Clean Development Pathways for India

Transcription

1 Prof. Valerie Karplus Asst. Professor, MIT Sloan School of Management Clean Development Pathways for India Evaluating Feasibility and Modeling Impact of Policy Options Arun Singh MS Student in Technology and Policy Program (TPP), MIT Dr. Niven Winchester Principal Research Scientist, MIT Joint Program on Global Change Image source: 40 th Annual IAEE International Conference Singapore June 21, 2017

2 Outline Motivation Research Questions Method Results Conclusion 2

3 Motivation: As a growing economy with low energy access levels, India needs to expand its energy base while limiting GHG emissions India is the third largest emitter of CO 2 in the world (Source: IEA 2015; Table below represents CO2 emissions in 2013) But India s per capita emissions are 1/3 rd of the global average (Source: WRI 2014) And 19% of the population lacks access to electricity (Source: IEA 2015) India s Nationally Determined Contributions (NDCs) to the Paris Agreement Reduce the emissions intensity of GDP by 33 to 35 percent by 2030 from 2005 level About 40 percent cumulative electric power installed capacity from non-fossil fuel based energy resources by 2030 with the help of transfer of technology and low cost international finance including from Green Climate Fund (GCF) 3

4 Research Question: How do India s policy alternatives for achieving its NDC impact the economy, emissions, and energy system, and what could be the synergies across policies? Economy Reducing emissions intensity of the GDP How does consumer welfare compare across policies? What are the observed carbon prices to achieve emissions intensity reduction with and without non-fossil electricity targets? Emissions What levels of emission reduction are achieved under different policies? What are the sector-wise emission trends under different policies? Energy System Enforcing non-fossil electricity capacity How does the electricity mix change under different policies? How would falling costs of wind and solar generation interact with the policy impacts? 4

5 Method: A Computable General Equilibrium (CGE) Model of India s Economy The activities of these agents and their interactions are defined by the following three conditions (Arrow & Debreu 1954; Wing 2004): Zero profit conditions Cost-benefit analyses for economic activities of the agents: output for firms and utility for households. In equilibrium, Marginal Cost equals Marginal Benefits. Market clearing Equilibrium price levels equalize supply and demand across all markets Income Balance Income levels of households and government should equate their spending 5

6 Method: CGE Model for India - Sectoral Representation and Benchmark Data 12 energy sectors Industrial Sectors 8 electricity: Coal, Oil, Gas, Nuclear, Hydro, Wind, Solar, T&D 4 other fuels: Coal, Crude Oil, Gas, Refined Oil 6 commodities Agriculture, Food, Minerals, Energy Intensive Goods, Manufacturing, Services Economic Data Data India s data from GTAP Power built from Input-Output Tables (2011) prepared by Ministry of Statistics and Programme Implementation LCOE Levelised Cost of Electricity Data for parametrizing Solar Electricity obtained from NITI Aayog (erstwhile Planning Commission) Report Qualitative Inputs Qualitative inputs on future of different energy sources based on interviews with diverse stakeholders: government, developers, research centers and IEA Reports Forecasts Forecasts for GDP, Emissions Intensity, and Fossil Fuel Prices from various official sources 6

7 Method: Key improvements - representation of non-fossil technologies Nuclear Hydro Wind Solar Current Capacity 5.8 GW 44.4 GW 28.7 GW GW Political Economy Domestic opposition International sanctions Displacement Ecological impacts Near grid parity; Favorable political and market environment Very favorable political and market environment Projection Policy target: uncertain; Production in 2030 = 164 TWh, compared to TWh in 2011 (IEA 2015) Policy target: uncertain; Production in 2030 = 253 TWh, compared to 165 TWh in 2011 (IEA 2015) Policy target: 60GW by 2022; Flexibility in expansion Policy target: 100 GW by 2022; Flexibility in expansion Technology representation Existing technology with TSF for resource/political constraint Existing technology with TSF for resource/political constraint Existing technology with TSF for resource/technology constraint New technology Parametrization Sufficient presence in benchmark data; Zero elasticity of substitution with TSF Sufficient presence in benchmark data; Zero elasticity of substitution with TSF Sufficient presence in benchmark data; Small elasticity of substitution with TSF to provide flexibility in expansion Negligible presence in benchmark data cost shares specified based on LCOE analysis; Small elasticity of substitution with TSF 7

8 Method: Policy Scenarios 1. Emissions Intensity Scenario (implemented via a CO 2 price) India s emissions intensity in 2030 as 25.84% lower from 2011 * 2. Non-Fossil Scenario India s installed electric power capacity to be comprised of 40% non-fossil sources (equivalent to 27% electricity production from non-fossil sources, accounting for capacity factors) 3. Combined Scenario Both (1) and (2) Sensitivity analysis with varying costs of wind and solar power * Taking upper limit of India s INDC: 35% drop in emission intensity in 2030 from 2005 levels. This corresponds to a linear drop of 1.36% per year, amounting to 25.84% drop from benchmark emissions intensity in

9 Results: Compared to reference case of no policies, Emissions Intensity target under a carbon price leads to lowest welfare loss 9

10 Results: Emissions fall under all policy scenarios; non-fossil electricity targets result in slightly higher emission levels than the two economy-wide scenarios 10

11 Results: Non-fossil electricity targets lead to leakage of emissions to other energy intensive industries 11

12 Results: Climate policies result in lower total electricity levels but higher penetration of non-fossil electricity sources 12

13 Results: Coal power drops sharply under climate policies, and is only mildly compensated by higher non-fossil power 13

14 Results: Combining emissions intensity target with non-fossil electricity targets result in lower carbon prices but higher welfare loss Falling costs of wind and solar power - Decrease carbon price in emission intensity scenario - Increase carbon price in combined scenario - Decrease welfare loss in both scenarios 14

15 Result: Falling costs of wind and solar power will result in lower welfare losses, likely paving way for stronger climate policies Decreasing costs 15

16 Result: Falling costs of wind and solar power will increase electricity production under non-fossil electricity targets 16

17 Conclusion An economy-wide emissions reduction policy simulated through a carbon price results in the lowest decline in consumer welfare Policies that enforce non-fossil electricity capacity targets are not only more expensive per unit of emission reduction, but are also less effective, as emissions leak to other energy intensive sectors Combining economy-wide emission reduction policy with non-fossil targets brings down the effective carbon price but increases welfare losses. The tradeoff needs to be better understood to design efficient and politically acceptable policies Welfare losses under all policies decrease sharply at lower wind and solar costs, paving way for more aggressive decarbonization policies in the future. 17

18 Questions? Thank You 18

19 Extra: Sector Aggregation Table 1: Aggregation of GTAP commodities into sectors in the model GTAP commodity Aggregated commodity GTAP commodity Aggregated commodity Paddy rice AGR Ferrous metals EINT Wheat AGR Metals EINT Cereal grains AGR Metal products EINT Vegetables, fruit, nuts AGR Motor vehicles and parts MANF Oil seeds AGR Transport equipment MANF Sugar cane, sugar beet AGR Electronic equipment MANF Plant-based fibers AGR Machinery and equipment MANF Crops AGR Manufactures MANF Cattle, sheep and goats, horses AGR Transmission and distribution TnD Animal products AGR Nuclear baseload ENUC Raw milk AGR Coal baseload ECOA Wool, silk-worm cocoons AGR Gas baseload EGAS Forestry AGR Wind baseload EW_S Fishing AGR Hydro baseload EHYD Coal COL Oil baseload EOIL Crude Oil CRU Other baseload ECOA Gas GAS Gas peak EGAS Minerals OMN Hydro peak EHYD Bovine meat products FOOD Oil peak EOIL Meat products FOOD Solar peak EW_S Vegetable oils and fats FOOD Gas manufacture, distribution GAS Dairy products FOOD Water SER Processed rice FOOD Construction SER Sugar FOOD Trade SER Food products FOOD Transport SER Beverages and tobacco products FOOD Water transport SER Textiles MANF Air transport SER Wearing apparel MANF Communication SER Leather products MANF Financial services SER Wood products MANF Insurance SER Paper products, publishing MANF Business services SER Petroleum, coal products OIL Recreational and other services SER Chemical, rubber, plastic products EINT Public Administration, Defense, Education, Health SER Mineral products EINT Dwellings SER 19