How Do We Prioritize the GHG Mitigation Options? Development of a Marginal Abatement Cost Curve for the Building Sector in Armenia and Georgia

Armenia and Georgia are taking the climate change agenda seriously and contributing to efforts for mitigating global climate change through various ways, including preparation of low-carbon development strategies for their future economic growth. The improvement of energy efficiency is one of the key elements of the low-carbon development strategies. This study develops a methodology to estimate a marginal abatement cost curve for energy efficiency measures and applies it to the building sector in both countries. The study finds that among the various energy efficiency measures considered, the replacement of energy inefficient lightbulbs (incandescent lamps) with efficient lightbulbs is the most cost-effective measure in saving energy and reducing greenhouse gas emissions from the building sector. Most energy efficiency improvement options considered in the study would produce net economic benefits even if the value of reduced carbon is not taken into account.


Policy Research Working Paper 7703
This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gtimilsina@worldbank.org.
Armenia and Georgia are taking the climate change agenda seriously and contributing to efforts for mitigating global climate change through various ways, including preparation of low-carbon development strategies for their future economic growth. The improvement of energy efficiency is one of the key elements of the low-carbon development strategies. This study develops a methodology to estimate a marginal abatement cost curve for energy efficiency measures and applies it to the building sector in both countries.
The study finds that among the various energy efficiency measures considered, the replacement of energy inefficient lightbulbs (incandescent lamps) with efficient lightbulbs is the most cost-effective measure in saving energy and reducing greenhouse gas emissions from the building sector. Most energy efficiency improvement options considered in the study would produce net economic benefits even if the value of reduced carbon is not taken into account.

Introduction
Armenia and Georgia are small countries in the Caucasus region located between the Black Sea, Russia, Turkey, Azerbaijan and Iran thereby connecting Eastern Europe to Western Asia. Both countries are currently developing low carbon strategies 3 for their economic development while promoting economic growth and prosperity. The strategies aim to reduce the growth of greenhouse gas (GHG) emissions. The low carbon strategies are also important because both countries depend on imports for their oil and gas supply. Both countries submitted their Intended Nationally Determined The low carbon strategies, NAMAs, NC and INDC all present list of potential options that could be implemented to reduce GHG emissions. While various approaches could be adopted to prioritize GHG mitigation options, the tool often used in low carbon strategies 6 , NAMA and NC, is the marginal abatement cost (MAC) curves. Energy efficiency options are the most common GHG mitigation options identified in a MAC curve analysis. These options are often found as the cheapest ones with negative costs of GHG abatement in most existing literature on green growth and low carbon strategies (see e.g., World Bank, 2014;World Bank, 2013;Cervigni et al. 2013;de Gouvello et al. 2010;Johnson et al. 2009;Mckinsey and Company, 2009, 2010, 2011. However, the economies of energy efficiency vary across countries depending on the rate at which future energy consumption is expected to increase, the current level of inefficiency and awareness of the availability of the best practice technologies to improve energy efficiency. The concept of MAC curves has been used in the literature for a long time in determining potential for GHG mitigation from various options in different economic sectors (production sectors, buildings, transportation sectors). Examples of early studies using MAC curves include UNEP (1992), Jackson (1991Jackson ( , 1995, Timilsina and Lefevre (1998), Timilsina et al. (2000). Apart from academia, private companies, such as McKinsey & Company (Mckinsey and Company, 2009, 2010, 2011, Bloomberg (Bloomberg, 2010); and international institutions, such as World Bank (World Bank, 2009;2010;2012) have been widely using the concept the MAC curve to prioritize climate change mitigation options/technologies in various countries.
Although the literature on MAC curve is rich, there are still some methodological issues that need to be resolved. For example, should an MAC curve analysis compare potential GHG mitigation options in a static fashion as if all mitigation potential of an option can be exploited now, or should the MAC curve be based on analysis over a time period in which the GHG mitigation option, such as improvement of energy efficiency, is implemented gradually? The latter approach can be interpreted as a dynamic MAC curve analysis and it would be more realistic because it is impractical to assume that the full GHG mitigation potential of a measure is ready for exploitation in a short interval of time.
Another issue is if the investment costs of GHG mitigation be the same when: (i) an energy inefficient device or process already installed in an existing facility be replaced with new energy efficient device or process and (ii) when consumers face a choice between energy efficient and inefficient device/process to install in a new facility (e.g., choice of roof and window insulations in a building to be built). Further, what assumptions are to be made regarding the penetration of a GHG mitigation measure in the baseline where no policy measures are introduced to incentivize the implementation of the measure?
Existing studies have used different approaches to address these issues. Most existing studies do not differentiate between the existing and new facilities while developing MAC curves and thus use the same opportunity costs in both cases. Therefore, MAC curves produced by various studies are not comparable and the use of the same opportunity costs is misleading. Moreover, existing studies use different assumptions on penetration rate of GHG mitigation measures in baseline as well as climate change mitigation scenarios. This also leads to different calculations of GHG mitigation potential of the same measure.

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This paper aims to present a methodology to contribute in resolving these issues. The methodology is then implemented to calculate the MAC curve for selected energy efficiency measures in the building sector in Armenia and Georgia. One of the key contributions of the methodology developed here is that it distinguishes between the existing buildings and new buildings. This is critically important because the likelihood of adoption of energy efficient appliances is higher in the new buildings due to several reasons, such as awareness of consumers and already introduced energy efficiency standards. Moreover, the probability of adopting energy efficient measures that require major reconstruction/remodeling of existing buildings would be low. For example, households might not be willing to dismantle walls of their houses to replaces them with energy efficient walls. Existing studies are not found to take into account these important issues, they assume all energy inefficient devices and processes are replaced and thus likely to overestimate GHG mitigation potential of a GHG mitigation option.
The study also differentiates between existing stock of appliances and new stock of appliances especially when they make assumptions on the cost of an appliance. 7 The use of an incremental costs (i.e., the difference between the capital costs of an efficient appliance and its inefficient counterpart that would have been considered in the baseline) approach when the existing stock of inefficient appliances is replaced with their efficient counterparts and thus underestimates the MAC of that energy efficiency measure. Our study suggests to use the full cost of the new efficient appliances instead of the incremental costs in such a situation. This is because normally there does not exist markets for already used energy inefficient technologies. Building owners might need to pay disposal costs when they want to discard their old appliance. Our study has accounted all these issues, which are often neglected in the existing literature.
The paper is organized as follows: Section 2 presents the methodology we developed followed by data and assumptions in Section 3. Results and sensitivity analysis are presented in Section 4 and 5, respectively. Finally, key conclusions are drawn in Section 6. 7 The differentiation between existing and new buildings and also between existing stock of appliances and new stock of appliances has different implications. This is because some existing buildings might have already adopted new stock of appliances, if that is the case those new stock of appliances would be part of baseline. On the other hand, some new buildings could still use inefficient appliances. Thus, a baseline could include not only existing buildings but also new buildings depending upon whether or not the buildings use inefficient or efficient appliances. The same logic is applicable for the mitigation scenario as well.

Methodology
Normally two types of approaches are used to calculate MAC curves: static and dynamic. The static approach considers all GHG mitigation technologies/options are compared assuming as if they are implemented now, and all emissions reductions can be realized immediately. However, in reality, realization of GHG mitigation potential of a technology/option occurs over time. For example, not all buildings with inefficient lightbulbs could adopt efficient lightbulbs immediately; it might take years.
Hence, it is important to take into account the gradual process of energy efficiency improvement. A dynamic approach to calculate MAC can account for more realistic adoption trajectories of efficient appliances where adoption of energy efficient appliances occurs over time. Note also that the rates at which efficient appliances are adopted vary across the appliances. While existing buildings could adopt energy efficient appliances over time, all new buildings yet to be built could be mandated to use efficient appliances. Therefore, it is important to distinguish new and old buildings while estimating MAC curves.
The (dynamic) marginal abatement cost of an efficient appliance/technology/measure (MAC) is calculated as follows: where C and E refers to discounted total costs and total emissions, superscripts M and B refers to GHG mitigation and baseline scenarios. We can express C and E as follows: where IC refers to annualized investment cost, FC refers to annual fuel and other O&M costs and AE refers to annual GHG emissions; t refers to time, expressed in terms of year and r is discount rate. 8 The annual cost of investment or annuity of a measure/option, no matter whether it is installed in the baseline or mitigation scenario, is calculated as follows: where INV is the initial, up-front investment cost of a measure (e.g., device or process) and n is economic life of that measure. Note that 'n' is different from 't' in Equation 2. Because, during the 20 years of study horizon we have considered here, a measure, such as electric lightbulbs, might be installed at multiple times if their economic life 'n' is smaller than the span of the study horizon (i.e., 20 years).
Energy efficient appliances will penetrate gradually in the buildings. The rate of penetration would be different in the existing buildings and the new buildings. If an existing building already have an energy consuming device (e.g., refrigerator), the lifetime of which is not spent yet, it has to pay the full costs of the new or efficient appliances to replace the existing inefficient appliances because it has already paid for existing device. In this case (i.e. if such replacement is done), the total investment cost also includes the residual value of the existing appliance that is being replaced with the efficient appliance. On the other hand, if a household is buying a new appliance because it does not have an appliance already or its previous appliance needs to be retired, it has two choices: (i) buying a new but still inefficient appliance and (ii) buying a new efficient appliance. In this case, the net investment of the household buying a new and efficient appliance would be the cost difference between efficient and inefficient appliances; it does not need to pay the full cost of the efficient appliances (it pays only the incremental cost). Equations (4) and (5)  where, FPt and FUt are price and quantity of fuel used by a device or process in year t and N refers to number of devices or process installed.
In the same manner, different annual investment costs (ICt) and annual emission released from them (AEt) are calculated, except the annual investment cost of replacing existing device or process, which, in case of mitigation scenario also includes the residual value of the appliance to be replaced.

The annualized investment cost
We assumed various penetration rates based on historical data and current stocks of appliances (see Appendix A for details). In addition, each year, when technologies from pre-existing stock retire, they need to be substituted by new ones. This has been modeled by gradually reducing the number of preexisting technologies and substituting them with new technologies. New technologies will be also installed in new buildings that are created in the future. Baseline and mitigation scenario differentiate the penetration of efficient technologies that can occur in this new stock. In addition, the mitigation scenario considers possibility of earlier switching from non-efficient technology to the efficient technology in pre-existing stock. Early switching, in this sense, means that household purchases efficient technology before its current inefficient one is retired. In this cases it pays full cost of the new efficient appliances instead of the incremental costs. 9 Since the variable N is the most critical one as it determines the penetration of energy efficient device or processes in the total stock of the devices and processes in a country, special attention is needed dealing with this variable in both the baseline and the mitigation scenario. Please see Appendix 9 An appliance installed before 2014, which we used as base year, will continue to operate until the expiry of its economic life. The owner of the appliance has two choices: wait until the appliance operates end of its economic life to replace it with a new efficient appliance or replace it before the end its economic life. In the former case, the actual capital costs to be borne by the appliance owner/user are the incremental costs (i.e., the difference of the costs between efficient appliance and the inefficient appliance). In the latter case, the owner/user of the appliance has to bear the full costs of the efficient appliance as there would be no market for his used appliance.

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A for more details on the estimation of

Data and Assumptions
A wide range of data is required to calculate MACs for buildings sector. In this section we discuss the data and their sources. The data that is needed to calculate MACC are following: Armenia's low carbon study (USAID, 2014) was also used for data and assumptions related to penetration rates in the baseline and climate change mitigation scenarios.

Data Sources for Georgia
A large number of secondary sources have been used to collect the required data for energy efficiency MAC analysis for Georgia. Some of the key sources include recent household energy survey carried out by the Winrock International EC-LEDS project (Winrock International, 2014), which provided data on average household area, average household size (number of persons per household), average percentage of heated area in dwellings, share of households using gas for heating, penetration of end-use technologies, both efficient and inefficient (refrigeration, washing machine, lighting bulbs) in households. Energy Audits carried out by EC-LEDS project (Sustainable Development Centre Remissia, 2014) was used to collect information on costs of different insulation 9 measures in residential and commercial buildings and also for natural gas consumption for heating.
The demographic and economic projections (e.g., projections of population, household size and GDP) and projections of natural gas prices and average electricity grid emission factor are obtained from the MARKAL-Georgia model. All data sources used by the MARKAL-Georgia model are identified in Appendix B. National energy balance and statistical year book were used for fuel consumption values (National Statistics Office of Georgia, 2014)). Electricity prices were obtained from the local electricity distributor Telasi Ltd. (http://www.telasi.ge/en/customers/tariffs). The detailed data for Georgia Energy efficiency MAC analysis and their sources are presented in Appendix B.

Selection of Energy Efficiency Measures and Associated Assumptions
The estimation of MAC curve for the residential sector depends on several factors including number of households, demolition rate of existing households, rate of new household construction, percentage of existing households with efficient appliances, percentage of newly built households with efficient appliances, energy consumption rates of both efficient and inefficient appliances, costs of those appliances and other variables. There are variety of energy utilizing devices and process which could be considered for GHG mitigation in the buildings, such as space heating devices (kerosene stoves, electric heaters), water heaters, refrigerators, washers and dryers, cooking appliances, electronic devices (TV, computers). Two options are normally available to reduce GHG emissions from these device or processes: improving their thermal efficiency and replacing more carbon intensive fuels used by these devices with less or none carbon intensive fuels. For space heating we considered the insulation improvement measures, such as increasing wall insulation, reducing heat leakage from windows and improving roof insulations. We did not consider energy efficiency improvements of space heating devices (e.g., kerosene stoves, electric heaters) because the efficiency of these devices is already relatively higher (> 87%) thereby leaving a small margin to further improve their thermal efficiencies. Central space heating units (a unit that can supply heat through out a building) could be an option, however a central heating system may not be feasible physically in the existing buildings as it require major remodeling of houses and it would be also too expensive.
Moreover, the central heating systems are for heating the entire house or buildings, which would increase energy consumption and GHG emissions. The insulation improving measures we have considered here are also recommended by the energy audits (Sustainable Development Centre Remissia, 2014). The energy audit also show that ranges (cooking devices) and water heaters, which mostly use natural gas do not offer much room for efficiency improvements; fuel substitution, for 10 example replacement of gas water heaters with solar water heaters would provide higher potential for GHG reduction than through increased efficiency of gas water heaters. Therefore, the study does not include efficiency improvements in ranges and water heaters.
Other measures we considered in reducing GHG mitigation from the residential sector include lightbulbs, TV, refrigerator and washing machines. The average number of bulbs per household is 8, and they operate around 5.26 hours daily. Almost 70% of households use incandescent bulbs, 8% efficient bulbs, and 22% have both. Almost all households have TV, from which around 11% is new LED type TVs. 84% of household own refrigerator and among them 48% state that their refrigerator is energy efficient. Around 68% have the washing machine, 43% of which is purchased after 2010.
As for the cooling systems the typical cooling system in Georgia is local split system, used to cool one or two rooms. The central cooling systems are very rare in country, and would result in increase of consumption.
The GHG mitigation potential is sensitive to the assumptions made on their future penetration.
Historical rate of penetration could be a guidance. Government policies and incentive mechanism available also affect the rates of penetration. Based on these factors we have made assumptions on the penetration rates of measures and options with potential to reduce GHG emissions.
Like in the residential buildings, space heating is the main energy end-user in the commercial and public buildings. Therefore, improved building insulation provides the highest potential to save building energy consumption and GHG mitigation. The selection of these options in Georgia are based on energy audits (Sustainable Development Centre Remissia, 2015) and Sustainable Energy Action Plans (SEAP) of cities (Zugdidi SEAP 2014, Batumi SEAP 2014, Kutaisi SEAP 2014. Other technologies, such as refrigerators, cooling systems, etc. could also be considered but required data were not available.
The discount rate used in the analysis is a social discount rate, which is assumed to be 7.5%, which is commonly used for infrastructure project evaluation in Armenia and Georgia. Since the results are sensitive to discount rates, we have carried out sensitivity analysis.

Results
The marginal abatement cost curve consisting of various GHG mitigation options related to energy efficiency improvement is presented in Figure 1. The residential and commercial sector energy efficiency measures considered in this study are estimated to reduce 16.4 million tons of CO2 in 11 Armenia over the 2015-2035 period and 6.2 million tons of CO2 in Georgia over the same time horizon.
As illustrated in Figure 1, replacement of energy inefficient lightbulbs (i.e., incandescent lamps) with efficient lightbulbs (i.e., Light Emitting Diode or LED) is the most cost effective measure in saving energy and reducing GHG emissions from the building sector in both countries. These   Table 1 presents the combined potential of GHG mitigation from the different options considered in the analysis. We have considered the most important energy-end uses in the residential sector including space heating, lighting, refrigeration, cooling, washing machine and most widely used electronic devices (television sets). In Armenia, the adoption of efficient appliances in both existing and new residential buildings has a potential of reducing 19% GHG emissions in 2020 to 41% reductions in 2035. The corresponding potentials are relatively smaller in Georgia (6% in 2020 and 15% in 2035) because most of energy efficiency in the residential buildings in Georgia is expected to occur in the baseline due to existing policies and programs. The potential reductions of GHG emissions in the commercial sector are relatively smaller as compared to that in the residential sector in absolute amount (i.e., million tons). However, in terms of percentage reduction, the commercial sector potential is much higher than that of residential buildings in Georgia thereby implying more space to improve energy efficiency in the commercial and public buildings. Note however that the aggregate emission reduction are, however sensitive to several parameters assumed, such as rate of penetration or adoption of efficient devices and process in the buildings. We have carried out later sensitivity analysis on these parameters. We have also distinguished the GHG mitigation potential between the replacement of preexisting stock of appliances (i.e., appliances already in use as of base year, 2014) and the new stock of appliances. The latter includes appliances in (a) existing buildings which have not used any of the electrical appliance and are expected to use appliances in the future and (ii) new buildings. While existing buildings may not replace their windows, walls and roofs to improve energy efficiency unless they are aged enough to replace, new buildings have a complete freedom of selecting either efficient or inefficient appliances/measures (see Table 2). This differentiation reveals an interesting insight.
For electrical appliances (i.e., lightbulbs, refrigerators, washing machine and TVs), almost entire GHG mitigation potential accrue through new stock of appliances. The reason is that households normally do not replace their existing appliance only for the purpose of energy savings as long as the appliance is operating. For example, a household may not discard an existing refrigerator and buy a new one because the latter is more energy efficient; it replace the existing refrigerator only when it stops to work, in such a situation it has two choices, buying energy inefficient refrigerator or buying efficient one. The probability of a households to choose energy efficient brand when it purchases electrical appliances is discussed in data and assumption section above. The same logic is applicable to other appliances and measures, particularly washing machine, television sets, walls and roofs of buildings.
In such appliances and measures majority of GHG mitigation comes through new stock of appliances/measures.
On the other hand, some measures, for example, windows have long life span. Both Georgia and Armenia have introduced norms for new buildings to use windows with high insulation starting from 2014. Therefore, energy efficient windows in the new buildings is part of baseline in our study.
However, most windows in existing building stocks (i.e., existing as of 2014) are energy inefficient (or have low insulation). Thus, most potential in improving energy efficiency through improved window insulation, particularly in the residential buildings, exist in the pre-existing stock of windows.

Sensitivity Analysis
The calculations of marginal costs, GHG mitigation potential and the MAC curves are sensitive to several parameters, such as assumptions on the penetration rates of energy efficient devices and processes in future years, evolutions of costs of energy efficient technologies overtime, etc. Among these variables, penetration rate would be very sensitive because it influences both the baseline and mitigation or energy efficient scenario. To measure the level of sensitivity of penetration rates, we carried out analyses with varying rates of penetration in both baseline and mitigation scenarios.

Penetration of energy efficient technologies in the baseline scenario
It is very important to distinguish between the penetration of efficient technologies that occur by itself and as a result mitigation policies or programs or incentives. The penetration that occurs itself, without designed policies should be reflected in baseline scenario. For example, if existing standards require all new buildings to use better insulated windows, the penetration of energy efficient windows will be high in the baseline. If such a standard does not exist and buildings either unware of energy efficient appliances or do not have an incentive to adopt the energy efficient appliances, penetration of efficient appliances in the baseline would be low. As the mitigation potential is measured by comparing the situation in the baseline, the potential is sensitive to what is assumed in the baseline for the penetration of efficient appliances. Thus, we conducted a sensitivity analysis by changing the penetration rates energy efficient measures in the baseline. First, we assumed to decrease the penetration rates by 5 percentage point, followed by increasing the penetration rates by 5 percentage point. The results of this sensitivity analysis are displayed in Table 3. Let us take an example of refrigerator in Georgia to explain the sensitivity results presented in Table 3. Earlier in the main analysis, we assumed that 20% of newly bought refrigerators will be energy efficient. The sensitivity analysis shows that if the baseline penetration rate of refrigerators in the residential buildings is decreased to 15% from 20%, the marginal abatement cost would increase (here it becomes less negative) by about 1% to US$ -138/tCO2 from US$139/tCO2, on the other hand, if the baseline penetration rate is increased to 25% from 20%, the marginal abatement cost would decrease (here it becomes more negative) by about 1% to US$ -140/tCO2 from US$139/tCO2. The potential GHG mitigation from energy efficient refrigerators would increase by 21%, if we assume 5 percentage point less penetration of efficient devices in baseline situation and the potential GHG mitigation from the energy efficient refrigerators would decrease by 22% if we assume 5 percentage point more penetration of efficient devices in baseline situation. Note that lower penetration of energy efficient appliances in the baseline implies would mean more potential of GHG mitigation remains under the mitigation scenario and vice versa. The sensitivity analysis results presented in Table 3 reveal that marginal GHG abatement costs of energy efficiency measures are not much sensitive to their level of penetration in the baseline. But their GHG mitigation potential is highly sensitive to the baseline penetration rates.

Rate of adoption of energy efficient technologies under the mitigation scenario
Higher the rate of adoption of energy efficient technologies, higher would be the GHG mitigation potential of an energy efficient measure. To test this, we decreased the annual rates of adoption or change in penetration rates of energy efficient appliances by one percentage points followed by increasing the rates by one percentage point. The results are presented in Table 4. Let us take the example of refrigerator in Georgia again to explain the sensitivity analysis results presented in Table 4. In the main analysis earlier, we assumed that the penetration rate of efficient refrigerator would increase by 3% annually under the mitigation scenario. If the annual growth of the penetration rate of efficient refrigerators in the residential buildings is increased to 4% from 3% before, the marginal abatement cost would increase (becomes more negative here) by 7.5% to US$ -149/tCO2 from US$ -139/tCO2. The GHG mitigation potential of efficient refrigerators would increase more significantly, by 57%, because more efficient technologies penetrate in mitigation scenario, more emissions are saved. If the penetration rate of efficient refrigerators is increased more slowly in mitigation scenario, by 2%, instead of 3% in the main analysis, the marginal abatement costs would decrease by 24%. The mitigation potential also decreases by 59% because if less efficient technologies penetrate in mitigation case less emissions are saved. Thus, the marginal abatement costs as well as the GHG mitigation potential are sensitive to the assumption on the change in penetration rates of energy efficient technologies.

Sensitivity on discount rate
The magnitude of marginal abatement costs are sensitive to discount rate. Many GHG mitigation measures with negative marginal abatement cost derived using social discount rates may not be implemented with higher discount rates. Private investors (either banks or individuals) normally uses higher discount rates because they see higher risks in new and emerging technologies. To reflect this situation, we doubled discount rate from 7.5% in the main analysis to 15% in the sensitivity analysis. All energy efficiency measures except energy efficient walls, windows and lighting systems in the commercial building, have now positive marginal abatement costs. Note that all energy efficiency options except television sets had negative marginal costs in the main analysis where 7.5% discount rate was used.

Conclusions
This study develops a methodology to estimate a marginal abatement cost curve for energy efficiency measures and applies the methodology in the building sector in Georgia. The key contribution of the methodology is that it distinguishes between the existing and new buildings and also between the existing stock of appliances and new stock of appliances.
The study shows that all energy efficiency improvement measures considered with exception of television sets are negative cost options meaning that these options saves more money by reducing energy consumption than that invested for their implementation, at the same time they reduce GHG emissions. However, this attractiveness of the energy efficiency options are highly sensitive to several parameters, particularly the discount rate. When discount rate is doubled to 15% in the sensitivity analysis from the original value of 7.5% in the main analysis, almost all GHG mitigation options turned to be positive cost options. The study also shows that the potential for GHG mitigation of a measure will depend on the assumption on their penetration rates under the baseline as well as under climate change mitigation or energy efficient scenarios. If a higher penetration rate of an energy efficient appliance is assumed in the baseline due to already existing standards and regulations, the The early retirement of non-efficient technologies in pre-existing stock will require additional new stock, which means that in mitigation scenario we will need more new stock of efficient technologies. The new stock of technologies in mitigation scenario, will be calculated as: is 100 sq.m. and 89% of households use modular heating options, such as electric heater, kerosene stoves that heat only a portion of the households 10 and on average 43% of dwelling living space is heated. Although about 60% of the total households have access to natural gas, only 38% use it for heating. Majority (57%) of the households still use firewood for space heating. Electricity is usually used as supplementary heating fuel for other types of fuels -only 6% use it as a main source. The general tendency is that households switch over to natural gas as their income rise in places where gas distribution infrastructure exists. We assumed that households not only switches to natural gas for space heating but also will increase the heated area as income rises. Since the Winrock survey is the first of its kind carried out in Georgia and there exist no earlier survey which would help to determine trends, expert assessment was used to make projections for future. It was assumed that annual rate at which heated area increases is 2% and the number of households switching to natural gas increases by 1% per year. The Ministry of Energy assumes that by 2035 75% of population will have access to gas.
Major appliances owned by households are TV, refrigerator and washing machines. Almost all households have TV, from which around 11% is new LED type TVs. 84% of household own refrigerator and among them 48% state that their refrigerator is energy efficient. 68% of households use washing machines, 43% of which is purchased after 2010. The average number of bulbs per household is 8, and they operate around 5.26 hours daily. 70% of households use incandescent bulbs, 8% efficient bulbs, and 22% uses both. As for the cooling systems the typical cooling system in Georgia is local split system, used to cool one or two rooms. The central cooling systems are very rare in country, and would result in increase of consumption. The key technical and economic characteristics of appliances used in the household buildings are summarized in Table A1.

Commercial Sector
Commercial sector includes commercial buildings as well as street lighting. The assessment of MACs for commercial sector was performed based on space area of commercial sector that uses gas for heating and number of bulbs used for street lighting. There are no statistics about the space amount of commercial sector of Georgia, so it was calculated based on energy audits of several commercial buildings performed in different cities of Georgia (Sustainable Dvelopment Centre Remissia, 2015). As a result, the average commercial area that is heated by gas was assessed as around 5 mln sq.ms. The growth rates than were based on GDP growth projections from commercial sector and elasticity from MARKAL-Georgia (MARKAL-Georgia, April 2015).
The key technical and economic characteristics of appliances used in the commercial buildings are summarized in Table A4. Other technologies, such as refrigerators, cooling systems, etc. can be also considered for commercial sector, but they were not assessed here because there are no data available for the moment about types of the technologies that are used in commercial sector and their penetration.
The future penetration rates of efficient technologies and insulation measures in commercial sector are based on expert assessment. Theble A5 shows the assumptions on shares of efficient technologies in new stock in baseline and mitigation scenarios.