Back Matter
Author: , , and Nate Vernon
• 1 https://isni.org/isni/0000000404811396, International Monetary Fund
• | 2 https://isni.org/isni/0000000404811396, International Monetary Fund

### Appendix 1. Model Equations

A discrete time period model is used where $t=0\dots \overline{t}$ denotes a particular year. Fossil fuels are first discussed, followed by fuel use in the power, road transport, and “other energy” sectors.

#### 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 ${p}_{t}^{i}$, is:

$\begin{array}{cc}{p}_{t}^{i}\text{\hspace{0.17em}}=\text{\hspace{0.17em}}{\tau }_{t}^{i}+{\stackrel{^}{p}}_{t}^{i}& \left(\text{A}1\right)\end{array}$

${\tau }_{t}^{i}$ is the specific tax on fuel i (or subsidy in the case of kerosene and LPG) including any excise or (future) carbon charge. ${\stackrel{^}{p}}_{t}^{i}$ is the pre-tax fuel price or supply cost. For fuels used in multiple sectors pre-tax prices and taxes are taken to be the same for all fuel users.

#### B. Power Sector

Residential, commercial, and industrial electricity consumption is aggregated into one economy-wide demand for electricity in year t, denoted ${Y}_{t}^{E}$, and determined by:

$\begin{array}{cc}{Y}_{t}^{E}=\left(\frac{{U}_{t}^{E}}{{U}_{0}^{E}}\cdot \frac{{h}_{t}^{E}}{{h}_{0}^{E}}\right)\cdot {Y}_{0}^{E},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{U}_{t}^{E}}{{U}_{0}^{E}}=\left(\frac{GD{P}_{t}}{GD{P}_{0}}{\right)}^{{\upsilon }^{E}}\cdot \left(\frac{{h}_{t}^{E}{p}_{t}^{E}}{{h}_{t}^{E}{p}_{o}^{E}}{\right)}^{{\eta }^{UE}},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{h}_{t}^{E}}{{h}_{o}^{E}}={\left(1+{\alpha }^{E}\right)}^{-t}\cdot {\left(\frac{{p}_{t}^{E}}{{p}_{0}^{E}}\right)}^{{\eta }^{hE}}& \left(\text{A2}\right)\end{array}$

${U}_{t}^{E}$ is usage of electricity-consuming products or capital or the stock of electricity-using capital times its average intensity of use. ${h}_{t}^{E}$ is the electricity consumption rate (e.g., kWh per unit of capital usage), or the inverse of energy efficiency. Product use increases with gross domestic product (GDPt) according to νE, the (constant) income elasticity of demand for electricity-using products. Product use also varies inversely with proportionate changes in unit electricity costs, or the user electricity price ${p}_{t}^{E}$ times the electricity consumption rate. ηUE < 0 is the (constant) elasticity of demand for use of electricity-consuming products with respect to energy costs. The electricity consumption rate declines (given other factors) at a fixed annual rate of αE ≥ 0, reflecting autonomous energy efficiency improvements. Higher electricity prices increase energy efficiency, implicitly through adoption of more efficient technologies: ηhE is the elasticity of the energy consumption rate with respect to energy prices.

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 ${\theta }_{t}^{Ei}$, is defined:

$\begin{array}{cc}{\theta }_{t}^{Ei}={\theta }_{0}^{Ei}\left\{\left(\frac{{g}_{t}^{i}}{{g}_{0}^{i}}{\right)}^{{\stackrel{˜}{\varepsilon }}^{Ei}}+{\mathrm{\Sigma }}_{j\ne i}{\theta }_{0}^{Ej}\left[1-\left(\frac{{g}_{t}^{j}}{{g}_{0}^{j}}{\right)}^{{\stackrel{˜}{\varepsilon }}^{Ej}}\right]/{\mathrm{\Sigma }}_{l\ne j}{\theta }_{0}^{El}\right\}& \left(\text{A3}\right)\end{array}$

where i, j, l = COAL, NGAS, OIL, NUC, HYD, REN, BIO. ${g}_{t}^{i}$ is the cost of generating a unit of electricity using fuel i at time t and ${\stackrel{˜}{\varepsilon }}^{Ei}<0$ is the conditional (indicated by ~) own-price elasticity of generation from fuel i with respect to generation cost. Conditional means the elasticity reflects the percent reduction in use of fuel i due to switching from that fuel to other generation fuels, per one-percent increase in generation cost for fuel i, for a given amount of electricity. Generation cost elasticities are larger than corresponding fuel price elasticities as an increase in all (fuel and non-fuel) generation costs has a bigger impact than an increase in fuel costs alone.

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 ${F}_{t}^{Ei}$, is given by:

$\begin{array}{cc}{F}_{t}^{Ei}=\frac{{\theta }_{t}^{Ei}.{Y}_{t}^{E}}{{\rho }_{t}^{Ei}}& \left(\text{A4}\right)\end{array}$

Fuel use equals the generation share times total electricity output and divided by ${\rho }_{t}^{Ei}$, the productivity of fuel use or electricity generated per unit of ${F}_{t}^{Ei}$. The total supply of power generation in each period is assumed equal to total electricity demand.

Unit generation costs are determined by:

${k}_{t}^{Ei}$ is unit capital, labor and other non-fossil fuel costs. Unit generation costs for fossil fuels decline with rising productivity (which is assumed to reduce fuel and non-fuel costs by the same proportion). Similarly, productivity improvements lower generation costs for non-fossil fuels. Productivity of generation by fuel i increases at rate αρi ≥ 0 per year implicitly from better production technologies and retirement of older, less efficient plants. Finally:

$\begin{array}{cc}{p}_{t}^{E}={\mathrm{\Sigma }}_{i}{q}_{t}^{Ei}{\theta }_{t}^{Ei}+{k}_{t}^{ET}+{\tau }_{t}^{E}& \left(\text{A6}\right)\end{array}$

The consumer price of electricity is the generation share times unit generation costs summed over fuels, plus unit transmission costs denoted ${k}_{t}^{ET}$, and any excise tax on electricity consumption ${\tau }_{t}^{E}$ (or subsidy if ${\tau }_{t}^{E}<0$).56

Analogous to (A1), gasoline and road diesel fuel demand at time t, denoted ${F}_{t}^{Ti}$, where i = GAS, DIES, LPG is gasoline, diesel and LPG respectively, is:

$\begin{array}{cc}{F}_{t}^{Ti}=\left(\frac{{U}_{t}^{Ti}}{{U}_{0}^{Ti}}\cdot \frac{{h}_{t}^{Ti}}{{h}_{0}^{Ti}}\right){F}_{0}^{Ti};\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{U}_{t}^{Ti}}{{U}_{0}^{Ti}}=\left(\frac{GD{P}_{t}}{GD{P}_{0}}{\right)}^{{\upsilon }^{Ti}}\cdot \left(\frac{{h}_{t}^{Ti}{p}_{t}^{i}}{{h}_{t}^{Ti}{p}_{0}^{i}}{\right)}^{{\eta }^{UTi}};\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{h}_{t}^{Ti}}{{h}_{0}^{Ti}}={\left(1+{\alpha }^{hTi}\right)}^{-t}\cdot {\left(\frac{{p}_{t}^{i}}{{p}_{0}^{i}}\right)}^{{\eta }^{hTI}}& \left(\text{A7}\right)\end{array}$

${U}_{t}^{Ti}$ is kilometers (km) driven by vehicles with fuel type i and ${h}_{t}^{Ti}$ is fuel use per vehicle km (the inverse of fuel economy). km driven in vehicle type i increases with GDP, according to the income elasticity of demand υTi, and varies inversely with proportionate changes in fuel costs per km ${h}_{t}^{Ti}{p}_{t}^{i}$, where ηUTi < 0 is the elasticity of vehicle km driven with respect to per km fuel costs.57 αTi ≥ 0 is an annual reduction in the fuel consumption rate due to autonomous technological change that improves fuel economy. Higher fuel prices also reduce fuel consumption rates (e.g., through promoting engine efficiency increases, lighter weight materials, encouraging people to drive smaller vehicles) according to ηhTi ≤ 0, the elasticity of the fuel consumption rate.

#### 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 ${F}_{t}^{Oqi}$, is:

$\begin{array}{cc}{F}_{t}^{Oqi}=\left(\frac{{U}_{t}^{Oqi}}{{U}_{0}^{Oqi}}\cdot \frac{{h}_{t}^{Oqi}}{{h}_{0}^{Oqi}}\right){F}_{0}^{Oqi};\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{U}_{t}^{Oqi}}{{U}_{0}^{Oqi}}=\left(\frac{GD{P}_{t}}{GD{P}_{0}}{\right)}^{{\upsilon }^{Oi}}\cdot \left(\frac{{h}_{t}^{Oqi}{p}_{t}^{i}}{{h}_{t}^{Oqi}{p}_{0}^{i}}{\right)}^{{\eta }^{UOi}};\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\frac{{h}_{t}^{Oqi}}{{h}_{0}^{Oqi}}={\left(1+{\alpha }^{Oi}\right)}^{-t}\cdot {\left(\frac{{p}_{t}^{i}}{{p}_{0}^{i}}\right)}^{{\eta }^{hOI}}& \left(\text{A8}\right)\end{array}$

where i = COAL, NGAS, KER, LPG, OIL, REN, and BIO. The interpretation for (A8) is analogous to that for (A2) and (A7) with ${U}_{t}^{Oqi}$ and ${h}_{t}^{Oqi}$ denoting respectively, use of products requiring fuel i at time t by group q and its fuel consumption rate. Parameters υOi, ηUOi, ηhOi, and αOi have analogous interpretations to previous notation and are taken to be the same across large and small users. Given the limited scope for substituting among different fuels used for very different products (compared with fuels producing a homogeneous product in the power sector), fuel switching possibilities are not modelled in the other energy sector.

#### E. Metrics for Comparing Policies

CO2 emissions. CO2 emissions from fossil fuel use at time t are:

$\begin{array}{cc}{\mathrm{\Sigma }}_{ji}{F}_{t}^{ji}\cdot {\mu }^{CO2i}& \left(\text{A}9\right)\end{array}$

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:

$\begin{array}{cc}{\mathrm{\Sigma }}_{ji}{F}_{t}^{ji}\cdot {\tau }_{t}^{i}+{Y}_{t}^{E}\cdot {\tau }_{t}^{E}& \left(\text{A10}\right)\end{array}$

Deaths from fossil fuel air pollution. At time t these are given by:

$\begin{array}{cc}{\mathrm{\Sigma }}_{ij}{F}_{t}^{ji}\cdot {m}_{t}^{ji}& \left(\text{A11}\right)\end{array}$

${m}_{t}^{ji}$ is mortality per unit of fuel i used in sector j, which may differ by sector due to differing use of control technologies.

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:

$\begin{array}{cc}{\mathrm{\Sigma }}_{ji}\left({\mathrm{\Gamma }}_{t}^{ji}-\frac{{\mu }^{CO2i}\cdot {\tau }_{t}^{CO2}}{2}\right)\cdot \left(-\mathrm{\Delta }{F}_{t}^{ji}\right)& \left(\text{A12}\right)\end{array}$
$\begin{array}{cc}\mathrm{\Delta }{F}_{t}^{ji}={F}_{t}^{ji}-{\stackrel{^}{F}}_{t}^{ji}& \left(\text{A14}\right)\end{array}$

where a ^ denotes a value in the BAU with no new mitigation policy and ${\mathrm{\Gamma }}_{t}^{ji}$ is the price distortion in a fuel market.

In (A13), ${\mathrm{\Gamma }}_{t}^{ji}$ consists (for fossil fuels and biomass) of local air pollution costs, equal to premature mortalities per unit of fuel use times VMORTt, the value per premature mortality. For road fuels, there is an additional environmental cost equal to the external costs of traffic congestion, accidents, and road damage expressed per unit of fuel use, ${\beta }_{t}^{Ti}$, and multiplied by the term in parentheses, which is the fraction of the change in fuel use in response to changes in fuel prices that comes from changes in vehicle miles driven as opposed to the other fraction that comes from (long run) improvements in average fleet fuel economy (which are assumed to have no effect on congestion, accidents, or road damage).60 For road fuels, the price distortion is also defined net of pre-existing road fuel taxes ${\stackrel{^}{\tau }}_{t}^{i}$, which drive up private costs and partly internalize environmental costs. For the renewable general fuel, the price distortion is the per unit subsidy ${S}_{t}^{EREN}$. In (A14), $\mathrm{\Delta }{F}_{t}^{ji}$ is the change in fuel use, relative to its baseline level ${\stackrel{^}{F}}_{t}^{ji}$.

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 ${\tau }_{t}^{CO2}$, the price on CO2 emissions at time t. There is also a small economic welfare loss from the increase in renewable generation, times the unit subsidy for renewables.

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). 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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. 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.

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.

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 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.

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.

Reforming Energy Policy in India: Assessing the Options
Author: Ian W.H. Parry, Victor Mylonas, and Nate Vernon