Appendix 1. Model Equations
A discrete time period model is used where
A. Fossil Fuels
Coal, natural gas, gasoline, road diesel, kerosene, LPG, and an aggregate of other oil products, are denoted by i = COAL, NGAS, GAS, DIES, KER, LPG, and OIL respectively. The consumer fuel price at time t, denoted
B. Power Sector
Residential, commercial, and industrial electricity consumption is aggregated into one economy-wide demand for electricity in year t, denoted
Power generation fuels potentially include coal, natural gas, oil, nuclear, hydro, (non-hydro) renewables (wind, solar, biofuels), and biomass, where the latter are denoted by i = NUC, HYD, REN, BIO. To accommodate flexible assumptions for the degree of substitution among fuels, the share of fuel i in generation, denoted
where i, j, l = COAL, NGAS, OIL, NUC, HYD, REN, BIO.
From (A3) fuel i’s generation share decreases in own generation cost and increases in the generation cost of other fuels, where the increase in fuel i’s generation share is the reduced share for fuel j≠i times the (initial) share of i in generation from all fuel alternatives to j.
Use of fossil fuel i in power generation at time t, denoted
Fuel use equals the generation share times total electricity output and divided by
Unit generation costs are determined by:
The consumer price of electricity is the generation share times unit generation costs summed over fuels, plus unit transmission costs denoted
C. Road Transport Sector
Analogous to (A1), gasoline and road diesel fuel demand at time t, denoted
D. Other Energy Sector
The other energy sector is decomposed into large and small energy users, the latter representing households and small entities (in the formal or informal sectors) with emissions below a threshold, denoted by q = LARGE, SMALL, respectively. Use of fuel i in the other energy sector, by group q, at time t, denoted
where i = COAL, NGAS, KER, LPG, OIL, REN, and BIO. The interpretation for (A8) is analogous to that for (A2) and (A7) with
E. Metrics for Comparing Policies
CO2 emissions. CO2 emissions from fossil fuel use at time t are:
where j = E, T, O denotes a sector and μCO2i is fuel i’s CO2 emissions factor (which is taken as zero for renewables, hydro, nuclear, and—in a lifecycle context—biomass).
Revenue. Revenue from fuel and electricity taxes is:
Deaths from fossil fuel air pollution. At time t these are given by:
Economic welfare gains. The economic welfare costs and benefits of policies are measured using applications and extensions of long-established formulas in the public finance literature (see Harberger 1964), based on second order approximations58 which simplifies the formulas. The information required to apply these formulas includes the size of price distortions in fuel markets (i.e., the difference between social costs of fuel use and private costs due to domestic environmental costs in fuel markets net of any fuel taxes/subsidies), any induced quantity changes in markets affected by these distortions (an output from the model), and any new source of distortions created by the policy scenarios.59
The economic welfare gains (excluding the global climate benefits) from a carbon tax in period t is computed using:
where a ^ denotes a value in the BAU with no new mitigation policy and
In (A13),
According to equation (A12), the net welfare gain from the increase in tax in the market for a particular fossil fuel product in a particular sector consists of: (i) the reduction in fuel use times the price distortion in that market less (ii) the ‘Harberger triangle’ equal to the reduction in fuel use times one-half of the tax increase, where the latter is the product of the fuel’s CO2 emissions factor and
The above formula is also used to calculate the net welfare gain from the ETS and coal tax. For the ETS no carbon charge applies to the transport sector or fuel consumption by small users in the other energy sector, while for the coal tax the CO2 charge applies only to coal use in the power and other energy sector.
Appendix 2. Model Parameterization
Data for each sector is described below, where the latest data available on fuel use and fuel price and taxes/subsidies is 2014.
A. Fossil Fuels
Pre-tax prices for coal, natural gas, gasoline, diesel, kerosene, LPG, and other oil products for 2013–16 are from a combination of the India PPAC,61 IEA (2016) and a country-level database compiled by the IMF 62 based on international reference prices of the finished product (e.g., gasoline), as this reflects revenue forgone by selling it domestically rather than overseas, and then adjusted for transport and distribution costs. These prices are then projected forward to 2030 based on averaging over IMF price projections and projections from the U.S. Energy Information Administration (EIA) where the latter offer more detailed (year-on-year) information with respect to the IEA (2016). The IMF projections are based on international commodity price indices for coal, natural gas, and crude oil out to 2021 and are approximately constant (they reflect futures prices) 63—from 2021 to 2030 we assume these prices remain constant. In the EIA projections, real crude oil prices double between 2015 and 2030, coal prices fall 6 percent, natural gas prices (averaging over LNG and non-LNG prices) rise 47 percent. For electricity, which is generally a non-traded good, the supply cost for 2013 and 2014 in the IMF database is the domestic production cost or cost-recovery price (from IEA 2016) with costs evaluated at international reference prices. Electricity prices are then projected forward using (A6) as a price index, and changes in fuel prices and generation shares in a future year relative to that in 2013.
The IMF database also provides estimates of prices to fuel users and the difference between these prices and producer prices is the estimated fuel tax (or subsidy), where for fuels consumed at the household level value-added tax (which is applied to general consumer goods) is subtracted from the household price, and for coal the tax is given by the statutory rate Rs 200 ($3) per ton in 2016. These prior taxes/subsidies are taken as constant for the projection period (from 2016 onwards), so future fuel user prices are given by the sum of these taxes/subsidies and the future supply prices.
B. Power Sector
Electricity consumption. This is obtained from IEA (2016) focusing on generation, as this is what matters for domestic emissions.
Income elasticity of demand for electricity-using products. Empirical studies for different countries suggests a range for this elasticity of around 0.5–1.5.64 We use a value of 0.9 which (along with other assumptions) leads to projected electricity use for India that is roughly consistent with projections (accounting for structural transformations in the Indian economy) from IEA (2015), when IEA price projections are used.
Price elasticities for electricity. A simple average across the 26 estimates of long-run electricity demand elasticities reported in Jamil and Ahmad (2011), Table 1, is about −0.5, and nearly all estimates lie within a range of about −0.15 to −1.0.65 A recent study for China by Zhou and Teng (2013) suggests an elasticity of −0.35 to −0.5. Evidence for the United States suggests the longrun price elasticity for electricity demand is around -0.4, with about half the response reflecting reduced use of electricity-consuming products and about half improvements in energy efficiency.66 Values of −0.25 are assumed for both the usage and energy consumption rate elasticities, implying a total electricity demand elasticity of −0.5.
Annual rate of efficiency improvement for electricity-using products. This parameter (which is of moderate significance for the BAU projection) is taken to be 0.01.67
Generation shares. These are obtained from IEA (2016) by the electricity produced from each fuel type divided by total electricity generation.
Own-price elasticities for generation fuels (conditional on total electricity output). Short run coal price elasticities among eight studies for various advanced countries, China, and India summarized in Trüby and Paulus (2012), Table 5, are around -0.15 to -0.35 (aside from one study where the elasticity is −0.6). For the United States, simulations from a variant of the U.S. Department of Energy’s National Energy Modeling System (NEMS) model in Krupnick and others (2010), suggest a coal price elasticity of around −0.15 (with fuel switching rather than reduced electricity demand accounting for over 80 percent of the response).68 On the other hand, Burke and Liao (2015) report somewhat larger size coal price elasticities for China of −0.3 to −0.7. A coal price elasticity in the power generation sector of −0.35 is assumed for India.
The elasticities in equation (A3) are defined with respect to (full) generation costs rather than fuel costs and can be obtained by dividing the fuel price elasticity by the share of fuel costs in generation costs, which is around 0.6 in 2013 (see below). This gives an approximate generation cost elasticity of −0.6. In the absence of solid evidence to the contrary, the same generation cost elasticity is assumed for other generation fuels as for coal.
Fossil fuel consumption and productivity. Consumption of power generation fuels is taken from IEA (2015). Electricity generated from a particular fossil fuel, divided by that fuel’s consumption, gives the productivity of that fuel.
Annual rate of productivity improvement. Productivity improvements at power plants reflect improvements in technical efficiency and retirement of older, less efficient plants. For coal, annual average productivity growth is taken to be 0.5 percent based approximately on IEA (2016), Figure 2.16. For natural gas, biomass, nuclear and hydro, there is likely more room for productivity improvements and baseline annual growth rate of 1 percent is assumed. For renewables, a productivity growth rate of 4.5 percent is used in the baseline case for this fuel. The resulting projected fuel mix for 2030 (when EIA energy price projections are used in our model) is very similar to that projected for India in IEA (2016).
Non-fuel generation costs. For coal plants these are taken to be 60 percent as large as 2013 fuel costs.69 For natural gas plants (which have low fixed and high variable costs), non-fuel generation costs are taken to be one quarter of those for coal plants.
Power transmission cost. This is taken to be 60 percent of the electricity generation cost in 2013.70
C. Road Transport Sector
Fuel use. Consumption of road gasoline and diesel is taken from IEA (2016) for 2013.
Income elasticity of demand for vehicle miles. Estimates of this parameter are typically between about 0.35 and 0.8, although a few estimates exceed unity (Parry and Small, 2005). However, these estimates come from countries (unlike India) with widespread vehicle ownership so they mainly reflect changes in the intensive margin. An elasticity of 0.9 is used for India, given the likely greater price responsiveness at the extensive margin.
Price elasticities. Numerous studies have estimated motor fuel (especially gasoline) price elasticities for different countries and some studies decompose the contribution of reduced vehicle miles from longer improvements in average fleet fuel efficiencies. Based on this literature, a value of −0.25 is used for each of these elasticities and for both gasoline and diesel—the total fuel price elasticities are therefore −0.5.71
Annual rate of decline in vehicle fuel consumption rates (from technological improvements). These are set at 1 percent a year (e.g., Cao and others 2013).
D. Other Energy Sector
Fuel use. We assume 50 percent of industrial fuel consumption is by large firms that are potentially covered by the ETS.72
Income and price elasticities for other energy products. Evidence on income and price elasticities for fuels used in the industrial and residential sectors is more limited. Income elasticities are chosen such that baseline projections of fuel use to GDP in 2030 are broadly consistent with those in IEA (2016), Annex A (Current Policies scenario), when IEA price projections are included in our model, implying elasticities of between 0.65 and 1.0. The price elasticities are taken to be the same as for electricity and road fuels.
Annual rate of productivity improvements. These are assumed to follow those for the same fuel as used in the power sector.
E. Miscellaneous
GDP growth. Projected GDP out to 2021 is from the IMF’s WEO and thereafter is assumed to gradually decline (from 7.8 percent a year in 2022 to 6.8 percent in 2030).
Mortality rates from fuel combustion. The major pollutant from coal combustion at power plants causing premature mortality is PM2.5, fine particulate matter with diameter up to 2.5 micrometers, which is small enough to penetrate the lungs and bloodstream. These emissions can be produced directly during fuel combustion and are also formed indirectly (and generally in greater quantities) from chemical reactions in the atmosphere involving sulfur dioxide (SO2) and nitrogen oxide (NOx) emissions. India is just starting to take steps to reduce local air emission rates through emissions control requirements on new plants.
Air pollution mortality and damage estimates are taken from Parry and others (2014), with some adjustments. Parry and others (2014) estimate damages from representative coal plants with emissions control technologies, and industry wide damages averaging over plants with and without control technologies. In the absence of other factors, we assume the mortality rate from coal combusted at power plants would converge linearly from the industry average in 2010 to the mortality rate from plants with control technologies by 2030 (as new plants with control technologies penetrate the coal plant fleet). However, in India the share of the population residing in urban areas is projected to rise over time, with both population growth and migration from rural to urban areas, increasing exposure to urban air pollution. A linear upward adjustment in the mortality rate each year is made to account for this.73 For large industrial coal users (e.g., steel plants) we assume the same mortality rates as for coal power plants. For small-scale coal users, mortality rates in 2010 are assumed equal to the industry average for coal plants emission, rising over time with urban population growth. Deaths from outdoor use of biomass is based approximately on Lelieveld et al. (2015).
Mortality rates for natural gas, gasoline, diesel, and oil products are also based on Parry and others (2014), adjusted for changes in population exposure.74
References
Allcott, Hunt, Allan Collard-Wexler, and Stephen D. O’Connell, 2016, “How Do Electricity Shortages Affect Industry? Evidence from India.” American Economic Review 106: 587–624.
Aldy, Joseph, and others, 2016. “Economic Tools to Promote Transparency and Comparability in the Paris Agreement.” Nature Climate Change, forthcoming.
Abdallah, Chadi, David Coady, Sanjeev Gupta, and Emine Hanedar, 2015. “The Quest for the Holy Grail: Efficient and Equitable Fiscal Consolidation in India.” Working paper 15/152, International Monetary Fund, Washington, DC.
Burnett, Richard T., C. Arden Pope, Majid Ezzati, Casey Olives, Stephen S. Lim, Sumi Mehta, Hwashin H. Shin, and others, 2013, “An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure” Health Canada, Ottawa, Ontario, Canada.
Brauer, Michael, Markus Amann, Rick T. Burnett, Aaron Cohen, Frank Dentener, Majid Ezzati, Sarah B. Henderson, Michal Krzyzanowski, Randall V. Martin, Rita Van Dingenen, Aaron van Donkelaar, and George D. Thurston, 2012, “Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution,” Environmental Science and Technology 46, 652–60.
Burke, Paul J. and Hua Liao, 2015. “Is the Price Elasticity of Demand for Coal in China Increasing?” CCEP Working Paper 1506, Crawford School of Public Policy, Australian National University.
Calder, Jack, 2015. “Administration of a U.S. Carbon Tax,” In Implementing a U.S. Carbon Tax: Challenges and Debates, edited by I. Parry, A. Morris, and R. Williams, New York: Routledge.
Cao, Jing, Mun S. Ho, and Dale W. Jorgenson, 2013. “The Economics of Environmental Policies in China.” In C.P. Nielsen and M.S. Ho (eds.), Clearer Skies over China: Reconciling Air Quality, Climate, and Economic Goals, MIT Press, Cambridge, MA, pp. 329–373.
Clements, Benedict, David Coady, Stefania Fabrizio, Sanjeev Gupta, Trevor Alleyene, and Carlo Sdralevich, eds., 2013. Energy Subsidy Reform: Lessons and Implications. Washington DC, International Monetary Fund.
Coady, David, Ian Parry, Louis Sears, and Baoping Shang, 2015. “How large are Global Energy Subsidies?” Working paper, International Monetary Fund, Washington, DC.
Coady, David, Ian Parry, and Baoping Shang, 2016. “Energy Price Reform: A Guide for Policymakers.” Unpublished manuscript.
Cropper, Maureen, Shama Gamkhar, Kabir Malik, Alex Limonov, and Ian Partridge, 2012, “The Health Effects of Coal Electricity Generation in India,” Discussion Paper No. 12–15, Washington DC, Resources for the Future.
Dahl, Carol, A., 2012. “Measuring Global Gasoline and Diesel Price and Income Elasticities.” Energy Policy 41: 2–13.
European Commission (EC), 1999, ExternE Externalities of Energy, Vol. 7—Methodology Update, Report produced for the European Commission, DG XII (Brussels: Office of Publications for the European Communities).
Energy Information Administration (EIA), 2016. International Energy Statistics. US Department of Energy, Washington. www.eia.gov/cfapps/ipdbproject/iedindex3.cfm.
Farid, Mai, Michael Keen, Michael Papaioannou, Ian Parry, Catherine Pattillo, Anna Ter-Martirosyan, and other IMF Staff, 2016. After Paris: Fiscal, Macroeconomic, and Financial Implications of Climate Change. Staff Discussion Notes 16/01 (Washington: International Monetary Fund).
Fullerton Don and Garth Heutel, 2011. “Analytical General Equilibrium Effects of Energy Policy on Output and Factor Prices.” The B.E. Journal of Economic Analysis & Policy 10: 1–26.
Goodkind, Andrew L., Jay S. Coggins, Timothy A. Delbridge, Milda Irhamni, Justin AndrewJohnson, Suhyun Jung, Julian Marshall, Bijie Ren, and Martha H. Rogers, 2012, “Prices vs. Quantities With Increasing Marginal Benefits.” Discussion paper, Department of Applied Economics, University of Minnesota.
Government of India 2015. “India’s Intended Nationally Determined Contribution: Working Towards Climate Justice.”
Harberger, Arnold C., 1964. “The Measurement of Waste”. American Economic Review 54: 58–76.
Hassett, Kevin, Aparna Marthur, and Gilbert Metcalf, 2009. “The Incidence of a US Carbon Tax: A Lifetime and Regional Analysis.” The Energy Journal 30: 155–178.
Helfand, Gloria, and Ann Wolverton, 2011, “Evaluating the Consumer Response to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource Economics 5, 103–46.
IEA, 2015. World Energy Outlook. International Energy Agency, Paris, France.
IEA, 2016. World Energy Outlook. International Energy Agency, Paris, France.
Institute for Health Metrics and Evaluation (IHME), 2013. Global Burden of Disease 2010.
International Monetary Fund (IMF), 2015. India: Staff Report for the 2015 Article IV Consultation. International Monetary Fund, Washington, DC. Available at: www.imf.org/external/pubs/ft/scr/2015/cr1561.pdf.
International Monetary Fund (IMF), 2016. World Economic Outlook. International Monetary Fund, Washington, DC.
International Monetary Fund (IMF), 2017, India: 2017 Article IV Consultation, IMF Country Report 17/54, Washington, DC.
Jamil, Faisal and Eatzaz Ahmad 2011. “Income and price elasticities of electricity demand: Aggregate and sector-wise analyses.” Energy Policy 39: 5,519–527.
Jenkins, Jesse D. and Valerie J. Karplus, 2016. “Carbon Pricing under Binding Political Constraints.” United Nations University World Institute for Development Economics Research, Helsinki, Finland.
Kelkar, V., I. Rajaraman, and S. Misra, 2012. “Report of the Committee on Roadmap for Fiscal Consolidation.” Government of India, Ministry of Finance, New Delhi.
Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and Michael J. Thun, 2009, “Extended Follow-Up and Spatial Analysis of the American Cancer SocietyStudy Linking Particulate Air Pollution and Mortality,” Research Report 140 (Boston, MA Health Effects Institute). http://scientificintegrityinstitute.net/Krewski052108.pdf.
Krupnick, Alan J., Ian W.H. Parry, Margaret Walls, Tony Knowles, and Kristin Hayes, 2010. Toward a New National Energy Policy: Assessing the Options. Washington DC, Resources for the Future and National Energy Policy Institute.
Krupnick, Alan J. and Ian W.H. Parry, 2011. “Decarbonizing the Power Sector: Are Feebates Better than a Clean Energy Standard?” Resources, Summer, 39–43.
Lelieveld, J., J. S. Evans, M. Fnais, D. Giannadaki and A. Pozzer, 2015. “The Contribution of Outdoor Air Pollution Sources to Premature Mortality on a Global Scale.” Nature, Letters, 2015/09/17 367-371. www.nature.com/nature/journal/v525/n7569/abs/nature15371.html.
Lepeule, J., F. Laden, D. Dockery, and J. Schwartz, 2012, “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-up of the Harvard Six Cities Study from 1974 to 2009,” Environmental Health Perspectives 120, 965–70.
Madlener, Reinhard, Ronald Bernstein and Miguel Ángel Alva González, 2011. “Econometric Estimation of Energy Demand Elasticities.” E.ON Energy Research Center, Series 3, Issue 8, Aachen University, Aachen, Germany.
Madheswaran, S. 2007. “Measuring the Value of Statistical Life: Estimating Compensating Wage Differentials Among Workers in India.” Social Indicators Research 84: 83–96.
Morris, Adele, 2016. “Build a Better Future for Coal Workers and Their Communities.” Climate and Energy Economics Discussion Paper, Brookings Institution, Washington DC.
Myers, Erica, Karen L. Palmer, and Anthony Paul, 2009. “A Partial Adjustment Models of U.S. Electricity Demand by Region, Season and Sector.” Discussion Paper 08-50, Resources for the Future, Washington, DC.
Nordhaus, William, D., 2016. “Projections and Uncertainties About Climate Change in an Era of Minimal Climate Policies.” National Bureau of Economic Research, Working Paper 22933, Cambridge, MA.
Parry, I., V. Mylonas and N. Vernon, 2017, “Energy Policy Reform in India: Assessing the Options,” Chapter 8 of, “India: Selected Issues,” IMF Country Report 17/55 (Washington: International Monetary Fund), pp. 57–62.
Parry, Ian W.H., Dirk Heine, Shanjun Li, and Eliza Lis, 2014a. Getting Energy Prices Right: From Principle to Practice. International Monetary Fund, Washington, DC.
Parry, Ian W.H. and Kenneth A. Small, 2005. “Does Britain or The United States Have the Right Gasoline Tax?” American Economic Review 95: 1,276–1,289.
Parry, Ian W.H. and Kenneth A. Small, 2005. “Does Britain or The United States Have the Right Gasoline Tax?” American Economic Review 95: 1,276–1,289.
Parry, Ian W.H. David Evans and Wallace Oates, 2014b. “Are Energy Efficiency Standards Justified?” Journal of Environmental Economics and Management 67, 104–125.
Poterba, James M., 1991. “Is the Gasoline Tax Regressive?” In David Bradford (ed.), Tax Policy and the Economy 5. National Bureau of Economic Research, Cambridge, MA.
Rausch, S., G.E. Metcalf, and J. M. Reilly, 2011. “Distributional Impacts of Carbon Pricing: A General Equilibrium Approach with Micro-Data for Households.” Energy Economics 33: S20–S33.
Shanmugam, K.R. 2001. “Self-Selection Bias in the Estimates of Compensating Wage Differentials for Job Risks in India.” Journal of Risk and Uncertainty 23, 263–275.
Sanstad, Alan H. and James E. McMahon, 2008. “Aspects of Consumers’ and Firms’ Energy Decision-Making: A Review and Recommendations for the National Energy Modeling System (NEMS).” Papers from the Workshop on Energy Market Decision-making for the New NEM. Energy Information Administration: Washington, DC.
Sterner, Thomas. 2007. “Fuel Taxes: An Important Instrument for Climate Policy.” Energy Policy 35: 3,194–202.
Trüby, Johannes and Moritz Paulus. 2012. “Market Structure Scenarios in International Steam Coal Trade.” The Energy Journal 33: 91–123.
United States Environmental Protection Agency (US EPA), 2011. The Benefits and Costs of the Clean Air Act from 1990 to 2020. Report to Congress (Washington: US Environmental Protection Agency).
United States Inter-Agency Working Group (US IAWG), 2013. “Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866.” Washington.
Watkiss, Paul, Steve Pye, and Mike Holland, 2005. CAFE (Clean Air for Europe) CBA: Baseline Analysis 2000 to 2020. Report to the European Commission. Brussels: Directorate-General for the Environment.
World Bank Group, 2016. State and Trends of Carbon Pricing 2016. Washington.
World Bank and State Environmental Protection Agency of China, 2007. Cost of Pollution in China: Economic Estimates of Physical Damages. Washington, World Bank.
World Bank and Institute for Health Metrics and Evaluation. 2016. The Cost of Air Pollution: Strengthening the Economic Case for Action. World Bank, Washington, DC.
Zhou, Shaojie, and Fei Teng, 2014. “Estimation of Urban Residential Electricity Demand in China Using Household Survey Data.” Energy Policy 61: 394–402.
Kennedy School, Harvard University.
We are grateful to Phillipe Wingender for help with the incidence analysis for this paper and to Christian Bogmans, Paul Cashin, Subir Gokarn, Akito Matsumoto, Delphine Prady, Elif Ture, and Xican Xi for helpful comments on an earlier draft.
This excise tax applies directly to the amount of raw/unprocessed coal extracted from a mine.
The urban population is projected to rise from 377 to 609 million between 2011 and 2030 (Government of India 2015).
Put another way, failing to fully reflect supply and environmental costs in fuel prices is tantamount to subsidizing fuel use relative to other products (Coady and others 2015). Coal taxes would have only modest implications for the balance of payments (given that oil imports are far larger than coal imports—IMF 2017, Figure 2).
Fiscal consolidation needs in India are discussed in IMF (2015), pp. 10–13, and Kelkar and others (2012). Currently coal tax revenues go to the National Clean Environment Fund (NCEF) to finance clean energy innovation and investment and broader environmental conservation and development projects, though enhanced revenues from the tax could go to the general budget.
The Paris Agreement came into force on November 4 2016, following ratification by at least 55 countries representing 55 percent of global emissions. Initially NDCs were called INDCs with ‘I’ referring to ‘independent,’ though after ratification they reverted to NDCs.
India’s large agrarian economy, expansive coastal areas, and sensitivity to extreme weather make it especially vulnerable to climate change (Government of India 2015).
For modelling exercises see, for example, Krupnick and others (2010) and for high-level support of pricing policies see www.carbonpricingleadership.org/carbon-pricing-panel.
See, for example, Parry and others (2014a). Other policies are also needed to address related market failures (e.g., that might deter adoption of cleaner technologies), infrastructure needs, and so on, though the net benefits from these individual measures are likely on a much smaller scale than those from comprehensive energy price reform.
See also Parry and others (2017).
It is assumed that CO2 emissions are required to fall in the same proportion as all GHGs to meet the pledge.
See, for example, Jenkins and Karplus (2016) for a discussion of political economy aspects.
See, for example, Nordhaus (2016), USIAWG (2013).
Instead of levying charges upstream on fuel supply, they can instead (though with greater administrative complexity) be levied downstream on CO2 emissions from large stationary sources, and combined with upstream charges on fuels used by small-scale sources (e.g., from buildings and vehicles).
The latter two pollutants react in the atmosphere to form fine particulates which are small enough to penetrate the lungs and bloodstream thereby elevating risks of various (e.g., heart and lung) diseases. PM2.5 is particulate matter with diameter up to 2.5 micrometers. Fuel combustion also leads to the formation of (low-lying) ozone, but the resulting mortality impacts are on a smaller scale to those from PM2.5.
Putting the onus on firms to demonstrate valid emissions reductions to obtain credits eases the burden on administrative capacity. For large stationary emitters in countries with emissions monitoring capacity, smokestack emissions can be charged directly, and can be varied with local population exposure.
Other environmental costs tend to be smaller in magnitude (e.g., impaired visibility, building corrosion, crop damage, annualized costs of leakage during transport and storage), difficult to quantify (e.g., despoiling of the environment at extraction sites), or the nature of the externality is unclear (e.g., energy security). Mortality impacts account for upwards of 85 percent of total air pollution damage estimates in U.S. EPA (2011), EC (1999), World Bank and State Environmental Protection Agency of China (2007), and Watkiss and others (2005).
For example, that European countries should lower their road fuel taxes to U.S. levels (Parry and Small 2005, Parry and others 2014a, Chapter 5). In computing efficient road fuel taxes, mileage-related externalities are multiplied by the fraction of the fuel reduction that comes from reduced mileage (usually assumed to be about half) as opposed to the fraction that comes from higher fuel economy (e.g., Parry and Small 2005), as only the former directly affects congestion, accidents, and road damage.
The estimates are broadly consistent with those for India reported in Lelieveld and others (2015).
This is based on mapping geographic data on the precise location of coal plants in a country to very granular data on population density. The approach ignores differences in meteorological and other factors between India and China that might affect pollution formation, though some cross-checks with an air quality model suggest any resulting bias may not be large (Parry and others 2014a, pp. 83–7).
Parry and others (2014a) assume that each one microgram increase in ambient PM2.5 concentrations would increase all causes of mortality by 1 percent, which is roughly consistent with U.S. studies (e.g., Krewski and others 2009, Lepeule and others 2012), current practice by the U.S. Environmental Protection Agency and (albeit limited) evidence for other countries (e.g., Burnett and others 2013). A caveat is that the responsiveness of mortality to additional pollution exposure could eventually flatten out at severe air pollution concentrations as people’s channels for absorbing pollution become saturated (paradoxically implying lower health benefits from incremental pollution reductions) though evidence on this is mixed (e.g., Goodkind and others, 2012).
For comparison, Cropper and others (2012) estimate corresponding deaths of 23, 10, and 9 per 1,000 tons of PM2.5, SO2, and NOx for India in 2008.
Parry and others (2014a) assume a value of INR 50 million ($0.75 million) per death, updated to 2013. For comparison, Madheswaran (2007) and Shanmugam (2001) report values for India of INR 15 million and INR 56 million respectively.
Updated from U.S. IAWG (2013).
The latter is because, per liter of fuel, heavy vehicles drive a shorter distance, implying smaller mileage costs per liter.
The model also abstracts from the possible use of carbon capture and storage technologies at power and large industrial plants, therefore taxing the carbon content of fuels upstream is equivalent to taxing CO2 emissions when these fuels are combusted.
Cross-price effects among the three energy sectors are also ignored as they are likely small for the foreseeable future, due to products being weak substitutes (e.g., higher prices for road fuels will have a minimal effect on the demand for residential and industrial electricity).
Improvements in energy efficiency reduce unit operating costs for energy consuming products, hence increasing their demand, though the resulting extra energy use from this ‘rebound effect’ offsets only about 10 percent of the savings from higher efficiency.
On average, combusting a ton of coal causes about 1.87 tons of CO2 emissions (see www.eia.gov/tools/faqs/faq.cfm?id=82&t=11).
See Calder (2015) for a discussion of administrative issues.
For comparison, this rate is in line with (albeit uncertain) estimates of the CO2 price needed by China to meet its INDC in 2030, though advanced countries would generally require substantially higher prices (e.g., Aldy and others 2016).
WBG (2016). This share will increase to about 80 percent if China implements a nationwide ETS in 2017.
Approximately the federal subsidy for solar and wind power generation in 2015 as reported by the Indian Renewable Energy and Energy Efficiency Policy Database.
See, for example, Bernard and others (2007) and Krupnick and Parry (2010) for more discussion.
Besides their environmental benefits, it is sometimes suggested that these policies address an additional market failure due to the private sector undervaluing the discounted energy savings from higher energy efficiency, though the evidence on this for advanced countries is mixed (e.g., Allcott and Wozny 2013, Helfand and Wolverton 2011). Allowing for this market failure could imply that, up to a point, policies to increase energy efficiency could have net economic benefits (before counting environmental benefits), though these net benefits appear to be small relative to those from directly pricing emissions (e.g., Parry and others 2014b).
In reality, much of this capital is difficult to regulate (e.g. smaller appliances, audio and entertainment equipment, industrial processes like assembly lines) and without extensive credit trading incremental costs may differ substantially across different efficiency programs.
The prices in New Delhi as reported by the India PPAC. The tax includes specific and ad-valorem portions.
See for example Poterba (1991), Hassett and others (2009).
The table was obtained from the Central Statistics Office, Ministry of Statistics and Programme Implementation of India. Although more recent tables are available from other sources, they lack the disaggregation of consumer products in the data used here.
As long as any trends reduce (or increase) energy budget shares for all household groups in roughly the same proportion, the relative incidence of fuel price reforms across households is largely unaffected. One exception might be the prospects for rising budget shares for gasoline among middle and lower income households with potential for growth in vehicle ownership rates among these groups.
For example, the first-order approximation (a rectangle) overstates the loss of consumer surplus (a trapezoid) by only about 5 percent when demand for a fuel product falls by 10 percent.
See for example Fullerton and Heutel (2011).
For example Rausch and others (2011).
A further caveat is that the distributional incidence of the domestic environmental benefits of fuel price reform are not considered. These benefits may be skewed to lower income households if these households are more likely to reside in severely polluted areas.
1 percent higher and 5 percent lower respectively in 2030.
For example, Lelieveld and others (2015), Extended Data Table 3, put outdoor pollution deaths in India at about 640,000 for 2010 (see also World Bank and Institute for Health Metrics and Evaluation 2016), though this includes some additional sources (e.g., agriculture, natural pollution).
Natural gas increases slightly due to switching to this fuel from coal.
Focusing on total, rather than outdoor, deaths takes account of increases in indoor air pollution deaths from policies that raise electricity prices, thereby causing a substitution from electricity to home biofuel use. This offsets about 7 percent of the reductions in outdoor air pollution deaths from less fossil fuel use under the coal and carbon tax and ETS. The offset is smaller for the policy to reduce the emissions intensity of the power sector, given the minimal impact of this policy on electricity prices.
Abdallah and others (2015) find that energy price reform in India is mildly regressive though the reason is that they focus on a large price increase for kerosene (which is heavily consumed by the poor) and a moderate increase in road fuel prices, rather than an increase in coal and electricity prices.
See Abdallah and others (2015). Subsidies for a ‘subsistence’ amount of electricity consumption, or for clean fuel technologies (e.g., solar water heaters) used by the poor, may also have a role.
See Morris (2016) for a discussion of the options.
The model abstracts from power outages which, according to Alcott et al. (2016), reduce revenues from the manufacturing sector by about 5 percent. Ideally, energy price reform would be accompanied by other measures to reduce outages, such as rising price schedules during periods of peak demand.
The model abstracts from substitution between use of gasoline and diesel vehicles given the different vehicle types (light-duty vehicles for gasoline and mostly heavy-duty vehicles for diesel) and that carbon pricing tends to increase user prices for gasoline and diesel in roughly the same proportion.
That is, taking fuel demand curves to be linear over the range of policy-induced fuel changes.
Induced quantity changes in markets with no price distortions have no implications for economic welfare costs (Harberger 1964).
See Parry and others (2014), Ch. 5, for a detailed discussion.
See http://ppac.org.in.
See www.imf.org/external/pubs/ft/weo/2015/02/weodata/weoselagr.aspx. The indices are for Australian thermal coal; Indonesian liquefied natural gas in Japan; and an average of Brent, West Texas Intermediate, and Dubai Fateh spot crude oil prices.
For example, Jamil and Ahmad (2011), Table 1, report 26 estimates of long-run income elasticities for electricity from 17 studies, almost all of them lying within the above range.
See Madlener and others (2011) for further discussion and broadly similar findings.
See Myers and others (2009), Parry and others (2014), Sanstad and McMahon (2008).
This is consistent with similar assumptions in other models, for example, for China in Cao and others (2012), pp. 389–90.
NEMS tends to be less price responsive than other models and the above simulation was for a carbon price which also raises natural gas prices, thereby dampening the reduction in coal use.
This is the same as assumption as used by Parry and others (2016) for China.
This is approximately consistent with Cao and others (2013), pp. 343.
These values represent a compromise between Sterner (2007), who reports globally averaged (long-run) gasoline price elasticities of around −0.7, and Dahl (2012) whose average estimate is about −0.25. For a summary of evidence on the decomposition of the fuel price elasticities into the vehicle mileage and fuel efficiency responses see Parry and Small (2005).
This fraction will depend on the threshold emissions level determining whether entities are covered by pricing schemes, which will depend in part on administrative considerations.
An increase of 2.56 percent was used in the model. This figure comes from India’s NDC documentation. The urban population increase accounts for 77 percent of the increase in air-pollution deaths between 2015 and 2030 in the BAU scenario.
Mortality rates for other oil products (which were not estimated by Parry and others 2014) are taken to be the same as for road diesel. For gasoline and road diesel, mortality rates (prior to adjusting for rising urban population shares) are assumed to linearly converge between 2010 and 2030 from the vehicle fleet average in 2010 to the mortality rates for representative vehicles in 2010 with advanced emission control technologies. The same adjustment is made for other oil products but not (due to lack of data) for natural gas, though air pollution damages from gas are relatively small.