from landfill sites, so this must be considered. information (usually for at least 10 years). national information on waste treatment. Default EFs are An important point is that the The IPCC 2006 Guidelines do not recognize the available. IPCC 2006 Guidelines do not Solid waste MC method, but it is preferred by cities. recognize the MC method disposal (presents only methods for Activity data: Mass of solid waste. ‘actual’, for example, FOD, not ‘potential’ emissions). Emission factors: There is no simple EF for Thus, cities must ensure they waste calculations. The equation uses many align methods with national different paraments, such as the Degradable approaches. Organic Carbon (DOC) content calculated from waste composition information, the methane correction factor related to landfill management, the methane in landfill gas, the fraction of methane recovered, and the oxidation factor. All of these values have international or regional defaults, but city-specific information will provide more accurate estimates. Activity data * Emission factor Activity data: Mass of dry or Cities will generally use default wet waste from city waste EFs for both of these sub- The method for calculating emissions from agency or scaled from sectors (biological treatment biological treatment of waste is fairly simple. national statistics. and incineration) due to the The most important information for cities to availability of data. Therefore, understand is the mass of waste treated. Cities Emission factors: Detailed changes in emissions will be Biological most commonly used default EFs, although some study/survey of city/ driven by the mass of weight treatment of cities have access to more detailed information national waste treatment, or treated by each treatment type, waste from studies. international default EFs from as well as the technologies IPCC Guidelines. used. Therefore, to be able to Activity data: Mass of dry or wet waste. capture changes in emissions and for it to be relevant for Emission factors: Amount of GHG produced per urban crediting, these activity unit of waste type treated by specific method. data need to be obtained and collected regularly. 63 Sector/Sub- Relevance to Crediting Typical Methods Used Typical Data Source sectors Approaches Activity data * Emission factor Activity data: Mass of waste Same applies as above. incinerated and total carbon The method for calculating emissions from and fossil carbon content incineration and open burning of waste is once from the facility, city waste again fairly simple. It involves multiplying mass agency or scaled from waste by an emission factor, depending on the national statistics. kind of incineration technology. The difficulty is in obtaining information about the incineration Emission factors: Detailed technology and conditions. Cities generally study/survey of city/national chose to use defaults from the IPCC guidelines. waste treatment process and conditions, or international Incineration Activity data: default EFs from IPCC and open CO2 emissions – Mass of waste incinerated Guidelines. burning at the facility, total carbon content of waste, fraction of carbon in the solid waste that originated from fossil fuels (for example, plastics). Non-CO2 emissions (CH4 and N2O) – Information on technology and conditions during the incineration process. Emission factor: Information on technology and conditions during the incineration process, or IPCC default factors. To quantify the methane emission for wastewater Activity data: Wastewater/ As noted, many cities choose treatment, data that is required is: sewage volumes from water to calculate emissions based • How wastewater and sewage are treated; companies or relevant city on the city population as well • Source of wastewater (for example, domestic department or scaled from the as basic information about or commercial); national level. Alternatively, how the wastewater is treated. • Organic content of wastewater (calculated base calculations on This approach would not from population and BOD population from city or national be very sensitive to policy • Proportion of waste attributed to the city; and statistics. Protein consumption changes, unless the policy • EF calculated from default parameters based data will most likely come from included significant changes on treatment type. national statistics or default to wastewater treatment Wastewater values. technology, which would To quantify the nitrous oxide emission for impact emissions by selecting wastewater treatment, data that is required is: Emission factors: IPCC different default factors. • Mass of nitrogen in discharged effluent Guidelines, unless specific (based on population and protein wastewater treatment consumption); and information is known. • EF for effluent from IPCC Guidelines. Many cities chose to calculate emissions based on the city population, as well as basic information about how the wastewater is treated. 64 MEASURE Models and tools Additional tools such as ClearPath and other Many models and tools are available for inventory-based tools also allow cities to cities to support them in reporting their GHG undertake a projections/scenario exercise. inventory. The most commonly used tool is the ClearPath has some advantages over CURB and City Inventory Reporting and Information System Pathways in that it is easier to use and is cloud- (CIRIS) developed by C40 Cities. This is an Excel- based, meaning collaboration is easier and data based reporting template, which also contains storage is safer. However, ClearPath is more various emission calculators. It is designed to simplistic than CURB and Pathway. ClearPath also capture all required data for a GPC-compliant lacks transparency, in that users enter data, and inventory. CIRIS is free, easy to use, flexible, and the calculations are done behind the scenes. By transparent. It also supports with the reporting and contrast, all calculation steps are visible in CURB visualization of results. In addition, CIRIS supports and Pathways. alignment with the IPCC 2006 Guidelines. The nature of the reporting tool used by cities does 3.1.3. Challenges not fundamentally affect the crediting approach, provided the categorization of sources is consistent There are a number of more general and highly and the inventory meets the quality principles. important challenges regarding the methodology and implementation of GHG inventories. that Recognizing that an inventory is only the are applicable to the development of a crediting starting point, there are increasing numbers approach. These largely relate to obtaining high of tools available to cities to manipulate their quality and appropriate activity data, as well as inventory data, undertake future projections ensuring the accuracy of the overall inventory. and set targets. Inventories can be transferred Without this, inventories cannot be developed or from CIRIS to other tools, such as the Climate used with confidence. Action for Urban sustainability (CURB)12 or the 1.5oC Pathways Tools13. These tools are designed Sourcing appropriate data for developing different emission scenarios for The biggest challenge for cities in adhering cities, thereby helping to support the policy and to the principles of accuracy, completeness, climate action planning process. They are not consistency, transparency and relevance in the inventory tools. Although these tools are flexible compilation of a GHG inventory is the ability to and insightful, they largely rely on highly simplistic source high quality data. data and models, and base BAU emission projections on simple population and/or GDP There are a range of sources from which a city growth. Relying on simplistic data will not provide could gather activity data. Activity data is the the most accurate set of projections/scenarios on record of a behavior that results in the emission which a city can base its policy decisions. However, of GHGs. It can be anything from the amount of cities do have the option to enter very detailed fuel bought or consumed, the distance travelled information on planned or potential mitigation on a mode of transport, to the volume of waste actions to understand the impact of such policies disposed. These sources usually include: on emissions. Tools such as CURB and 1.5oC • Government departments and statistics Pathways are highly useful to cities starting to agencies undertake climate action planning. They are quick - Census data, city tax records and easy to use models that provide enough detail - Country’s national greenhouse gas inventory to inform mitigation action. However, they will report require further analysis for future actions. 65 • International organizations Beyond this, if no data is available, then proxy data - IPCC default assumptions of another equivalent city could be used. - Food and Agriculture Organization (FAO) statistics Data availability and quality Data availability is a serious challenge for cities • Universities, research institutes and non- in less-developed countries; oftentimes, a governmental organizations (NGOs) combination of these data sources is required - Local surveys, project reports to obtain a reasonably accurate inventory. - Scientific and technical articles in In many developing country cities, a lack of environmental books, journals and reports capacity (both human resources, and knowledge/ • Local utilities and service providers understanding) combined with a lack of awareness - Waste contractor collection data about data availability, and poor inter-departmental - Metered consumption data communications or data sharing protocols means • Sector experts/stakeholder groups/ city that the compilation of a GHG inventory, at least government colleagues utilizing ‘expert initially, will rely on many publicly available data judgement’, as well as other assumptions or sources. These could include national statistical internally generated data and information. publications, which are often scaled to the city boundary using simple metrics such as population. Data sources vary widely depending on the source of the emission being reported, Although a city should aim to improve the the capacity of the city, and the culture of quality of its data, following the concept of transparency and data sharing in the city and a ‘data hierarchy’, this is often a significant country. A city would likely first look internally to challenge. The higher up the hierarchy a city’s other departments within the city administration data is, the more it will be able to implement a that might have appropriate data for the given crediting system. sources. The collected data could include population figures, building occupancy data, The ‘data hierarchy’ aligns with the GPC’s data transportation volumes or traffic counts, waste quality classification, and delineates three disposal figures, data on building type and floor categories: high, medium and low. area, energy billing and land cover. • High quality data is city-specific, and it includes real-consumption/ generation data by sub- If city-level data is unavailable, then the city might sector. look to the regional or national government for • Medium quality data could be modelled on data. Such data could be scaled down to the recent activity data using robust assumptions or city level. Research in the public sphere can be recently conducted surveys. extremely helpful in plugging data gaps, including reports from universities or non-governmental • Low quality data could be highly modelled; organizations. However, there are data points that uncertain, incomplete or aggregated; scaled regional or national level administrations may be regional or national data; or proxy data from unable to help with, for example, fuel sales values. similar cities/countries. Thus, the city might have to source this data from fuel providers or utilities. The key principle that cities should follow in assessing their data landscape is to aim Finally, should none of these sources be available, for completeness over accuracy. There are expert opinion can offer a reasonable estimate to techniques to fill data gaps, but if there is no data at work from, and is better than having no data at all. all, then the inventory suffers. This challenging data 66 MEASURE landscape for cities, especially in less-developed the International Council for Local Environmental countries, poses issues for a crediting mechanism. Initiatives (ICLEI). This provides a greater level For instance, even if the data are available, it may be of confidence in the results and ensures the a complex process to integrate into an inventory that inventory is as complete and accurate as possible has likely been developed on a top-down (and based — although there is no consistent review process. on fuel-sales data for example). Also, the quality is dependent on the expertise of the reviewers, and the ability of the city to follow In less-developed cities, these challenges up on any queries. There are also some checks typically become more difficult. They could undertaken by organizations and initiatives that also be exacerbated by factors such as a lack manage, disseminate and report the data, such as of staff capacity to gather, rationalize or even CDP. However, these checks are largely automated collect the necessary data necessary, as and confined to completeness of reporting rather well as by stakeholders who are unwilling to than accuracy or consistency, such as time series share data. Even if the data is available, a lack of checks. This means that for many cities the data uniformity can make it very difficult to integrate the has not undergone any checks for transparency, new data within the existing system. The data might completeness, consistency, comparability or not match the year required, the time-period or the accuracy (TCCCA) or relevance (TCCRA) (TCCCA in boundary. Although this can be overcome through the IPCC quality principles, TCCRA in the GPC).14 extrapolation and manipulation, it can significantly challenge the inventory’s accuracy. Appropriateness of data Generally, the aim of GHG emission Crediting approaches will be most valuable in inventories is to enable mitigation planning. the cities of developing countries, where large- An understanding of where the emissions are scale investments in infrastructure are occurring coming from allows a city to plan and target its — and where effective financing mechanisms interventions. However, an inventory does not allow are most valuable. However, these are the cities for the monitoring of whether mitigation actions that generally have the lowest capacity, data and have had an impact. This is especially the case information critical for inventory compilation. for one compiled using low quality data sources. They also have the most difficult in meeting the transparency and accuracy requirements. For these cities, it will be a case of making the most of what they have and using the structure of the GPC to achieve as complete an inventory as possible. In addition, they will try to ensure alignment with a national level inventory, if it exists. Quality Assurance/Quality Control (QA/QC) and review procedures Many cities implement their own internal checks and controls on data, but routine checking and review are not widely undertaken. Furthermore, the review procedures undertaken for compiled data vary considerably. For instance, some municipal authorities undertake their own QA (or outsource it to consultants) of submitted city data or rely on organizations such as C40 and 67 Furthermore, many inventories are by necessity implementing programs and policies supporting compiled with whatever data might be available. In wider shifts in the way they function. Such shifts many developing cities, the availability of data is are required for achieving deep decarbonization, such a significant challenge that very poor quality such as Transit Oriented Development. However, data and methods are employed. For example, the impacts of these policies cannot be identified in where national household energy data are scaled to a GHG inventory. the city-level using population figures, any change in emissions reported year-on-year is solely due Improve the ability to track the impact of mitigation to changing population figures and/or the national actions in the inventory requires: (i) a dramatic value for household energy. An inventory compiled improvement in the policy-sensitivity of the in this manner is useful only for achieving an inventory (through the use of very specific, detailed initial understanding of activities and their likely activity data linked to specific policies and actions); contributions, as well as an initial prioritization of (ii) detailed modelling studies (which will also have mitigation action and data quality improvements. complex data demands and which can be costly exercises where there are multiple actions and An inventory compiled using city-specific sectors); or (iii) a set of indicators for tracking the data can show that certain emissions have impact of actions alongside higher-level inventory changed, but it cannot show whether the data. The third option is recommended due to the mitigation actions directly caused the change greater simplicity and cost-effectiveness for cities, or whether it was due to other factors. To make as well as the greater likelihood that such indicator that determination, a more thorough monitoring, data will be more easily available (for example, reporting and verification system is needed. In transit ridership through ticket sales, or commuter this context, an inventory is a key component. volumes through traffic surveys). Also, such Greater insight can be gained through tracking data is more suitable for tracking the impact of other indicators, such as the stages of project cross-sectoral programs, such as transit-oriented implementation. development (TOD). Ideally, such indicators will be identified and implemented early in the process of The effect of data disconnected from actions planning and implementing the mitigation action. is exaggerated when national level data is Where data is of lower quality and the effect of scaled to the city level because there is a much mitigation actions cannot be so well demonstrated greater discrepancy between that data and any in the inventory, then discounting approaches can interventions taking place in the city. For most be used to account for the uncertainty. cities, it dramatically decreases the usefulness of an inventory. At the same time, it increases the Timeseries and recalculations need for a more comprehensive MRV system. There is currently no explicit requirement Although there are opportunities to address data for cities to either report time series data gaps, with national data being one example, cities or to recalculate, unless the inventory and/ must work toward improving the quality and local- or city has undergone changes that might specificity of the data used in their calculations in trigger a recalculation, such as the use of a order to identify and track mitigation actions. new methodology/data source or a change in boundary. The lack of routine reporting of Many city mitigation actions are by their time series data often leads cities to have poor very nature systemic, cross sectoral, and consistency between years. Also, cities are not in multi-faceted. From the perspective of tracking the habit of revising the time series when new, better mitigation actions and crediting, it is ‘easier’ at data is available or when revised assumptions or the project level. However, cities are increasingly trends are identified. This means that where there 68 MEASURE are multiple years of data, there may not be a true the national GHG inventory is adjusted and the ‘time series’’. There are also many cases where emissions are lower than in year X. This might have cities vary in the use of their methods in a given serious consequences for the crediting program, year for a sector. As such, they do not recalculate which would need to be considered during the previous inventories because the specific data for development of the carbon crediting program. that new method is only available for the given year. This limits comparability, as well as the ability of the 3.1.4. Summary city to establish trends and track progress against a baseline and target. Carbon crediting in cities using a GHG inventory requires careful consideration of a number of The issue of recalculation is also important to key factors. This is especially the case when it consider when using scaled national or regional is used as a tool to set the baseline, crediting level data. At the national level, recalculations are threshold and track progress — and potentially often performed across the time-series data. This align with the national inventory. Data limitations, must be reflected and replicated in the city-level unclear boundaries, poor application of methods data to avoid having significant inconsistencies and inconsistent approaches would significantly between the two sources. A further complication constrain the ability of the inventory to accurately would be if the time periods are misaligned between demonstrate real progress toward targets and the national and sub-national levels, and then support investor confidence in the reliability of the recalculations are conducted at the national level. data. Alignment of the city inventory to the national This would make performing the same recalculations inventory (and then NDC targets) is a beneficial on the city-level data more difficult. development and the GPC framework is conducive to this, also preventing double-counting. Another issue specifically pertaining to crediting concerns the risk of a scenario where certificates The key issues that cities should consider are are issued in year X (based on alignment with summarized below. They are addressed in the the national GHG inventory), but in year X+1 methodological steps set out in the next section. 69 Key methodological issues City inventory standards and guidance • The GPC was developed to allow for more credible, meaningful and consistent reporting of city-scale GHG emission inventories. It is considered the ‘global standard’. • It largely aligns with the Intergovernmental Panel on Climate Change 2006 Guidelines for National Inventory Compilers (IPCC 2006). Boundaries and scopes • There is no set guideline regarding the geographic boundary, but the use of an administrative boundary supports easier aggregation and helps to avoid double-counting. • The most significant boundary issue is the inclusion of sources and scopes, reflecting the location of emissions in or out of the boundary. As such, it allows for aggregation and integration with the national level GHG inventory. In addition, it reduces the risk of double-counting. Scope 3 sources will require additional alignment to be included in a crediting approach. Reporting framework • The Territorial approach (for in-boundary, Scope 1 emissions) better supports the avoidance of double-counting. However, it does not support an understanding of the city’s emission-generating activities and associated mitigation actions. • The City-induced framework better represents city-level activities and mitigation action areas, but it introduces additional complexity and the risk of double-counting. • The choice of reporting framework will affect the crediting approach. Sources • Cities must report various sources within different sectors, the completeness of which will vary depending on the reporting framework. • Emission sources can be mapped between the GPC and IPCC, as well as, theoretically, scaled-up and scaled-down between cities and national inventories (for Scope 1 sources). Base year • Cities are encouraged to produce an inventory for their ‘base year’ and for subsequent years. • There is no prescription of a base year in the GPC. However, most international reporting initiatives request that the most recent inventory submitted by a city be no more than 4 years prior to the reporting year. • Targets are set either relative to the base year, a BAU projection, or a fixed goal, which can then be translated into absolute emissions. 70 MEASURE Recalculations • There is no requirement for cities to either report time-series data or recalculate, unless the inventory and/or city has undergone changes that might trigger such an action. • Cities may apply recalculations inconsistently, which would not allow policy makers to track progress. Retrospective recalculation may have an impact on a crediting program, whereby the level of credited emission savings would be affected. Methods • Approaches used to calculate emissions depend on the types of sources, the Scope, the availability of data to undertake different kinds of calculation, and the capacity of the inventory compiler(s). • The methods are largely aligned with the IPCC 2006 Guidelines (activity data x emission factor), with some deviation for solid waste methodologies, as well as allocation of emissions in and out of boundary using bottom-up transport methods. Tools • The nature of the reporting tool used by cities does not fundamentally affect the crediting approach, provided the categorization of sources is consistent and the inventory meets the quality principles. • Additional tools such as CURB and 1.5 Pathways have been developed to assist cities in undertaking projections of their inventories. Such tools may be useful in establishing baselines and potential mitigation actions. Activity Data • The biggest challenge for cities is the ability to source high-quality data. Such data would better support the implementation of a crediting system. • Scaled national data is suitable only as an indication of the scale of an emission source. Most cities would not have access to the city-specific data, which would allow them to reflect on the extent of emission reductions achieved in a given city. Reflecting mitigation actions • An inventory compiled with city-specific data can show that certain emissions have changed. However, it cannot show whether the mitigation actions directly caused the change or whether it was due to other factors. • Scaled national data cannot reflect changes at the city level. It can only reflect changes at the level of the data and parameters employed (such as population, national level activity). • Additional indicators are required as part of a broader MRV system in order to associate emission reductions with mitigation actions. • Discounting could be used to account for issues of data quality. 71 3.2. CITY GHG INVENTORY ENHANCEMENT FOR URBAN CREDITING City-level GHG inventories serve as the primary Step A: Define the boundary of the tool for the tracking of city emission reduction GHG inventory and align it with the achievements. Therefore, it is central to both the crediting approach establishment of the carbon crediting program and its alignment with a NDC, whether initially or at a The first step is to define the boundary of later stage. The city-level GHG inventory would the GHG inventory to be used as the tool ideally need to be aligned — to the extent feasible to measure urban mitigation progress, — with the national GHG inventory, particularly with as well as the sectors reported relevant regard to assumptions and methodologies. Where to a crediting approach, including an this is not possible for reasons of data quality, or assessment of any limitations. due to constraints originating from the national level (poor data or granularity of reporting, lack Step A is highly important as it defines the activities of recent national GHG inventory, national targets to be included within the crediting boundary and not quantified, and so on), then cities may wish to against which the city will measure progress. proceed on the basis of non-alignment. As such, These include: they could seek to align in future years once local • Geographic boundary or national capacity and data are improved. • Temporal boundary The following steps are recommended to address • Emission sources and sectors the features and challenges, while utilizing a city • Emission scopes and reporting framework(s) GHG inventory for urban crediting. • Base year for the inventory and time series data. Cities should consider the issues identified in Table 3.2. 72 MEASURE Table 3.2: Boundary Issues and Solutions for Urban Crediting Criteria Solutions to Ensure Alignment with Crediting Approach Geographic If the inventory boundary is larger than the desired crediting area, adjust the data to provide an account for only the area relevant to crediting. This could be done by either removing the data for the excluded additional geographic areas from the account (preferred), or by scaling the data. If the inventory covers an area smaller than the proposed crediting boundary, the inventory must be A geographic expanded. Options include: boundary must be - Undertake additional sourcing of data and calculation of emissions from the new areas to add to the clearly defined, and existing inventory (preferred). an administrative area would be - Scale existing inventory to account for additional geographic areas, ensuring principles of completeness preferred and accuracy (that is, ensuring there are no new sources in the new area that will be missed; that the emissions profile of the new area(s) are consistent with the existing inventory in terms of sources and activities; and that the scaling factors are robust and representative). - Ensure no double-counting occurs and that geographic adjustment is undertaken for the crediting year. Temporal boundary If an inventory year does not align, adjust the data reported to provide an account for the 12-month period A temporal relevant to the crediting year. Where national alignment is desired, the 12 months reported should also align. boundary must This might mean: be done for a - Undertaking additional sourcing of data and calculation of emissions from the new time period to add to 12-month period, existing inventory (preferred). calendar year. This - Scale existing inventory to the new time period using scaling factors that are robust and representative. would be preferred Ensure that any change in sources in the new and excluded periods are accounted for, for example, plant for national closures, activity spikes, new facilities, and so on. alignment - Ensure that no double-counting occurs. Emission sources and sectors / sub-sectors Where BASIC sources are not included in the inventory, but are occurring (‘Not Estimated’), these must be calculated and added. Where they are not present, ‘Not Occurring’, they should be clearly stated and confirmed. Sources that are BASIC+ must be identified as such, and there should be no double-counting between in- These must be boundary and out-of-boundary. clearly defined and should include all Where sources are reported but are not disaggregated by sub-sector (for instance, the use of notation key BASIC sources ‘Included Elsewhere’), action should be taken to either: reported for all - Seek additional data to enable the reporting of activities by sub-sector, for example, by allocation of total sectors occurring fuel to sub-sectors (preferred). in the boundary - Apply scaling factors or assumptions deemed appropriate to apportion non-disaggregated activities to sub-sectors. - Where neither are possible, the data should be noted as low quality and excluded from the crediting boundary. Alternative, a discounting may be applied. Emission scopes included and reporting framework(s) Data must be associated with a Scope in the inventory. This would enable clear delineation of in- and out-of- boundary sources. Scopes must Scopes 1 and 2 must be reported for all BASIC sources. be noted, and Where there is some difficulty in ascertaining a split between Scope 1/3 (for example, the exact proportions emissions reported of waste in-/out-of-boundary, or road transport flows), double-counting must be avoided. Action should be accordingly. Any taken to either: potential double- counting between - Seek additional data to enable the reporting of activities by Scope, for example, initiate further studies or territorial and city- consultations with facility operators, conduct surveys, and so on, to apportion the data between Scopes. induced sources - Apply scaling factors or assumptions deemed appropriate to apportion non-disaggregated activities to should be avoided. Scopes. Scopes 1 and 2 - Report all activities within a Scope and use a notation key to indicate the approach: Where data is allocated are required, and to Scope 3, this should be excluded from the crediting boundary until the data can be improved enough Scope 3 must to include it. Where allocated to Scope 1, the data should be noted as low quality, and excluded from the meet additional crediting boundary. Alternatively, discounting may be applied consistent with the low data quality factor. criteria Note that in the latter case, there is the potential for the baseline to be significantly higher where additional emissions are allocated in-boundary. This comes with greater risks of over-allocating emission savings. 73 Criteria Solutions to Ensure Alignment with Crediting Approach Base year for the inventory and time series data The choice of base year for the inventory is less important for the development of the urban crediting approach. However, several issues are important to note to ensure consistency and transparency, including: - Targets and pathways should be set on the basis of an absolute emission, which will be relative to the base year. The base year itself does not matter so much as the target and the absolute emissions associated with that target. For example, BAU scenario targets should also be translated into absolute targets. - Aligning the base year with the national inventory (that is, ensuring there is at least one year of consistent data between the city and national levels) will help to facilitate greater alignment and reduce uncertainties associated with scaling between years. Where methods are improved, assumptions are adjusted, or disaggregation methods are applied (some examples noted above), it is important to apply to all years in the time series, even if this means recalculating previous years. Although this cannot impact credits already issued, it may impact the BAU and baseline. (This point is further addressed in Step 5 in section 4.2.2.) Scope 1 and 2 sources must be reported by cities as part of their GPC requirements, as well as for the major sources present in cities that must be mitigated. Scope 3 sources provide additional complexity. In particular, there is a potential for double-counting between those sources reported in the Territorial inventory and under a BASIC/ BASIC+ city induced framework. The criteria in Table 3.3 are suggested for inclusion of sources, such as Scope 3 and Territorial Sources in the crediting boundary. The effort level relates to the level of effort by cities in typically reporting each source and meeting the crediting requirements: 74 MEASURE Table 3.3: Suggested Requirements for Boundary of Sources and Scopes Frequency of Effort Requirements for Inclusion in Crediting Source Recommendation reporting by cities1 level Boundary Scope 1 & 2 All cities – sub- Must be included Ensure quality principles are maximized. Stationary Energy sectors vary Scope 1 Fugitive High (gas), Must be included Ensure quality principles are maximized. Low (coal/oil) Energy Generation Low Recommend excluding Important to track and report data, but electricity (Territorial) from crediting boundary supplied to the grid is allocated to end users. Thus, unless locally owned it is not included within city crediting. and controlled Scope 3 High BASIC+, but advise Generic (national) factors for TOD losses. including to maximize Recommended to use. Can be easily aligned and national alignment double counting can be avoided. opportunity by incorporating all emissions associated with electricity supply Scope 1 & 2 All cities Must be included Transport - Road Where possible, by mode Ensure methodology for separating scope; 1/3 Scope 1 & 2 High Must be included is robust. Avoid double-counting electricity for Transport - Rail Scope 2 more important Transport in Stationary. where electric metro/ LRT etc. Scope 3 Transport High Advise including only A significant double-counting risk exists. Highly - Road data and methods of transparent and accurate methods require a clear high quality; emissions differentiation of Scopes. that can be clearly Scope 3 Transport High attributed; and when City should ensure no double-counting with national - Rail policy objective and other city/regional level actions through supports inclusion. clear differentiation of emissions by scope; close May be a relevant coordination of activities and data included in the source where city is crediting threshold; Policy makers should agree implementing cross- on a share of emissions and savings that can be sectoral programs allocated to the city due to cross-cutting/regional (for example, TOD) or projects (for example, two cities may each assume supporting regional low 50 percent of the commuter traffic emissions carbon initiatives that between their cities, and within their crediting enhance connectivity. boundary provided no double-counting occurs with Scope 1 in either city). Scope 1 & 2 Medium Scope 1 must be Ensure methodology for separating scope 1/3 is Transport - (scope 2 low) included. robust. Waterborne Scope 2 rarely reported Ensure source is reported separately and not as IE (where fuel sales used). Scope 3 Transport Medium Suggest excluding as Only include where there is a clear policy rationale - Waterborne outside scope of city for doing so and/or when it is important to national control policy and alignment. Accurate data and attribution methods are required. Scope 1 & 2 Low Suggest excluding Where source is significant, that is, when local Transport - Aviation aviation is key to economy, it should be included. In most instances, the source is small or not reported. Accurate data will be required. Scope 3 Transport High Suggest excluding as Only include where there is a clear policy rationale - Aviation outside scope of city for doing so and/or important to national policy and control alignment. Accurate data and attribution methods are required. 75 Frequency of Effort Requirements for Inclusion in Crediting Source Recommendation reporting by cities1 level Boundary Scope 1 & 2 High Low priority as data If data reported as ‘IE’ and cannot be disaggregated, Transport – Off- (Scope 2 low) usually poor and scaled then sector is notionally included. Accurate data road or ‘IE’ in road transport and attribution methods are required to fully include sector. Scope 1 Waste High Must be included Ensure methodology for separating Scopes 1/3 (all disposal routes) is robust. This is usually simpler for waste, as it is based on the location of facilities. Scope 3 waste (all High Recommend including High double-counting risk, but potential to disposal routes) as part of city-induced mitigate this easily. framework but clear differentiation of scopes Important source for cities to take some and communication responsibility for — even if the facility is located needed. elsewhere. Accurate data and attribution methods are required to fully include the sector. Cities may wish to decide whether to include Scope 3 Waste, or Scope 1 Territorial on the basis of volumes, management ability/influence, and mitigation actions. Ideally, the city should ensure no double-counting with national and other city/regional level actions by clearly differentiating waste volumes through close coordination of activities and data included in the crediting threshold. For example, there could be an agreement on a share of emissions and savings that can be allocated to the city. Scope 1 Territorial Low Exclude where there is Accurate data and attribution methods required to Waste no national alignment. fully include sector and ensure no double-counting. Consider including where national alignment Transparency required about the inclusion of any occurs and/or where it imported waste within the crediting threshold. is a significant mitigation priority. Cities may wish to decide whether to include Scope 3 Waste, or Scope 1 Territorial, or both on the basis of volumes, management ability/influence, and mitigation actions. IPPU (scope 1 only) Low Include where a Accurate data and attribution methods are required significant source, and to fully include sector. AFOLU (scope 1 Low when cities have the only) ability to track and report. Other scope 3 Low Exclude – methods not n/a compatible. Source: Ricardo Energy and Environment Note: 1 This is judged on author’s perception and experience, not on systematic review of reported emissions by cities. In addition, many cities report only BASIC inventory, so this issue does not likely have full coverage at present. 76 MEASURE Step B: Assess the quality of the The assessment of emission factor data quality available data and methods employed should also be included and should be factored into decisions about overall data quality, for in calculating the inventory example: The inventory should be assessed for • Where country specific emission factors are quality to determine whether there are available but not used, the data quality should any grounds for exclusion of certain be assessed as lower. sectors or sources, and to inform the use • Where there are no country-specific emission of discounting (Step 6). factors and both national and city inventories use IPCC defaults, the activity data data quality Inventory data should represent the best possible assessment should take precedence (that is, it estimate of emissions released and removed can be assessed as High even though the EFs from the atmosphere in a given year. The activity are technically low). data, emission factors and methods used to • Where electricity grid emissions are highly calculate emissions must therefore meet the quality variable and the emission factor is not reflective principles of the IPCC 2006 Guidelines and the of the current year, the data quality should be GPC. These are Transparency, Completeness, assessed as lower. Consistency, Comparability, Relevance, Accuracy. Further description of the data quality principles This initial assessment of data quality should be can be found in Box 3.2. Assessment of the data used to define the sources included in the crediting quality should be undertaken with reference to boundary and flag any immediate concerns. It these principles. In addition, the data quality should also serve as an opportunity to review and principles should be applied with a view to how well update data and methods as appropriate, thereby the inventory can reflect local change in emissions, ensuring that the most robust dataset can be that is, the effectiveness of mitigation actions. used for crediting purposes. A second phase of data quality assessment is undertaken in Step 6. Note that in assessing the data quality of the GHG Discounting is applied where data quality is not inventory, the assessment should be applied to the high so as to account for the likely uncertainty. activity data, which largely informs the method. Table 3.4: General Data Quality Assessment and Examples Quality Description Example Activity data likely to represent real activity in the city and can City-specific measured data, such as billed change over time. It reflects actual change and can be used energy consumption by sub-sector and fuel High to inform policy, track mitigation activities, and implement type, is aligned with the inventory boundary. crediting with more confidence. Likely representative of the city within an acceptable range Modelled data, that is, consumption data from of uncertainty if scaled based on recent trends and using <3 years is scaled to city population/boundary/ Medium justifiable factors. building stock. Data for inventory year for part of the city is scaled to the whole city. Does not represent consumption within the city boundary Scaled national or regional level data. with any degree of accuracy; only indicative of general, likely Low trend/level. Does not reflect local-scale mitigation activities. should be encouraged to implement improvements. Source: GPC and Ricardo Energy and Environment 77 Table 3.5: Emission Factor Data Quality Assessment and Examples EF Data Quality Example Description Highest quality, reflects locally specific fuel characteristics or High City-specific emission factor (EF). energy mix, derived from measurements or calculated from specific plant or activity data. Country-specific EF, consistent with national Aligns with national approach, considered to be accurate and Medium level reporting. consistent. May be used by many cities and countries. Acceptable where Low Default EF. used at both scales. Source: GPC and Ricardo Energy and Environment BOX 3.2: DEFINITIONS OF DATA QUALITY • Transparency: Activity data, emission • Comparability: The inventory is reported sources, emission factors, and accounting in a way that allows it to be compared with methodologies require adequate other cities or the national greenhouse documentation and disclosure to enable gas inventory. This comparability should verification. There is sufficient and clear be reflected in appropriate prioritization of documentation such that individuals or sources, as well as the use of standardized groups other than the inventory compilers can tools and approaches. understand how the inventory was compiled. • Relevance: The reported GHG emissions Good practice requirements can be assured. shall appropriately reflect emissions occurring • Completeness: Estimates are reported for all as a result of activities and consumption relevant categories of sources and sinks and patterns in the city. The inventory will also gases within the inventory boundary. Where serve the decision-making needs of the elements are missing, their absence should be city, taking into consideration relevant local, clearly documented together with a justification subnational, and national regulations. The for exclusion or use of notation keys. principle of relevance applies when selecting data sources and determining and prioritizing • Consistency: Estimates for inventory years, data collection improvements. gases and categories are made in such a way that differences in the results between • Accuracy: The inventory does not contain years and categories reflect real differences over- or under-estimates, so far as can be in emissions. Inventory annual trends, as judged. This requires making all efforts to far as possible, should be calculated using remove bias from the inventory estimates. the same method and data sources and Accuracy should be sufficient to provide inventory boundary in all years. They should decision makers and the public with aim to reflect the real annual fluctuations in reasonable assurance of the integrity of the emissions or removals. Furthermore, they reported information. Uncertainties in the should not be subject to changes resulting quantification process shall be reduced to the from methodological differences. extent possible and practical. Source: Adapted from the IPCC 2006 Guidelines and the GPC. 78 MEASURE Step C (i): Establish inventory crediting boundary well as the requirements for crediting. These should be used to assess the extent to which sources are Based on the assessment of the boundary applicable for inclusion in the crediting boundary. conditions, relevant sources and sinks, Based on the GPC emission categorization and and prioritization criteria, define the scopes framework, a suggested prioritization of sub-sectors included those within the scopes and sources for inclusion within the urban crediting boundary. crediting boundary is presented below. These are also assigned to IPCC categories to aid national alignment. Emission sectors/sub-sectors may have higher priority for inclusion in an urban crediting approach It is possible to include all emission sources than others. This may be due to their contribution and scopes within the urban crediting boundary to overall city emissions; the frequency of reporting if appropriate steps are taken to avoid double- by cities; accounting approaches and complexities; counting, ensure transparency and meet minimum the importance and ability of cities to influence and requirements for reporting. However, some reduce those emissions; the potential boundary sectors should be prioritized, and others may be and double-counting issues presented; and the excluded (or maximum discounting applied) until data quality issues presented above. Steps 1 and such minimum requirements are achieved. The 2 should be used to inform the inclusion of sources prioritization below should be taken together in the crediting boundary. Table 3.3 presents with the data quality assessment to define the suggested boundaries of sources and scopes, as sources included. Table 3.6: Prioritization of Sources for Crediting Boundary GPC Ref GPC Description BASIC/ BASIC+ GPC Scope IPCC codes Priority I.1.1 Residential BASIC 1 1A4b I.1.2 Residential BASIC 2 1A1a end-user I.1.3 Residential BASIC+ 3 1A1a end-user I.2.1 Commercial/Institutional BASIC 1 1A4a I.2.2 Commercial/Institutional BASIC 2 1A1a end-user I.2.3 Commercial/Institutional BASIC+ 3 1A1a end-user I.3.1 Manufacturing Industry/Construction BASIC 1 1A2 I.3.2 Manufacturing Industry/Construction BASIC 2 1A1a end-user I.3.3 Manufacturing Industry/Construction BASIC+ 3 1A1a end-user I.4.1 Energy Industries BASIC 1 1A1 I.4.2 Energy Industries BASIC 2 1A1 I.4.3 Energy Industries BASIC+ 3 1A1 I.4.4 Energy Industries 47 BASIC (T) 1 (T) 1A1 I.5.1 Agriculture/Forestry/Fishing BASIC 1 1A4c I.5.2 Agriculture/Forestry/Fishing BASIC 2 1A1a end-user I.5.3 Agriculture/Forestry/Fishing BASIC+ 3 1A1a end-user 79 GPC Ref GPC Description BASIC/ BASIC+ GPC Scope IPCC codes Priority I.6.1 Non-specified sources BASIC 1 1A5 I.6.2 Non-specified sources BASIC 2 1A1a end-user I.6.3 Non-specified sources BASIC+ 3 1A1a end-user I.7.1 Fugitive coal BASIC 1 1B1 I.8.1 Fugitive oil and gas BASIC 1 1B2 II.1.1 Road transport BASIC 1 1A3b II.1.2 Road transport BASIC 2 1A1a end-user II.1.3 Road transport BASIC+ 3 1A3b II.2.1 Rail BASIC 1 1A3c II.2.2 Rail BASIC 2 1A1a end-user II.2.3 Rail BASIC+ 3 1A3c II.3.1 Waterborne BASIC 1 1A3dii II.3.2 Waterborne BASIC 2 1A1a end-user II.3.3 Waterborne BASIC+ 3 1A3d II.4.1 Aviation BASIC 1 1A3aii II.4.2 Aviation BASIC 2 1A1a end-user II.4.3 Aviation BASIC+ 3 1A3a II.5.1 Off-Road BASIC 1 1A3eii II.5.2 Off-Road BASIC 2 1A1a end-user II.5.3 Off-Road BASIC+ 3 1A3e III.1.1 Solid Waste BASIC 1 4A III.1.2 Solid Waste BASIC 3 4A III.1.3 Solid Waste BASIC (T) 1 (T) 4A III.2.1 Biological treatment BASIC 1 4B III.2.2 Biological treatment BASIC 3 4B III.2.3 Biological treatment BASIC (T) 1 (T) 4B III.3.1 Incineration/Open Burning BASIC 1 4C III.3.2 Incineration/Open Burning BASIC 3 4C III.3.3 Incineration/Open Burning BASIC (T) 1 (T) 4C III.4.1 Wastewater BASIC 1 4D III.4.2 Wastewater BASIC 3 4D III.4.3 Wastewater BASIC (T) 1 (T) 4D IV.1 Industrial Processes BASIC+ 1 2A-E & 2H IV.2 Product Use BASIC+ 1 2F & 2G V.1 Livestock BASIC+ 1 3A V.2 Land Use BASIC+ 1 3B V.3 Aggregate Sources BASIC+ 1 3C VI.1 Other Scope 3 3 N/A Source: Ricardo Energy and Environment Note: 1 Unless the utility is owned and operated by the city and important to strategic planning – note that this source includes grid- supplied only. Generation for onsite use (Auto-generation) is reported under the sub-sector it is used in. 80 MEASURE 3.2.1. GHG Inventory Review and can affect the GHG emission levels to be reported. Where urban crediting covers land-use and land- Alignment with Urban Crediting use change, reported emissions would have to use the same accounting approaches as at the national The GPC largely refers to the IPCC 2006 level. Roughly a quarter of the submitted NDCs Guidelines, making alignment feasible where do not specify whether the land-use sector will be desired. However, at present, such alignment included and, if so, how it will be accounted for is theoretical only. In practice, it might lead to (Herold, Siemons and Herrmann, 2018). greater efforts to compile a GHG inventory of cities. Furthermore, with regard to the waste sector, the To address these issues, alignment with NDC GPC provides a methodology for landfill emissions tracking will be required, particularly through from solid waste, which is not mirrored in the the use of the national GHG inventory. The IPCC 2006 Guidelines. Alignment would also be necessary actions are presented in the form of required with regard to GHG inventory scopes steps. However, some of these steps can be carried and the reporting of city GHG inventories. All out partly in parallel, where desired. GHG emissions reported by cities would in some form be reflected in the national GHG inventory, for example, Scope 2 emissions from power consumption in a city GHG inventory would be Step C (ii): Identify inventory categories reflected in GHG emissions from power generation where urban/national alignment is in national GHG inventories (1A1a). City-level desired reporting for urban crediting purposes would have to clearly state which city-level categories relate to Mapping of the GPC inventory categories which national level categories to avoid any double against the national inventory categories counting. Although the GPC does not explicitly should be undertaken to identify those map sources, tools such as CIRIS do support sectors where emissions reporting is this, particularly in cases where cities have the comparable, and crediting is desired. granularity of data to report against each category Review the methodologies, data sources, within the sub-sectors. assumptions and emission factors to identify any major discrepancies in Apart from the methodological approaches, sectors where alignment is desired. the GHG inventory methodologies will not always apply the highest level of granularity Alignment will be required for all inventory for reasons of cost-effectiveness, or because categories whose emissions might be affected of the need to prioritize completeness over by mitigation actions to be credited. It is accuracy. This impacts the ability to reflect understood that city-level crediting programs GHG reductions achieved by mitigation actions would eventually aim to cover all city’s emissions. (Choudrie and others 2017) and the amount of However, at present, given the poor quality of reductions credited. Crediting based on a city GHG certain data categories and other methodological inventory might lead to mitigation options having a difficulties, considering only city-wide crediting less attractive reduction potential, where reductions may considerably delay the start of the program. cannot be fully reflected in the city GHG inventory. Therefore, should it be found that certain categories of emissions cannot be accurately From the perspective of carbon accounting, accounted for in the city-level inventory in the Article 4 of the Paris Agreement requires a near future, a sector-wide approach may become country to perform accounting for their NDC. a transitional stage on the way to the full urban For example, with regard to land-use change, this coverage of the crediting program. 81 A city might decide to initially focus on only Step C (iii): Identification of general those sectors and categories where the largest GHG inventory alignment requirements reductions might be obtained, for example, energy and transport. Initially, then, the urban This requires an assessment of high- crediting baseline would only be established for level approaches to be aligned with the these specific sectors. Regarding the identification national-level GHG inventory. of the relevant GHG inventory categories, the city would ideally need an assessment of mitigation Ideally this takes place the form of discussions potential and costs, thereby allowing for the between the national-level GHG inventory team (or identification of areas of most cost-effective teams, if several cities are aiming to establishing reduction potential. Suggested criteria and urban crediting systems) and the city-level GHG considerations for prioritizing and including inventory team. inventory categories are found in Step 2. • IPCC Standards used: The GPC refers to the Once key sectors / categories affected by IPCC 2006 Guidelines (IPCC 2006) for national the most cost-effective reduction potential GHG inventories as the preferred standard. have been identified, they must be matched to However, many developing countries have categories in the national level GHG inventory. submitted a NDC target based on the use of the However, this is not always a straightforward Revised IPCC 1996 Guidelines for national GHG process. For example, reducing GHG emissions inventories. The methodologies are generally from power consumption would be covered under similar, but there are a few distinct cases where the energy sector, Scope 2 for city-level GHG methodologies differ between the standards. inventories. Such a category does not exist in Default emission factors also tend to differ. the national level GHG inventories. Instead, such Negotiations under the UNFCCC have not yet reductions related to power consumption would come to an agreement as to whether or when be reflected in the national GHG inventory under developing countries should transition to the category 1A1a Main Activity Electricity and Heat IPCC 2006 Guidelines. Therefore, the city GHGI production (IPCC 2006 GL categorization). As team needs to understand whether/when the indicated, in some cases the IPCC guidelines use national-level GHGI team intends to transition to more granular categories than the GPC, whereas the IPCC 2006 Guidelines. in other cases it is the opposite. The city-level GHG inventory team might need to align with Where the Revised 1996 Guidelines are the national-level GHG inventory team to ensure currently being used for the national-level GHG city and national level categories are correctly inventory and a transition is planned, the easiest matched, as well as to understand whether GPC approach for the city GHG inventory might categories might need further disaggregation to be continue using the IPCC 2006 Guideline match IPCC categories or vice versa. methodologies for the time being and ensure alignment once the transition takes place. In any General alignment of GPC and IPCC categories is case, the national-level GHG inventory will have shown in Appendix 2. Additional alignment detail to recalculate its full time series data after the may be possible, for example, in cases where cities transition. This means that crediting amounts report by mode within Road Transport. cannot be calculated before the transition. However, once the transition has happened, the amounts could be calculated and issued for all relevant years. Of course, this approach might not be adequate if a transition is only planned 82 MEASURE briefly before the NDC target year/period – • Quality Assurance/Quality Control: An meaning a significant delay in the issuance of alignment in QA/QC measures is not required as certificates. Considering that the urban crediting such. However, the city GHG inventory requires approaches are intended to help in achieving a high data quality for the urban crediting. Also, the NDC target, a discussion involving both the national level GHG inventory team is likely national and city level(s) might be required to to have more experience with QA/QC planning bring the intended transition time forward. and implementation. Aligning processes and approaches, where appropriate and feasible, • Global Warming Potentials (GWP): The same can be an easy way of transferring existing set of global warming potential data would have experience. National-level GHG inventories to be used. This alignment is straightforward in include QA/QC plans, which could simply be practice. Similar to the point related to the IPCC copied/pasted and adjusted as appropriate for Guidelines, many developing countries use older the city level. Of course, developing countries sets of GWPs, for example, from the IPCC’s not regularly producing a national-level GHG Second Assessment Report. However, they inventory might not yet have developed a are likely to transition to a more recent set over detailed QA/QC plan. The alignment process time, for example, from the 4th or the 5th IPCC might then be an opportunity for the city- and Assessment Report. Where a city uses a newer national-level teams to develop such a plan set of GWPs and the national level intends to together. QA/QC plans evolve over time as transition to that level, the easiest approach for experience grows. Thus, a long-term alignment the city would be to continue using these GWPs. process ensuring that checks related to aligned However, the issuance of certificates would only categories are carried out both at the city and be feasible once the national-level GHG inventory national levels should be considered. has transitioned to the same set of GWPs. • The time period covered: National GHG inventories adhere to the calendar year as a time period. This is relatively easy to achieve in terms of setting the next city GHG inventory to be compiled. However, the recalculation for previous GHG inventory years might be complex because the available activity data is not disaggregated into the right time period. This might require assumptions to be made, thereby increasing the uncertainty in emission estimations. However, where consistent time series are available for the city GHG inventory, the uncertainty would only relate to allocating GHG emissions to a specific calendar year. It would not relate to the level of GHG emissions. • Recalculations: When a recalculation takes place in the national GHG inventory, it also triggers a recalculation for any aligned categories at the city level. A process needs to be set in motion to ensure such recalculations are mirrored at the city level and in full methodological alignment. 83 Step D: Identification of necessary (ethane) emissions resulting primarily from alignment at the category level aluminium production. Where this data is available at either the national or city level, For the relevant categories, city- and it could be used for both GHG inventories. national-level inventories inventory Confidentiality of data might also play a should be compared by category to role if there are only a small number of such better understand the differences in installations at either the city or the national methodologies, data, and assumptions. level. This problem can be overcome by Discussions should be conducted reporting GHG emissions aggregated over regarding how such differences could be several categories and using appropriate realistically overcome. considering city notation keys (such as, C – confidential, IE – level capacity, data availability, cost and included elsewhere). other factors. • Emission factors: - If default emission factors are used, To achieve the best outcome, this should take place alignment will be straightforward. Cities in the form of discussions between the city-level should also use nation-specific emission GHG inventory team (or teams, if several cities are factors where these are available. seeking to establish urban crediting systems) and - A more complex situation arises if cities use the national-level GHG inventory team. emission factors which are more specific than the national-level emission factors to The category assessment should include the allow for a better representation of local following elements: circumstances (such as factors at the city • Completeness: All relevant categories should or regional level). This is not a common be reported at the city level. Alignment would approach among developing countries. not be feasible if reporting does not occur. • Activity data: Ideally activity data comes • Methodologies used: from the same source and has the same - In a majority of cases, this will be the granularity, and quality. It would also use the emission factor multiplied by the activity same assumptions. This will require a detailed data. It will not require further alignment from discussion between city- and national-level a methodological perspective. GHG inventory teams. The national level data can be disaggregated to the city level without - Where modelling takes place, alignment limiting the ability to reflect GHG reductions at will be more complicated. A simplified the city level. If this is not possible, it can also approach would be to disaggregate the be scaled down based on other key indicators modelling results to the city level. However, (for example, population, GDP, and so on). this would limit the ability of the city-level However, this will significantly compromise the GHG inventory to reflect the amount of GHG applicability of this data for carbon crediting as reductions achieved. A considerably costlier it will not be able to reflect emission reductions solution would be for the city to replicate achieved at the city level. There will be cases modelling at the city scale. It would be where city-level data might be more granular necessary though to assess whether this than national level data, for example, where a would provide full alignment, depending on large production plant might share data relevant the specific modelling approach used. to the IPPU sector with the city directly, whereas - Direct GHG measurements are typically at the national level, statistical data would be only carried out for specific categories, used. Although expectations that data reported for example, for CH4 (methane) or C2H6 by individual plants should aggregate to the 84 MEASURE statistical data, differences between the two by sharing data, reviewing, and so on. Alignment are commonly found. Thus, it would be activities should ideally be integrated with the necessary to understand the level of difference next city-level GHG inventory compilation cycle. to avoid inconsistencies between the city and In addition, these processes are necessary to national levels. maintain alignment over time in case of changes to either of the inventories. Such changes may include: Assessing the need for alignment might lead to a reconsideration about the types of mitigation • Additional sectors/categories could be included actions urban crediting can entail. Such in the urban crediting approach. This might be considerations might include: triggered by NDC updating or by an increase in • Mitigation actions cannot be sufficiently city capacity or resources, allowing for further reflected in the national GHG inventory and alignment, technological changes as well as would lead to a limited amount of certificate access to new cost-effective reduction potential issues. at the city level, and so on. • The necessary alignment would lead to • Changes in the national-level GHG inventory excessive costs at the city level. methodologies, data, assumptions, data • The necessary alignment is, at present, collection processes could also occur. These technically unfeasible at the city level, for changes could be driven by new data becoming example, the relevant capacity and/or data may available, new emission sources becoming not be available. This somewhat mirrors the relevant ( for example, certain industrial point about costs, as capacity and/or data could production processes) at both the city and potentially be acquired based on the availability national levels. Changes could also occur in of sufficient resources. transitioning to the IPCC 2006 Guidelines or starting the use of the 2019 IPCC refinement to the IPCC 2006 Guidelines, which is currently under development. Such changes would Step E: Planning, implementing and require recalculations of data for previous maintaining alignment reporting years. This entails implementation of the alignment activities identified, including These processes would require regular any data improvements required to communication between the city- and national-level support a crediting approach. Also, it will GHG inventory teams, for example, at the start of be important to establish a system for each compilation cycle. Specific events related ongoing alignment. to the national level GHG inventory – for example, methodological changes, or recalculations – could also trigger exchanges to ensure alignment action Once the need for alignment has been agreed, is taken as soon as possible. alignment activities should be planned and responsibilities defined. This includes the national GHG inventory team providing support for example, 85 3.2.2. Summary The GHG inventory will become the main tracking While the barriers associated with the low data method for the urban crediting program. As such, quality are addressed in the methodology through it will be necessary to ensure its alignment with the application of a discounting factor, one of the national GHG inventory in all relevant areas. the objectives of the crediting approach is to This assessment should be undertaken either incentivise a higher-quality inventory compilation prior to or in parallel with the design of an urban and development of the necessary capacity crediting program. to ensure inventory preparation on a regular basis. By offering cities an opportunity to earn The assessment of what is feasible in terms of a higher number of credits for their mitigation program coverage needs to be undertaken, and actions through high-quality GHG inventories, this it should be based on the available city data and methodology may offer the financing necessary to its granularity. Should it be concluded that all enable a higher quality emission reporting on the GHG emissions of a given city can be covered city level. However, additional donor support may (full program coverage), it will then be required be needed to build capacity of city governments to identify general alignment necessities at the in improving the process of continuous data program level. The sectoral-level alignment collection and quality assurance. requirement will also need to be identified. Once these steps are completed, the necessary alignment steps will need to be implemented. Also, processes should be in place to maintain the achieved alignment in case of any further changes. If the city is not yet capable of covering all sectors and emission categories due to data quality/ availability issues or other restrictions, it will be necessary to define which sectors are suitable for coverage under the crediting program. Once the program scope is identified, the same steps as in the full coverage case will be required. In this case, however, it is advised to continue working on improving the data quality in the uncovered sector — with the intention of eventually expanding the coverage to the city level. These steps are demonstrated in Figure 3.3. 86 MEASURE Figure 3.3: GHG Inventory Review and Alignment Steps Step A DEFINE BOUNDARY OF THE GHG INVENTORY AND ALIGN WITH CREDITING APPROACH Full coverage Partial coverage Identify suitable sectors Step B ASSESS THE QUALITY OF THE AVAILABLE DATA AND METHODS EMPLOYED IN CALCULATING THE INVENTORY Non-NDC aligned NDC-aligned  Step C (i) Step C (ii) ESTABLISH INVENTORY IDENTIFY INVENTORY CATEGORIES CREDITING BOUNDARY WHERE URBAN/NATIONAL ALIGNMENT IS DESIRED PRIORITIZE SOURCES FOR Step C (iii) CREDITING BOUNDARY IDENTIFY GENERAL GHG INVENTORY ALIGNMENT REQUIREMENTS Step D IDENTIFY NECESSARY ALIGNMENT AT THE CATEGORY-LEVEL Step E PLAN, IMPLEMENT AND MAINTAIN ALIGNMENT Source: Developed by Ricardo Energy and Environment 87 4 application Application to the city of Amman, Jordan 88 APPLICATION This section examines the application of the principles and steps within the context of the city of Amman, as well as Jordan’s climate change commitments. It is based on the most recent available (published) data and information submitted by the city. It also includes data from the country contained in its NDC and Biennial Update Report (BUR1). 4.1 CONTEXT 4.1.1. Jordan: Specific Country Context In November 2016, Jordan published its Intended Jordan’s first GHG inventory was reported for NDC (INDC).15 It committed to reducing its 2006 and submitted as part of the country’s Third greenhouse gas emissions by 14 percent from the National Communication (TNC) in November 2014. Business-as-Usual (BAU) scenario by the year 2030. This inventory was the basis for the development It defined the unconditional outcome target as 1.5 of the country’s NDC baselines and targets (as percent of GHG emission reductions below the BAU above), below are the net GHG emissions by sector scenario by 2030, as well as a conditional target of in Figure 4.1. However, it was not updated in the 12.5 percent of emission reductions below the BAU first Biennial Update Report (BUR1) to the UNFCCC scenario by the same year. This is to be achieved submitted in November 2017. It reported Jordan’s through the implementation of more than 70 projects anthropogenic emissions by source and removals under the National Climate Change Policy of the by sink of all greenhouse gases not controlled Hashemite Kingdom of Jordan 2013-2020. by the Montreal Protocol for the years 2010 and The Jordanian NDC notes the following key points: 2012, using the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The BUR1 updated • “Emissions are expected to grow according to the mitigation projections previously undertaken the 2006 baseline scenario used in the Third using the 2006 data (but not the 2006 inventory National Communication (2014) to 38,151 GHG, data). Also, it should be noted that the methods, 51,028 GHG and 61,565 GHG of carbon dioxide data and IPCC Guidelines used are different (CO2) equivalent in the years 2020, 2030 and from 2006 to 2010-12, where it appears some 2040, respectively, due to normal growth improvements were undertaken. models.” • “The role of the energy sector and sub-sectors The NDC target was based on implementing as the leading emitters of GHGs is expected to projects, some of which were part of the “43 increase in the future from 73 percent of total sectoral projects” resulting from the mitigation emissions in the year 2006 to 83 percent in scenario assessment articulated in the Third the year 2040, according to the BAU scenario. National Communication Report, as well as Therefore, it is anticipated to focus the priority projects proposed in emission sectors mitigation efforts of the Country on this sector.” (after developing the TNC and communicated to the Ministry of Environment). The implementation • “The baseline scenario was based on 2014 of some of these projects has already begun. conditions, which were deeply relying on The BUR1 also notes the projects and expected imported fossil fuel and the delay in renewable actions that will deliver future energy scenarios and alternative energy projects, as well as for the country, as well as the current committed interruptions in gas supplies. Now, in 2015, the and planned actions (largely relating to the energy conditions have changed with more emphasis sector. These include, for example the rapid on renewable and gas. We believe when Jordan increase in nuclear and renewable power: develops its BUR based on 2010 inventory the baseline scenario will lead to a peak year.” 89 Figure 4.1: Jordan’s Net GHG emissions by Sector (%) 2010 2012 Subsectors (Gg of CO2eq) (Gg of CO2eq) ENERGY SECTOR 19410.88 22756.83 Fuel Combustion Activities 19409.61 22755.78 Energy Industries 9112.78 11296.10 Manufacturing Industries and Construction 2306.36 1249.19 Transport 5296.81 7391.60 Other Sectors (Residential, Commercial and Agriculture) 2275.97 2334.00 Non-Specified 417.70 484.92 Fugitive Omissions (Oil and Natural Gas) 1.26 1.02 Source: Jordan NDC (2014) • Nuclear: By 2020, Jordan plans to have nearly 1350 MW, representing 25% of all installed 60 percent (according to the projected energy generating capacity and contributing 20% of balance) of its electricity generation drawn from generated electricity.” two 1000 Megawatt (MW) nuclear power stations • Fluctuating role of natural gas: The BUR1 expected to come onstream in 2023-2025: “The also notes the fluctuating role of natural gas in JAEC has selected the nuclear power plant site the country’s energy mix: “Jordan has the ability in Amra. [The] JAEC announced that Rosatom’s to meet the country’s needs of natural gas for reactor export subsidiary, Atom Story Export electricity generation and for industrial uses (ASE), would supply two nuclear units on a build, following construction of [the liquified natural own, and operate (BOO) basis with a capacity gas] LNG terminal in Aqaba, which was put in of 1000 MW each. The first reactor will be commercial operation in September 2015. In commissioned in 2023 and the second in 2025 as 2016, 90 percent of electricity generation in the stated in the new energy strategy 2015-2025.” Kingdom was based on natural gas delivered • Renewables: “The total capacity of renewable through the Aqaba LNG terminal. The demand energy by the end of 2020 will be around for natural gas for electricity generation during Table 4.1: Sample of Third National Communication Scenario Energy Projects (now part of the BUR1 baseline scenario) Project Name Status Loss Reduction in Electricity Transmission and Still valid and considered in the current mitigation scenario Distribution Network Improving Combustion in Rehab Power Plant Cancelled. No longer valid. Cancelled due to reduction of natural gas production and delay in Combined Cycle Gas Turbine in Risha Plant implementation of Risha field development. Still valid and considered in the current mitigation scenario to include Zarqa Distribution Network of Natural Gas in Aqaba and Amman in addition to Aqaba. Demand Side Management Still valid and considered in the current mitigation scenario. Moved from the TNC mitigation scenario and considered within current BUR Nuclear Power Plant (1000 MW) baseline scenario according to the updated energy strategy 2015-2025. Source: Jordan NDC Note: BUR= Biennial Update Report; TNC= Third National Communication; MW= megawatt. 90 APPLICATION the period 2016-2022 was estimated to be 350- 4.1.2. Amman’s Inventory Analysis 420 Million Cubic Feet per Day (MMCFD). During and Suitability for Crediting the period 2023-2025, the estimated demand dropped to 150-250 MMCFD due to substitution Program by oil shale and nuclear power which will come Scope. The geographic scope of Amman’s online during the period in question.” inventory is defined as the 22 districts (out of a total of 27 districts) currently within the Greater Amman Furthermore, many actions are now moved out of Municipality (Figure 4.2). The included area totals the third national communication mitigation scenario 800 square kilometers (km2). (for the NDC) into the BUR1 baseline scenario, such as the project for ‘Reduction by using hybrid cars Amman currently has two inventories covering for public passengers’ and ‘Reduction by Amman – the calendar years 2014 and 2016. The inventory Zarqa Bus Rapid Transit (BRT)’. follows the BASIC reporting level, covering all main emission sources in the stationary energy, Apart from the overarching national policy transport and waste sectors, with the exception of objectives and commitments, there are references waste incineration. No additional BASIC+ or ‘Other to specific projects in Amman in the BUR1, Scope 3’ emission sources were included. including the Introduction of the Zero Emission Electric Vehicle (ZEV) in Jordan, a program The geographic boundary of the crediting system, implemented through a partnership between the policies and the inventory should align for better Greater Amman Municipality (GAM), the Ministry of functioning of an urban crediting program. As Environment and the private sector. Also included noted, the current inventory only covers the 22 is a ‘mega project’, the Amman Bus Rapid Transit districts of the GAM. It is recommended that Project, a US$ 160 million project currently under Amman extend the geographical scope to include construction in Amman. It runs on 3 routes from all 27 districts in Amman. Sweileh in West Amman to the Jordan Museum in Ras Al-Ain. GAM also plays a significant role in Results. The 2014 and 2016 inventory results are solid waste and wastewater management, operating depicted in Figure 4.3 In both years, the stationary the country’s only sanitary landfill. It is part of a energy use of residential buildings was the number of new energy projects, including new gas largest contributor to the city’s emissions. It was distribution pipelines and renewable energy. followed by on-road transportation, commercial Figure 4.2: Geographic Scope of Amman’s Inventory GAM AMMAN Source: Amman Inventory 2014 91 Figure 4.3: Overview of Inventory Results 2014 2016 Source: CURB Model for Amman using GHG Emissions Inventory 2014 2,731,171 2,726,383 2,452,196 2,267,555 2,081,121 1,974,328 328,154 346,126 364,292 313,462 237,991 258,990 RESIDENTIAL COMMERCIAL AND MANUFACTURING ON-ROAD SOLID WASTE WASTEWATER BUILDINGS INSTITUTIONAL INDUSTRIES AND TRANSPORTATION BUILDINGS AND CONSTRUCTION FACILITIES Source: CURB Model for Amman using GHG Emissions Inventory 2014 and institutional buildings and facility stationary are included within Scope 1. Wastewater is treated energy use, manufacturing industries, construction beyond the city boundary and is considered as stationary energy use, wastewater and solid waste Scope 3. Between 2014 and 2016, there was about emissions. Scope 2 (electricity consumption) a 12 percent increase in emissions. emissions dominate stationary energy, with a smaller amount of Scope 1 energy use. All The tables below (Table 4.2 and Table 4.3) give emissions from on-road transport and solid waste results by scope and sector for the 2014 and 2016 inventories, respectively. Table 4.2: Emissions by Sector and Scope (2014) tCO2e Basic+ Other Scope 3 Scope 1 Scope 2 Scope 3 Stationary 861,874 3,892,804 Transportation 2,267,555 Waste 264,292 237,991 Source: Amman Inventory 2014 Table 4.3: Emissions by Sector and Scope (2016) tCO2e Basic+ Other Scope 3 Scope 1 Scope 2 Scope 3 Stationary 945,235 4,213,183 Transportation 2,726,383 Waste 313,462 258,990 Source: Amman Inventory 2014 92 APPLICATION Stationary Energy. The main data source used 2012-2016, with potentially significant impacts on in the stationary energy sector is the National emissions. The fact that Amman is using a calculated Energy Balance. This was scaled based on either factor for 2012 from IEA data for all the relevant years population (for residential buildings) or GDP data is a significant data weakness. As such, there is (for commercial buildings). A 2012 grid electricity substantial uncertainty in the calculations. factor, estimated from the IEA Energy Balance, was used in both the 2014 and 2016 inventories. No Transport. Only Scope 1 road transport emissions Amman-specific data was used for either electricity were estimated in Amman because there are consumption or other stationary fuel use. All activity no railways or waterborne transport activities. data was considered ‘low’ quality. Also, in-boundary aviation and off-road transport activities were considered insignificant. Estimating Currently, the approach adopted by Amman to emissions from Scope 3 transport activities (for estimate emissions in the stationary energy sector example, transboundary journeys) is not required uses ‘low’ quality national-scale activity data, as for a BASIC inventory. well as many international default emission factors. These cannot reflect the impacts of policies in the The main activity data for Amman’s road transport sector with any degree of sensitivity. To improve the sector came from the 2012 VISUM traffic model. detail and sensitivity of the inventory to city-specific This contains data for Vehicle Kilometers Travelled policies, data improvements are outlined for each (VKM) for private vehicles, taxis, school buses sub-sector in Table4.4. and vehicles containing goods. The VISUM traffic model data was considered to be of ‘medium’ There has been considerable fluctuation in the quality. Estimated VKM data for mini-buses, buses supply mix of electricity generation in Jordan from and white taxis comes from the Transportation Table 4.4: Data/Approach and Improvements for the Stationary Energy Sub-sector Sub-sector Data / Approach Improvement Priority and Needs Activity data: National Energy Balance/National High priority: Obtain actual consumption data Statistics scaled by Amman population. for electricity. Residential Buildings Emission factors: 2012 Electricity grid emission factor Medium priority: Obtain actual consumption estimated from energy balance and IPCC Default EFs for data for other fuels. other stationary fuels. Activity data: National Energy Balance/National High: Obtain actual consumption data for Commercial Statistics scaled by Amman GDP. electricity split by end use e.g. municipal, and Institutional commercial. Emission factors: 2012 Electricity grid emission factor Buildings estimated from energy balance and IPCC Default EFs for Medium: Obtain actual consumption data for other stationary fuels. other fuels. Activity data: 2012 Industrial sector data scaled to 2014 High: identify scale of relevant industries in Manufacturing, using GDP – assume no heavy industry in Amman. Amman. Industry and Emission factors: 2012 Electricity grid emission factor Construction Medium: Obtain actual consumption data for estimated from energy balance and IPCC Default EFs for industry and construction sector. other stationary fuels. Activity data: National energy data scaled to population of GAM. High: obtain Jordan-specific annual electricity Grid Electricity (all) grid factor for the correct inventory yea(s) – Emission factors: 2012 Electricity grid emission factor potentially calculated by MEMR. estimated from energy balance. Source: Ricardo Energy and Environment Note: EF= Emission Factor; GAM= Greater Amman Municipality; GDP=Gross Domestic Product; IPCC= Intergovernmental Panel on Climate Change; MEMR= Ministry of Energy and Mineral Resources. 93 Table 4.5: Data/Approach and Improvements for the Transport Sub-Sector (road transport only) Sub-sector Data / Approach Improvement Priority and Needs Activity data: VKT from Low: VISUM Traffic Model and - Detailed datasets and bottom-up estimation approach available. the Transportation Planning - Assess changes to vehicle fleet year-on-year to ensure capture in inventory Road Department. - Identify Amman/Jordan specific vehicle emission factors. Transportation - Include ‘transboundary’ journeys in the inventory. Emission factors: vehicle emission factors from Zarqa Medium: obtain fuel sales data for GAM area to cross-check transport study (2011). model. Source: Ricardo Energy and Environment Note: GAM= Greater Amman Municipality; VKT= Vehicle Kilometers Travelled. Planning Department. This data was considered study from 2010 was also used to understand what as ‘low’ quality. Emission factors for each vehicle the waste contains. In addition, abio-gas study were taken from a World Bank-supported project, provided estimates of how much methane was Developing an Energy Efficient Urban Transport recovered, and how it was used. Experts16 estimated Plan for Zarqa City Downtown Area (World Bank the capture rate at 85 percent. Activity data was of 2011). The emission factors from this study were ‘medium’ quality. The CIRIS solid waste emissions considered as being of ‘medium’ quality. calculator was used to calculate emissions. Nitrous oxide (N2O) is not estimated using the methane The approach to estimating road transport commitment approach. For estimates of emissions emissions using a mix of modelled and estimated from landfills to fully reflect mitigation policies, the VKT data and regional emission factors is an GAM MSW and waste characterization data would improvement on using scaled national data. need to have been updated for each inventory in However, policy or mitigation actions to reduce order to reflect both changes in waste volumes and emissions from road transport would only be characteristics. reflected in the inventory if changes to the vehicle fleet are captured annually and if Amman/Jordan- To date, waste incineration emissions were not specific vehicle emission factors are available. included in the inventory. However, they are likely to occur in hospitals. This is a clear gap which should To improve the detail and sensitivity of the be addressed. inventory to city-specific policies, suggested data improvements are outlined for the road The C40 wastewater emissions calculator was transportation sub-sector in Table 4.4. The most used to estimate Amman’s wastewater emissions. important improvement would be to ensure that This is a simple tool which is driven by population detailed data used in the inventory is frequently numbers and uses a variety of international, updated to capture changes to emissions as a regional and national default values to estimate result of mitigation actions/policies/measures. wastewater emissions. To improve sensitivity of this calculation to polices, more detailed information Waste. The methane commitment method was with better characterization of treatment methods, used for estimating emissions from solid waste and wastewater volumes and destinations should treated at landfill sites within the city. The activity be obtained. data consists of local GAM 2014 waste data for tonnage of municipal solid waste (MSW) generated Table 4.6 outlines the main data/approach and in the boundary from the GAM waste treatment necessary improvements for each of the waste department. Data from a MSW composition analysis sub-sectors. 94 APPLICATION Table 4.6: Data/Approach and Improvements for Each Waste Sub-sector Sub-sector Data / Approach Improvement priority and needs Medium: Update waste composition study Methane commitment method is used. Waste generation (2010-11) to ensure changes are captured (for Solid Waste to statistics for Amman combined with waste composition example, increased recycling). Landfill study data and landfill site characteristics. High: Confirm/verify or obtain updated information on flaring at landfill site. High: Not currently reported in the inventory, Incineration Not Estimated presently. but incineration in hospitals is identified and should be included. Medium: Small source but potential to improve Population and C40 calculator (default assumptions, estimates with better characterisation of Wastewater except for ‘Middle East’ BOD value and national annual treatment methods, and wastewater volumes protein consumption). and destinations. Source: Developed by Ricardo Energy and Environment Summary. Overall, the Greater Amman city-level policies would not be reflected in the city- Municipality has managed to go through two level emission figures reached through scaling of cycles of inventory compilations, reporting the the national emissions. Following the methodology results in a clear, consistent and transparent outlined in this report, such data quality issues are manner. However, the data used for these dealt with through the application of discounting. inventories is largely considered to be of low This would help to address likely data inaccuracies quality. As such, it will need to be considerably and encourage incremental improvement over time. improved to accommodate the introduction of a city-level carbon crediting program. Improvements and refinements are needed to enhance the ability of the city inventory to be used as a basis for an urban crediting system in Amman. These improvements largely pertain to making data more city-specific. This would help to ensure that the impacts of policies to reduce emissions are sufficiently tracked at the city scale in the context of the urban crediting methodology. The main improvements are in the stationary energy sector. Currently, no city-specific fuel or electricity consumption is used, and the electricity emission factor is out of date. The method used involves scaling national data to Amman by using either population or GDP; this approach is not preferable for use within an urban crediting context because 95 4.2 DEVELOPMENT OF AN URBAN CREDITING PROGRAM IN AMMAN 4.2.1. Inventory Enhancement Steps Step A: Define the boundary of the • Temporal boundary: This must be for a 12-month period; a calendar year is preferred GHG inventory and align with the for national alignment. crediting approach • Emission sources and sectors: This must be As a first step, Amman should define the clearly defined, and all BASIC sources reported boundary of the GHG inventory to be used for all sectors operating within the boundary. as a tool to measure urban mitigation • Emission scopes included and reporting progress, as well as the sectors reported framework(s): The Scopes must be noted as relevant to a crediting approach — and any potential double-counting between including an assessment of any limitations. territorial and city-induced sources noted. • Base year for the inventory and time series: The inventory must define the following: This should be noted, and any recalculations or • Geographic boundary: This must be clearly time series issues flagged (see also Table 4.7). defined; an administrative area is preferred. Table 4.7: Defining the Inventory Boundary Boundary Amman Case Strengths and Limitations Priority Definition Geographic Amman reports an inventory covering the 22 - Administrative boundary should be clearly districts (of a total of 27 districts) currently defined. within the Greater Amman Municipality (GAM). - Double-counting likely limited. The included area totals 800 square kilometres - It does not include all areas of ‘Amman’, (km2). The boundary is that of the metropolitan including some areas containing high-emitting area, and it is not marked by any significant industry – an issue only for completeness. geographical feature. Temporal Calendar year 2014 and 2016. Consistent 12-month period. boundary Emission sources Amman reports a BASIC inventory, covering See Table 4.7. and sectors all required Scope 1 and 2 sources within the reported boundary. Emission Emissions reported consistent with City-Induced - Energy industries NO no double-counting scopes included BASIC Framework. issues. and reporting - Waste imported to landfill will need to be framework considered as within the crediting boundary. Base year for the 2014 inventory is the first reported by Amman. Two consistent years are available. inventory and 2016 inventory has also been reported and Emissions total 7.5m tons of carbon dioxide timeseries Amman is looking to institutionalize reporting on equivalent (CO2e) in 2014; increased to 8.5m a regular basis. tons of CO2e in 2016 2014 was recalculated on the basis of improved assumptions in 2016. Source: Ricardo Energy and Environment 96 APPLICATION Table 4.8: Amman’s 2014 GHG Emissions by Sub-sector Total GHGs (metric tonnes CO2e) GPC ref GHG Emissions Source (By sector and Sub-sector) Scope 1 Scope 2 Scope 3 Total I STATIONARY ENERGY I.1 Residential buildings 483,249 1,968,947 NE 1,452,196 I.2 Commercial and institutional buildings and facilities 324,178 1,650,150 NE 1,974,328 I.3 Manufacturing industries and construction 54,447 273,707 NE 328,154 I.4.1/2/3 Energy Industries NO NO NO I.4.4 Energy generation supplied to the grid NO I.5 Agriculture, forestry and fishing activities NO NO NO I.6 Non-specified sources NO NO NO Fugitive emissions from mining, processing, storage and I.7 NO transportation of coal I.8 Fugitive emissions from oil and natural gas systems NO Sub-total (City induced framework only) 861,874 3,892,804 4,754,678 II TRANSPORTATION II.1 On-road transportation 2,267,555 NO NE 2,267,555 II.2 Railways NO NO NO II.3 Waterborne navigation NO NO NO II.4 Aviation NO NO NO II.5 Off-road transportation NO NO NO Sub-total (City induced framework only) 2,267,555 2,267,555 III WASTE III.1.1/2 Solid waste generated in the city 264,292 NO 264,292 III.2.1/2 Biological waste generated in the city NO NO III.3.1/2 Incinerated and burned waste generated in the city NO NO III.4.1/2 Wastewater generated in the city NO 237,991 237,991 III.1.3 Solid waste generated outside the city 46,640 III.2.3 Biological waste generated outside the city NO III.3.3 Incinerated and burned waste generated outside the city NO III.4.3 Wastewater generated outside the city NO Sub-total (City induced framework only) 264,292 237,991 502,283 IV INDUSTRIAL PROCESSES AND PRODUCT USES IV.1 Emissions from industrial processes occurring in the city boundary NE IV.2 Emissions from product use occurring within the city boundary NE Sub-total (City induced framework only) V AGRICULTURE, FORESTRY AND OTHER LAND USE V.1 Emissions from livestock NE V.2 Emissions from land NE V.3 Emissions from aggregate sources and non-CO2 emissions on land NE Sub-total (City induced framework only) VI OTHER SCOPE 3 VI.1 Other Scope 3 NE Total (City induced framework only) 3,393,721 3,892,804 237,991 7,524,516 Source: Amman’s Emission Inventory Note: All BASIC sources are reported or are noted as Not Occurring. 97 Step B: Assess the quality of the available data and methods employed in calculating the inventory. The inventory should be assessed for quality to determine whether there are any grounds for exclusion of certain sectors or sources, and to inform the use of discounting (Step 6). The details of Amman’s inventory data quality is summarized in Table 4.9. Table 4.9: Amman’s Inventory Data: Quality and Completeness GPC ref GPC Description BASIC/BASIC+ GPC Scope Data quality IPCC codes I.1.1 Residential BASIC 1 L 1A4b I.1.2 Residential BASIC 2 L 1A1a end-user I.1.3 Residential BASIC+ 3 NE 1A1a end-user I.2.1 Commercial/Institutional BASIC 1 L 1A4a I.2.2 Commercial/Institutional BASIC 2 L 1A1a end-user I.2.3 Commercial/Institutional BASIC+ 3 NE 1A1a end-user I.3.1 Manufacturing Industry/Construction BASIC 1 L 1A2 I.3.2 Manufacturing Industry/Construction BASIC 2 L 1A1a end-user I.3.3 Manufacturing Industry/Construction BASIC+ 3 NE 1A1a end-user I.4.1 Energy Industries BASIC 1 NO 1A1 I.4.2 Energy Industries BASIC 2 NO 1A1 I.4.3 Energy Industries BASIC+ 3 NO 1A1 I.4.4 Energy Industries BASIC (T) 1 (T) NO 1A1 I.5.1 Agriculture/Forestry/Fishing BASIC 1 NO 1 1A4c I.5.2 Agriculture/Forestry/Fishing BASIC 2 NO 1 1A1a end-user I.5.3 Agriculture/Forestry/Fishing BASIC+ 3 NO 1A1a end-user I.6.1 Non-specified sources BASIC 1 NO 2 1A5 I.6.2 Non-specified sources BASIC 2 NO 1A1a end-user I.6.3 Non-specified sources BASIC+ 3 NO 1A1a end-user I.7.1 Fugitive coal BASIC 1 NO 1B1 I.8.1 Fugitive oil and gas BASIC 1 NO 1B2 II.1.1 Road transport BASIC 1 M/L 1A3b II.1.2 Road transport BASIC 2 NO 1A1a end-user II.1.3 Road transport BASIC+ 3 NE 1A3b II.2.1 Rail BASIC 1 NO 1A3c 98 APPLICATION GPC ref GPC Description BASIC/BASIC+ GPC Scope Data quality IPCC codes II.2.2 Rail BASIC 2 NO 1A1a end-user II.2.3 Rail BASIC+ 3 NO 1A3c II.3.1 Waterborne BASIC 1 NO 1A3dii II.3.2 Waterborne BASIC 2 NO 1A1a end-user II.3.3 Waterborne BASIC+ 3 NO 1A3d II.4.1 Aviation BASIC 1 NO 3 1A3aii II.4.2 Aviation BASIC 2 NO 1A1a end-user II.4.3 Aviation BASIC+ 3 NO 1A3a II.5.1 Off-Road BASIC 1 NO 3 1A3eii II.5.2 Off-Road BASIC 2 NO 1A1a end-user II.5.3 Off-Road BASIC+ 3 NO 1A3e III.1.1 Solid Waste BASIC 1 M 4A III.1.2 Solid Waste BASIC 3 NO 4A III.1.3 Solid Waste BASIC (T) 1 (T) M 4A III.2.1 Biological treatment BASIC 1 NO 4B III.2.2 Biological treatment BASIC 3 NO 4B III.2.3 Biological treatment BASIC (T) 1 (T) NO 4B III.3.1 Incineration/Open Burning BASIC 1 NO 4 4C III.3.2 Incineration/Open Burning BASIC 3 NO 4C III.3.3 Incineration/Open Burning BASIC (T) 1 (T) NO 4C III.4.1 Wastewater BASIC 1 NO 4D III.4.2 Wastewater BASIC 3 L 4D III.4.3 Wastewater BASIC (T) 1 (T) NO 4D IV.1 Industrial Processes BASIC+ 1 NE 2A-E & 2H IV.2 Product Use BASIC+ 1 NE 2F & 2G V.1 Livestock BASIC+ 1 NE 3A V.2 Land Use BASIC+ 1 NE 3B V.3 Aggregate Sources BASIC+ 1 NE 3C VI.1 Other Scope 3 3 NE N/A Source: Ricardo Energy and Environment Note: L= Low quality ; M= Medium quality ; NE= Emissions not estimated ; NO= Not occurring ¹Source may occur as there are agricultural areas, but these are not current reported. Thus, there may be a potential gap. 2 Amman does not report any military activity in the inventory. 3 Reported as ‘insignificant’ – NO permitted. 4 Likely that incineration is occurring in hospitals, but not current reported. 99 Step C (i): Establish Inventory Crediting Boundary Based on the assessment of the boundary conditions, relevant sources and sinks, and prioritization criteria, define the sub-sectors included within the crediting boundary. The following sub-sectors are recommended for inclusion within Amman’s crediting boundary at the present time (Table 4.10). Table 4.10: Inventory Crediting Boundary for Amman GPC ref GPC Description BASIC/BASIC+ GPC Scope IPCC Codes Comment I.1.1 Residential BASIC 1 1A4b I.1.2 Residential BASIC 2 1A1a end-user I.2.1 Commercial/Institutional BASIC 1 1A4a I.2.2 Commercial/Institutional BASIC 2 1A1a end-user I.3.1 Manufacturing Industry/Construction BASIC 1 1A2 I.3.2 Manufacturing Industry/Construction BASIC 2 1A1a end-user II.1.1 Road transport BASIC 1 1A3b To be added over II.1.2 Road transport BASIC 2 1A1a end-user time, if possible III.1.1 Solid Waste BASIC 1 4A Source: Ricardo Energy and Environment 4.2.2. Development of Carbon As part of the development of a non-NDC aligned Crediting Approach urban crediting program, Amman would need to go through the necessary steps outlined in the urban Given the current state of Jordan’s NDC crediting methodology. To better understand how development, Amman may prefer to consider these steps may be applied in the case of Amman, the implementation of a non-NDC aligned urban some city specific considerations are discussed crediting program in the short term. As noted in below. Chapter 2, opting for this approach would allow the city to build the necessary capacity for the Step 1: NDC target metric alignment management of an urban crediting program, as well Not applicable. as improve its emission-related data quality. This would then support its potential link to the NDC in Step 2: Establishing a NDC pathway the future. Not applicable. 100 APPLICATION Step 3: Define the city’s BAU emissions Should insufficient data be available for meaningful assessment at the sectoral level, Amman should define a reliable BAU the CURB tool can be applied to develop emission trajectory for the city to a basic BAU scenario. It will primarily base ensure the environmental integrity of the its assumption on the GDP and population crediting program. projections, which would significantly simplify the BAU development exercise. However, it will The city of Amman will first need to develop its result in a very low level of precision. As a result, BAU emission trajectory to better understand it may significantly compromise the principle of how its future emission reduction targets compare the environmental integrity. to it. This can be done using simple projection methods or simulation modelling, depending on In the case of Amman, options for a BAU scenario available data and resources. have been developed using the CURB model (from the 2014 Amman GHG inventory). They were The most simplified approach would require developed by the World Bank team and updated the projection of emissions of each sector to reflect some new parameters. CURB has been based on historical data, as well as educated used to model a series of possible BAU scenarios assumptions about future changes in the for the target years 2025, 2030 and 2050, as shown sectors. Each sector activity data will need to in Figure 4.4. This also includes a BAU based on be separated from emission intensity, and then sectoral growth rates from Jordan’s BUR1. each would need to be projected and adjusted separately. This exercise will involve the evaluation In order from highest to lowest scenario, these of some underlying factors, which usually include include: the following: • Historical population and GDP/capita annual • Rates of retirement and retrofits for existing growth rate (5.5 percent); facilities/practices; and growth and market • GAM-supplied population (1.795 percent) plus penetration of new facilities/practices and their GDP/capita17 (2 percent) annual growth rate; associated technologies/practices; • GAM-supplied population and GDP/capita • Economic conditions; growth rates (as above) applied differently by • System operation policies or constraints; sub-sector – as shown in Table 4.11.Sectoral • Environmental conditions; and growth rates as specified in the BUR1 baseline scenario assumptions (and derived from the • Legal and regulatory frameworks (PMR 2013). projected energy balance), shown in Table 4.12. The activity data would usually undergo • Historical population growth only (2.3 percent). stronger changes than emission intensity. • Sectoral growth rates as specified in the BUR1 It can be forecasted using simple projections baseline scenario assumptions (and derived (extrapolation of historic trends) or regressions from the projected energy balance) are shown in based on historic dependencies between various Table 4.12. They include baseline assumptions input factors. These variables will be sector specified in the BUR1 related to mitigation specific. measures and electricity supply (red line – see below). • GAM supplied population growth only (1.8 percent). 101 Figure 4.4: Possible BAU Scenarios for Amman 60,000,000 5.5% Historical population & 50,000,000 GDP/cap growth 40,000,000 30,000,000 3.8% Population & GDP/capita growth Population & GDP/capita rates by sub-sector BUR1 sectoral energy growth rates 20,000,000 2.3% Historical population growth BUR1 sectoral growth rates and committed actions 10,000,000 1.8% Population growth 0 Target, 2050 2014 2016 2018 2020 2022 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2024 2026 2050 Table 4.11: Application of CURB Growth Drivers by Sub-sector Sub-sector BAU Growth Driver Justification Residential Fuels Population Assumes constant usage per person, and no increase with rising affluence. Assumes increase with population and affluence, for example, more Residential Electricity Population & GDP/capita appliances, higher demand. Commercial & GDP/capita Assumes increase with economic growth. Institutional Fuels Commercial & Assumes increase with population and, for example, higher demand for Population & GDP/capita Institutional Electricity services, higher consumption levels. Manufacturing Fuels GDP/capita Assumes increase with economic growth. Manufacturing Electricity GDP/capita Assumes increase with economic growth. Assumes increase with population and affluence, for example, higher Road Transport Population & GDP/capita demand for on-road vehicles, more travel, and greater distances travelled. Municipal Solid Waste Population Assumes waste generation remains constant per person. Wastewater Population Assumes wastewater generation remains constant per person. Source: Ricardo Energy and Environment 102 APPLICATION Table 4.12: BUR1 Specified and Calculated Growth Rates 2014- 2025- 2030- Sub-sector Fuel/ Activity Explanation 2025 2030 2050 BUR1 mitigation analysis states that “electricity generation Electricity All usage 0.041 0.041 0.041 requirements will grow during the period 2015-2040 at an average annual growth rate of 4.1 percent.” BUR1 mitigation analysis- calculates growth from energy Residential All fuels 0.0227 0.0139 0.0054 balance end-consumption. BUR1 anticipates “medium growth of 1.4 percent annually.” Commercial & BUR1 mitigation analysis states “5 percent”, energy balance All fuels 0.0567 0.0495 0.031 Institutional calculations slightly varied by period. Manufacturing BUR1 mitigation analysis calculates growth rate based Industries & All fuels 0.0568 0.0494 0.031 on projected energy balance end-consumption. BUR1 Construction anticipates “high average growth rate of 5 percent annually.” BUR1 mitigation analysis calculates growth rate based on Transport All fuels/ modes 0.0193 0.0136 0.0049 projected energy balance end-consumption. BUR1 projects a decline in intensity and low overall growth. Population growth rate. BUR1 states “Solid waste Solid waste All 0.01795 0.01795 0.01795 production rate per capita is assumed to be fixed at 0.9 kg/ capita/day.” Population growth rate. BUR1 states “current water Wastewater All 0.01795 0.01795 0.01795 consumption and generated wastewater per capita is assumed to be fixed.” Source: BUR1 Jordan Note: The years 2030-2050 use 2030-2040 rates. Figure 4.5: Amman’s Projected Emissions and National Energy Supply Actions (in BUR1) 60,000,000 50,000,000 5.5% Historical population & GDP/cap growth 40,000,000 30,000,000 3.8% Population & GDP/capita growth Population & GDP/capita rates by sub-sector BUR1 sectoral energy growth rates 20,000,000 2.3% Historical population growth BUR1 sectoral growth rates and committed actions 10,000,000 1.8% Population growth Target, 2050 0 2014 2016 2018 2020 2022 2044 2046 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2048 2050 Source: Ricardo Energy and Environment 103 A scenario incorporating the key energy Additionally, Amman could select the BUR1 assumptions in the BUR1 mitigation analysis growth rate and policy scenario as the baseline. baseline was developed as a potential ‘policy- However, given the uncertainties related to the adjusted BAU’, as shown in the red line in Figure implementation of national actions vis-à-vis the 4.5. Here, the yellow wedge reflects the planned grid supply, this approach is not recommended. changes to the grid mix and electricity intensity as specified in the BUR1 baseline scenario. It is recommended that the BAU baseline However, this has been discounted for use as a selected for Amman be based on the BAU given the uncertainty surrounding the likely application of BUR1 growth rates. This would implementation of these actions. As such, only be in keeping with the conservative principle. BAU scenarios considering projected growth (for Based on this, the BAU for Amman can be found example, ‘without measures’) are considered. in Table 4.13. • Selecting the BAU projection: In the case of It is recommended that Amman set baselines Amman, the population and GDP growth rates separately for each sector to ensure that the result in varying projections for 2050, ranging most conservative baseline could be chosen from around 12 million tons of CO2e to over 50 and tailored for each sector. This will also million tons of CO2e. The outlier projections are enable the option in the next step to choose unlikely to be accurate. Therefore, selecting a different baseline dynamics for each sector. conservative and accurate baseline is important. This option will also reinforce the environmental As such, Amman could choose the following for integrity of the scheme, as some sectors might the BAU projection: Population and GDP/capita have more significant or frequent changes in data growth rates applied by sub-sector availability and quality. When the BUR1 sectoral growth rates in Table 4.11 are applied to the 2014 • BUR1 sectoral growth rates baseline inventory, the following BAU emissions are derived (Figure 4.6 and Table 4.14). Table 4.13: Projected Business-as-Usual Emissions Scenario for Amman Target/Projected Emissions (t CO2e) Options Base Year 2025 2030 2050 2014 BAU 7,524,516 10,778,751 12,612,696 23,159,133 BUR1 projected sectoral growth rates: most conservative/ realistic Source: Ricardo Energy and Environment 104 APPLICATION Figure 4.6: BAU Emissions for Amman (based on BUR1 sectoral growth rates) 8,000,000 Residential energy 7,000,000 Residential electricity 7,000,000 Institutional & commercial energy Institutional & 6,000,000 commercial electricity tons CO2e Manufacturing industries 5,000,000 & construction energy Manufacturing industries 4,000,000 & construction electricity Road transport 3,000,000 Solid waste Wastewater 2,000,000 1,000,000 0 2014 2016 2020 2030 2040 2050 inventory Source: Ricardo Energy and Environment Table 4.14: Projected Business-as-Usual Emissions for Amman by Sector Total Projected Sector Actual Projected Emissions (tCO2e) under BAU Scenario Volume of Emissions 2014-2050 (tCO2e) 2014 Inventory 2016 2025 2030 2050 Total Residential Energy 483,249 505,437 618,588 662,792 738,167 23,838,783 Residential Electricity 1,968,947 2,133,710 3,063,318 3,744,947 8,364,891 164,363,529 Institutional & Commercial 324,178 361,982 594,637 757,120 1,394,241 29,999,014 Energy Institutional & Commercial 1,650,150 1,788,236 2,567,329 3,138,594 7,010,511 137,751,033 Electricity Manufacturing Industries & 54,447 60,808 99,977 127,234 234,303 5,041,463 Construction Energy Manufacturing Industries & 273,707 296,611 425,837 520,591 1,162,817 22,848,446 Construction Electricity Road Transport 2,267,555 2,355,928 2,798,209 2,993,734 3,301,187 107,918,525 Solid waste 264,292 273,865 321,421 351,323 501,459 13,714,128 Wastewater 237,991 246,612 289,435 316,362 451,557 12,349,392 TOTAL 7,524,516 8,023,189 10,778,751 12,612,696 23,159,133 517,824,311 Source: CURB analysis by Ricardo Energy and Environment 105 From the analysis used to derive Table 4.14, the It should be possible to develop a number projected total volume of emissions under the of baselines using different assumptions; of business-as-usual scenario is as follows: these, the most conservative approach should be chosen for emission baselining, unless there • 2014-2025: 108,628,773 tons CO2 e are clear reasons not to select it. For example, • 2026-2030: 59,260,529 tons CO2 e different time periods (shorter and longer data series) can be used to develop projections. Once • 2030-2050: 349,935,009 tons CO2 e the most conservative baseline is established, all savings below this baseline should be eligible to Inclusive of the period 2014-2050, under the earn credits under the crediting program. BAU scenario, Amman is projected to emit 517,824,311 tons CO2e. As noted, for Amman, it is recommended that baselines be set separately for each sector to ensure that the most conservative baseline can be chosen and tailored to each sector. This will also enable the option in the next step to choose Step 4: Set a crediting baseline different baseline dynamics for each sector. This will also reinforce the environmental integrity of A crediting baseline should be set for the scheme, as some sectors might have more a city carbon crediting program, which significant or frequent changes in data availability would be more ambitious than the BAU and quality. projection. It should be aligned with city carbon mitigation targets. In the case of Amman, city-wide targets would be as follows: 14 percent on BAU by 2025; 40 For a non-NDC aligned carbon crediting percent by 2030; and 99.5 percent by 2050. program, the key point is to ensure These have been used to determine the crediting environmental integrity in setting a carbon baseline and volumes (Table 4.15). Some further crediting baseline. This means that the credited issues and considerations are presented in the savings are “real” savings. summary section. Table 4.15: Crediting Baseline for Amman Target/Projected Emissions (t CO2e) Options Base year 2025 2030 2050 2014 BAU 7,524,516 10,778,751 12,612,696 23,159,133 BUR1 projected sectoral growth rates: most conservative/ realistic Target (BAU scenario) -14% -40% -99.5% Crediting baseline (target emissions on BAU) 7,524,516 9,269,726 7,567,618 115,796 Source: Ricardo Energy and Environment 106 APPLICATION Table 4.16: Projected Target Emissions for Amman by Sector Total Projected Projected Emissions (tCO2e) under Target (crediting) Sector Actual Volume of Emissions Scenario – Reductions on BAU 2014-2050 (tCO2e) 2014 inventory 2016 2025 (-14%) 2030 (-40%) 2050 (-99.5%) Total Residential Energy 491,765 531,986 397,675 3,691 9,822,990 14,015,793 Residential Electricity 2,075,994 2,634,453 2,246,968 41,824 49,434,061 114,929,468 Institutional & Commercial 352,190 511,388 454,272 6,971 9,244,881 20,754,133 Energy Institutional & Commercial 1,739,865 2,207,903 1,883,156 35,053 41,430,073 96,320,960 Electricity Manufacturing Industries & 59,163 85,980 76,340 1,172 1,553,685 3,487,778 Construction Energy Manufacturing Industries & 288,587 366,220 312,355 5,814 6,871,910 15,976,535 Construction Electricity Road Transport 2,292,200 2,406,460 1,796,240 16,506 44,901,740 63,016,784 Solid waste 266,457 276,422 210,794 2,507 5,261,595 8,452,532 Wastewater 239,941 248,914 189,817 2,258 4,737,998 7,611,394 TOTAL 7,524,516 7,806,163 9,269,726 7,567,618 115,796 173,258,933 From the analysis used to derive Table 4.16, the projected total volume of emissions under the target (crediting) scenario is as follows: • 2014-2025: 100,314,584 tons CO2 e • 2026-2030: 41,090,013 tons CO2 e • 2030-2050: 31,854,335 tons CO2 e. Inclusive of the period 2014-2050, under the BAU scenario, Amman is projected to emit 173,258,933 tons CO2 e. Should Amman achieve targets and emissions consistent with the target/crediting baseline, the following volumes could be avoided (based on the BAU). These would then become eligible for credit generation under the urban credits program (Table 4.17). 107 Table 4.17: Project Emissions Available for Credit Generation Indicative Projected Aggregated Emissions Savings Eligible Sector Total 2-year Period for Credit Generation 2014-16 2015-2025 2026-2030 2031-2050 2015-2050 Residential Energy 20,402 493,523 982,944 12,539,326 14,015,793 Residential Electricity 85,628 2,310,799 5,321,574 107,297,095 114,929,468 Institutional & Commercial Energy 14,456 428,559 1,062,063 19,263,511 20,754,133 Institutional & Commercial 71,764 1,936,652 4,459,945 89,924,364 96,320,960 Electricity Manufacturing Industries & 2,428 72,033 178,507 3,237,238 3,487,778 Construction Energy Manufacturing Industries & 11,903 321,228 739,761 14,915,546 15,976,535 Construction Electricity Road Transport 95,202 2,256,638 4,441,977 56,318,169 63,016,784 Solid waste 11,072 260,331 517,628 7,674,573 8,452,532 Wastewater 9,970 234,425 466,117 6,910,852 7,611,394 TOTAL 322,825 8,314,189 18,170,515 318,080,675 344,565,379 Source: Ricardo Energy and Environment Step 5: Define the crediting baseline two inventories for 2014 and 2016. While it is the dynamics city’s intention to prepare its inventory annually, the two-year interval appears more suitable for the Policy makers should determine whether crediting baseline update. It would allow the city a static or dynamic baseline will be used, to maintain the relevancy of the baseline, while and how often it will be revised. This avoiding the effect of short-term fluctuations in will require an assessment of a trade- any of the underlying parameters. off between investment certainty and environmental integrity. Following this approach, once an updated inventory is available, the evaluation of the The city of Amman will need to identify the underlying factors affecting the emission city’s priorities for the crediting program projections should be undertaken and the in order to define the approach to updating crediting baseline updated. The evaluation the crediting baseline. The program will should be done following the same methodology require extensive capacity building for the local used for development of the original crediting government, building trust among investors and baseline. supporting the credibility of the scheme. Thus, it may be beneficial to choose the balanced Amman may undertake improvements to the approach requiring a dynamic ex-ante baseline. quality of the GHG inventory. Thus, consideration of the impact of data quality improvements on The review of the baseline can be performed historical and current inventories, and baseline every two years following the publication of the projections will be required. The options for doing city GHG inventory. To date, Amman has prepared so are summarized in Figure 4.7. 108 APPLICATION Figure 4.7: Options for Updating the Baseline with Improved Data Options for updating the baseline when improved data is available Baselines are set ex-ante for each two-year period Baseline projections are No No update dependent on same data as city in baseline inventory used for ‘actuals’ necessary Crediting perspective: Inventory ‘good practice’: No change made to Update baseline in past as well current baseline only as in future Yes for future periods Yes Is historic data Update in quality available to be of data? updated? No Significant Yes change in emission Dependent on Yes factors No data available No OPTION 1 OPTION 2a OPTION 2b OPTION 3 OPTION 4 OPTION 5 Use Continue to use Ex-post Update the base Estimates should The target year baseline the old data until update of year inventory be made of the inventory should as set the next scheduled baseline by using the same emissions that may be additionally and apply update of the changing improved have been generated calculated using discounting baselines (which updated methods and if using revised the previous lower factor to will be less than emission data sources, methods, through quality data/ address 2 years away). factor data and re-project scaling or projecting method(s) to enable uncertainty. Keep using same only, follow the BAU backwards for the a comparison of the discounting factor option 2a scenario using sector(s) in question. likely magnitude of for uncertainty of for rest of the same growth This should then change between previous data. dataset. rates applied to be the basis for a the new and old the revised base re-projection for methods fand this year to enable a the BAU scenario to factor applied to comparison with enable a comparison uplift/downscale the target year. with the target year. the base year and BAU scenario. Investor certainty Environmental integrity Ease of implementation Source: Ricardo Energy and Environment 109 It is recommended that baselines be set ex-ante • Updating the base year inventory using the for each two-year period. In this case, if the same improved methods and data sources, and baseline projections are based on (some of) the re-projecting the BAU scenario using the same same data used for the inventory to calculate growth rates applied to the revised base year. actuals, then a decision on updating the baseline This would enable a fair comparison with the needs to be taken if new or updated data target year. becomes available. • If no historic data is available, estimates can be made of the emissions that may have been If the quality of data is improved within the two- generated if revised methods were used. This year time frame, it may be decided that the city could be done through scaling or projecting will continue to use the old data until the next backwards for the sector(s) in question. It can scheduled update of the baseline (which will be less then be the basis for a re-projection for the BAU than two years away). The calculated discounting scenario, thereby enabling a comparison with factor (see step 6) will still apply to the emission the target year. reductions for which credits will be issued. This • Depending on data availability, the target year option is easier to implement, but it risks that inventory can be calculated using the previous credits will not represent real emission reductions. lower quality data/method(s) to enable a comparison of the likely magnitude of change If a substantial change in the emission factors between the new and old methods. This factor occurs, for example, if a nuclear plant comes can then be applied to uplift/downscale the base online, an ex-post update of the baseline can year and BAU scenario. be made by changing the updated emission factors. For the rest of the data, no update will Figure 4.8 shows that there may be a need to be made until the scheduled biennial update. This revise the baseline ex-ante based on revised option might be harder to implement based on data, as well as improved data quality. For the availability of emission factor data. However, instance, emissions have grown faster than as the most substantial changes in data are expected under the BAU for 2014, and over the expected to be due to a different electricity mix, it 2-year period to 2016. Therefore, it would be is recommended separating electricity data and its important to determine if the assumptions for corresponding discounting factors from the general growth require revision, or whether emissions are dataset. As noted, while discounting factors and growing at a faster rate than would be expected baselines may be applied on a sectoral level, under BAU conditions. The BAU has not been credits will be issued on a city-wide level. redrawn at this point as this example is indicative. However, it would be possible to update the CURB In order to improve the inventory quality, model with the 2016 inventory and generate a new other updates to the baseline may instead be set of BAU projections. undertaken. All of these options may impact on the supply of credits, but they can improve the Taking the period 2014-2016 as an example of environmental integrity of the scheme. The options a two-year crediting period (with no application include: of discounting in this instance), Table 418 shows that Amman would, at a city-wide level, generate no credits because emissions have increased 12 percent, and are 5.4 percent higher than the BAU projection. In addition, they are 8.3 percent higher than the target (assuming a linear target to 2025). 110 APPLICATION Figure 4.8: Comparison of Amman’s 2014 and Actual versus Projected BAU 2016 Emissions 2014 actual 2016 actual 2016 BAU 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 RESIDENTIAL COMMERCIAL MANUFACTURING ON-ROAD SOLID WASTE WASTEWATER BUILDINGS AND INSTITUTIONAL INDUSTRIES TRANSPORTATION BUILDINGS AND AND FACILITIES CONSTRUCTION Source: Ricardo Energy and Environment Table 4.18: Comparison of Actual versus Projected Emissions for 2016 (target emissions and actual savings achieved) Tons CO2e Actual Sector Projected 2014 Actual Actual Savings 2016 BAU 2016 Actual 1 2016 Target Savings (baseline) minus BAU relative to 2015-162 Target Residential Energy 483,249 505,437 563,668 58,231 491,765 20,402 71,903 Residential Electricity 1,968,947 2,133,710 2,167,503 33,793 2,075,994 85,628 91,509 Institutional & Commercial 324,178 361,982 324,178 -37,804 352,190 14,456 -28,012 Energy Institutional & Commercial 1,650,150 1,788,236 1,756,943 -31,293 1,739,865 71,764 17,078 Electricity Manufacturing Industries & 54,447 60,797 57,389 -3,408 59,163 2,428 -1,774 Construction Energy Manufacturing Industries & 273,707 296,611 288,737 -7,874 288,587 11,903 150 Construction Electricity Road Transport 2,267,555 2,355,928 2,726,383 370,455 2,292,200 95,202 434,183 Solid waste 264,292 273,865 313,462 39,597 266,457 11,072 47,005 Wastewater 237,991 246,612 258,990 12,378 239,941 9,970 19,049 TOTAL 7,524,516 8,023,189 8,457,254 434,065 7,806,163 322,825 651,091 Source: Ricardo Energy and Environment Note: 1 Negative number indicates a savings relative to the BAU. 2 Based on the BAU and target trajectory. 111 Step 6: Apply discounting Overall, the discounting factors for each category of emissions within Amman’s inventory can be Given the high uncertainty and often low generated and applied as shown in Figure 4.14. quality data typical of city-level emissions data, a discounting approach will need to The assessment of Amman’s data quality be developed. resulted in an overall inventory-weighted discount of 56 percent (sectoral) and 54 percent The discounting approach will need to be when including overarching criteria. The largest applied to accommodate data quality issues and factor contributing to the score was the temporality uncertainties. If detailed information about data of the data (the data was not consistent with the uncertainties is available, a more detailed approach inventory year), followed by the use of national data toward the calculation of discounting factors can scaled to the city. The sector with the lowest overall be applied. As such, the discounting factor would score was Stationary Energy, which then inflated always reduce the amount of achieved savings to the the overall score of the sector, thereby contributing lowest extreme of the uncertainty range. the most to overall emissions. Given that the Amman inventory data primarily The discounting factors applied to each sector falls within the “low” quality category, a more or subsector must be reviewed and evaluated conservative approach can be applied. In this following the publication of each new inventory, instance, a tool covering the main data quality together with the baseline updating. This means issues has been developed to objectively assess that as cities improve their data quality, their and score data quality. It is based on set criteria scoring improves. The discount also decreases, that align with the GPC’s classification of data providing an incentive to enhance the accuracy and as high, medium or low quality. It would then be relevance of the data underpinning their inventory used to arrive at a discount factor weighted by and the crediting approach. The following tables the magnitude of the emissions for each sector. show the weighted discounts applied for Amman. Table 4.19: Inventory: Weighted Discounts for Amman Sector/ 2014 Sub-sector Emissions GPC Ref No. Sector and Sub-sector Scope Data Quality (metric tons Score CO2e) 1 0.63 861,874 I STATIONARY ENERGY 2 0.45 3,892,804 II TRANSPORTATION 1 0.72 2,267,555 III.1.1/2 Solid waste generated in the city 1 0.86 264,292 III.4.1/2 Wastewater generated in the city 1 0.41 237,991 Source: Ricardo Energy and Environment Table 4.20: Overarching Data Quality Discount for Amman Total Inventory Quality Total Discount Medium quality 0.95 Assurance 56% Inventory weighted discount (sectoral) 54% Inventory weighted discount (sectoral + overarching) 112 APPLICATION Scores are combined using the sum product of the scores, weighted by emissions, and multiplied by the overarching discount. Table 4.21: Indicative Discounted Emission Volumes Eligible for Credits Projected Discounted, Aggregated Emission Sector Inventory Total Savings Eligible for Credit Generation Weighted Discount Score 2015-2025 2026-2030 2031-2050 2015-2050 Residential Energy 0.63 295,374 588,292 7,504,787 8,388,452 Residential Electricity 0.45 983,476 2,264,862 45,665,644 48,913,982 Institutional & Commercial Energy 0.63 293,134 726,451 13,176,242 14,195,827 Institutional & Commercial 0.45 824,239 1,898,153 38,271,809 40,994,201 electricity Manufacturing Industries & 0.63 43,112 106,836 1,937,487 2,087,435 Construction Energy Manufacturing Industries & 0.45 136,715 314,842 6,348,056 6,799,613 Construction Electricity Road Transport 0.72 1,543,540 3,038,312 38,521,628 43,103,480 Solid waste 0.86 211,454 420,443 6,233,672 6,865,569 Wastewater 0.41 90,863 180,667 2,678,646 2,950,176 Discounted total (sectoral weighted scores + overarching 4,421,907 9,538,859 160,337,970 174,298,735 discount) Discounted total (total inventory weighted score + overarching  0.54 3,860,665 8,437,416 147,699,661 159,997,742 discount) Table 4.22: Amman: Assessment of Data Quality for Discounting Combining data Accompanying (national data) Inventory year Uncertainty in (project data) quality score Data checks Data scaling Data scaling Data source Sector/Sub- information sector data Proxy data Overall EF boundary Inventory reporting quality quality Scope National 1 LQ 0.7 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 MQ 0.9 0.63 Energy Data National 2 LQ 0.7 MQ 0.8 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 LQ 0.8 0.45 Energy Data 1 Traffic Model HQ 1 MQ 0.8 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 MQ 0.9 0.72 Waste volume HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 MQ 0.95 MQ 0.9 0.86 1 Wate 0.71 HQ 1 LQ 0.6 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 MQ 0.95 MQ 0.9 0.51 composition GAM 1 HQ 1 LQ 0.6 MQ 0.85 HQ 1 HQ 1 HQ 1 HQ 1 HQ 1 LQ 0.8 0.41 population Most critical data 5.4 4.8 5.85 6 6 6 6 5.9 5.2 qulaity issue 113 4.3 IMPLEMENTATION OF THE PILOT URBAN CREDITING SCHEME To test the urban carbon crediting program It is recommended that the piloting phase last in Amman, a pilot scheme is currently being for 6 years, disaggregated into three 2-year considered. Its implementation is expected to be crediting periods. To achieve a higher level of based on the methodological recommendations. environmental integrity, the baseline should be updated every two years. With the retrospective Implementation timeline. Taking into consideration application of the piloting scheme, the re- that Amman’s NDC was announced in 2016 baselining for the second crediting period will and the emission saving policies have been not be necessary as the initial baseline will be in the process of implementation since then, developed after the start of the second crediting it is recommended to start the piloting period period. For the third crediting period, however, the retrospectively from 2017. This would allow the re-baselining rule should be applied in line with city to gain credits for the achieved savings, the suggested methodology. which is expected to further encourage its mitigation ambitions. Following this approach, the Once Amman GHG inventories for the two years of implementation timeline is expected to look as the first crediting period (2017-2018) are available, shown in Figure 4.9. achieved emission savings can be calculated, which will in turn allow issuance of carbon credits. These credits will then be available for sale to international donors, as well as for voluntary carbon offsetting purposes. Figure 4.9: Potential Pilot Implementation Timeline End of the first End of the second End of the third crediting period crediting period crediting period 2017 2018 2019 2020 2021 2022 2023 Start of the scheme Baseline reassessment 2019 Inventory published Baseline developed Baseline updated 2017 onwards 2021 onwards 2017 Inventory 2018 Inventory published published Credits issued for the Source: Ricardo Energy and Environment first crediting period Note: Only the first crediting period is fully covered. 114 APPLICATION Piloting scheme projections Table 4.23 shows the potential volumes of Projections will need to be undertaken from the emission savings for each of the crediting periods. year 2017. The 2017 GHG inventory is currently Table 4.24 shows the potential discounted being compiled and should serve as the basis for volumes from a 2014 baseline. the pilot projections. Role of the Carbon Partnership Facility (CPF). The examples illustrate the steps that will need The CPF is expected to play the leading role in to be taken using the 2017 inventory once it is facilitating the implementation of the piloting available. The same steps have been applied scheme and becoming the major donor using the current base year of 201418, with BAU purchasing the generated urban carbon credits. and target projections from this baseline used to This will reward energy-saving measures generate the likely emission volumes eligible for implemented in the city. In addition, it will provide credit generation in the piloting periods 2017-19, the necessary funding for future climate change 2019-21, and 2021-23. mitigation policies and projects in Amman. 115 Table 4.23: Potential Aggregate Emission Savings Eligible for Credit Generation during Pilot Periods (2017-2023) Sectors Potential Aggregated Emission Savings eligible for Credit Generation (tCO2e) 2017-2019 2019-2021 2021-2023 2017-2023 Reduction on BAU scenario -7% -9% -12% Total Residential Energy 64,035 95,416 128,738 288,189 Residential Electricity 282,889 436,608 610,261 1,329,759 Institutional & Commercial Energy 49,871 79,289 114,178 243,338 Institutional & Commercial Electricity 237,086 365,916 511,452 1,114,455 Manufacturing Industries & Construction Energy 8,380 13,325 19,192 40,897 Manufacturing Industries & Construction Electricity 39,325 60,694 84,833 184,852 Road Transport 295,945 438,072 587,157 1,321,175 Solid waste 34,286 50,618 67,666 152,570 Wastewater 30,874 45,581 60,932 137,387 TOTAL 1,042,691 1,585,519 2,184,410 4,812,621 Table 4.24: Potential Discounted Aggregate Emission Savings Eligible for Credit Generation during Pilot Periods (2017-2023) Inventory Potential Discounted Aggregated Emission Sector Total Weighted Savings Eligible for Credit Generation (tCO2e) Discount Score 2017-2019 2019-2021 2021-2023 2017-2023 Residential Energy 0.63 38,325 57,106 77,050 172,481 Residential Electricity 0.45 120,935 186,650 260,887 568,472 Institutional & Commercial Energy 0.63 29,848 47,455 68,335 145,638 Institutional & Commercial electricity 0.45 101,354 156,429 218,646 476,429 Manufacturing Industries & Construction 0.63 5,015 7,975 11,487 24,477 Energy Manufacturing Industries & Construction 0.45 16,811 25,947 36,266 79,024 Electricity Road Transport 0.72 202,427 299,641 401,616 903,683 Solid waste 0.86 28,011 41,355 55,283 124,649 Wastewater 0.41 12,025 17,754 23,733 53,512 Discounted total (sectoral weighted 554,752 840,312 1,153,302 2,548,366 scores + overarching discount) Discounted total (total inventory  0.54 563,053 856,181 1,179,582 2,598,815 weighted score + overarching discount) Source: Ricardo Energy and Environment 116 APPLICATION 4.4 SUMMARY The crediting approach taken here is one One option for Amman might be to set a based on a clearly defined GPC-compliant crediting baseline for a new straight-line target GHG inventory covering the GAM area, not from 2014 to 2050. This would have several currently aligned with the national inventory. advantages. First, it is below the ‘BUR1 with Due to data limitations, the overall discount policies’ line in 2025 and (especially for NDCs) applied is quite high, but small improvements 2030. Thus, it would reflect some additional action to inventory quality can impact this score at the city level. Second, it would be conservative, significantly. as it would be below the city’s own target. The BAU projections result in several options Future emissions in Amman and future for Amman which should be reviewed and crediting levels could be highly dependent on tested, particularly with regard to the impact the timely delivery and initial operation of two of BUR1 baseline projection assumptions. nuclear power projects, as well as renewable Applying the BUR1 growth rates (generated and other major infrastructure projects. The through the BUR1 mitigation analysis and LEAP recommended approach is to update the biennial modelling) results in the most conservative ex-ante baseline, reflecting changes in views baseline. Thus, it has been used for as the about those future periods on the grounds that basis for these calculations. The ‘BUR1 with ex-post baseline updates are too complicated and policies’ scenario includes expected changes costly. In such a situation, the option of adjusting due to current policy, which results in a very the crediting baseline on an ex-post basis should optimistic trajectory early on — largely as a be considered. It would be based on actual result of significant changes to the grid mix, emissions factors (but not anything else), and such as nuclear and renewable power. There are may be a better balance between simplicity and questions of feasibility and achievability which accuracy (option 2b in Figure 4.7). lead to hesitation in selecting such an ambitious trajectory as a crediting baseline (indeed, one The potential volume of emissions eligible for lower than the current target trajectory). This credits, as estimated for the piloting phase, provides a second reason for the selection of the is based on the 2014 inventory and BAU. BUR1 growth rates scenario as the BAU. However, To date, there has been significant divergence if this ‘policy scenario’ is achievable (because they form the BAU as shown in the 2016 inventory, are changes that have already occurred ), then the demonstrating the need for re-baselining at the non-policy growth rates and the target should be end of crediting periods. The 2017 inventory for revised (if compared with the current position). In Amman is currently in development and should addition, the baseline data and assumptions about be used as the basis for the generation of the energy demand and the grid mix in CURB are in crediting BAU and baseline once it has been need of updating, particularly the emission factor published. and energy consumption data. This will result in a more accurate understanding of the impact of such BUR1 baseline policies. Furthermore, it will likely change the BAU and crediting baselines, as well as volumes generated. 117 MAIN RECOMMENDATIONS ✔ Work toward including additional sources and increase the geographic coverage to include all of ‘Amman’ (particularly where national alignment is sought). ✔ Improve inventory data quality in key sectors (particularly energy) and where updates can be easily made to enhance accuracy and improve the discount applied. ✔ Report an annual inventory where possible; as a minimum, report a biennial inventory. ✔ Undertake updated modelling of the BAU and crediting baseline based on the 2017 inventory and updated growth rate assumptions. ✔ Use a 2-year dynamic, ex-ante baseline approach. ✔ Apply discounting to account for data limitations ✔ Consider adjusting the crediting baseline on an ex-post basis; this would be based on actual emission factors only, where there is a considerable shift in conditions. ✔ Move toward national alignment. 118 APPENDIX 1 APPENDIX 1. GPC EMISSION SOURCES, SECTORS, SUB-SECTORS AND SCOPES Sectors and sub-sectors Scope 1 Scope 2 Scope 3 STATIONARY ENERGY Residential buildings ✔ ✔ ✔ Commercial & institutional buildings and facilities ✔ ✔ ✔ Manufacturing industries & construction ✔ ✔ ✔ Energy industries ✔ ✔ ✔ Energy generation supplied to the grid ✔ ✔ ✔ Agriculture, forestry and fishing activities ✔ ✔ ✔ Non-specified sources ✔ ✔ ✔ Fugitive emissions from mining, processing, storage and transportation of coal ✔ Fugitive emissions from oil and natural gas systems ✔ TRANSPORTATION On-road ✔ ✔ ✔ Railways ✔ ✔ ✔ Waterborne navigation ✔ ✔ ✔ Aviation ✔ ✔ ✔ Off-road ✔ ✔ WASTE Disposal of solid waste generated in the city ✔ ✔ Disposal of solid waste generated outside the city ✔ Biological treatment of waste generated in the city ✔ ✔ Biological treatment of waste generated outside the city ✔ Incineration and open burning of waste generated in the city ✔ ✔ Incineration and open burning of waste generated outside the city ✔ Wastewater generated in the city ✔ ✔ Wastewater generated outside the city ✔ INDUSTRIAL PROCESSES AND PRODUCT USE (IPPU) Industrial processes ✔ Product use ✔ AGRICULTURE, FORESTRY AND OTHER LAND USE (AFOLU) Livestock ✔ Land ✔ Aggregate sources and non-CO2 emission sources on land ✔ OTHER SCOPE 3 Other Scope 3 Sources covered by GPC Sources required for BASIC reporting Sources required for BASIC+ reporting Sources required for territorial but not for BASIC/BASIC+ reporting Sources included in Other Scope 3 Non-applicable emissions 119 Stationary energy (BASIC) directly by combusting fuel (Scope 1 if from travel The stationary energy sector covers emissions occurring within the city boundary; Scope 3 if from the combustion of fuels in buildings (Scope from trans-boundary journeys); or indirectly by 1); fugitive emissions (Scope 1); the consumption of consuming grid-supplied energy [Scope 2]; and grid-supplied electricity, steam, heating or cooling Scope 3 for transmission and distribution losses). (Scope 2); and emissions from the transmission and It is often one of the largest contributors to a city’s distribution of electricity (Scope 3). This sector is GHG emissions. broadly similar to the national-level energy sector in the IPCC Guidelines, specifically, the sub-sectors All cities have emissions from on-road of fuel combustion of 1A1 Energy Industries, 1A2 transportation occurring within the city (II.1), the Manufacturing Industries and Construction and 1A4 others depend on the geography, that is, whether Other Sectors. the city has a rail network (II.2), access to water (II.3) or has an airport within the city boundary All cities have residential (sector I.1 in the GPC (II.4). Aviation can be difficult and depends on notation) and commercial/institutional (I.2) sources, proximity of the airport to the city, where it lies and some have industries (I.3) (to a greater or lesser relative to the boundary. It also depends on the extent). The presence of energy industries (I.4) ability to obtain data from the mostly private varies considerably, and depends on the industrial operators, as well as the complexity of how the makeup of the city and the location and structure of city allocates the passenger numbers to the city. the energy supply within the country. For obvious The final component of transportation is off-road reasons, many power stations are not located in (II.5). Gathering accurate data is very challenging cities. Agriculture, forestry and fishing activities as there are only a relatively small number of off- (I.5) more often take place in developing cities and road vehicles and operations in transport hubs. can be hard to report (or at least disaggregate) It can be very hard to disaggregate the vehicle- due to their informal nature and the low data related data from other data on the site. quality/ lack of data. The reporting of non-specified sources (I.6) is rare, and is usually used to report Waste (BASIC) emissions related to military activities within the Waste is a sector that most cities have, and it city boundary. Fugitive emissions from mining, often comprises a small proportion of the city’s processing, storage and transportation of coal (I.7) total. However, it is an important sector, as it is are also rarely reported in cities. However, fugitive often something the city has a greater degree emissions from oil and natural gas systems (I.8) of control over as compared to other sectors. are often significant. Leakage from gas networks is The categories in the GPC for the waste sector usually reported here, when present in cities. match those in the IPCC. Waste emissions are generated through solid waste disposal (landfill) Transportation (BASIC) (III.1); biological treatment (anaerobic digestion This is one area of clear distinction between the and composting) (III.2); incineration and open way city and national scale emission inventories burning (III.3); and wastewater treatment (III.4). If are reported. Transportation is accounted for at the treatment occurs within the city, the emissions using BASIC. At the city level, emissions from are accounted for under Scope 1. If grid-supplied transportation are reported separately from other electricity is used in the waste treatment, these energy activities in the city, specifically reported in emissions are accounted for under Scope 2 in the category of Stationary Energy. However, this stationary energy. Finally, if the waste is treated is only really a minor accounting difference as the outside the city boundary, these emissions are sectors largely align. The sector covers emissions accounted for under Scope 3. The type of process from vehicles that produce GHG emissions varies depending on what technology the city 120 APPENDIX 1 has available to it. Incineration is occurs more Agriculture, Forestry and Other Land Use frequently (in hospitals) than people report, and (AFOLU) (BASIC+) open burning is an issue in developing cities. Emissions from AFOLU are produced from land- Thus, these activities are generally not well use changes altering the composition of the quantified. soil, as well as from the digestive processes of livestock and nutrient management. If these Industrial Processes and Product Use (IPPU) activities occur within the city-boundary, they (BASIC+) are accounted for under Scope 1. If the activity IPPU covers all non-energy related industrial itself occurs outside the city boundary and activities and product uses that generate the agricultural products are imported for emissions, but which do not originate from fuel consumption within the city-boundary, then they combustion. These are all Scope 1, as Scope 3 are accounted for under Scope 3. However, they emissions from IPPU are not yet covered in the are not included this sector, but in Other, Scope GPC. For most developing cities, it is not reported, 3. It is a sector that is not routinely reported. as these cities would likely be reporting a BASIC However, it is not generally problematic as it is (which does not require IPPU emissions reporting). not a significant source of emissions in most However, where there are significant industries, that cities. At the same time, it is also not a very well is, cement and steel, the IPPU reporting should be understood sector. Urban agriculture is often an in place. Product use is occurring in all cities, but important policy area, so greater efforts may be it is not routinely reported. Also, it generally uses needed to report on it. The key element is land scaled national data. use change, as many cities have been static in terms of action in this sub-sector for some time. Large development/expansion/land use changes should be quantified, but they are not likely to be a significant emissions source compared to others. 121 APPENDIX 2 APPENDIX 2. GPC-IPCC SOURCE CATEGORY MAPPING Priority for GPC BASIC/ GPC GPC Description IPCC Codes Inclusion in Urban Ref BASIC+ Scope Crediting Boundary I.1.1 Residential BASIC 1 1A4b I.1.2 Residential BASIC 2 1A1a end-user1 I.1.3 Residential BASIC+ 3 1A1a end-user I.2.1 Commercial / Institutional BASIC 1 1A4a I.2.2 Commercial / Institutional BASIC 2 1A1a end-user I.2.3 Commercial / Institutional BASIC+ 3 1A1a end-user I.3.1 Manufacturing Industry/Construction BASIC 1 1A2 I.3.2 Manufacturing Industry/Construction BASIC 2 1A1a end-user I.3.3 Manufacturing Industry/Construction BASIC+ 3 1A1a end-user I.4.1 Energy Industries BASIC 1 1A1 I.4.2 Energy Industries BASIC 2 1A1 I.4.3 Energy Industries BASIC+ 3 1A1 I.4.4 Energy Industries BASIC (T) 1 (T) 1A1 I.5.1 Agriculture/Forestry/Fishing BASIC 1 1A4c I.5.2 Agriculture/Forestry/Fishing BASIC 2 1A1a end-user I.5.3 Agriculture/Forestry/Fishing BASIC+ 3 1A1a end-user I.6.1 Non-specified sources BASIC 1 1A5 I.6.2 Non-specified sources BASIC 2 1A1a end-user I.6.3 Non-specified sources BASIC+ 3 1A1a end-user I.7.1 Fugitive coal BASIC 1 1B1 I.8.1 Fugitive oil and gas BASIC 1 1B2 II.1.1 Road transport BASIC 1 1A3b II.1.2 Road transport BASIC 2 1A1a end-user II.1.3 Road transport BASIC+ 3 1A3b II.2.1 Rail BASIC 1 1A3c II.2.2 Rail BASIC 2 1A1a end-user II.2.3 Rail BASIC+ 3 1A3c II.3.1 Waterborne BASIC 1 1A3dii Note: II.3.2 Waterborne BASIC 2 1A1a end-user 1 Scope 2 energy II.3.3 Waterborne BASIC+ 3 1A3d2 is reported on an II.4.1 Aviation BASIC 1 1A3aii end-user basis, II.4.2 Aviation BASIC 2 1A1a end-user but generation II.4.3 Aviation BASIC+ 3 1A3a2 is reported II.5.1 Off-Road BASIC 1 1A3eii under 1A1a (for II.5.2 Off-Road*** BASIC 2 1A1a end-user electricity). Thus, II.5.3 Off-Road*** BASIC+ 3 1A3e emissions should III.1.1 Solid Waste BASIC 1 4A be aligned here. III.1.2 Solid Waste BASIC 3 4A 2 This should III.1.3 Solid Waste BASIC (T) 1 (T) 4A be split by III.2.1 Biological treatment BASIC 1 4B cities between III.2.2 Biological treatment BASIC 3 4B domestic and III.2.3 Biological treatment BASIC (T) 1 (T) 4B international, III.3.1 Incineration/Open Burning BASIC 1 4C so they can be III.3.2 Incineration/Open Burning BASIC 3 4C aligned with either III.3.3 Incineration/Open Burning BASIC (T) 1 (T) 4C 1A3di/ii. Scope 3 III.4.1 Wastewater BASIC 1 4D can include both domestic and III.4.2 Wastewater BASIC 3 4D international. III.4.3 Wastewater BASIC (T) 1 (T) 4D 3 This is very IV.1 Industrial Processes BASIC+ 1 2A-E & 2H rarely reported by IV.2 Product Use BASIC+ 1 2F & 2G cities and is not V.1 Livestock BASIC+ 1 3A significant. V.2 Land Use BASIC+ 1 3B V.3 Aggregate Sources BASIC+ 1 3C VI.1 Other Scope 3 3 N/A 122 APPENDIX 2 1 Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories Solvent Land – Use Energy Industrial and Other Agriculture Change & Waste Processes Product Use Forestry 2006 IPCC Guidelines for National Greenhouse Gas Inventories Industrial Agriculture, Energy Processes and Forestry & Other Waste Product use Land Use Source: IPCC 123 APPENDIX 3 APPENDIX 3. REFERENCES Asian Development Bank (ADB). 2017. Future Carbon Fund. Delivering Co-Benefits for Sustainable Development. Available at: https://www.adb.org/sites/default/files/publication/389821/future- carbon-fund.pdf Briner, G. and S. 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Exploring New Crediting Approaches to Deliver Carbon and Climate Finance.” Available at: http://documents.worldbank.org/curated/en/269631538069214396/ pdf/130258-WP-LowCarbonCities-PUBLIC.pdf World Resources Institute and World Business Council for Sustainable Development. 2004. The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard. Available at: https:// ghgprotocol.org/corporate-standard World Resources Institute GHG Protocol. 2014. “Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC).” Available at: https://ghgprotocol.org/sites/default/files/standards/ GHGP_GPC_0.pdf 126 FOOTNOTES 1 Under the Clean Development Mechanism, emission-reduction projects in developing countries can earn certified emission reduction credits. These saleable credits can be used by industrialized countries to meet a part of their emission reduction targets under the Kyoto Protocol.(UNFCCC) 2 A network of the world’s megacities committed to addressing climate change (www.c40.org). 3 An extreme manifestation of this can be found in South Africa, where municipal governments are required to purchase power from Eskom, the government utility, despite its reliance on dirty technology. The City of Cape Town is currently embroiled in a court case with the national government to gain the right to purchase low carbon power from independent power producers. 4 For example, where there are large variations in key variables which determine the GHG emissions, such as changes in the output of the projects or variations in key economic drivers. 5 Formulae for error propagation calculation can be found in Volume 1, Chapter 3, page 3.28 of the IPCC 2006 Guidelines for national GHG Inventories. See https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_3_Ch3_Uncertainties.pdf 6 https://www.energyplan.eu/othertools/national/markaltimes/ 7 Based on: https://openknowledge.worldbank.org/bitstream/handle/10986/21824/951940Partners00Box385315B00PUBLIC0. pdf;sequence=1 8 See Appendix 2 for a mapping of GPC sources against IPCC 2006 Guideline source categories. 9 Plus Scope 3 waste includes emissions from the city’s waste disposed of out of its boundary. 10 Indeed, some inventory reporting tools such as the ‘CIRIS’ (a tool developed by C40 for GPC reporting) include IPCC nomenclature in the emission categorization to aid this alignment and encourage greater disaggregation. 11 Note that where countries are using older versions of the IPCC Guidelines (1996) there will be misalignment of sectors here, as there was been an evolution of the reporting categories for the AFOLU sector. 12 https://staging.c40.org/programmes/climate-action-for-urban-sustainability-curb 13 Unreleased tool developed by C40. 14 Note that the GPC quality principles substitute Comparability for Relevance, suggesting a more inward-focussed approach than outward, in terms of global methodological or reporting comparability. 15 ... 16 From a C40-organized GPC workshop. 17 Data supplied by C40. 18 As the 2017 inventory is still in development, the 2014 inventory has been retained as the basis for projections in this example. 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