1. Prioritizing GHG Mitigation Options in Georgia: Development of MAC Curves for the Building Sector Govinda R. Timilsina, The World Bank, Washington, DC Anna Sikharulidze, SDC, Tbilisi, Georgia Eduard Karapoghosyan, SRIE, Yerevan, Armenia 33 rd USAEE/IAEE North American Conference Oct. 25-28 2015, Pittsburgh

2. Presentation Outline  Introduction  Methodology & Data  Results and Sensitivity Analysis  Conclusions

3. Introduction (1/2) -- Georgia Source: www.Google.co.in Small country in Central Asia with population 3.75 million Party to UNFCCC and ratified Kyoto Protocol in 1999 Actively participated in international climate negotiation, submitted INDC in Sept. along with 76 other parties; currently developing Low carbon development strategy Mainly depends on imports for energy supply (75%) More than 50% of electricity is generated from hydro

4. Introduction (2/2) – MAC Curve  Appeared as a main tool to prioritize GHG mitigation options in the last few years, particularly by business communities (e.g., McKinsey, Bloomberg)  However, the concept has been used in research and academia since early 90’s (UNEP 1992; Jackson 1991 & 1995; Timilsina and Lefevre 1998; Timilsina et al. 2000)  Simple theory and quickly implementable (therefore popular) and good for the first step screening of GHG mitigation projects  Has several limitation (e.g., project treatment in isolation)  Since it is extensively used despite its limitations, this study aims to correct some methodological drawback starting from the building sector

5. Methodology (1/4) C and E are discounted total costs and total emissions, superscripts M and B refers to GHG mitigation and baseline scenarios IC is annualized investment cost, FC is annual fuel and other O&M costs; AE is annual GHG emissions; t and r are year and discount rate

6. Methodology (2/4) Baseline scenario: Existing inefficient devices/processes Existing efficient appliances New inefficient device/processes New efficient devices/processes Mitigation scenario: Gradual (or rapid) replacement of existing inefficient devices/processes Continuation of existing efficient appliances Implementation of efficient device/processes instead of inefficient ones when new devices/processes are purchased Existing Building Replace now Wait until expiration Inefficient Efficient Inefficient Efficient New Building Efficient Inefficient

7. Methodology (3/4) Key difference compared to existing methodology  Dynamic approach (energy efficiency improvement would be realized gradually overtime)  When an existing device or process is replaced, investment cost includes not only the full cost of a new device/process but also remaining value of the existing device/process unless a market is available for an used device/process

8. Methodology (4/4) Sectors – Residential and commercial Devices – Light bulbs, refrigerators, electronic devices Processes – Roof insulation, wall insulation and window insulation

9. Data & Assumption (1/2) Data/assumption for both scenario:  No. building in the base year (2014)  Rate of building growth adjusting the demolishment of old buildings  Device/process penetration rate (% of total HH with refrigerators)  Investment costs of device/technologies  Energy consumption rate of device/technologies (or efficiency)  Operating hours of device/processes  Economic lives of devices/processes  Energy service requirement in a service area (e.g., heating or cooling service required in a living area)  Energy prices  Emission rate  Discount rate

10. Data & Assumption (2/2) Main Assumption - Baseline Scenario:  No replacement of existing inefficient device/process until it expires  Probability of adopting efficient devices/processes when existing expires lifespan  Probability of efficient device/process in the new buildings (e.g., 100% due to existing mandates) Main Assumption – Mitigation Scenario:  Increased penetration of energy efficient device/process in existing buildings  Increased penetration of energy efficient device/process in new buildings

11. Results (1/2)

12. Results (2/2) 2020202520302035 Residential Sector Total GHG emissions from heating, lighting, refrigeration, TV and washing machine use (million tons) 1.562.192.93.58 GHG mitigation from the options considered in this study (million tons) 0.090.190.350.52 % reduction of GHG emissions due to the options considered in this study 5.7%8.9%11.9%14.6% Commercial/public Sector Total GHG emissions from heating, lighting and street lighting (million tons) 0.240.310.390.45 GHG mitigation from the options considered in this study (million tons) 0.040.080.140.20 % reduction of GHG emissions due to the options considered in this study 16.3%26.4%35.9%43.3%

13. Sensitivity Analysis (1/2)  Changing the penetration rate of energy efficient technologies in the baseline scenario  Changing the rate of adoption of energy efficient technologies under the mitigation scenario  Changing the discount rate

14. Sensitivity Analysis (2/2)

15. Conclusions  The study made a methodological contribution by distinguishing between the existing and new buildings and also between the existing stock of appliances and new stock of appliances.  At the 6% discount rate all energy efficiency measures, but efficient TV sets, are found to be no-regret options (negative MAC), if discount rate is increased to 15%, all measures show positive MAC.  Energy inefficient lighting is the most attractive energy efficiency measure to reduce GHG emissions in Georgia, followed by efficient refrigerators, energy efficient roofing in commercial buildings, etc.  Residential lighting and window insulation offer the highest energy savings and GHG mitigation potentials in Georgia.  Experimentation on the likelihood of technology adoption in both existing and new buildings would be an natural extension of the study (especially when such an adoption is anticipated as a response to a market policy).

16. Reference  Timilsina, G. R., T. Lefevre and S. Sherstha (2000), Techno-Economic Databases for Environmental Policy Analysis in Asia: Requirements and Barriers, Pollution Atmospherique, pp. 79-88.  Jackson, T. (1995). Joint Implementation and Cost Effectiveness under the Framework Convention on Climate Change, Energy Policy, Vo. 23, pp 117-.  Jackson, T. (1991). Least-cost greenhouse planning supply curves for global warming abatement. Energy policy, Vol. 19, No. 1, pp. 35-46.  United Nations Environmental Program (UNEP) (1992) Atmospheric Brown Clouds. Regional Assessment Report with Focus on Asia, UNEP Nairobi.