Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Appendix A. Prior Literature on Climate Policy Options for China

There are a number of engineering models of the energy system in China focusing on technological pathways for reducing future CO2 emissions, for example, Mischke and Karlsson (2014) review 18 models developed by Chinese universities. The projections from these models serve as a useful check on the baseline projections in the present analysis (with some caveats about very recent structural shifts in the Chinese economy and reductions in energy prices). However, the models generally do not incorporate the impacts of specific mitigation policies and the key behavioral response assumptions implicit in the models are not always transparent.

Green and Stern (2016) also provide baseline projections for China using extrapolations of very recent trends in the energy intensity of GDP and the emissions intensity of energy, rather than an explicit structural model. Their CO2 emissions projections, which emphasize structural changes in the economy, are lower than in the present analysis (see Appendix B and Section 3).

As regards modelling of specific carbon mitigation policies, Cao and others (2013) provide a sophisticated analysis of a carbon tax in China using a model that integrates a detailed treatment of air pollution damages into an economic-energy model incorporating capital dynamics and disaggregating 33 different industries. With similar assumptions about the price responsiveness of coal use, our results on the long run effectiveness of carbon taxes on reducing future CO2 emissions are consistent in a broad sense, as are the reductions in deaths from less local air pollution.42 The present analysis differs from Cao and others (2013), both in terms of scope and modelling approach. The focus here is on a broad range of fiscal and regulatory carbon mitigation instruments (well beyond carbon taxes) and these policies are also evaluated against a broader range of metrics (beyond emissions and public health effects). And the modelling strategy is highly simplified, given the goal of providing a user-friendly spreadsheet tool to readily accommodate updating and additional policy and sensitivity analysis.

The present analysis, in terms of its comparison of multiple instruments against multiple metrics, is closest in spirit to that in Krupnick and others (2010) who simulated a wide range of policies and policy combinations to reduce U.S. CO2 emissions and oil consumption. Besides focusing on another country, that study also employed a far more sophisticated modelling approach, based on a variant of the U.S. Department of Energy’s National Energy Modelling System (NEMS), with each model simulation run by a consultancy firm specializing in the use of NEMS. The qualitative rankings of many policies in terms of their effectiveness at reducing CO2 are consistent with those here, though this is not surprising given that effectiveness depends on the range of behavioral responses for reducing emissions exploited by different policies.

Finally, the International Energy Agency (IEA) makes annual forecasts for China in its World Energy Outlook. Our baseline projections are compared with those in their latest report (IEA 2015) (see Section IIIA).

Appendix B. Model Parameters

Quantity and price data are taken from the IEA and the IMF respectively, and behavioral response parameters are based on empirical literature. Data for each sector is described below and is summarized in Table B1. Where there is significant uncertainty for parameters, a range of values is considered for sensitivity analysis.

Table B1.

Parameter Values

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(i) Fossil Fuels

Fuel prices and taxes/subsidies. Pre-tax prices for coal, natural gas, gasoline, diesel, and oil products for 2013 are from an IMF data based on international prices. These prices are then projected forward to 2030 based on splitting the difference between IMF43 and IEA (2015) projections of international commodity price indices for coal, natural gas, and crude oil.

Also from IMF sources, pre-tax excises for gasoline and diesel are RMB 1.0 ($0.16) and RMB 0.85 ($0.13) per liter, while there are no other significant excises (or subsidies) for other fossil fuel products.

(ii) Power Sector

Electricity consumption. From IEA (2015), total electricity consumption in China in 2013 is 386,971 kilotons of oil equivalent (ktoe).44

Income elasticity of demand for electricity-using products. Empirical studies for different countries suggests a range for this elasticity of around 0.5–1.5.45 However, China is currently undergoing an important, partial transition away from energy-intensive industries to services, suggesting that demand for energy products will increase by substantially less than in proportion to GDP growth.46 This trend can be accounted for in the model by choosing a lower value for the income elasticity of electricity and other industrial energy products. A baseline value of 0.5 is used for the income elasticity for electricity which (along with other assumptions) produces projections of future energy intensity that are about in the middle of projections from other studies (see Section IIIA). A range of 0.25-0.75 is used for sensitivity analysis.

Price elasticities for electricity. A simple average across the 26 estimates of long-run electricity demand elasticities reported in Jamil and Ahmad (2011) is about -0.5, and nearly all estimates lie within a range of about -0.15 to -1.0.47 A recent study for China by Zhou and Teng (2014) suggests an elasticity of -0.35 to -0.5. Evidence for the United States suggests the long-run 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 (e.g., Myers and others 2009, Parry and others, 2014, Sanstad and McMahon 2008). Baseline values of -0.25 are assumed for both the usage and energy consumption rate elasticities, each with ranges of -0.1 to -0.4, implying a total electricity demand elasticity of -0.5, with range -0.2 to -0.8.

Annual rate of efficiency improvement for electricity-using products. This parameter (which is of modest importance for the results) is taken to be 0.01 in the baseline,48 with range 0.005 to 0.015.

Generation shares. These are obtained from IEA (2016) by the electricity produced from each fuel type divided by total electricity production.

Own-price elasticities for generation fuels (conditional on total electricity output). The price responsiveness of coal (in the power and other energy sector) is the most critical parameter determining the effectiveness of major CO2 mitigation policies. 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 US 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).49 On the other hand, Burke and Liao (2015) report a coal price elasticity for China of -0.3 to -0.7 for 2012 using a panel of province-level data. A baseline coal price elasticity of -0.35 is assumed here, with range -0.2 to -0.5.

However, the elasticities in equation (3) 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 taken to be 0.6 in 2013 (see below). This gives (approximately) a generation cost elasticity of -0.6 with range -0.35 to -0.85.

Evidence to parameterize other generation cost elasticities is less solid, though the results are generally not very sensitive to different values. For simplicity, the same baseline generation cost elasticity, and range, is assumed for other generation fuels as for coal.

Fossil fuel consumption. This is consumption of coal, natural gas and oil in power generation, taken from IEA (2016). Electricity produced 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 during 2003 to 2013 was 2 percent, though IEA (2015) Figure 2.16 projects sharply lower productivity growth in the future (e.g., because average coal plant efficiency in China has now surpassed that in advanced countries): a baseline annual productivity growth rate of 0.5 percent is assumed for both coal (to be approximately consistent with IEA 2015) as well as oil and hydro, with a range of 0 to 1.0 percent used for sensitivity analysis. For natural gas and nuclear, there is likely more room for productivity improvements and baseline annual growth rate of 2 percent is assumed (with range 1 to 3 percent). For renewables, annual productivity growth from 2003 to 2013 was the most striking at 6 percent, though this seems unlikely to be sustainable out to 2030—a productivity growth rate of 4.5 percent is used in the baseline case for this fuel, with range 3 to 6 percent.

Non-fuel generation costs. For coal plants these are taken to be two-thirds as large as 2013 fuel costs,50 or RMB 0.18 ($0.028) per kWh. 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.

Renewables subsidy. This is obtained by dividing subsidy outlays on renewables (RMB 45.5 ($7) billion—see Figure 1) by renewable generation in 2013 (208 GWh), which yields a subsidy of RMB 0.2 ($0.03) per kWh.

Power transmission cost. This is taken to be 60 percent of the electricity generation cost in 2013, or RMB 0.63 ($0.05) per kWh.51

(iii) Road Transport Sector

Fuel use. From IEA (2015), consumption of road gasoline and diesel was 96,471 ktoe and 170,729 ktoe respectively in 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). A value of 0.6 is used here, with range 0.4 to 0.8.

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 improvements in average fleet fuel efficiencies. Based on this literature, a value of -0.25 is used, with range -0.1 to -0.4, for each of these elasticities, and for both gasoline and diesel—the total fuel price elasticities are therefore -0.5 with range -0.2 to -0.8.52

Annual rate of decline in vehicle fuel consumption rates (from technological improvements). These are set at 1 percent, with range 0.5 to 2 percent a year (e.g., Cao and others 2013).

(iv) Other Energy Sector

Fuel use. We assume 50 percent of fuel consumption in IEA (2016) from mining and quarrying, iron and steel, chemical and petrochemical, non-ferrous metals, paper, pulp and print, and non-metallic minerals is by large firms, to be consistent with projections that an ETS would cover about 50 percent of economy-wide CO2 emissions.53

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 and, based on judgement, similar assumptions are made as for the power and transport sectors.

The same baseline value and range is used for goods produced with coal and oil as for the income elasticity for electricity products (given the structural shift away from heavy industry noted above), while a baseline value of 1.0, with range 0.75 to 1.25, is assumed for goods produced with natural gas and renewables. The central values and ranges for the usage and efficiency price elasticities for all fuels are taken to be the same as those for road fuels and electricity consumption.

Annual rate of productivity improvements. These are assumed to follow those for the same fuel as used in the power sector, reinforcing the structural shift towards cleaner energy sources.

(v) Miscellaneous

GDP growth. Projected GDP out to 2021 is from the IMF’s World Energy Outlook. From 2022 onwards, real GDP growth is assumed to decrease linearly from 6 to 5 percent in 2030.54

Mortality rates from fuel combustion. Coal accounts for the vast majority of air pollution deaths from fossil fuel combustion in China. The problem 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 are produced directly during fuel combustion and are also formed indirectly (and in greater quantities) from chemical reactions in the atmosphere involving sulfur dioxide (SO2) and nitrogen oxide (NOx) emissions. China has taken steps to require new coal plants are fitted with flue-gas desulfurization (FGD) equipment, close small-scale (high polluting) plants, and require other existing plants are retrofitted with FGD. As of 2010, FGD equipment had been installed on around 80 percent of electric coal plants (Cao and others 2013, pp. 343), though even with these technologies plants still emit some SO2, in addition to NOx and (modest amount of) direct PM2.5.

Air pollution mortality and damage estimates used are taken from Parry and others (2014), with some adjustments. Parry and others (2014) estimate that the average coal plant in China caused 10.4 air pollution deaths in 2010 per petajoule (PJ), or 0.435 deaths per ktoe, and the average coal plant with control technologies caused 5.3 deaths per PJ. These estimates are extrapolated from an air quality model for China by Zhou et al. (2006), after adjusting for changes in the average population exposure to coal plants emissions and changes in emission rates. In the absence of other factors, we assume the mortality rate from coal combusted at power plants and large industrial sources would converge linearly from 10.4 to 5.3 deaths per PJ between 2010 and 2030 (due to previously enacted environmental regulations). However, the share of the Chinese population residing in urban areas is projected to increase by about 25 percent between 2010 and 2030 (Cao and others 2013) and it is the urban population that is mostly exposed to air pollution. We therefore make a linear upward adjustment in this mortality rate each year to account for this, where the upward adjustment reaches 25 percent by 2030. For small-scale coal emissions, we assume the mortality rate is 10.4 deaths per PJ in 2010, rising in proportion to the rising share of the urban population.

Also based on Parry and others (2014), the mortality rates for natural gas, gasoline, diesel, and oil products for 2010 are taken to be 1.1 per PJ, 36 per billion liters, 124 per billion liters, and 20 per million barrels of other oil products, and again these are scaled up for the rising urban population (though even combined these fuels contribute only a small share to total mortality).

One caveat is that some evidence suggests people’s channels for absorbing air pollution become saturated at very high outdoor pollution concentrations implying, paradoxically, that the health benefits from incremental reductions in fuel combustion are smaller at high pollution concentrations than at more moderate concentrations.55 In this regard, our analysis may overstate the domestic health benefits of carbon mitigation policies as it assumes incremental benefits are the same, regardless of pollution concentrations.

Appendix C. Formulas for Measuring Domestic Benefits and Costs of Policies

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), using second order approximations56 which greatly simplifies the formulas. To apply these formulas, all we need to know is the size of price distortions in fuel markets, in other words, the difference between social costs of fuel use and private costs (in the model, these include road fuel taxes, renewables subsidies, and domestic environmental costs in fuel markets) any quantity changes in markets affected by these distortions (an output from the model), and any new source of distortions created by policies in directly affected markets.57

The net domestic welfare gains from a carbon tax in period t is computed using the formula:

Σji(ΓtjiμCO2iτtCO22)(ΔFtji)(C1)
Γtji=VMORTtmtji,forjTandjiEREN;ΓtTi=VMORTtmtTi+(ηhTiηhTi+ηUTi)βtTiτ^ti;ΓtEREN=stEREN(C2)
ΔFtji=FtjiF^tji(C3)

where a ^ denotes the baseline value in a period with no carbon tax and Γtji is the price distortion in a fuel market.

In (C2), Γtji consists (for fossil fuels) 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, βtTi, 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 km driven as opposed to the other fraction that comes from improvements in fuel economy (which essentially have no effect on congestion, accidents, or road damage).58 For road fuels, the price distortion is also defined net of pre-existing road fuel taxes τ^ti which drive up private costs and partly internalize environmental costs. For the renewable general fuel, the price distortion is the per unit subsidy stEREN.

In (C3), ΔFtji is the change in fuel use, relative to its baseline level F^tji.

According to equation (C1), 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 τtCO2, the price on CO2 emissions at time t. In addition, there is a small 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, coal tax, and higher road fuel taxes. For the ETS no carbon charge applies to the transport sector or small fuel use in the other energy sector; for the coal tax the CO2 charge applies only to coal use in the power and other energy sector; and for the road fuel tax scenario carbon charges apply only to these two fuels.

For the electricity tax, welfare gains are calculated from:

τtEΔYtE2+ΣiΓtEI(ΔFtEi)(C4)

This expression is the Harberger triangle in the electricity market (a negative term equal to one half the tax rate times the change in electricity consumption), plus environmental benefits from reduced use of fossil fuels in power generation, plus a (small) gain from offsetting the distortion from the renewable subsidy.

Welfare gains for the renewable subsidy are calculated using:

ΣiΓtEI(ΔFtEi)stEREN+s^tEREN2ΔFtEREN(C5)

This is the environmental benefits as use of fossil fuel falls in response to the greater subsidy, less a welfare loss trapezoid in the renewable generation market, with base equal to the increase in renewable generation and average height equal to the average of the pre-existing subsidy and the new subsidy.

Welfare gains from the policy to lower the CO2/kWh rate for power generation are computed from:

Σi(ΓtEiμCO2iτtCO2implicit)(ΔFtEi)(C6)

where τtCO2implicit is the ‘implicit charge’ on CO2 emissions from power generation, that is the incentive per ton to reduce emissions created by the policy. Welfare gains are somewhat smaller than for the case of an equivalently scaled direct tax on the carbon content of power generation fuels, because the latter policy has a greater impact on reducing electricity demand (because revenues raised by the tax are reflected in higher electricity prices) and hence reducing fossil fuel use.

Welfare gains for the energy efficiency policy for electricity-using capital are computed from:

ΣiΓtEi(ΔFtEi)(τtEimplicit2)ΔhTEh^tEY^tE(C7)

Again, the first term is environmental benefits from the reduction in fossil fuel use (and from counteracting the renewables subsidy). The second expression is a welfare loss triangle with base equal to the reduction in electricity consumption from improved energy efficiency and height equal to the implicit charge on energy efficiency, τtEimplicit, that is, the incentive at the margin created by the policy to increase energy efficiency.

Finally, welfare gains from the energy efficiency policies for gasoline vehicles and large users in the other energy sector are:

ΓtTG(ΔFtTG)(τtTGimplicit2)(1+ΔhtTGh^tTG)F^tTG+βtTG{FtTG+ΔhtTGh^tTGF^tTG}(C8)
ΣiΓtOi(ΔFtOLARGEi)(τtOLARGEiimplicit2)ΔhtOLARGEih^tOLARGEiF^tTOLARGEi(C9)

These formulas are essentially analogous to those for the energy efficiency policy for electricity with τtTGimplicit the implicit incentive at the margin for improving the fuel economy of gasoline vehicles and τtOLARGEiimplicit the implicit marginal incentive for large users in the other energy sector to reduce use of fuel i through higher efficiency. The notable exception in (C8) is the last term, which reflects the increase in km-related externalities as lower fuel costs per km slightly increase vehicle usage59 (a similar term does not appear in (C7) or (C9) because there are no externalities analogous to road congestion and so on associated with use of products using electricity or other fuels in the other energy sector).

For all of the above formulas, where welfare gains are cumulated over the 2017-2030 period, they are converted into present values using a discount rate of 3 percent.

Appendix D. Fully Efficient Pricing Policy

The fully efficient pricing policy is taken from Parry and others (2014), and updated. The policy has three components.

First is a carbon charge on fossil fuels equal to their CO2 emissions factor times an estimate of the global damage from CO2 emissions—the so-called ‘social cost of carbon’.

Second is a charge for local air pollution costs, which primarily come from coal. This can be implemented downstream on emissions out of the smokestack or upfront on coal use with rebates provided for downstream coal users demonstrating (through continuous emission monitoring systems) that emissions released into the environment are less than embodied emissions in coal input. This pricing scheme both reduces coal use and air emission rates per unit of coal and is taken to be feasible for the power sector and large industrial firms but not for coal consumed by small users in the other energy sector (where abatement equipment is less practical). In the latter case, a local air pollution charge is still applied (upfront) to coal supply, but there is assumed to be no change in emission rates relative to the baseline.

Charges in line with local air pollution costs for natural gas and oil products are also imposed, with no effect on emission rates (an unimportant assumption given the small contribution of these fuels to air pollution deaths).

The third charge is applied to road fuels to fully reflect the external costs of congestion, accidents, and road damage, though quantitatively this component is far less important than the carbon and air pollution charges.

Appendix E. Additional Parameters for Welfare Calculations and Fully Efficient Pricing Policy

Social cost of carbon. We rely on the widely cited study by the U.S. government for the social cost of carbon (US IAWG 2013, Table), RMB 390 ($60) for per ton of CO2 for in 2030 (expressed in year 2015 RMB), while recognizing that the literature on this is much disputed (due in particular to disagreements over long-range discounting and the modelling of extreme climate risks).

Value per premature mortality. Parry and others (2014) use a value of RMB 7.35 ($1.13) million per premature mortality in China for year 2010 based on extrapolating empirical evidence from advanced countries under an assumption that the income elasticity for this valuation is 0.8. This figure is first increased by 15 percent to update it to year 2015 RMB based on the average increase in consumer prices between 2010 and 2015 (see IMF 2016). And the 2010 figure is updated to future periods based on future (real) per capita income relative to 2010 raised to the power 0.8.

Km-based externalities for road vehicles. Parry and others (2014) estimate congestion, accident, and road damage externalities for gasoline and diesel vehicles, βtTG and βtTD, at RMB 5.6 ($0.86) and RMB 3.6 ($0.56) respectively for year 2010. This figure is updated to future periods in the same way as for the value of mortality.

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1

In this paper and its accompanying spreadsheet all prices and results are reported in constant 2015 prices.

3

China’s INDC also pledges to expand forest coverage, though this is beyond the paper’s scope. Non-CO2 greenhouse gases are also beyond our scope (and are not in China’s INDC targets): in 2012 methane and nitrous oxide emissions were 19 and 6 percent respectively as large as CO2 emissions (World Bank 2016).

5

The tool is available online at www.imf.org/environment.

6

The approach avoids a lot of technical detail on, and derivations from, underlying household preferences over goods and firm production technologies.

7

Cross-price effects across the three sectors are not modelled as they are likely small for the time horizon due to products being poor substitutes for one another (e.g., higher prices for transport vehicles will have a minimal effect on the demand for fuels for space heating).

8

VAT effectively applies only to fuels consumed at the household level and, moreover, does little to reduce consumption of those fuels as it raises the price of all consumer goods by the same proportion (rather than raising the price of fuels relative to other consumer goods).

9

The taxes discussed below might be viewed as an effective rate, equal to the nominal rate times the fraction of the tax that is passed forward.

10

Disaggregation is not needed for the policy analysis as this does not consider taxes differentiated by type of electricity user.

11

Explicitly modelling dynamic capital adjustments would require a far more complex numerical simulation model, defeating the purpose of the flexible spreadsheet tool developed here.

12

This is a neutral assumption implying, for example, that other generation fuels are scaled up in proportion as a coal tax reduces coal use. In practice, proportionate increases for nuclear and renewables might be greater than for hydro, though what matters for our purposes is the total increase in non-carbon fuels (rather than its composition between nuclear, renewables, and hydro).

13

The model abstracts from substitution between use of gasoline and diesel vehicles given the very different vehicle types and that the policy scenarios increase gasoline and diesel prices in roughly the same proportion.

14

Given the focus on policies to reduce fossil fuels, the model does not capture (non-combustion) CO2 emissions released, for example, during the cement-making process.

15

A threshold of 26,000 tons of CO2 has been suggested for participation in the ETS, though this has yet to be confirmed.

16

These three fuels are assumed to have no environmental costs either. There are risks with nuclear power (e.g., radiation leaks from plant operation and waste storage) though these are not easily quantified and may be limited by appropriate safeguards.

17

Local air pollution causes a range of other damages beyond mortality (morbidity, impaired visibility, building corrosion, crop damage, acidification of lakes) but previous studies suggest their combined damages are modest relative to mortality damages (e.g., NRC 2009, WB/SEPAC 2007).

19

An exchange rate of RMB 6.5 per $1 is assumed.

20

The specific industries include petro and other chemicals, building materials, iron and steel, non-ferrous metals, and paper (see http://carbon-pulse.com/14353).

21

From IEA (2015). For more detail on country-level renewables policies see REN21 (2015).

22

The case for subsidizing R&D into renewable technologies appears to be more solid than for subsidizing deployment of these technologies (e.g., Dechezleprêtre and Popp 2016, Löschel and Schenker 2016).

23

Allowing greater subsidies to accelerate the rate of technological improvement in renewable generation in the model would enhance, but only moderately, their environmental effectiveness.

24

Allowing for this market failure could imply that, up to a point, policies to increase energy efficiency could have net economic benefits (before even counting environmental benefits), though these are small relative to the net benefits from directly pricing emissions (e.g., Parry, Evans, and Oates 2014).

25

In reality, policy will be less efficient as some products and capital may be difficult to regulate (e.g., smaller appliances, audio and entertainment equipment, industrial processes such as assembly lines) and (in the absence of extensive credit trading provisions), the incremental cost per ton of CO2 reduced may differ substantially across different energy efficiency programs.

26

Implementing regulations for heavy trucks, for example, is complicated given that fuel economy is very sensitive to the weight of freight (see Harrington 2012).

27

The data underlying the figures below is available from the accompanying spreadsheet.

28

These energy demand and CO2 projections are broadly consistent with those from the range of energy models for China summarized in Mischke and Karlsson (2014), Figures 2 and 3—GDP growth is moderately larger in the present model, though this is offset by a faster decline in the energy intensity of GDP. Green and Stern (2016) project a similar decline in the energy intensity of GDP but a somewhat larger decline (20 percent) in the CO2 intensity of energy by 2030.

29

For comparison, with the IEA price scenarios economy-wide CO2 emissions in 2030 are about 5 percent higher than projected in the IEA (2015) pp. 635 ‘Current Policies Scenario’. In our baseline base, emissions are 18 percent higher than in IEA (2015).

30

China recently introduced a domestic oil price floor of RMB 260 ($40) per barrel but this is non-binding in our scenarios.

31

This excludes outdoor air pollution deaths from non-fossil emissions sources (e.g., agriculture, plastics, refrigerants, landfills, mining).

32

The rebound effect offsets about 10 percent of the energy savings from higher efficiency in the power sector, and similarly in the transport and other energy sectors.

33

A cautionary note here is that the uncertainties surrounding the effects of such dramatic policy changes are especially large.

34

Regional analyses as well as full general equilibrium effects on production, labor, income and consumption are beyond the scope of this paper.

35

Incidence with pre-existing market distortions should ideally be measured by the consumer surplus loss trapezoid. The direct impact measure is very close to the first order losses we estimate—e.g., only a 5 percent difference between the two for a policy change that reduces fuel consumption by 10 percent.

37

The CFPS is conducted by the Institute of Social Science Survey at Peking University. The survey covers about 95 percent of the Chinese population in 25 provinces. Income distribution and poverty studies have found the CFPS to be consistent with other large-scale nationally representative household surveys in China. Zhang and others (2014) find that poverty levels are much higher in these surveys than those reported in official statistics. Xie and others (2014) find the sex–age structure of the 2010 CFPS survey closely tracks the 2010 Census.

38

Spending on electricity is estimated from electricity consumption taken from the CFPS and average residential electricity prices by province taken from Fridley and others (2014).

39

The difference between the two curves is unclear and might be due to the very high savings rate of Chinese households, especially among higher incomes. Underreporting of income might also explain some of this divergence. Further breakdowns of these effects reveal that the difference in the degree of progressivity operates almost entirely through the direct impact of energy prices. This result also differs from the United States. Morris and Mathur (2015) use broadly similar methodologies and find that the regressivity of a carbon tax is higher when using income as a measure of socioeconomic status

42

Cao and others (2013) caution that chronic mortality effects of air pollution—which are the much larger component—are far more speculative than the acute effects, as the former is based pollution exposure/mortality relationships estimated for the United States rather than China (both chronic and acute mortality is implicit in the present analysis). However a recent study by Burnett and others (2013) suggests that (albeit limited) evidence on this relationship for other countries is broadly consistent with that for the United States.

43

See www.imf.org/external/pubs/ft/weo/2015/02/weodata/weoselagr.aspx. The indices (based on futures prices) 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.

44

Generation, rather than consumption, is what matters for fuel use and emissions, though the difference between them (reflecting electricity exports and imports) is well below 1 percent.

45

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.

47

See Madlener and others (2011) for further discussion of the literature and broadly similar findings.

48

This is consistent with similar assumptions for China in Cao and others (2012), pp. 389–90.

49

NEMS tends to be less price responsive than other models and the above simulation was for a carbon tax which also raises natural gas prices and dampens the reduction in coal use.

50

Based on Cao and others (2013), pp 341 (after accounting for differences in coal prices).

51

This is approximately consistent with Cao and others (2013), pp. 343.

52

There is, however, significant variation among studies: for example, Sterner (2007) reports globally averaged (long-run) gasoline price elasticities (the sum of the two elasticities noted above) of around –0.7 while individual country estimates in Dahl (2012) are closer to about –0.25 on average (see Charap, da Silva and Rodriguez 2013 for further discussion). 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). The responsiveness of fuel efficiency to taxation will be dampened in the presence of binding fuel economy regulations, though this issue is not relevant for the present analysis which compares policies in isolation (rather than jointly).

54

IMF (2016) projects a growth rate of 5.7 percent for 2015-2020, though growth rates beyond that are expected to be a little lower (e.g., Green and Stern 2016).

55

That is, the relationship between mortality and pollution concentrations may start to flatten out at severe pollution concentrations (e.g., Burnett and others 2013).

56

That is, assuming fuel demand curves are linear over the range of fuel changes induced by policies.

57

Changes in quantities in markets with no distortions have no impacts on economic welfare.

58

See Parry and others (2014), Ch. 5, for a detailed discussion.

59

See Fischer, Harrington, and Parry (2007) for further discussion.