metadata: authors: 0: author: Cao,Jing
1: author: Ho,Mun-Sing
2: author: Timilsina,Govinda R.
count: China
docna: 0: docna: Impacts of carbon pricing in reducing the
            carbon intensity of China's GDP
docty: Policy Research Working Paper
subtopic: Climate Change Mitigation and Green House Gases,Energy Policies & Economics,Macroeconomic Management,Energy Demand,Energy and Environment,Energy and Mining
teratopic: Environment,Health, Nutrition and Population,Science and Technology Development
abstracts: cdata!: In contributing to global climate change
            mitigation efforts as agreed in Paris in 2015, China has set
            a target of reducing the carbon dioxide intensity of gross
            domestic product by 60-65 percent in 2030 compared with 2005
            levels. Using a dynamic computable general equilibrium model
            of China, this study analyzes the economic and greenhouse
            gas impacts of meeting those targets through carbon pricing.
            The study finds that the trajectory of carbon prices to
            achieve the target depends on several factors, including how
            the carbon price changes over time and how carbon revenue is
            recycled to the economy. The study finds that carbon pricing
            that starts at a lower rate and gradually rises until it
            achieves the intensity target would be more efficient than a
            carbon price that remains constant over time. Using carbon
            revenue to cut existing distortionary taxes reduces the
            impact on the growth of gross domestic product relative to
            lump-sum redistribution. Recycling carbon revenue through
            subsidies to renewables and other low-carbon energy sources
            also can meet the targets, but the impact on the growth of
            gross domestic product is larger than with the other
            policies considered.
geo_reg_key: 517191,517198,119225
repnme: repnme: Impacts of carbon pricing in reducing the
            carbon intensity of China's GDP
display_title: Impacts of carbon pricing in
            reducing the carbon intensity of China's GDP
listing_relative_url: /research/2016/06/26537193/impacts-carbon-pricing-reducing-carbon-intensity-chinas-gdp
topicv3: Current Account m1326278 1135,Carbon Tax m1326372 1845,Energy and Extractives m1326595 507,Power and Electricity Sector m1327094 515,Carbon Pricing m1327308 1846
publishtoextweb_dt: 2016-06-29T00:00:00Z
historic_topic: Environment,Health, Nutrition and Population,Science and Technology Development
no_of_pages: 108
author_keys: 0: author_key: 995995
1: author_key: 1372182
2: author_key: 940446
model_extractions: WPS7735 Policy Research Working Paper 7735 Impacts of Carbon Pricing in Reducing the Carbon Intensity of China’s GDP _Jing Cao Mun Ho Govinda R. Timilsina_ Development Research Group Environment and Energy Team June 2016
Policy Research Working Paper 7735

# **Abstract**

In contributing to global climate change mitigation efforts as agreed in Paris in 2015, China has set a target of reducing the carbon dioxide intensity of gross domestic product by 60-65 percent in 2030 compared with 2005 levels. Using a dynamic computable general equilibrium model of China, this study analyzes the economic and greenhouse gas impacts of meeting those targets through carbon pricing. The study finds that the trajectory of carbon prices to achieve the target depends on several factors, including how the carbon price changes over time and how carbon revenue is recycled to the economy. The study finds that carbon pricing that starts at a lower rate and gradually rises until it achieves the intensity target would be more efficient than a carbon price that remains constant over time. Using carbon revenue to cut existing distortionary taxes reduces the impact on the growth of gross domestic product relative to lump-sum redistribution. Recycling carbon revenue through subsidies to renewables and other low-carbon energy sources also can meet the targets, but the impact on the growth of gross domestic product is larger than with the other policies considered.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Impacts of Carbon Pricing in Reducing the Carbon Intensity of China’s GDP<sup>1</sup>

Jing Cao, Mun Ho, and Govinda R. Timilsina<sup>2</sup>

**Key Words** : Climate change policies, Carbon pricing, China, NDC, CGE modeling

## **JEL Classification** : Q54, D58

> 1 The authors would like to thank Bert Hofman, Todd Johnson, Garo Batmanian, Karlis Smits, Ying Fan and Mike Toman for their valuable comments and suggestions. The views and interpretations are of authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. We acknowledge World Bank’s Knowledge for Change (KCP) Trust Fund and the Harvard Global Institute for financial support.

> 2 Jing Cao (caojing@sem.tsinghua.edu.cn) is an Associate Professor at the School of Economics and Management, Tsinghua University, Beijing, China; Mun Ho <u>(HO@rff.org) is a Visiting Scholar at the Resources for Future,</u> Washington, DC and also a member of Harvard  China Project; Govinda Timilsina (gtimilsina@worldback.org) is a Senior Economist at the Development Research Group, World Bank.
# **1 Introduction**

In its Nationally Determined Contribution (NDC) coming out of the Paris Agreements reached at the 21<sup>st</sup> meeting of the Conference of Parties to the United Nations Framework Convention on Climate Change (UNFCCC), the Chinese government has set a goal to cut CO2 emissions per unit of GDP by 60 to 65 percent by 2030, compared to 2005 levels. It also has reiterated previously announced goals that carbon emissions would peak, and that the share of non-fossil fuels would increase to 20% of total energy consumption, by 2030. Earlier in 2016 the government announced the 13<sup>th</sup> Five-year Plan, with the goals of reducing energy per unit GDP by 18% between 2015 and 2020 and to reduce CO2 per unit GDP by 20%. The longer term targets imply that China would need additional low carbon infrastructure investments, including further deployment of renewable energy, as well as further improvements in energy efficiency.

This study addresses the following “what-if” question: what would be the economic consequences of China seeking to meet its NDC target with carbon pricing? In practice, China can be expected to use a range of policy instruments to that end. However, an examination of economic consequences using a hypothetical least-cost instrument, a dynamic carbon price, provides a useful context for evaluating more complicated policy portfolios. Moreover, an examination of carbon pricing is consistent with the expressed desire of the government to rely more on market mechanisms.

The analysis is carried out using a multi-sector growth model of China, including a household model component that allows us to discuss the impacts of policies on different demographic groups. While there exist a large number of studies analyzing impacts of a hypothetical carbon tax in China, studies analyzing the potential impacts of carbon pricing to meet China’s NDC do not exist. To the knowledge of authors, this is the first analysis that examins implications of meeting China’s NDC through a carbon pricing mechanism.

We first use the model to develop a base case up to 2030. The base case includes existing plans to expand the renewable energy and nuclear energy included in China’s 12<sup>th</sup> and 13<sup>th</sup> Five year plans out to 2020. The base case does not include carbon pricing. The government has announced an intention to extend current experimental carbon trading programs to a national system beginning in 2017. However, no details of the level of cap and coverage of sources have been given, and so the planned national emission trading scheme is not included in the study. In the base case, the carbon emissions are rising continuously during the 2012-2030 period, though ‐2‐
they peak around 2030 through various GHG mitigation measures already in the base case. The base case is then compared to scenarios with carbon pricing as well as a policy for additional investment in renewables for electricity generation. Carbon pricing is implemented in the model through surcharges on fossil fuel prices based on their carbon contents.

The study considers the following policy formulations. First, “lower” carbon pricing and “higher” carbon pricing trajectories are implemented to achieve, respectively, 60% or 65% reduction of carbon intensity of GDP from 2005 levels. For the 65% target, we compare a fixed carbon surcharge on fossil fuels over time to a trajectory where the surcharge starts at a low level but gradually rises.

As has been emphasized by many others in the literature, the method of recycling the revenues generated from carbon pricing is another important policy component, as it has an important impact on the net economic cost of such a pricing policy. We consider two approaches to recycle the carbon revenue to the economy– a lump sum transfer to households, and a cut in the VAT and capital income tax. In addition, the study considers a policy that subsidizes renewable energy with revenue generated through the carbon pricing.

We find that the carbon surcharge on fossil fuels aimed at achieving a 60% reduction in CO2 intensity of GDP would rise from 1.6 yuan /ton of CO2 in 2016 to 26 yuan/ton of CO2 in 2030 (all units measured in 2010 yuan). This carbon price will reduce the absolute level of 2030 emissions by 3.3% with GDP only 0.11% lower than the base case, if carbon revenue is recycled to cut existing taxes. In this scenario, 2030 energy use falls by 2.6% and electricity use by 1.5%. With the carbon pricing policy to achieve a 65% reduction in CO2 intensity of GDP, the carbon surcharge on fossil fuels rises to 157 yuan/ton of CO2 in 2030, generating a 13% reduction in energy use, a 16% reduction in CO2 and a 0.74% lower GDP, all relative to the base case..

If instead of cutting existing taxes, the carbon revenues are recycled by giving them to households as a lump sum rebate, then GDP is 1.2% lower than the base case in 2030, instead of 0.74%. In this case, the same carbon price slows aggregate output growth more, and hence also slows growth energy consumption and emissions more. This reinforces the lesson emphasized by many others – recycling revenues from carbon pricing by reducing existing tax wedges is a useful opportunity for softening adverse economic impacts.

The paper is organized as follows. Section 2 briefly highlights the CGE methodology developed for this study (detailed description of the model is presented in the Appendix A), ‐3‐
followed by results of the model for the base case scenario in Section 3. Section 4 presents results of carbon pricing scenaruios to meet China’s NDC, followed by sensitivity analysis in which the baseline is altered in Section 5. Section 6 concludes the paper.

# **2. The CGE model**

The CGE model used for this study is a dynamic recursive growth model where the main agents are the household, producers, government and the rest of the world. Household savings, enterprise retained earnings and government-funded investments are the main sources of investment; unlike most developed economies the government role in China is much larger. Detailed description of the model is presented in Appendix A, here we summarize its key features.

One of the key features of this model that distinguishes it from large number of CGE models available for China for climate policy analysis is that it allows for heterogeneity among households, whereas most existing CGE models have a single representative household. In our model, households are distinguished by size, presence of children, age of head and region. The income elasticity is different for different consumption items and thus projects a different structure of consumption in the future when incomes rise. Such a function allows us to distinguish the impact of policies on different households via the consumption channel.<sup>3</sup> Labor is supplied inelastically by households.

The private household savings rate is set exogenously and total national private savings is made up of household savings and retained earnings of enterprises. These savings, together with allocations from the central plan, finance national investment. They also finance the government deficit and the current account surplus. Investment in period _t_ increases the stock of capital that is used for production in future periods. The plan part of the capital stock is assumed immobile in any given period, while the market part responds to relative returns. Over time, plan capital is depreciated and the total stock becomes mobile across sectors.

The government imposes taxes on value added, sales and imports. On the expenditure side, it buys commodities, makes transfers to households, pays for plan investment, makes interest payments on the public debt and provides various subsidies. Expenditures on interest and

> 3 See Equations A20-A24 for the household demand function in Appendix A.

‐4‐
transfers are exogenous, and the exogenous deficit target is met by making government spending on goods endogenous.

Finally, the rest of the world supplies imports and demands exports. Domestically produced goods are imperfect substitutes for imports. World relative prices and the current account balance are set exogenously in this one-country model, and an endogenous variable for terms of trade clears the current account equation. The world price of commodity _i_ relative to _j_ is assumed to be at base year ratios throughout the projection period with the exception of world oil prices, where projections from the U.S. Energy Information Administration 2013 _Annual Energy Outlook_ are used (and treated as exogenous).

On the supply side, 33 industries are distinguished, each producing output that is given by a nested series of constant-returns-to-scale CES functions.<sup>4</sup> Primary factors include capital, labor and land. Pure TFP growth and biased technical change are allowed; in particular, energy input per unit output can decline faster than other input-output ratios.

Since the electricity sector is the main contributor of CO2 emissions in China, in the version of the model used for this study, the electricity sector is disaggregated into one transmission subsector and 9 distinct generation sub-sectors – coal, gas, nuclear, hydro, other, wind, solar, coal-CCS and gas-CCS. The tier structure of electricity production is given in Figure 1. At the top node total output is the aggregate of Generation and Transmission, and Generation is in turn an aggregate of Baseload and Intermittent Renewables.<sup>5</sup> The Baseload aggregate is composed of the output from coal, gas, nuclear, hydro, miscellaneous other minor sources, and the potential technologies, coal with carbon storage and sequestration (CCS) and gas-CCS. The Intermittent Renewables are wind and solar. The elasticities of substitution among these generation sources are presented in Table A2 in Appendix A and also noted in Figure 3; we assume a high degree of substitution among the baseload sources, and we follow other studies in assuming an elasticity of one between Baseload and Renewables.

This structure reflects the reality that average generation costs are different and yet different generation sources co-exist; there are considerations beyond average costs that determine the share of various sources in a highly regulated sector. We should make a technical note regarding this formulation: the imperfect substitution of kWh’s means that the quantity

> 4 The production structure is given in Figure A1 and equations A4-A12 in Appendix A.

> 5 This aggregation of Baseload and Renewables is similar to that in C-GEM (Qi et al. 2014).

‐5‐
index of output of generation source _l_ ( _QltEGEN_ in Appendix A) is not identical to the kWh measure ( _QltkWh_ ); the total generation output index ( _QtEG_ ) is not a simple sum (  _l_<sup>_Q_</sup> _ltkWh_ ) but a CES index of the components that take into account the different prices.

The output from each generation source is also expressed as a nest of CES functions; for nuclear, hydro, wind and solar there is a “Resource” input that represents non-produced inputs such as suitable rivers, and land with wind and sun. In any given period, these resources are given by an upward sloping supply curve to represent the short-run costs of developing such regions. This is similar to the representation in Vennemo et al. (2013) and Sue Wing et al. (2014) as discussed in the Appendix A.

The electricity generation and distribution system in China is dominated by a few large enterprises and tightly regulated by the NDRC. The prices are set by the NDRC after negotiation among the generators, distribution monopolies and major users. The dispatch order (which generation units are used at any moment) is determined for the most part by a “fairness” principle and a loose aim to promote renewables; it is not set according to least-cost dispatch, and it does not reflect bid prices for electricity in a competitive wholesale generation market. For a carbon price to work in the electric power sector, there must thus be reforms that would allow price signals to matter – that the net cost of fossil fuels reflects the carbon price, and that dispatch is sensitive to the prices asked by the generators. Our policy simulation here is predicated on the assumption that such reforms will take place.

There are 33 markets for the commodities; that is, there are 33 endogenously determined prices that equate supply with demand for the domestic commodities identified in the model. The total supply consists of domestically produced goods and imported varieties; these are assumed to be imperfect substitutes. There are three markets for the factors of production – land, capital and labor – and three prices to clear them.

The base case simulation is determined by the projections of the exogenous variables and the initial stocks of debt, capital and labor force.<sup>6</sup> Given the initial stocks, we solve for the three factor prices and the 33 commodity prices that clear the markets in the first period. This gives us all the quantities for the first period, including investment that augments the next period stock.

> 6 The projections of the exogenous projections are described in detail in Appendix A. The population projections come from UN Population Division’s _World Population Prospects: 2012 Revision_ , downloaded from their web site, <u>http://esa.un.org/unpd/wpp/unpp/panel_population.htm</u> .

‐6‐
The details of the construction of the base year data for the power sector and the base case projections of the various generation sources is given in Appendix B. We discuss the impact of using this electricity projection for the base case in section 5.

# **Figure 1. Structure of electricity sector**

<!-- Start of picture text --> Electricity<br> σED=0.7<br>Transmission<br>Generation<br> See Fig A2b σ GE =1<br>Base load Intermit.<br> σ BL =4 Renewables<br> σ RE =4<br>Wind Solar<br>Coal Gas Nuclear Other<br>Hydro Coal-CCS Gas-CCS<br> σM =Coal σM =Nuclear Fig A2b σM =CoalCCS<br><!-- End of picture text -->

# **3. The Base Case Scenario**

GDP growth in China between 1978 and 2007, the eve of the Global Financial Crisis, was 9.9% per year. With the stimulus to fight the effects of the Crisis in 2008 and 2009, the growth rate between 2007 and 2011 was maintained at a high 9.6%, however, with the end of the stimulus and the continuing weakness in the world economy, it decelerated to about 7.5% during 2012-14.<sup>7</sup> In the 13<sup>th</sup> Five-year plan announced in March 2016, the goal was to reduce the energy per unit GDP by 18% between 2015 and 2020, and reduce CO2 per unit GDP by 20%.

> 7 Cao and Ho (2014) discuss the sources of growth in China for the past 30 years – aggregate productivity and industry productivity performance – and discuss how these might inform projections of future growth. They relate this growth accounting of China to the literature about the middle-income trap and growth slowdowns.

‐7‐
The base case growth path is driven by the exogenous variables including population, labor force quality, capital quality growth, total factor productivity and saving rates. Our assumptions for the drivers are based on our reading of the historical record (Cao and Ho (2014) and are described in Section A.4 in the Model Appendix A. This base case is not designed to replicate precisely any particular projection; it is only intended to provide a point of comparison for the policy cases. The base case growth is summarized in Table 1 and Figure 2. Between 2010 and 2030 the population is projected to rise from 1,360 to 1,470 million, while the working age population falls from 938 to 883 million. With the assumed increase in average hours worked per person (including longer work lives) and labor quality, effective labor supply increase by 8% over these 20 years despite the fall in working population. Our model projects that GDP will grow with an average rate of 6.4% per year during 2015-20 and decelerates to 4.6% during 202030.<sup>8</sup> The 6.4% rate for the 2015-2020 period is very close to that assumed in 13<sup>th</sup> Five-year plan (6.5% for the 2016-2020 period). The consumption share of GDP rises from 35% in 2010 to 54% in 2030 due to falling trends in household saving rates. More precisely, growth decelerates due to lower TFP, lower savings rate, and falling working age population.

Primary energy use grows at 3.9% during 2015-20, implying a fall in energy intensity of 3.4% per year, close to the 13<sup>th</sup> FYP target of a 18% reduction in intensity. This is to be compared to the historical record given in Figure 2; the intensity index was quite volatile and averaged a decline of 4.1% per year during the 11<sup>th</sup> Five-year Plan (2006-10). During 2020-30 projected energy use decelerates more, but with the slowing GDP growth, the intensity falls at only 2.1% per year. There is a big change in the composition of energy sources; coal use only grows at 2.2% compared to oil at 4.3% and gas at 7.7% per year during 2015-20.

> 8 GDP projection is endogenous to the model which is calculated based on several variables which are exogenous. They include  population growth, share of working age population to total population, saving rates, dividend payout rates, government taxes and deficits, world prices for traded goods, current account deficits, rate of productivity growth, rate of improvement in capital and labor quality, The values of these variables are provided in Table A3 in Appendix A. Note that GDP projections vary from across the sources. For example, IMF (2014) projections were 6.9% for 2012-2020 and 5.3% for 2020-30. World Bank (2013) projections were 7.0% for 2016-2020 and 5.0% for 2026-30. Projections change across the sources and also overtime from the same source due to different assumptions.

‐8‐
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Electricity use is projected to grow at 4.4% during 2015-20 and at 3.0% during 2020-30. The change in the structure of generation sources is given in Table 2 and graphed in Figure 3; we see that electricity use grows faster than coal due to the rise of renewables and nuclear energy – coal-fired electricity grows at 2.7% during 2015-20, nuclear at 12%, wind at 11%. Solar starts from a very tiny base but rises rapidly, at 40% per year. In the 2020-30 period, renewable growth decelerates sharply but is still faster than coal, with the exception of hydro growth which falls to 0.4% per year.

As a result of the shift towards cleaner fuels driven by the assumed improvements in energy efficiency and the endogenous changes in prices in the base case, CO2 emissions, including those from cement manufacture, only grows at 2.6% per year during 2015-20. That is, the CO2 intensity falls faster than energy intensity; by 2030 it is 58.7% lower than the 2005 level (compared to the NDC target of a 60-65% reduction). CO2 emissions rise steadily from 8,300 million tons in 2010 to 12,800 million in 2030, thereafter we project a stabilization around 13 billion tons for at least the next 10 years. Coal use rises to 4.1 billion tons in 2020 and then plateaus at about 4.6 billion beginning in 2030.<sup>9</sup>

# **4. Simulations of Carbon Pricing Scenarios**

In our base case the CO2 intensity falls by 58.7% in 2030 relative to 2015. We consider carbon price trajectories that can reduce CO2 intensity by 60 or 65% by 2030, as in the NDC. To that end, we consider flat rates that apply for all years, and prices that start low and rise every year so as to achieve the same cumulative emission reductions. We also consider two alternative approaches for recycling the carbon revenue to the economy: (i) recycle the carbon revenue by cutting existing taxes and (ii) rebating it lump-sum to households.

These policy scenarios are defined in Table 3. In the R1CUT scenario, we find through trial-and-error that a carbon price starting at 1.6 yuan/ton of CO2 in 2015,<sup>10</sup> and rising gradually to 26 yuan in 2030, hits the 60% reduction goal.<sup>11</sup> In this scenario, the carbon revenues collected

> 9 Our energy projection is based on IEA (2014). In the “Current Policies” scenario of IEA, China’s primary energy use is projected to grow at 2.9% during 2012-20 and 1.9% for 2020-30, and project coal use to grow at 2.1% (201220) and at 1.3% (2020-30).

> 10 All yuan units are measured in 2010 yuan, the base year of the model.

> 11 We should digress here to clarify what is meant by a statement like “a carbon price of  ¥10 per ton in each year of the projection.” In an economy with no TFP growth and little capital accumulation, absolute (and thus relative) commodity prices will differ little from the base year. In this case there is little ambiguity about what ¥10 in each year means. With rapid growth in output and incomes per capita, the price of commodities relative to the price of labor is

‐11‐
<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>ee ee<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>40<br>20 ee<br>0<br>2015 2020 2025 2030<br>———RICUT ——R2CUT — — F2CUT<br><!-- End of picture text -->
Table 3. Policy scenarios

|Scenario<br>definition|Scenario<br>name|Target to be achieved under the<br>scenario|Revenue recycling<br>scheme|Carbon<br>CO|surcharge<br>2, yuan20|(per ton<br>15)|
|---|---|---|---|---|---|---|
|||||2015|2025|2030|
|Base case|B0|58.7% reduction of CO2<br>intensity without carbonpricing|||||
|Low carbon pricing<br>with risingrate|R1CUT|60% reduction in CO2intensity<br>by2030|Cut VAT and capital<br>tax|1.6|18|26|
|High carbon pricing<br>with rising rate|R2CUT|65% reduction in CO2intensity<br>by2030|Cut VAT and capital<br>tax|9.8|108|157|
||R2LUMP|Same cumulative CO2<br>reductions asR2cut|Lump sum rebate to<br>households|9.3|102|149|
|High carbon pricing<br>with flatrate|F2CUT|Same cumulative CO2<br>reductions asR2cut|Cut VAT and capital<br>tax|82|82|82|
|Rising renewable<br>subsidies to reach<br>IEA's New Policies<br>scenario|RRSUB|Renewable subsidies financed<br>by carbon revenue|Carbon tax to offset<br>renewables costs|2.8|36|58|

‐13‐
<!-- Start of picture text --> '0.14i SS<br><!-- End of picture text --> <!-- Start of picture text --> a~<br>2<br>0.12 ><br>2 0.10 ;—— —Base case<br>So cate<br>o> 0.08 60% target<br>0.06 65%9, target<br><!-- End of picture text --> <!-- Start of picture text --> 0.00<br>2010 2015 2020 2025 2030<br><!-- End of picture text -->
fossil fuels, and raised the price of electricity, leading to a rise in the prices of energy intensive goods relative to the base case as shown in Figure 7 for 2030. Coal price at the mine-mouth rises by 6.2% while crude oil price rises by 1.2% inclusive of the tax, leading to a 1.4% rise in electricity prices.

# **Table 4. The effects of achieving higher reductions with higher carbon price case**

(R1CUT vs R2CUT; rising carbon prices with offsetting cuts in VAT)

|||2015<br>R1CUT|R2CUT||2030<br>R1CUT|R2CUT|
|---|---|---|---|---|---|---|
||Base case|:<br>Low C<br>price<br>|:<br>High C<br>price<br>|Base<br>case|:<br>Low C<br>price<br>|:<br>High C<br>price<br>|
|Variable|values|% chang<br>ba|e from<br>se<br>|values|% chan<br>ba|ge from<br>se|
|GDP (billion yuan 2010)|58,956|-0.004|-0.027|126,235|<br>-0.11|<br>-0.74|
|Consumption (bil yuan 2010)|25,290|-0.003|-0.017|<br>67,264|<br>-0.11|<br>-0.71|
|Investment (bil yuan 2010)|25,374|-0.007|-0.045|<br>43,747|<br>-0.13|<br>-0.86|
|Government consumption (bil yuan 2010)|6,973|0.000|0.000|<br>12,785|<br>0.00|<br>0.00|
|Energy use (million tons of sce)|4,005|-0.21|-1.21|<br>5,958|<br>-2.6|<br>-13.1|
|Coal use (million tons)|3,666|-0.29|-1.71|<br>4,610|<br>-4.1|<br>-20.0|
|Oil use (million tons)|537|-0.04|-0.27|<br>835|<br>-0.7|<br>-4.0|
|Gas use (billion cubic meters)|166,354|-0.09|-0.53|381,163|<br>-1.8|<br>-9.4|
|Electricity (billion kWh)|5,554|-0.11|-0.65|<br>8,951|<br>-1.5|<br>-7.9|
|CO2 emissions(inc cement; mil tons)|9,819|-0.23|-1.33|12,797|<br>-3.3|<br>-16.0|
|Cumulative CO2 (2015-2030)||||<br>166,675|<br>-1.9|<br>-9.8|
|Carbon price yuan/ton CO2||¥1.6|¥9.8||¥26.2|<br>¥157.1|
|Carbonprice as a share of total revenue||0.11%|0.66%||1.1%|5.8%|
|Electricity generation|values|% ch|ange<br>|values|% ch|ange|
|Coal|3967|-0.18|-1.04|<br>5782|<br>-2.7|<br>-14.2|
|Gas|152|0.02|0.11|<br>459|<br>-0.3|<br>-2.2|
|Nuclear|190|0.06|0.35|<br>678|<br>1.0|<br>5.4|
|Hydro|902|0.06|0.35|<br>1132|<br>0.9|<br>5.2|
|Other|133|0.11|0.67|<br>271|<br>1.6|<br>8.5|
|Wind|191|0.002|0.01|<br>498|<br>0.01|<br>0.12|
|Solar|19|0.004|0.02|<br>130|0.04|<br>0.25|

‐15‐
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These changes in relative prices, and the reduction in capital stocks, lead a fall in industry output relative to the base case, as shown in Figure 8. By 2030, the consumption of coal falls in relative terms by 4.1%, oil by 0.7% and electricity by 1.5% (Table 4). The output of energy intensive goods, including primary metals and cement (building materials sector), fall more than the output of consumer goods and services industries. These price distortions reduce GDP, leading to lower investment every year. By 2030 GDP is lower y 0.11%, aggregate consumption is lower by 0.11%, investment by 0.13%, and total energy by 2.6%. As a result of these reductions, CO2 emissions fall by 3.3% in 2030 in Policy R1CUT.

The composition of electricity generation change at the same time that total electricity output falls by 1.5%; coal generation falls by 2.7% in 2030 while nuclear and hydro rise by about 1% (bottom section of Table 4). The price of intermittent renewables (wind and solar) are essentially unchanged and their output remains unchanged leading to a rise in their share contribution.

When we raise taxes much higher in the R2CUT scenario, the effects are amplified. The tax by 2030 is 157¥/ton, and the price of coal rises by 37% instead of 6%.The impact of lower investment and capital stocks due to these price shocks is a 0.23% reduction in 2020 GDP, and a 0.74% reduction in 2030 (compared to the 0.11% cut in GDP under the low tax policy). The tax on CO2 raises revenues equal to 5.8 percent of total revenues and enables a corresponding cut in the VAT and capital taxes. The lower GDP and higher energy prices lead to a 13% reduction in energy use compared to 2.6% under policy R1. Carbon emissions are 16% lower in order to reach the 65% intensity target. Within the electricity group, coal generation falls by 14% while nuclear and hydro rises by more than 5%. This is due to the rise in coal price leading to a rise in the relative price, P(coal electricity)/P(hydro), of 5.6%. Solar power output rises by only 0.3% given that we assumed that the elasticity between intermittent renewables and baseload is only 1, that is, most of the substitution is from coal to nuclear and hydro and not to intermittent sources.

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plotted in Figure 4). The comparison of impacts is shown in Table 5 for the first and final years. Note that the reduction in cumulative CO2 emissions over 2015-30 is the same, 9.8%, and that the first year the tax is 82¥/ton in F2CUT compared to 9.8¥ in the rising tax case.

With the higher tax rate in the first year of F2CUT, the larger distortion lead to a 0.26% fall in GDP relative to base case, compared to 0.03% in R2CUT. There is a bigger impact on investment that cumulates to a 0.56% reduction in the 2030 capital stock relative to base case, compared to a 0.51% relative reduction in the rising tax scenario.

The other variables such as energy consumption, electricity composition and CO2 emissions follow the same pattern – much bigger changes in year 1 and a more modest change in 2030, but cumulative losses that are bigger. The cumulative reduction in CO2 is the same but the reduction relative to base case in year 2030 is only 9.5% in the flat tax case compared to 16% in the rising tax case. In that year the changes relative to base case in coal generated electricity are -8.2% versus -14.2%, and in hydro they are +2.7% versus +5.2%.

To summarize, the flat tax path that delivers the same cumulative CO2 reduction as the rising tax trajectory imposes greater GDP losses. A richer model that takes explicit account of adjustment costs would generate even bigger differences between a gradually rising tax and a flat tax profile.

# _Options for recycling carbon tax revenue_

To give an illustration of the importance of how recycling carbon tax revenues is done, we compare the previous R2cut scenario where we cut existing VAT and capital income tax to a case where the revenues are given back to households as a lump sum transfer. The results for this R2LUMP case are shown in Table 6 for 2030 together with R2cut. These two paths of rising carbon tax rates are set so that the cumulative change in CO2 emissions over 2015-2030 are the same -9.8%. It turns out that the relatively weaker economic performance in the lump-sum case generates lower emissions, and so the same cumulative reduction requires a lower carbon price than in R2CUT. This difference means that the change in carbon intensity in 2030 is not exactly equal to the 65.0% reduction in the R2cut case, but very close.

‐20‐
# Table 5. The effects of flat versus rising carbon taxes (F2cut vs. R2cut)

(High tax cases, revenue recycled by cutting VAT and TK)

|||2015<br>F2CUT:|R2CUT:||2030<br>F2CUT:|R2CUT:|
|---|---|---|---|---|---|---|
||Base<br>case|<br>Flat C<br>Tax|<br>High C<br>Tax|Base<br>case|<br>Flat C<br>Tax|<br>High C<br>Tax|
|Variable|values|% change f|rom base|values|% chan<br>ba|ge from<br>se|
|GDP (billion yuan 2010)|58,956|<br>-0.26|<br>-0.027|126,235|<br>-0.61|<br>-0.74|
|Cumulative GDP (not discounted)|||||-0.50|<br>-0.42|
|Consumption (bil yuan 2010)|25,290|<br>-0.17|<br>-0.017|67,264|<br>-0.54|<br>-0.71|
|Investment (bil yuan 2010)|25,374|<br>-0.41|<br>-0.045|43,747|<br>-0.64|<br>-0.86|
|Capital stock (bil yuan 2010)|138,851|<br>0.00|<br>0.00|386,091|<br>-0.56|<br>-0.51|
|Energy use (million tons of sce)|4,005|<br>-8.9|<br>-1.21|5,958|<br>-7.8|<br>-13.1|
|Coal use (million tons)|3,666|<br>-12.4|<br>-1.71|4,610|<br>-11.9|<br>-20.0|
|Oil use (million tons)|537|<br>-2.2|<br>-0.27|835|<br>-2.4|<br>-4.0|
|Gas use (billion cubic meters)|166,354|<br>-4.3|<br>-0.53|381,163|<br>-5.4|<br>-9.4|
|Electricity (billion kWh)|5,554|<br>-5.0|<br>-0.65|8,951|<br>-4.6|<br>-7.9|
|CO2 emissions(inc cement; mil tons)|9,819|<br>-9.8|<br>-1.33|<br>12,797|<br>-9.5|<br>-16.0|
|Cumulative CO2 (2015-2030)|||<br>|184,840|<br>-9.8|<br>-9.8|
|Carbon tax yuan/ton CO2||¥82.5|<br>¥9.8||¥82.5|<br>¥157|
|Carbon tax as a share of total revenue||5.0%|0.66%||3.3%|5.8%|
|Electricity generation|values|% change f|rom base|values|% chan<br>ba|ge from<br>se|
|Coal|3967|<br>-7.96|<br>-1.04|5782|<br>-8.2|<br>-14.2|
|Gas|152|<br>0.41|<br>0.11|459|<br>-1.1|<br>-2.2|
|Nuclear|190|<br>2.78|<br>0.35|678|<br>2.9|<br>5.4|
|Hydro|902|<br>2.78|<br>0.35|1132|<br>2.7|<br>5.2|
|Other|133|<br>5.22|<br>0.67|271|<br>4.6|<br>8.5|
|Wind|191|<br>0.09|<br>0.01|498|<br>-0.1|<br>0.12|
|Solar|19|0.17|0.02|<br>130|0.1|<br>0.25|

The carbon tax rate in 2030 is ¥149 in the R2lump case but slightly higher at ¥157 in the tax cut case. The lack of an offsetting cut means that the VAT’s in the lump sum case remain high and generate greater distortions. The net effect for many goods is a higher price in R2lump; for example, the price of primary metals rise by 5.7% versus 4.6% in R2CUT, for food manufacturing it is 2.1% versus 0.9%, and for construction it is 2.9% versus 1.8%. These greater price distortions contribute to a smaller GDP. The cut in capital taxes in R2CUT also allows ‐21‐
enterprises to retain more earnings and invest more, and thus the capital stock by 2030 falls relative to base case by only 0.51% compared to a 1.3% reduction in R2lump. As a result of this lower capital stock and higher value-added tax rates in the lump sum case, GDP in 2030 is 1.2% below base case, compared to only 0.74% lower in the tax cut case.

With lower aggregate output and lower carbon taxes prices in the lump sum case, the reduction in energy consumption is very similar, 13.2% versus 13.1%. The changes in CO2 emissions are corresponding similar, -16%, and thus the fall in CO2:GDP intensity relative to base case is slightly smaller in the R2lump case. Hydro generation is +4.7% in R2lump versus +5.2% in R2CUT. The change in intermittent renewables is different; in the lump-sum case the reduction in total energy demand leads to a 0.22% relative fall in wind power, while it rose by 0.12% in the tax cut case.

In sum, we have similar reductions in emissions at the cost of somewhat greater reduction in GDP growth (relative to base case) with the lump-sum transfer case than the tax-cut case. The slower GDP growth is compounded over time. Note that our gains in efficiency by reducing existing value-added taxes and capital taxes are calculated for a recursive model; in a dynamic model with foresight the impact of cuts in capital taxation would be even bigger, that is, the slowdown in GDP with the tax cuts would be even more modest than we have calculated.

‐22‐
# Table 6. The impact of different methods of recycling carbon revenues

(R2CUT vs. R2LUMP, cases with high and rising carbon tax)

|||2030<br>R2CUT|R2LUMP|
|---|---|---|---|
||Base<br>case|:<br>Cut in<br>VAT, TK|:<br>Lump sum<br>transfer|
|Variable|values|% change|from base|
|GDP (billion yuan 2010)|126,235|-0.74|-1.17|
|Consumption (bil yuan 2010)|67,264|-0.71|-0.77|
|Investment (bil yuan 2010)|43,747|-0.86|-2.13|
|Capital stock (bil yuan 2010)|386,091|-0.51|-1.28|
|Energy use (million tons of sce)|5,958|-13.1|-13.2|
|Coal use (million tons)|4,610|-20.0|-19.9|
|Oil use (million tons)|835|-4.0|-4.5|
|Gas use (billion cubic meters)|381,163|-9.4|-9.4|
|Electricity (billion kWh)|8,951|-7.9|-8.1|
|CO2 emissions(inc cement; mil tons)|12,797|-16.0|-16.1|
|Cumulative CO2 (2015-2030)|184,840|-9.8|-9.8|
|Carbon tax yuan/ton CO2||¥157|¥149|
|Carbon tax as a share of total revenue||5.8%|5.5%|
|Electricity generation|values|% change|from base|
|Coal|5782|-14.2|-14.3|
|Gas|459|-2.2|-2.4|
|Nuclear|678|5.4|4.9|
|Hydro|1132|5.2|4.7|
|Other|271|8.5|7.8|
|Wind|498|0.12|-0.22|
|Solar|130|0.25|0.00|

_Subsidies for renewables and other low-carbon sources of electricity_ In the policy RRsub we subsidize the cost of nuclear, hydro, wind, and solar using the revenues from a fossil fuel tax in proportion to their carbon contents. The subsidies are chosen so that the share of each of these sources in generation is equal to the projection by IEA (2014) under their “New Policies Scenario (NPS)”. We focus on the kilowatt-hours generated rather than the capacity of the various sources which are also projected separately in IEA (2014).

‐23‐
Table 7 gives the shares of electricity generation (by kWh) projected in IEA (2014) under their two scenarios – “Current Policies” and “New Policies” for two selected years. We first compute the change in shares for each year of the projection period between the two policies, e.g. under NPS the coal share in 2030 falls by 8.8 percentage points, and the wind share rises by 2.2 points. Although our base case shares not identical with the IEA’s Current Policies projections in each year, they are set to be very close. We set the annual renewable targets in our policy scenarios as our base case shares plus the differences between the IEA scenarios, as illustrated in Table 7 for 2020 and 2030. In the RRsub policy we set subsidies separately for nuclear, hydro, wind, solar and other to hit the higher shares under the New Policies Scenario.

The results of using the carbon tax to subsidize renewables and other low-carbon energy sources are given in Table 8, together with those from R1cut and R2cut for comparison. In RRsub, the subsidies for renewables start at low rates but become quite substantial in the later years as given in the last column of Table 7. The share for wind has to rise from 5.7% in the base case to 7.9%, which requires a subsidy of 48%, while the share of hydro has to rise from 12.8% to 14.7%. As noted in Section 2, these renewables require natural resource inputs that have upward sloping supply curves and thus require higher returns to meet higher resource demands.

Table 7. Renewables target from IEA (2014) “New Policies Scenario” and subsidies in the model.

|||2020|<br><br>|<br>||2030<br>||
|---|---|---|---|---|---|---|---|
||Current<br>Policies<br>|New<br>Policies|Differ-<br>ence<br>|<sup>Current</sup><br>Policies|<br>New<br>Policies|Differ-<br>ence|RRsub<br>subsidy (%)|
||%|contribut|ion|%|contribut|ion||
|Coal|65.4|61.9|-3.6|<br>64.1|<br>55.3|-8.8||
|Nuclear|5.8|6.3|0.5|<br>7.6|<br>9.2|1.6|<br>18.9|
|Hydro|15.6|17.1|1.4|<br>12.8|<br>14.7|1.9|<br>13.2|
|Wind|4.8|5.6|0.8|<br>5.7|<br>7.9|2.2|<br>48.3|
|Solar|1.4|1.8|0.4|<br>1.5|<br>2.5|1.0|<br>46.7|
|Gas, other|7.0|7.4|0.5|<br>8.3|<br>10.4|2.1||
|Total|100|100|0|100|100|0||

The value of output of the electricity sector was ¥3240 billion in 2010, with a value added of 810 billion equaling 2.1% of GDP. These high subsidies thus require a sizable carbon tax to finance them. By 2030 the CO2 tax needed is ¥58/ton, which is in between the ¥26 carbon price ‐24‐
in R1cut and ¥157 in R2cut. These subsidies result in a 2.2% fall in the price of electricity compared to the base case and thus the 1.9% reduction in the consumption of electricity is only slightly bigger than the 1.5% reduction in the R1cut scenario with its much lower carbon price. That is, the carbon prices that raised the price of electricity in R1cut encouraged conservation and generated a 3.3% cut in total CO2 emissions at a modest cost of reducing GDP by 0.11% in 2030. In this Renewable subsidy scenario, the subsidies discourage electricity saving and mutes the total CO2 impact of generating less coal electricity.

Total CO2 emissions in 2030 fall by 7.9% relative to base case, given the 58 yuan carbon price. The cumulative 2015-2030 relative cut in emissions of 2.1% is only slightly bigger than the 1.9% cut in R1cut, with the 26 yuan carbon price. The large carbon price and distortions of the electricity price generate a relative GDP loss of 0.53% in 2030 compared to only 0.11% loss in R1cut. The GDP loss in R2cut is bigger at 0.74% but that achieved a cumulative CO2 reduction of 9.8% compared to 2.1% for RRsub. That is, the ambitious renewable targets require large distortions in the later years, which generate CO2 reductions as a byproduct of a greater slowdown in GDP growth.

With the subsidies, coal generation in 2030 falls to 58.4% of total kWh compared to 64.6% in the base case and 63.8% in R1CUT, and hydro rises to a 14.5% share compared to 13.0% in R1CUT. These changes result in a larger reduction coal use in 2030, 10% versus 4.1% in R1cut, and CO2 emissions fall by 7.9% compared with 3.3% in R1CUT.

# **5. Sensitivity Analysis on Non-Fossil Fuels Growth Assumptions**

In the base case the projection of resource inputs and capital inputs into the electricity generation functions is set according to the projections under the “Current Policies” scenario (CPS) for China in IEA (2014). As shown in Table 2, this scenario assumes a rapid growth of nuclear, wind and solar generation. The rapid expansion of relatively expensive generation resources (i.e., nuclear, wind, solar) implies higher subsidies and ultimately higher costs to the society.

‐25‐
# Table 8. Subsidies to renewables financed by carbon taxes (scenario RRsub).

|||2015|||203|0||
|---|---|---|---|---|---|---|---|
||Base case|RRsub|R1CUT|Base case|RRsub|R1CUT|R2CUT|
|Variable|values|% change f|rom base|<br>values|% cha|nge from|base|
|GDP (billion ¥2010)|58,956|-0.018|-0.004|126,235|-0.53|<br>-0.11|<br>-0.74|
|Consumption (bil ¥2010)|25,290|<br>-0.014|-0.003|67,264|-0.45|<br>-0.11|<br>-0.71|
|Investment (bil ¥2010)|25,374|-0.020|-0.007|43,747|-0.61|<br>-0.13|<br>-0.86|
|Government consumption (bil¥)|6,973|-0.023|0.00|12,785|-0.40|<br>0.00|<br>0.00|
|Fossil energy use (mil tons of sce)|4,005|-0.41|-0.21|5,958|-5.8|<br>-2.6|<br>-13.1|
|Coal use (million tons)|3,666|-0.63|-0.29|4,610|-10.1|<br>-4.1|<br>-20.0|
|Oil use (million tons)|537|-0.03|-0.04|835|-1.1|<br>-0.7|<br>-4.0|
|Gas use (billion cubic meters)|166,354|-0.29|-0.09|381,163|-6.0|<br>-1.8|<br>-9.4|
|Electricity (billion kWh)|5,554|<br>-0.09|-0.11|8,951|-1.9|<br>-1.5|<br>-7.9|
|Electricity price||-0.18|0.10||-2.2|<br>1.4|<br>7.9|
|CO2 emissions(mil tons)|9,819|-0.48|-0.23|<br>12,797|-7.9|<br>-3.3|<br>-16.0|
|Cumulative CO2 (2015-2030)|||<br>|184,840|-2.1|<br> <br>-1.9|<br> <br>-9.8|
|Carbon taxyuan/ton CO2||¥2.8|¥1.6||¥58|<br>¥26|<br>¥157|
|Electricity generation|share o|f total gene|ration|sha|re of total|generatio|n|
|Coal|0.714|0.709|0.714|0.646|0.584|<br>0.638|<br>0.602|
|Gas|0.027|0.027|0.027|0.051|0.046|<br>0.052|<br>0.054|
|Nuclear|0.034|<br>0.035|0.034|0.076|0.092|<br>0.078|<br>0.087|
|Hydro|0.162|0.166|0.163|0.127|0.145|<br>0.130|<br>0.145|
|Other|0.024|<br>0.024|0.024|0.030|0.038|<br>0.031|<br>0.036|
|Wind|0.034|0.035|0.034|0.056|0.075|<br>0.057|<br>0.061|
|Solar|0.003|0.004|0.003|0.015|0.020|0.015|0.016|

In order to get an idea of the economic impacts of this push away from coal and towards non-fossil fuels in the IEA Current Policies Scenario, we conducted an alternative set of simulations with a “no coal reduction” (NCR) scenario and a “CPS targets” scenario. In the NCR case we allow the coal share to fall from the first year 2010 to 2016 as in the base case, but then maintain close to that share out to 2030. The share of hydro in the base case is falling due to the projected difficulty in finding more hydro resources, and we maintain this in NCR. Note that even with the falling share the absolute production of hydro power is rising over time. The ‐26‐
remainder of the electricity demand is met by the other sources (gas, nuclear, wind, solar, others) that rise modestly in their share contribution to offset the falling hydro contribution.

In the CPS case, we do not change the resource and capital supplies exogenously as in the base case, but let them remain at the NCR paths and use a system of taxes and subsidies to hit the higher targets for renewables and nuclear. We impose taxes on coal and subsidies for the rest in order to attract more resources and capital into the non-fossils and discourage coal generation. We require that the tax revenue exactly equal the subsidies so that there is no net (new) transfer to the government.

The comparison of these two scenarios is given in Table 9. In the NCR case, total coal consumption in 2020 is 4,147 million tons compared to 4,083 in the base case. The coal power is cheaper and thus the electricity demand in NCR is slightly higher, 6,949 billion kWh in 2020 compared to 6,886 in the base case. The impact of achieving the CPS targets via taxes and subsidies is substantial even in 2020; coal use is 3.5% lower than in NCR leading to a 2.1% reduction in total energy consumption. The price of average electricity is 1.3% higher leading to a 2.7% reduction in 2020.

By 2030 the impact is magnified by the cumulative GDP losses and reduction in capital stock. Coal use in 2030 is 5.5% lower in the CPS case while total energy consumption in 3.5% lower. Aggregate GDP is 0.29% lower due to these distortions with reductions in both consumption and investment. The generation mix shifts from 70.6% coal and 4.9% nuclear to 65.3% coal and 7.6% nuclear. The wind contribution rises from 4.7% to 5.7%.

While our specifications of the cost functions for the renewables and nuclear are simple, it allows a representation of the costs of finding suitable sites for such generation methods, and also possibly higher costs of transmission. The simulated costs are comparable to the renewable subsidies case shown in Table 8 given that the same cost function elasticities are used. There the cost of reducing CO2 emissions by 7.9% in 2030 is a 0.53% relative cut in GDP, here the 4.0% reduction in CO2 reduces GDP relative to base case by 0.29%.

‐27‐
Table 9. Comparing “no coal reduction (NCR)” with IEA “Current Policies” scenarios

||20|20<br>CPS|20|30<br>CPS|
|---|---|---|---|---|
||NCR|vs<br>NCR|Base case|vs<br>NCR|
|Variable|values|% change|values|% change|
|GDP (billion ¥2010)|80,494|-0.10|<br>126,235|-0.29|
|Consumption (bil ¥2010)|38,784|-0.13|<br>67,264|-0.35|
|Investment (bil ¥2010)|31,532|-0.07|<br>43,747|-0.25|
|Government consumption (bil<br>¥2010)|8,958|-0.14|<br>12,785|-0.27|
|Fossil energy use (million tons of<br>sce)|4,915|-2.1|<br>6,113|-3.5|
|Coal use (million tons)|4,147|-3.5|<br>4,794|-5.5|
|Oil use (million tons)|663|0.12|<br>836|-0.16|
|Gas use (billion cubic meters)|239,647|2.1|<br>361,272|-1.5|
|Electricity (billion kWh)|6,949|-2.7|<br>9,102|-4.3|
|Electricity price||1.3||3.4|
|CO2 emissions (inc cement; mil<br>|11,236|-2.3|<br>13,073|-4.0|
|tons)|||<br>||
|Cumulative CO2(2015-2030)||<br>|<br> <br>187,684|-2.8|
||share o|f total|share o|f total|
|Electricity generation|gener|ation|gener|ation|
|Coal|0.678|0.657|<br>0.706|0.653|
|Gas|0.037|0.037|<br>0.040|0.039|
|Nuclear|0.047|0.058|<br>0.049|0.076|
|Hydro|0.157|0.156|<br>0.127|0.128|
|Other|0.025|0.030|<br>0.022|0.031|
|Wind|0.046|0.048|<br>0.047|0.057|
|Solar|0.010|0.014|<br>0.009|0.015|

# **6. Conclusion**

As part of the Paris Agreement, China has set for itself targets for reducing energy intensity by 60-65% in 2030 compared to 2005 levels. This study examines the economic consequences if the targets were met through a carbon pricing mechanism, where all fossil fuels are taxed in proportion to their carbon contents. The study uses a recursive dynamic computable general equilibrium model of the Chinese economy for the purpose this analysis.

‐28‐
The study finds that the carbon pricing policy would lead to a such a 65% reduction in intensity at a modest cumulative cost to GDP, which would be 0.7% lower than the base case by 2030, when revenues generated from the carbon pricing are recycled to the economy to cut existing value-added taxes. The goal of reducing the intensity by 60% is projected to cut GDP by only 0.1% relative to the base case by 2030. The carbon price not only reduces energy demand but also causes a shift away from more energy intensive industries to less energy intensive ones; it also causes a larger-scale deployment of renewable energy in electricity and heat generation.

A policy focused on promoting renewables in electricity generation using subsidies financed by a fossil fuel tax in proportion to their carbon contents would achieve the carbon reduction at a somewhat greater relative loss in GDP growth than a carbon pricing case. Putting more of the burden of adjustment on the power sector, when there are costs to a rapid ramp-up of hydro, nuclear, wind and solar power, results in a greater distortion of the price of electricity and a more difficult adjustment. A more careful study of the costs of resources going into these renewables is needed, including land, and suitable rivers.

As noted, this study does not attempt to model China’s actual plan to achieve its NDC, which includes both market and non-market measures. Instead, it focuses on the cost of achieving the NDC through a market mechanism, carbon pricing. Comparison of the economic consequences of the carbon pricing mechanism considered here with that of government’s actual plan of implementing the NDC is a natural extension of this study. Moreover, this study focuses only on CO2 emissions from fossil fuel combustion, and does not include other GHG emissions from fossil fuel combustion and also GHG emissions from industrial processes and land use change. This is a limitation of the study to be addressed in future work.

# **References**

Cao, Jing, Mun S. Ho and Dale Jorgenson. 2013. “The Economics of Environmental Policy in China” in Chris Nielsen and Mun Ho (eds.) _Clearer Skies over China: Reconciling Air Quality, Climate and Economic Goals_ , MIT Press, Cambridge, MA.

Cao Jing and Mun S. Ho. 2014. “China: Economic and GHG emissions Outlook to 2050”. The Chinese version of this is in 重塑未来: 未来20 年的中国与世界, 清华大学能源环境经 济研究所 _(Reshaping the Future: China and the World in the next 20 years_ ), Tsinghua University, Institute for Energy and Environmental Economics.

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Dollar, David. 2015. “China’s rise as a regional and global power” _Horizons - Journal of_ _International Relations and Sustainable Development_ . Summer, Issue 4:162-172.

Financial Times. 2016. Hudson Lockett and Lucy Hornby. “China routes RMB 5tn into transport infrastructure,” May 11 2016.

International Energy Agency (IEA). 2010. _Projected Costs of Generating Electricity_ , IEA and OECD, Paris.

International Energy Agency (IEA). 2014. _World Energy Outlook 2014_ , IEA, Paris. International Monetary Fund (IMF) (2014), World Economic Outlook, IMF, Washington, DC.

Nielsen, Chris and Mun Ho (eds.). 2013. _Clearer Skies over China: Reconciling Air Quality, Climate and Economic Goals_ , MIT Press, Cambridge, MA.

- Qi, Tianyu, Niven Winchester, Da Zhang, Xiliang Zhang and Valerie Karplus. 2014. The Chinain-Global Energy Model. MIT JPSPGC Report No 262, May.

Sue Wing, Ian, Kathryn Daenzer, Karen Fisher-Vanden and Katharine Calvin. 2011. Phoenix Model Documentation. PNNL Joint Global Change Research Institute. Available at: <u>http://www.globalchange.umd.edu/models/phoenix/</u> Vennemo, Haakon, Jianwu He and Shantong Li. 2014. Macroeconomic Impacts of Carbon Capture and Storage in China. Environmental and Resource Economics, 59(3):455-477.

World Bank and Development Research Center (DRC). 2013. China 2030. The World Bank, Washington DC and Development Research Center of the State Council, People’s Republic of China.

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## **Appendix A:  Economic-Environmental Model of China**

In this appendix we describe the model for China in some detail, beginning with the modeling of each of the main economic agents. Then in section A.2 we describe the data and parameters underlying the model. A previous version of this model of the Chinese economy is used in Nielsen and Ho (2013) and here we describe the updates to it. This is a multi-sector model of economic growth where the main drivers of growth are population, total factor productivity growth and changes in the quality of labor and capital. It has a dynamic recursive structure, i.e. where investment is determined by fixed savings rate as in the Solow model. Consumption demand is driven by a translog household model that distinguishes demand by different demographic groups. The electricity sector is composed of 7 different generation technologies.

# **A.1 Structure of the Model**

We discuss the five main actors in the economy in turn – producers, households, capital owners, government and foreigners. The electricity sector is described in greater detail explaining how the different generation technologies are allocated. For easy reference Table A1 lists variables which are referred to with some frequency. In general, a bar above a symbol indicates that it is a plan parameter or variable while a tilde indicates a market variable. Symbols without markings are total quantities or average prices. To reduce unnecessary notation, we include the time subscript, _t_ , only when necessary to note the time dependence.

## **A.1.1. Production**

Each of the 33 industries is assumed to produce its output using a constant returns to scale technology. Except for electricity, for each sector _j_ the output at time t, _QI jt_ , is expressed as: (A1) _QI j_  _f_ ( _KDj_ , _LDj_ , _TDj_ , _A_ 1 _j_ ,..., _Anj_ , _t_ ) , where _KDj_ , _LDj_ , _TDj_ , and _Aij_ are capital, labor, land, and intermediate inputs, respectively.<sup>13</sup> In sectors for which both plan and market allocation exists, output is made up of two components,

> 13 _QIj_ denotes the quantity of industry _j_ ’s output.  This is to distinguish it from, _QCj_ , the quantity of commodity _j_ . Each industry may produce more than one commodity and each commodity may be produced by more than one industry.  In the language of the input output tables, we make use of both the Use and Make (or Supply) matrices.

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~ the plan quota output ( _QI j_ ) and the output sold on the market ( _QI j_ ). The plan quota output is sold at the state-set price ( _PI j_ ) while the output in excess of the quota is sold at the market price ~ ( _PI j_ ). The _PI_ and _QI_ names are chosen to reflect that these are domestic _industry_ variables, as opposed to commodities ( _PC_ ) or total supply ( _PS_ ), the sum of domestic output and imports.

A more detailed discussion of how this plan-market formulation is different from standard market economy models is given in Garbaccio, Ho, and Jorgenson (1999). In summary, if the constraints are not binding, then the “two-tier plan/market” economy operates at the margin as a market economy with lump sum transfers between agents. The capital stock in each industry consist of two parts – the fixed capital, _Kj_ , that is inherited from the initial period, and the market ~ portion, _KDj_ , that is rented at the market rate. The before-tax return to the owners of fixed capital in sector _j_ is: For each industry, given the capital stock _Kj_ and prices, the first order conditions from maximizing equation A2, subject to equation A1, determine the market and total input demands.

We represent the production structure with the cost dual, expressing the output price as a function of input prices and an index of technology. The 3 primary factors and 33 intermediate inputs for each industry are determined by a nested series of constant elasticity of substitution (CES) functions taken from the GTAP model (version 7). The nest structure is given in Figure A1 and applies to all industries except electricity which is treated separately below.

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<!-- Start of picture text --> QI<br>σ QI =.15<br>VE M<br>σ=1<br>σVE =.5<br>VA E<br>VA σ E =.5<br>σ<br>Capital Land Coal Oil‐mine Gas‐mine Refining Electricity Gas<br>Labor<br><!-- End of picture text --> Figure A1. Production structure for each industry except electricity.

At the top tier, output is a function of the primary factor-energy basket (VE) and the nonenergy intermediate input basket (M), _QI jt_  _f_ ( _VEjt_ , _M jt_ , _t_ ) . The VE basket is an aggregate of value added (VA) and the energy basket (E). Value added is a function of the 3 primary factors – capital (K), labor (L) and land (T). The energy aggregate is a CES function of coal, oil mining, gas mining, petroleum refining & coal products, electricity and gas commodities. The materials aggregate (M) is a Cobb-Douglas function of the 27 non-energy commodities.

For the top tier, the value equation and cost function are, respectively: _VE_ (A3) _PI jtQI jt_  _Pjt VEjt_  _PM jtM jt_ _QI_ where  _Mj_ is the weight for all non-energy inputs into industry _j_ , and<sup>1 /</sup>  _jt_ is the elasticity of substitution between the two inputs. _gjt_ is the index of the level of technology where a rising value indicates positive TFP growth and falling output prices. We assume that the change follows an ‐33‐
exponential function: _g_<sup></sup> _j_ ( ) _t_  _Aj_ exp(  _jt_ ) . This implies technical change that is rapid initially, but gradually declines toward zero.

The primal function corresponding to the above dual cost is: The input demands derived from the CES cost function are: The weights for the CES functions are explained in Rutherford (2003) and Klump, McAdam and Willman (2011); these are calibrated using the base year values: The corresponding value, price and input demand equations for the primary factor-energy basket (VE) and the value-added basket (VA) are: ‐34‐
The parameters of the value-added-energy and value-added nodes are calibrated to base year values in the following way: Note that the cost functions for the sub-aggregates do not have an index of technology like the top tier; however, the share coefficients –  _Ejt_ ,  _Kjt_ , etc. – are allowed to change over time to reflect biases in technical change.

The energy basket equations give the demands for the 6 types of energy: _PE E_  _PS A_ (A11) _jt jt_  _kt kjt k_  _IE_ ‐35‐
The non-energy basket is a Cobb-Douglas function and the corresponding equations are: We set the energy share  _Ej_ to fall gradually over the next 40 years while the labor coefficient,  _Lj_ , rises correspondingly. The composition of the aggregate energy input _Ej_ (i.e. the _E_  _kj_ coefficients) are also allowed to change over time.

The price to buyers of industry output includes the indirect tax on output, the externality ad-valorem tax, the externality unit tax: _t t xv xu_ (A13) _PIi_  (1  _ti_  _ti_ ) _PIi_  _ti_

# Industries versus Commodities

The model distinguishes industries from commodities as in the official Use and Make input-output tables. Each industry may make a few commodities and each commodity may be made by a few industries; e.g. the Refining industry produces Refining commodity and Chemical commodity, and the Chemical commodity comes from Refining, Chemical, Primary Metal and other industries. The quantity of domestic commodity is denoted _QC_ and its price _PC_ ; the sum of column _i_ in the Make matrix gives the value of commodity _i_ , and the sum of row _j_ is the industry output value. The relation between commodity and industry output and prices are written as: ‐36‐
_r c_ where _m_ is the row share and _m_ is the column share. _ji ji_ A.1.2. The electricity sector In version 17 of the China Model we disaggregate the electricity sector into different generation technologies unlike the previous versions that represent the output of electricity using the common production function (A.4b) above. The production and input structure of this sector is illustrated in Figure A2; this consists of a nested structure of CES functions. At the top tier, electricity output is an aggregate of Transmission & Distribution and Electricity Generation. The price of electricity output (sector 22) is a function of the price of transmission _Pjtt TD_  ,22, _t_ and the price of generation _PjEG_  22, _t_ : The superscript _tt_ denotes that this is a price inclusive of indirect business taxes that are levied at the level of the generating sectors and transmission. This is explained in (A21b) below. The quantity and value equations are: _PI ttj_  _elec t_ , _QI j_  _elec t_ ,  _PtEGQtEG_  _Pttt TD_ , _QtTD_ ‐37‐
At the Generation node we assume that this consists of Base load sources and Renewables (intermittent) in a way similar to the C-GEM model (Qi et al 2014). The price of Electricity Generation is thus a function of the price of Base load Electricity ( _PEtBL_ ) and price of Renewable Electricity ( _PEtRE_ ): Figure A2. Structure of electricity sector ‐38‐
<!-- Start of picture text --> Electricity<br> σED=0.7<br>Transmission<br>Generation<br> See Fig A2b σ GE =1<br>Base load Renewables<br> σ BL =4 σ RE =4<br>Wind Solar<br>See Fig A2b<br>Coal Gas Nuclear Other<br>Hydro Coal-CCS Gas-CCS<br> σM =Coal σM =Nuclear Fig A2b σM =CoalCCS<br>M<br>σ VE M VR M VE<br>m σVE=.5 σm σVR=.4 σm σVE<br>VA E<br>σVA=.4 σE=.25 VEσVE R VAσVA=.4 F&Seqσseq=0<br>K L Noncoal Coal E Seques<br>σNC=.5 VA E K L σE=.2<br>σVA=.4 σE=.5 Noncoal<br>Coal<br>σNC=.5<br>Elec, Refine, Gas K L<br>Elec, Refined, ..<br>Elec, Refine, Gas<br><!-- End of picture text --> ‐39‐
# Figure A2. Structure of electricity sector

# (b) Transmission and Renewables structure

<!-- Start of picture text --> Wind/Solar<br>Transmission<br> σRN=0<br> σTD=.7<br>M VR<br>M VE σm σVR=.6<br>σm=1 σVE=.5<br>Intermed ..<br>VE<br>R<br>Intermed .. σVE=.5<br>VA E<br>σVA=1 σEN=.5 VA E<br>σ =.2 σ =.5<br>VA EN<br>K L<br>Elec, Refined, ..<br>K L Elec, Refined, ..<br><!-- End of picture text --> ‐40‐
The quantities of Base load output and Renewable output are: Renewables here consist only of Wind and Solar which are intermittent sources and requires either parallel storage capacities, or conventional backup. We thus assume that such electricity is imperfectly substitutable with base load sources and specify an _GE_ elasticity of substitution,  , in a way similar to Qi et al. (2014) for our main parameter value of 1.0.<sup>14</sup> All other sources of electricity contribute to the Base load aggregate with a high elasticity of substitution,  _BL_ =4.<sup>15</sup> In the base year, these sources include conventional coal, gas, hydro, nuclear and a minor “other” (oil, biomass, geothermal, etc.) In the future years we allow the options of coal with CCS and gas with CCS. The price of Base load electricity is thus a function of the component prices _Pcoaltt_ , _EGEN_ , _t_ , The price variables have a _tt_ superscript to denote that they are inclusive of (net) output taxes and subsidies. The value equation and quantities of the various Base load technologies are:

> 14 In the Phoenix model (Sue Wing et al 2011), the elasticity of substitution between “peak load” (which includes wind and solar) and “base load” sources is also 1.

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The price to the purchasers of such electricity is _Pltt_ , _t_ , _EGEN_ which includes the net output tax, The input demand structure for coal generation is given in Figure A2; at the top tier coal power is produced by a low elasticity CES function of value-added-energy (VE) and non-energy intermediates _BL_ (M).<sup>16</sup> Productivity growth in this sector is represented by the _gct_ term in the price function (A22). The _VE_ VE bundle is a CES function of value-added (VA) and energy (E) with  =0.5, while the VA node has _VA_ an elasticity  of 1.0 between capital and labor. The Energy aggregate is a function of coal and noncoal energy which is a small item that includes electricity and refined petroleum products (lubricants and _E_ vehicle fuels) as described in the data appendix. We set the elasticity between them to a low value ( 

> 15 Our specification of base load and renewables follows EPPA‐4, which assumes perfect substitution among the base load sources. We have, however, chosen to use an elasticity of 4 as used in the Phoenix model; in a similar setup, Vennemo et al (2014) use an elasticity of 20.

> 16 In Qi et al. (2014) this is set to be a Leontief function, here we use the low general elasticity between materials and value‐added‐energy in GTAP of 0.15.

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=0.2). The non-coal quantity is an aggregate of only electricity and refined oil since the other energy inputs (oil mining, gas) are zero.

The input structure for gas generation is similar to the one for coal for the top tiers for output price, price of VE, price of VA and price of M. The equations for the top tiers, in terms of the producer prices, are: ; c=coal, coal_ccs, gas, gas_ccs ‐43‐
For the energy tier, the equations for coal and noncoal (NC) inputs in sector _c_ are: NC={oil, gasmine, refine, elect, gas} The elasticities of substitution are summarized in Table A2. The top tier is Leontief between _M VE_ Materials and the VE bundle (  _BL c_ ,  0). The substitution between energy and value added (  _BL_ , _c_ ) is set ‐44‐
at 0.5, the value used in the GTAP model.<sup>17</sup> Labor input is a very small share in the electricity sector and _VA_ we set  _BL c_ ,  1following EPPA and Qi et al. (2014). This model explicitly recognizes the small amount of energy used besides the main fuel source unlike the other models mentioned so far; in the case of coal, the non-coal inputs include refined petroleum, electricity and gas. The substitution between the main fuel and the small non-coal energy bundle is set at 0.25, this is similar in spirit to the value of energy-capital substitution used in Phoenix for electricity generation. The substitution among the components of noncoal energy is set at 0.5, the general elasticity for energy inputs in GTAP and EPPA.

The input structure for gas-fired power plants is similar to that for coal, except that in the bottom tier for energy, gas inputs (GS) are aggregated with non-gas (NG; electricity and refined petroleum). The value equations for the gas and gas_ccs nodes are: The price functions of the energy aggregate, the gas aggregate and the non-gas aggregate are: 17 The EPPA (Paltsev et al, Table 3) model uses an elasticity of 0.4‐0.5; Qi et al. (2014) uses 0.1 and Phoenix (Sue Wing, Fig 2) uses 0.25.

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The values of the substitution elasticities follow that of coal and are given in Table A2.

We also project fossil fuel technologies that will be cost competitive in the future with a high carbon price: coal integrated gasification with carbon capture (IGCC) and natural gas with carbon capture (NGCC). The IGCC tier structure is given in Figure A2; the NGCC structure is identical. It is the same as the coal tier structure except that the energy node is replaced by a “fuel and sequestration” node. This structure follows Sue Wing et al. (2011, p33) where the fuel and sequestration technology in combined in a Leontief function, and the sequestration input is a fixed factor resource with an upward sloping supply curve.

The equations for _Pc_  _BLcoalccs_ , _t_ are the same as (A.22), and (A.23) for _PMcBL_  _coalccs t_ , , (A.25) for _PBLVA_ , _c_  _coalccs_ , (A.26) for _PEcBL_  _coalccs_ , and (A.27) for _PBLNC_ , _c_  _coalccs_ . The price for the fuel-sequestration sub-aggregate is given, in general, by: In the case of a Leontief function with  _coalccseseq_  0 , the equation is simply: ‐46‐
where we may normalize the units of the fuel-sequestration bundle to be the same as the fuel units,  _coalccsE seq_ ,  1 and the sequestration coefficient  _coalccsseq_ reflects to addition cost for this technology, per unit fuel.

The sequestration resource supply is given by: A parallel set of equations hold for NGCC with the price of fuel-sequestration in NGCC given by: The modeling of nuclear power supply is always treated specially in the models cited given its unusual nature. While there is no obvious constraint like the availability of rivers for hydro power, it is recognized that actual construction of nuclear plants has been difficult with substantial opposition by those worried about safety. Vennemo et al. (2014) uses an upward sloping supply function due to “the political suitability of different locations.” Sue Wing et al. (2014) also use a “fixed factor” in their specification of nuclear power, but justify it by saying that a “supply curve is used to parameterize the mining and milling of resources to produce the fuel rods.” EPPA-4 also has a fixed factor in nuclear generation but interprets it as a stock of knowledge that builds over time with cumulative output.

We follow the logic of Vennemo at al., and the Phoenix model, and require a non-reproducible resource input for nuclear power. In the second tier (see Figure A2), the VR bundle is an aggregate of resource ( _R_<sup>_NUCL_</sup> ) and value-added-energy (VE) with an elasticity of  _VR_ =0.4, following the EPPA-4 ‐47‐
value (Phoenix uses a top elasticity of 0.5). We follow Vennemo et al. in using a nuclear resource supply elasticity of 2.5.

The specification of the hydro power production function is another complicated matter. Rivers suitable for hydro power is an obvious input, and one might think of the stock of such water resources and the cost of using the marginal river. If the next unused water source requires a more costly structure (or implementation costs including relocation of people) than existing dams, then one may represent this as a production function with a lower TFP factor, or as a paying more for a fixed quantity of effective water resource R. For simplicity we have chosen the latter approach and set up the hydro function like that of nuclear, with a rising supply curve for the water resource input, _R_<sup>_HYDR_</sup> . In the same spirit, our wind and solar output functions follow those of nuclear and hydro with specific Resource inputs. This is illustrated in Figure A2(b). The parameterization of the resource input shares follows that given in EPPA 4 (Paltsev et al. 2005 Table 11); the resource share for wind and solar is set at 0.05 and we assume the resource share for hydro is also 0.05 of total gross output.

The cost functions for the renewables – nuclear, hydro, wind, solar and “other” – are the same The following equations are for the sub-aggregates – output price _PbEGEN_ , _t_ , intermediate input price _PM bBL_ , _t_ , value-added-resource price _PBLb tVR_ , , value-added-energy price _PBLb tVE_ , , the energy price _PEb tBL_ , : ‐48‐
‐49‐
In Vennemo et al. (2014) the wind resource grows in a logistic manner, while in Phoenix the supply curve is a constant elasticity one. EPPA-4 introduces a “Fixed Factor” for wind and solar and interprets it as knowledge that grows with cumulated output, and sets an elasticity between the Fixed _VR_ Factor and value-added (  _BLb_ ) at 0.6. Paltsev et al. (2005) states that the “Choice of the substitution elasticity creates an implicit supply elasticity of wind in terms of the share of electricity supplied by the technology.” We interpret of the resource variable for hydro, wind and solar as a limited supply of sites suitable for such technologies, and set the Nuclear, Hydro, Wind and SolarPV resources to grow in the base case at the rates projected in IEA (2014). In any period _t_ , in the policy case, the supply curve for Wind or SolarPV resources is given by: _R_ (A.36) _Rtr_  _Rtr PRrt_<sup></sup> _r_ r=NUCL, HYDR, WIND, SOLR where<sup>_R_</sup> _tr_<sup>is the projected base case resource availability. This means that opening an additional river, or</sup> wind farm, or solar farm, will require paying a price for the resource that is higher than the base case price. We follow Vennemo et al. (2014) in using an elasticity of 2.5, which means that this price is only slightly higher than base case resource price. It may be reasonable to not assume an identical function for all possible values of the base case resources availability, that is, when there is a high utilization of these water or wind sites, the marginal cost might increase substantially. That is, imposing a lower elasticity in the future years when there is a high penetration of such renewable sources.

The final element of the electricity sector is transmission and distribution. There is little data on this; even the 500-sector IO table for the U.S. has just one sector for total electric utilities. In the data appendix we describe how the data for Electric Utilities is separated into two sets for Generation and Transmission. The resulting input vector for Transmission includes almost all 33 commodities identified in the model. The tier structure for it is given in Figure A2(b) and is similar to the general structure for all other regular sectors given in Figure A1. The equations for the top tier of the Transmission sector, in terms of the producer price, are: ‐50‐
The equations for the lower tiers are: The elasticity of substitution between materials and VE (  _TDQI_ ) is set at 0.7, and  _TDVA_  1.0 following Sue Wing (2011, p 30).

_Note on aggregation and units of measurement_ Equation (A17) is the cost dual of the quantity function that expresses the index of total generation output (<sup>_Q_</sup> _tEG_ ) as a CES function of baseload output ( _QEtBL_ ) and intermittent renewables ( _QEtRE_ ). _EGEN_ Baseload output is, in turn, a CES aggregate of the output coal, gas, nuclear, wind and others, { _Ql_ , _t_ }, _BL EGEN_ that is, _QEt_ is not a simple linear sum of the _Ql_ , _t_ ’s. We must thus be careful and distinguish between ‐51‐
_EGEN_ the output of kWh of each generation source _l_ , and the aggregate indices. _Ql_ , _t_ is the output measured in billions of Y2010, and the kWh is given by a conversion coefficient: One may express the output index as a product of a quality index and the total kWh: _EG EL kWh_ (A40) _Qt_  _t QTOT_ , _t_ _EL_ The quality, or composition, index  _l_ represents the impact of changes in shares of components that have different relative prices. For example, the price per kWh of coal is much lower than that of solar and if the share of solar kWh rises, then the quality index rises. This is analogous to the relation between effective labor input, quality of labor and hours worked; the index of labor input is the product of labor quality and total hours, and a rising labor quality indicates that hours from more highly paid workers is rising as a share of total hours worked by all workers. One may think that the term “quality” of electricity may be misleading since kilowatt hours as perfectly homogenous and substitutable, it however, represents an economically meaningful distinction between kWh and value of kWh. The electrons that are identical from the user point of view are distinguished by the method of production – clean versus polluting, steady _EL_ versus intermittent, near versus far. The  _l_ index represents changes in the costs of production as the _EG_ composition of methods change;<sup>_Q_</sup> _t_ is an index of the (marketed) economic resources that went into producing _QTOT tkWh_ , kWh of electricity. The non-marketed resources such as clean air, or clean water, are accounted for separately.

# A.1.3. Households

Private consumption in this model is driven by an aggregate demand function that is derived by aggregating over different household types. Each household derives utility from the consumption of commodities, is assumed to supply labor inelastically, and owns a share of the capital stock. It also receives income transfers from the government and foreigners, and receives interest on its holdings of public debt. Aggregate private income is the sum over all households, and this income, after taxes and the payment of various non-tax fees ( _FEE_ ), is written as: ‐52‐
The relationship between labor demand and supply is given in equation A63 below. Aggregate supply _LS_ is a function of the working age population, average annual hours, and an index of labor quality: _DIV_ denotes dividends from the households’ share of capital income and is explained below in A75. _G_I_ and _G_transfer_ represent interest and transfers from the government, and _R_transfer_ is transfers from the rest-of-the-world. _T LUMP_ is the lump sum tax that is used in policy simulations.

Household income is allocated between consumption ( _VCCt_ ) and savings. In this model we use a simple Solow growth model formulation with an exogenous savings rate ( _s t_ ) to determine private savings ( _Stp_ ): Total consumption expenditures are allocated to the 33 commodities identified in the model. We do this with a demand function estimated over household consumption survey data. This consumption data is at purchaser’s prices and follows the expenditure classification; these have to be linked later to the IO classifications and the factory-gate prices of the IO system. We arrange the demand system in a tier structure shown in Table 1. At the top tier total expenditures is allocated to Food, Consumer Goods, Housing and Services. In the sub-tiers these four bundles are allocated to 27 items.

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Table 1. Tier structure of household consumption

|||Name|Components in Consumer Expenditures|
|---|---|---|---|
|1|C|Consumption|Food, Consumer goods, Services, Housing<br>_CC =CC(FD, CG, SV, HS)_|
|2|FD|Food|Food & tobacco, Dining out<br>_C_<sup>_FD_</sup>_= C_<sup>_FD_</sup>(C1, C2)|
|3|CG|Consumer goods|Clothing, Residential goods,<br>Recreational & misc. goods, Vehicles & parts<br>_C_<sup>_CG_</sup>_= C_<sup>_CG_</sup>(CL,RG,RM,C14)|
|4|SV|Services|Communication, Education, Recreational svc,<br>Health, Other services, Imputations, Transportation<br>_C_<sup>_SV_</sup>_= C_<sup>_SV_</sup>(C19,C22,C23,C24,C26,C27,TR)|
|5|HS|Housing|Rental & housing services, Utilities-Energy<br>_C_<sup>_HS_</sup>_= C_<sup>_HS_</sup>(C5,_EN_)|
|6|CL|Clothing|Clothes-shoes, Clothing services<br>_C_<sup>_CL_</sup>_= C_<sup>_CL_</sup>(C3, C4)|
|7|RG|Residential<br>goods|Furniture, Appliances, Interior Decorations,<br>HH daily-use articles<br>_C_<sup>_RG_</sup>_= C_<sup>_RG_</sup>(C10, C11,C12,C13)|
|8|RM|Recreational<br>& Misc. goods|Communications equip, Recreational articles, Books,<br>Other goods<br>_C_<sup>_RM_</sup>_= C_<sup>_RM_</sup>(C18, C20,C21,C25)|
|9|EN|Energy (dom)|Water, Electricity, Coal, Gas<br>_C_<sup>_EN_</sup>_= C_<sup>_EN_</sup>(C6,C7,C8,C9)|
|10|TR|Transportation|Gasoline, Vehicle svcs, Transportation fees<br>_C_<sup>_TR_</sup>_= C_<sup>_TR_</sup>(C15,C16,C17)|

Household _k_ ’s indirect utility function over the four aggregates in the top tier, _V(p,Mk)_ , is of a form that allows for exact aggregation: _k k k k_ where _Mk_ is the expenditures of household k, and _p_  ( _pFD_ , _pCG_ , _pHS_ , _pSV_ )' is the price vector of the 4 bundles. Each household type has its own distinct utility function and _Ak_ is a vector of demographic dummy ‐54‐
variables to indicate the size of the household, the presence of children, the age of the head, and the region. The budget constraint for household _k_ is: _k k k_ Let _wi_  _pi ci_ / _Mk_ denote the share of expenditure allocated to bundle i. Applying Roy’s Identity we get the demand share vector: The aggregate demand is obtained by summing over all household types. Let _nk_ be the number of households of type k; the aggregate share vector is then: The above equations (A52) and (A53) are estimated simultaneously, with (A52) estimated over one year of cross-sectional consumer expenditure data, and (A53) estimated using time series national prices and aggregate consumption expenditures.

To use the estimated equation (A53) in the model that include projections into the future we make some modifications. Firstly, the consumer survey data does not include some items that are in the National Accounts such as imputed rentals for owner-occupied housing and FISIM. We make some adjustments to the  _p_ ’s to scale the shares to match the consumption in the Input-Output table for our base year 2010. We project the distribution and demographic terms to account for the aging impact and thus re-write the share demand system as: Next, the aggregate expenditures on the 4 bundles are allocated to the 27 commodities according to the tier structure in Table 1. This is done with a linear logarithmic function that allows the shares to ‐55‐
change over time. For example, for the Transportation bundle, the value of expenditures ( _vcTR_ ), the price _CE_ index ( _pTR_ ) and implied quantity is: The demand for gasoline, item 15, is then: The consumption items listed in Table 1 are those used in the consumption survey and must be linked to the factory gate values in the Input-Output Accounts. For example, Food & tobacco in the Consumption accounts consist of commodities from Agriculture, Food Manufacturing, Trade (Commerce) and Transportation in the IO categories. The CE superscript denotes that these are prices for the consumption expenditure items. Table 2 gives the bridge that links these two accounts in the benchmark year 2010 for urban consumption. Column _i_ of the bridge **H** _u_ gives the shares to allocate consumption item _i_ to the 33 IO commodities. A similar table is constructed for rural consumption. Let be _VCtu CE_ , the vector of consumption values for the urban sector, then the vector of consumption in IO terms is given by: (A57) _VCtu IO_ ,  **H** _uVCtu CE_ , The prices of the consumption commodities are linked to the prices of the IO commodities via the same share matrix: The total value of consumption of commodity _i_ is the sum of the urban and rural components: The value of national consumption in equation (A48) is the sum over all the commodities: ‐56‐
Table 2. Bridge to link Consumption Expenditures (urban) to Input-Output accounts

|||Food,|Dining||Applian-|Health-|
|---|---|---|---|---|---|---|
|||Tobacco|Out|Clothes|ces|care|
|||1|2|3<br>...|<br>11|24<br>...|
|Agri|1|0.141|0.280|0.000|0.000|0.000|
|Coal|2|0.000|0.000|0.000|0.000|0.000|
|Crude|3|0.000|0.000|0.000|0.000|0.000|
|natgas|4|0.000|0.000|0.000|0.000|0.000|
|nonenergy|5|0.000|0.000|0.000|0.000|0.000|
|Food|6|0.781|0.100|0.000|0.000|0.000|
|textile|7|0.000|0.000|0.030|0.000|0.000|
|apparel|8|0.000|0.000|0.776|0.000|0.000|
|lumber|9|0.000|0.000|0.000|0.000|0.000|
|paper|10|0.000|0.000|0.000|0.000|0.000|
|Refine|11|0.000|0.000|0.000|0.000|0.000|
|Chem|12|0.002|0.000|0.000|0.011|0.130|
|Build|13|0.000|0.000|0.000|0.000|0.000|
|pmetal|14|0.000|0.000|0.000|0.001|0.000|
|metal|15|0.000|0.000|0.000|0.000|0.000|
|machin|16|0.000|0.000|0.000|0.020|0.000|
|tequip|17|0.000|0.000|0.000|0.030|0.000|
|emachin|18|0.000|0.000|0.000|0.638|0.088|
|electro|19|0.000|0.000|0.000|0.162|0.000|
|Instru|20|0.000|0.000|0.000|0.000|0.000|
|Other|21|0.000|0.000|0.006|0.010|0.074|
|Elect|22|0.000|0.000|0.000|0.000|0.000|
|gasprod|23|0.000|0.000|0.000|0.000|0.000|
|constr|24|0.000|0.000|0.000|0.000|0.000|
|transp|25|0.006|0.000|0.026|0.023|0.000|
|commun|26|0.000|0.000|0.000|0.000|0.000|
|commerc|27|0.069|0.000|0.156|0.106|0.083|
|Hotel|28|0.000|0.620|0.000|0.000|0.000|
|finance|29|0.000|0.000|0.000|0.000|0.079|
|realest|30|0.000|0.000|0.000|0.000|0.000|
|business|31|0.000|0.000|0.000|0.000|0.000|
|service|32|0.000|0.000|0.006|0.000|0.546|
|admin|33|0.000|0.000|0.000|0.000|0.000|
|Sum of share||1.000|1.000|1.000|1.000|1.000|

‐57‐
# **A.1.4. Government and Taxes**

In the model, the government has two major roles. First, it sets plan prices and output quotas and allocates investment funds. Second, it imposes taxes, purchases commodities, and redistributes resources. Public revenue comes from direct taxes on capital, labor, value-added taxes, indirect taxes on output, tariffs on imports, the externality tax, and other non-tax receipts: where _Dj_ is the depreciation allowance and _X i_ and _M i_ are the exports and imports of good _i_ .

Externality taxes, such as those on SO2 or CO2, may be on the value of output, or on the quantities. We allow for both, the total revenue from the externality tax on output is: In one application of the model described in Ho and Nielsen (2007, Chapter 10), the externality tax rate is set proportional to the marginal air pollution damages ( _MD_<sup>_O_</sup> ) from output _j_ : (A63) _t xjt_  _MDOjt_  1 When we consider a tax on fossil fuels based on the carbon content, the externality tax per unit of fuel _j_ is: where<sup>_c_</sup> _j_<sup>is the carbon content per unit of fuel of type</sup><sup>_j_.</sup> The nontax payments to the government are set as a fixed share of household income:

- (A65) _FEEt_  _NHHYt p_

Total government expenditure is the sum of commodity purchases and other payments: Government purchases of specific commodities are allocated as shares of the total value of government expenditures, _VGG_ . For good _i_ : ‐58‐
Transfers are set equal to a fixed rate of the population multiplied by the wage rate: _tr_ (A69) _G_ transfer_  _PLPOPt t_ The difference between revenue and expenditure is the deficit,  _G_ , which is covered by increases in the public debt, both domestic ( _B_ ) and foreign ( _B_<sup>_G_*</sup> ):

> (A70)  _Gt_  _Expend t_  _Revt_ ,

The deficit and interest payments are set exogenously and equation A70 is satisfied by making the level of total nominal government expenditure on goods, _VGG_ , endogenous in the base case. In simulating policy cases we would often set the real government expenditures in the policy case equal to those in the base case. In this counterfactual we would use some endogenous tax variable to meet equation A70.

# **A.1.5. Capital, Investment, and the Financial System**

We model the structure of investment in a fairly simple manner. In the Chinese economy, some state-owned enterprises receive investment funds directly from the state budget and are allocated credit on favorable terms through the state-owned banking system. Non-state enterprises get a negligible share of state investment funds and must borrow at competitive interest rates. There is also a small but growing stock market that provides an alternative channel for private savings. We abstract from these features and define the capital stock in each sector _j_ as the sum of two parts, which we call plan and market capital: (A72) _Kjt_  _Kjt_  _K_<sup>~</sup> _jt_ .

The plan portion evolves with plan investment and depreciation: ‐59‐
_I_ The rate of depreciation is  , and  _t_ is an aggregation that converts the investment units to capital stock units.<sup>18</sup> In this formulation, _K j_ 0 is the capital stock in sector _j_ at the beginning of the simulation. This portion is assumed to be immobile across sectors. Over time, with depreciation and limited government investment, it will decline in importance. Each sector may also rent capital from the total stock of market capital, _K_<sup>~</sup> _t_ : The allocation of market capital to individual sectors, _K_<sup>~</sup> _jt_ , is based on sectoral rates of return. As in equation A2, the rental price of market capital by sector is _P_<sup>~</sup> _jKD_ . The supply of _K_<sup>~</sup> _jt_ , subject to equation A72, is written as a translog function of all of the market capital rental prices, ~ _KD KD Kjt_  _Kj_ ( _P_<sup>~</sup> 1 ,..., _P_<sup>~</sup> _n_ ) : To simplify the modeling of the capital supply in the electricity sector, we first allocate _K_<sup></sup> _elect_ , _t_ according to (A74b) and then allocate that to the various generation subsectors, using a similar function of the rental rates in the various electricity subsectors: In three sectors, agriculture, crude petroleum and gas mining, “land” is a factor of production. We have assumed that agricultural land and oil fields are supplied inelastically, abstracting from the complex property rights issues regarding land in China. After taxes, income derived from plan capital, market capital, and land is either kept as retained earnings by the enterprises, distributed as dividends, or paid to foreign owners:

> 18 Both K and I are aggregates of many asset types, ranging from computer equipment to structures. The composition of total investment and total capital stock are different and an aggregation coefficient is needed to reconcile the historical series.

‐60‐
where _tax_ ( _k_ ) is total tax on capital and value added (the first two terms on the right hand side of equation A2).<sup>19</sup> As discussed below, total investment in the model is determined by savings. This total, _I VII_ , is then distributed to the individual investment goods sectors through fixed shares,  _it_ : A portion of sectoral investment, _I t_ , is allocated directly by the government, while the remainder, ~ _I t_ , is allocated through other channels.20 The total, _I t_ , can be written as: As in equation A73 for the plan capital stock, the market capital stock, _K_<sup>~</sup> _jt_ , evolves with new market investment:

# **Non-reproducible assets**

In addition to the capital stock, the households own the non-reproducible assets – land, renewable resources and sequestration resources. The supply of land (or mining resources) is simply assumed fixed for each type (agriculture, coal mining, oil mining): The supply curves for nuclear and hydro resources are assumed to be upward sloping curves.

> 19 In China, a substantial part of the “dividends” are actually income due to agricultural land.

> 20 It should be noted that the industries in the Chinese accounts include many sectors that would be considered public goods in other countries.  Examples include local transit, education, and health.

‐61‐
# **A.1.6. The Foreign Sector**

Trade flows are modeled using the method followed in most single-country models. Imports are considered to be imperfect substitutes for domestic commodities and exports face a downward sloping demand curve. We write the total domestic supply of commodity _i_ as a CES function of the domestic ( _DCi_ ) and imported good ( _Mi_ ): 1 (A81) _DSi_  _A_ 0  _d DCi_    _mM i_   <sup>,</sup> where _DC_ is the quantity of domestically produced goods that are sold domestically. The elasticity is   1/ (1   ) . The cost dual corresponding to the above primal function is: and the value of total domestic supply is: The purchaser’s price for domestic goods, _PDi_ , is related to the commodity supply price _PCi_ and is discussed in the export section below. _PSi_ is the price of the basket of commodity _i_ to domestic purchasers. The price of imports to buyers is the foreign price plus tariffs (less export subsidies), multiplied by a world relative price, _e_ : _r_ * _ru_ (A83) _PMi_  _e_ (1  _ti_ ) _PMi_  _ti_ .

From (A82) we may derive the demand for imports as: Domestically produced commodities (QC) are allocated to the domestic market and exports according to a constant elasticity of transformation (CET) function: The ratio of exports to domestically sold goods depends on the domestic price (PD) relative to world prices _e_ adjusted for export subsidies ( _sit_ ): ‐62‐
The value identity is: The weights and constant terms are set using base year values: _x_ The share parameters  _it_ are projected exogenously to take into account the rising role of exports during 1980-2010 and a falling role in the future. The price _PC_ is given in equation (A14) above, and is also an implicit dual function of (A85), PC=f(PX,PD).

The current account balance is equal to exports minus imports (valued at world prices before tariffs), less net factor payments, plus transfers: Like the government deficits, the current account balances are set exogenously and accumulate into stocks of net foreign debt, both private ( _Bt_ *) and public ( _BtG_ *): (A88) _Bt_ *  _BtG_ *  _Bt_ *  1  _BtG_  *1  _CAt_ .

# **A.1.7. Markets**

The economy is in equilibrium in period _t_ when the market prices clear the markets for the 33 commodities and the three factors. The supply of domestically produced commodity _i_ must satisfy the total of intermediate and final demands: (A89) _DSi_   _Aij_  _Ci_  _Ii_  _Gi_ , _i_ = 1, 2, ..., 33. _j_ For the labor market, we assume that labor is perfectly mobile across sectors so there is one average market wage which balances supply and demand. As is standard in models of this ‐63‐
type, we reconcile this wage with the observed spread of sectoral wages using wage distribution coefficients,  _jtL_ . Each industry pays _PLjt_   _Ljt PLt_ /(1  _tVj_ ) for a unit of labor. The labor market equilibrium is then given as: For the non-plan portion of the capital market, adjustments in the market price of capital, _P_<sup>~</sup> _jKD_ , clears the market in sector _j_ : _K_ where  _jt_ converts the units of capital stock into the units used in the production function. The rental _PT_ price _j_ adjusts to clear the market for “land”: In this model without foresight, investment equals savings. There is no market where the supply of savings is equated to the demand for investment. The sum of savings by households, businesses (as retained earnings), and the government is equal to the total value of investment plus the budget deficit and net foreign investment: (A93) _S p_  _RE_  _G_ INV_  _VII_   _G_  _CA_ .

The budget deficit and current account balance are fixed exogenously in each period. The world relative price ( _e_ ) adjusts to hold the current account balance at its exogenously determined level.

The model is a constant returns-to-scale model and is homogenous in prices, that is, doubling all prices leaves the economy unchanged. We are free to choose a price normalization.

# **A.1.8 Welfare Other accounting identities**

The household welfare function (A50) is chosen to allow aggregation over different households. The aggregation issues are discussed in Jorgenson et al. (2013, Chapter 3); equation (A54) gives the aggregate demand function for the four consumption bundles. Jorgenson et al. expresses social welfare as a function that takes into account the different compositions of ‐64‐
households (different size and number of children), using the concept of household equivalents. The welfare function depends on the average level of consumption as well as inequality of consumption (efficiency and equity). Here we compute only the average levels to give the efficiency measure which is given by:

> <sup>_V_</sup> _k_<sup>is the household utility in (A50), and</sup> _m_ 0<sup>(</sup> _p_<sup>,</sup> _Ak_ ) is the household equivalent to the reference household which is aged 18-34, male, elementary school, two members and in the East. The equivalence scale is explained in Jorgenson and Slesnick (1987) and is given by:

The money measure of welfare is given by a social expenditure function (Jorgenson and Slesnick 1987, eq. 5.15): The money measure of the change in welfare due to a policy (from _W_ 0 to _W_ 1) is a function of the 0 policy case measured at base case prices ( _p_ ): Gross domestic product in nominal terms is the sum of consumption, investment, government spending, plus net exports: (A97) _VGDP_  _VCC_  _VII_  _VGG_  _VX_  _VM_ To construct real, constant yuan, GDP we need to first define real consumption, investment, etc. These are expressed as the divisia aggregate of the 33 commodities that make up each component, for example, real personal consumption expenditures is: Real GDP is then a divisia index of these components: ‐65‐
# **A.1.9 Energy, emissions and environmental accounting**

To account for atmospheric environmental damages we consider a range of criteria pollutants: particulate matter (PM25 and PM10), sulphur dioxide, nitrogen oxides, VOCs, ammonia. We also account for greenhouse gas emissions, in particular carbon dioxide. The PM concentration is due to primary PM emissions as well as secondary particles such as sulfates and nitrates which are formed from sulfur dioxide and NOx respectively. The emissions inventory is described in _Clearer Skies_ , Chapters 4-6. To illustrate the calculations we describe here a simplified account of energy flows and primary PM, SO2 and NOx emissions.

We begin by describing the energy variables. Very often a simple indicator of total primary energy production and consumption is produced by summing the energy equivalents of the fossil fuels and primary electricity and heat. This may not be a very useful indicator given that a joule of energy from burning coal is very different in the ease of use from a joule from gasoline or a joule of electricity; a difference that is reflected in the prices per joule. Nevertheless, for comparison with well-known series we compute the standard coal equivalent (sce) of these primary sources of energy.

First, recall that we distinguish between industry output ( _QI_ ) and commodity output ( _QC_ ). _QCft_ is the constant yuan quantity of commodity produced (billions of 2010 yuan). _QP_<sup>_f_</sup> , the total quantity of coal, crude, and gas _produced_ (whether combusted or not) in year _t_ is given by the commodity output ( _QC_ ) _f_ multiplied by the fuel conversion coefficient,  _mean_ :

> _f_  _f_ (A101) _QPt_  _meanQC f_ , _t_ f=coal, crude oil, gas mining, electricity

where  _meanf_ is the quantity of the commodity output (in million tons, million m<sup>3</sup> , or billion kWh) per billion yuan of commodity output. For example, the quantity of raw coal produced in million tons is given by _rawcoal coal_  _QPt_  _meanQCcoal_ , _t_ . Since electricity is only a part of the “Electricity, Steam & Hot water” sector, the quantity of electricity produced (in billion kWh) is: where _a_<sup>_el_only_</sup> is the electricity share of the “Electricity, Steam, & Hot water” sector’s commodity output. We compute energy consumption in two ways. The first way simply uses the total output of fuels (production based account); the second way sums over the industry consumption of energy that is calibrated to the official estimates in the base year (consumption based account). First, _E_<sup>_PROD_</sup> , the total sce of energy produced domestically, is: ‐66‐
where<sup>_e_</sup> _f_<sup>is the energy content of a unit of fuel</sup><sup>_f_(e.g. tons of sce per ton of oil) and the PRI superscript</sup> denotes primary electricity from renewables and nuclear.( In this calculation we ignore the tiny amount of heat from natural sources.) We set _α_<sup>_PRIelec_</sup> , the share of electricity produced from primary sources,

> exogenously by considering the projected generation of renewables and nuclear power. Then _QP_<sup>_PRIelec_</sup> , the quantity of primary electricity produced from renewables and nuclear, is:

_E_<sup>_EXP_</sup> , the total sce of energy exported, on net, is:

> where _Xf_ is the value of exports of fuel _f_ (in billion yuan) and _Mf_ is the value of imports of fuel _f_ (in billion

yuan). Exports of electricity are not counted in this measure since it is a secondary energy, the pollution due to the generation of electricity for exports is located in the country and they are not exported.

_E_<sup>_CONS_</sup> , the total energy consumed in China (in tons sce) is then given by production less net exports, less _INV_ changes in inventory (<sup>_E_</sup> _t_ ): The second expression in (A105) substitute in (A102) and (A104) to show that it is the constant yuan output less net exports, multiplied by the fuel conversion coefficient, and multiplied by the energy content ‐67‐
coefficient. In (A106), the variables _CFtcoal_ , _CFtoil_ , _CFtgas_ denote the quantity of fuel consumed in million tons or million m<sup>3</sup> . Here, _CF_<sup>_f_</sup> is calculated as the sum of commodity output ( _QC_ ) and imports ( _M)_ less exports ( _X_ ), multiplied by the fuel conversion coefficient (  _meanf_ ) to convert the constant yuan of fuel consumed into the quantity of fuel consumed (in million tons or million m<sup>3</sup> ).

Second, in consumption-based accounting, we also calculate national energy consumption by adding over each industry, using industry specific information about the consumption of coal, coke, liquid _f_<sup></sup> _j_ fuels, etc. We first define consumption coefficients ( ) by taking the data on fuel actually used (in million tons, million m<sup>3</sup> , or billion kWh) from the China Statistical Year book (CSY 2012, Table 7-9 “Consumption of Energy by Sector”) and dividing by the value of energy purchases given in the Input-Output table.

To disambiguate, the fuel conversion coefficient (  _meanf_ ), presented in equation (A101), is computed using the production data at the aggregate level: the _total_ quantity of the commodity output divided by the _f total_ value of the commodity output. In contrast, the consumption coefficient (<sup></sup> _j_<sup>), presented here, is</sup> computed using the consumption data at the industry level: industry _j_ ’s consumption of fuel _f_ (in million tons, million m<sup>3</sup> , or billion kWh) divided by the value of industry _j_ ’s purchases of fuel _f_ (in billion yuan).

Secondary fuels are produced by the Petroleum Refining & Coal Products sector which we group as coke, refined liquids, and other petroleum products. The “other petroleum products,” such as bitumen and lubricants, are assumed to be not combusted (i.e. not contributing to CO2 emissions). Each industry _j_ purchase a different share of coke (coal products) from this sector and we write the value of coke input as a share of the value of Refining & Coal Products in the Use matrix:  _refcoalpr_ co_ , _jUrefine_ , _j_ where _a_<sup>_f_</sup> _s, j_ is the share of fuel _f_ in sector _s_ that industry _j_ purchases, and _Uf, j_ is the value (in billion yuan) of inputs of fuel _f_ for industry _j_ from the Use matrix.

That is, the value of refined liquids is the product of: 1) the share of liquids in total refined petroleum products; 2) the share of _non_ -coal products in the Refining & Coal Products sector that industry _j_ purchases; and 3) the value of Refining & Coal Products purchased by industry _j_ . Similarly, the value of Other Petroleum Products input (on the right) can be interpreted as the product of: 1) the share of _non_ -liquids in total refined petroleum products; 2) the share of _non-_ coal products in the Refining & Coal Products sector that industry _j_ purchases; and 3) the value of Refining & Coal Products purchased by industry _j_ .

The energy consumption coefficients for coke and liquid fuels are thus: ‐68‐
where _FTcokeCSY_ , _j_ , _baseyr_ is the quantity of coke consumed by _j_ in million tons in the base year 2010. _FTliquidfuelCSY_ is the sum of the quantity of gasoline, kerosene, diesel and fuel oil consumed (given in CSY 2012), and _FTotherpetroleumCSY_ is the sum of the quantity of lubricant, bitumen, naphta, etc. consumed (given in the LBL’s China Energy Databook).

is the quantity share of liquids consumed in consumption of total refined petroleum products for industry _j_ . Our model distinguishes between the Gas Mining sector and the Gas Utilities (or Gas Products) sector; most industries purchase only from Gas Products, while a few purchase from Gas Mining for transformation and combustion – Chemicals, Electricity and Gas Products. For all industries _j_ other than Gas Products the consumption coefficient is the _quantity_ of natural gas purchased by industry _j_ divided by the sum of the _values_ of natural gas and gas products purchased by industry _j_ in the base year: In contrast, the energy consumption coefficient for the Gas Products industry is divided by only the value in the Gas Products cell, excluding the Gas Mining cell: We now move on from calculating energy consumption to calculating energy combustion. The above consumption coefficients refer to the purchases of the different fuels. Some of these oil and gas inputs are not combusted but converted to other products such as fertilizer or bitumen. In the Refining sector part of the crude input is combusted but most are converted to liquid fuels or other petroleum products; the un- _ref_ loss_  _ref_ loss_ combusted portion is represented by the “refining loss” coefficient,<sup></sup> _j_ where (1<sup></sup> _j_ ) is the _ref_ loss_ fraction of un-combusted crude input. (For industries other than j=Refining,<sup></sup> _j_ is simply 1, reflecting that 100% of the crude input is combusted.) In the Gas Products (Utilities) industry, gas is purchased from the Natural Gas Mining sector and sold to consumers, that is, there is assumed to be no combustion in this industry. In the Chemicals sector, raw gas is purchased from the Gas Mining sector and part of it is converted to plastics and other products. The _gas_ loss gas_ loss_ combusted portion is represented by  _j_  _Chemical_ . (For industries other than j=Chemical,  _j_  _Chemical_ is simply ‐69‐
1, reflecting that 100% of the raw gas is combusted). Thus, these loss adjustment coefficients can be thought of as the share of fuel _f_ that _is_ combusted for industry _j_ .

_FT CSY_ To disambiguate in advance: _fj_ (used in the previous section) refers to the quantity of fuel _f_ _FT f purchased_ , while _j_ (used below) refers to the quantity of fuel _f combusted_ .

The quantity of fuel **combusted** ( _FT_ ) is given by the constant yuan of fuel ( _Aij_ ) multiplied by the _f_ consumption coefficients (<sup></sup> _j_<sup>that converts the value of fuel</sup><sup>_f_to physical quantities (tons of coal, tons of</sup> _f_ loss_ oil, m<sup>3</sup> of gas, kWh of electricity)), and multiplied by these loss adjustments (<sup></sup> _j_ ). The following equations describe the quantity of fuel combusted for coal, oil, other petroleum products ( _nonliqref_ ), and gas in terms of fuels at a finer classification: where that the constant yuan quantity of energy input is given by the value in the Use matrix divided by the fuel price: _Aij_  _Uijt_ / _PSit_ . _Q_<sup>_i_</sup> is the quantity of energy input _i_ (at the finer classification) combusted, and the quantity of fuel _f_ combusted is the sum over the _i_ finer types to give _FT_<sup>_f_</sup> _._ _elec_ For electricity,  _elect_ is the share of electricity in Electricity, Steam & Hot Water, and we may similarly define _FT_<sup>_elect_</sup> , the quantity of electricity purchased (in billion kWh) as: _elect elec elec_ (A113) _FTjt_  _elect_  _t Uelect,j_ / _PSelect_ The above equations (A112) and (A113) are for the industry purchases of energy, a similar set of equations hold for household and investment use of energy: ‐70‐
> where _Celect, Ccoal, Crefining, Coil, Cgas, and Cgasprod_ denote the constant yuan value of Consumption by households of those fuels (in billion yuan). _If_ is the value (in billion yuan) of purchases of fuel _f_ by the Investor (these are essentially business inventories in the Investment column of the input-output accounts).

The un-combusted portions in this version are the other petroleum products (“nonliqref”) and part of the gas use by the Chemicals industry. We denote the un-combusted fuel use by FU:

[In this version we have not separated out the combusted portion of “other petroleum products (nonliqref)”. A more detailed accounting would have refined products divided to “refined liquids”, “other combustible refined products”, and “noncombustible refined products”. The equations would be: The other combustible refined products are LPG, Refinery Gas and other gases and should be counted in the calculation of emissions. In the current version we combine them with the noncombustible refined products (wax, asphalt, etc) to give total “nonliqref”.

The total energy consumed by industry _j_ or households is the sum of these physical units of primary fossil fuels combusted multiplied by the energy conversion coefficient ( _ef_ , e.g. tons of SCE per ton of coal) plus the electrical energy, plus the un-combusted portions: When we express energy consumption as above we are counting _j_ ’s use of electricity as energy consumed by _j_ , not as energy consumed by the Electric Utilities when it burns coal to generate electric ‐71‐
power. For a consistent accounting of total national consumption, the net energy consumed by Electric _U_ Utilities is only the generation loss plus the Utilities own electricity consumption ( _elect,elect_ ).The generation loss is given by the energy embodied in the fuels combusted in the power plants _less_ the energy embodied in the delivered thermal electricity (total electricity minus renewables and nuclear ( _QPtelec_  _QPtPRIelec_ ). The net energy consumed by Electric Utilities, _EINDj=elect_ , is thus: The national total energy consumption is then the sum over all industries and final demand: _CONS_ This should be equal to<sup>_E_</sup> _t_ , the total computed from the production data in equation A107.

# _Emissions_

The national emissions of carbon dioxide may be computed from the production accounts by adding over the emissions from all fossil fuels _f_ . This is given by the quantity of fuel consumed ( _CFt f_ ), multiplied by the energy content coefficient ( _ef)_ , and multiplied by the CO2 intensity, (<sup>_c_</sup> _f_<sup>, tons of CO2 per sce of fuel</sup> _f_ ):

> The quantity of fuel _f_ consumed, _CFt f_ , is given in equation A107 above. For non-combustion sources of CO2 we only consider those from cement production processes; this is expressed as an emission factor ( _ccement_ ) multiplied by the cement component of the output of the Building Materials industry:

_noncmb cement_ (A120) _EMCO_ 2, _t_  _ccement_  _Build QIBuild_ , _t_ where _a_<sup>_cement_</sup> is cement’s share of the Building Materials industry’s output, and _QIBuild_ is the value of the output of the Building Materials industry (in billion yuan) which also includes glass and clay products.

Total carbon emissions are then the sum of the fossil emissions and non-combustion ones: (A121) _EMCO_ 2, _t_  _EMCOfos_ 2, _t_  _EMCOnoncmb_ 2, _t_ _Local pollutants_ ‐72‐
> Primary emissions of pollutant _x_ from sector _j_ at period _t_ ( _EMjxt_ ) are produced from fossil fuel combustion and from non-combustion production processes. The combustion emissions are obtained by multiplying the energy input by an emission factor,  _jxft_ ,  while the process emissions are output multiplied

by the emission factor,  _jxt_ . Total emissions from j are thus: _x_ = PM10, SO2, NOX, f = coal, oil, gas where _QIj_ is the output of industry _j_ ’s (in billion constant yuan2010) and _FTj_ is the quantity of fuel _f_ combusted by industry _j_ . The combustion emission factor (  _jxft_ ) is given in tons of emissions of pollutant _x_ per ton of fuel, while the process emission factor (  _jxt_ ) is given in tons of primary emissions of pollutant _x_ per billion yuan of industry output.

Households’ use of fuels also generates pollutants: The estimation of emissions in 2005 is reported in _Clearer Skies_ (Chapters 4-6) and an updated version for 2010 is used to calibrate theses emission factors. The emission factors are projected based on planning documents of the NDRC and other government agencies.

The emissions are then used by the GEOS-Chem atmospheric model to compute the concentration of various criteria pollutants at each grid cell as described in _Clearer Skies_ (Chapter 7). We consider the impact of PM and ozone on human health, and concentrate on the main effects – mortality risks, hospital admission due to cardiovascular reasons and due to respiratory reasons, and outpatient visits. The health effect _h_ due to a change in concentration of x ( <sup>_C_</sup> _x_ ) induced by a policy change is given by ( _Clearer Skies_ Chap. 8): (A124)  _HEhx_  _fhx_ (  _Cx_ )  _Pop_  _BIh_ where Δ _HEh_ denotes the change in the number of cases of health endpoint _h_ ; _f_ is the C-R function ; _Pop_ represents the population exposed to the pollutant; and _BIh_ represents the baseline incidence of the health endpoint _h_ . The total impact, say for mortality, is the sum over all pollutants for _h_ = mortality,  _HEh_    _HEhx_ . _x_ We also consider the impact of ozone on agriculture output. There is less agreement in the literature about how to model this impact, and as discussed in _Clearer Skies_ (Chap. 8) we use three different measures ‐73‐
_I i_ of ozone exposure (indices _O_ 3 , i=SUM6, AOT40, W126), to compute the impact on the output of maize, rice and wheat. The percentage change in yields is given by: The final step is to calculate the monetary value of these damages. The value is given by the health _V_ impact from (A125) multiplied by the willingness to pay value of each type of health effect ( _ht_ ), and the value of crop damages is the value of the crop ( _Vcrop t_ , ) multiplied by the percentage change in crop yields: ‐74‐
# **A.2 Implementing tax and subsidy policies; environmental policies**

In this we describe the implementation of the main policies studied with this model. We first describe carbon tax scenarios, one that is offset by a lump sum rebate to households and one that is offset by cut in existing taxes. We then describe electricity policies, one which subsidize clean energy and one with imposes Renewable Portfolio Standards.

# **A.2.1 Carbon Tax**

We represent a simple carbon tax with revenue neutral recycling as a tax on fossil fuels in proportion to the carbon content of fuel _j_ (<sup>_c_</sup> _j_<sup>). We put the tax upstream – on the producers of these</sup> (primary) fuels and on the imports of all fuels; the tax per unit output (constant yuan2010) is the carbon content (tons of carbon per yuan) multiplied the carbon tax rate ( _txu_ , yuan per ton of C): _xu u_ (A130) _t j_  _tx cj_ j=coal, crude, natgas and the unit tariff on import of fuel commodity _i_ is: The unit tax<sup>_t_</sup> _xuj_ is the term that appears on the right hand side of the purchaser’s price equation (A13), while the unit tariff is on the right side of the import price (A83).

The above simple setup means that all purchasers of fuel _j_ will pay the carbon tax. A more complicated policy of imposing different rates on different users of fossil fuels will require a tax on intermediate input _Aij_ .

If this tax is to be recycled as a lump sum transfer to households then this is represented by a negative tax, _T LUMP_ , in income equation (A45) above. If this tax is to recycled as a tax cut we introduce _scale_ a new endogenous variable,<sup>_t_</sup> _i_ , that is applied to all tax rates in period _t_ , e.g. the tax on capital income is _k_ this scaling variable multiplied by the base case rate ( _t_ 0 ): These offsetting tax variables are chosen to maintain revenue and spending neutrality; we choose the lump sum payment or the tax rate factor such that the real level of aggregate government consumption (GG) and deficit are the same as the base case value for each period: _base base_ (A134) _GGt_  _GGt_ ;  _Gt_  _Gt_

# **A.2.2 Renewables promotion policy**

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One common policy examined is the use of subsidies to promote renewables in electricity _EL_ generation. In the base case there is a net output tax ( _ttl_ , _t_ ) on electricity generated by source _l_ , giving the purchaser price, _Pl ttt_ , , _EGEN_ , in equation (A21b). A new subsidy for renewables is _EL_ represented by _sbt_ , : The total payment for this new subsidy is expressed as a negative revenue from the electricity generation sector, _R_EGEN_ : This is added to the total revenue term in equation (A61): A renewable portfolio standard (RPS) policy requires a certain minimum share of total electricity output to come from renewable sources. In practice, this may be imposed on each company, or imposed on a region and allowing companies to meet the target collectively. We represent the RPS policy in this model as requiring the national share of the kWh from source _l_ be no less than some target, _rpslt_ : The RPS may be implemented by subsidies or by allowing producers to pass the higher costs to consumers. A simple way to implement this is a system of taxes and subsidies to the different generators such that the net fiscal burden on the government is zero, and the consumer bears the _RPS_ net impact on the average price of electricity. Let _tl t_ , be the tax on the output from source _l_ ; this is negative if it is a subsidy. The price equation (A21b) is then rewritten as: (A138) _Pltt_ , _t_ , _EGEN_  (1  _ttlEL_ , _t_  _tlrps_ , _t_ ) _Pl_ , _EGENt_  _ttlELxu_ , _t_ ; l=coal,gas,hydro,wind,...

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Producers will change output given this new vector of prices according to the cost functions given in A17, A19 and A21. This is illustrated in Figure A3.

Revenue neutrality requires: We thus have potentially 8 rates for the _tlrps_ , _t_ , to hit a maximum of 8 independent target shares for the different sources of power; only relative prices matter and the shares must sum to 1.

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Figure A3. Using shadow taxes to meet renewable portfolio standards <!-- Start of picture text --> `<br>Wind<br>S<br>1  s P<br>( w ) c B<br>P<br>P c<br>w<br>P<br>w<br>0<br>P<br>c<br>0<br>P<br>w<br><!-- End of picture text --> <!-- Start of picture text --> Coal<br><!-- End of picture text --> In this diagram the arc represents the production possibility frontier for coal versus wind. In the 0 0 base case the producer, and consumer, relative price for coal versus wind is _Pc_ / _Pw_ and output and consumption is at point B. The small arcs represent the iso‐utility curves of eq. A17. _P_ / _P_ A subsidy for wind of sw will move the relative price to _c w_ and production to point S. The price to the consumer becomes (1  _sw_ ) _Pc_ / _Pw_ ‐78‐
# **A.3 Parameterizing the electricity sub-model**

The parameters of the electricity sub-model are based on three main sources of data – the Input-Output table, the Electric Power Industry Statistics,<sup>21</sup> and the International Energy Agency (2010) “Projected Costs of Generating Electricity”.

The elasticities are summarized in Table A2

# **A.4 Parameters, exogenous variables and data sources**

The key input into the model is the Social Accounting Matrix (SAM) for 2010. This traces the flow of commodities and payments among the producers, household, government and rest of the world. The SAM is assembled from the 2007 benchmark input output table.<sup>22</sup> A summary of this SAM is given in Figure A4, the actual matrix used is disaggregated to the 33 sectors and commodities. From this we derive the labor and capital incomes, the tax revenues for each type of tax, the expenditures on specific commodities by the household, government and foreign sectors, and government payments of all types in equation A76.

These payments are combined with employment and capital input data to give the compensation rates for labor and capital for each sector. The estimates for employment and capital stocks by sector are taken from a productivity study of China (Cao, Ho, Jorgenson, Ren, Sun and Yue 2009) that supplements the official data with labor force surveys. The various tax and subsidy rates are not statutory rates but are implied average rates derived by dividing revenues by the related denominator – value of industy output, capital income, total value added, and imports.

The exogenous variables in the model include total population, working age population, saving rates, dividend payout rates, government taxes and deficits, world prices for traded goods, current account deficits, rate of productivity growth, rate of improvement in capital and labor quality, and work force participation. These variables may, of course, be endogenous (i.e. they interact among each other) but we ignore this and specify them independently. Our assumptions for these exogenous drivers are summarized in Table A3.

The assumption that affects the growth rate the most is the household savings rate, _st_ . Our assumption is to have _st_ beginning at the observed 41.2% for 2010 and gradually falling to 22% in 2020 and 15% in 2050. National private savings is household savings plus the retained earnings of enterprises.

> 21 电力工业统计资料汇编 .

> 22 The 2010 input‐output table is given in NBS (2014). The benchmark IO table for 2007 is derived from detailed enterprise data, the 2010 IO table is extrapolated by the NBS using simpler aggregated data.

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The share of retained earnings is assumed to fall, and dividend payouts to rise to reflect the diminishing role of state enterprises in the economy. The dividend rate, i.e. the share not used for retained earnings, was 38.9% in 2010 and we project it to rise to 58% by 2020. It should be pointed out that national savings and investment in the Chinese data includes capital such as roads and other public infrastructure, items that are excluded from the “gross fixed private investment” item in most other countries National Accounts.

In the labor supply expression eqn. (A47) we have the product of the working-age population, annual average hours and quality. In Cao and Ho (2014) we discussed various population growth scenarios including the different two-child policies. Projections by age groups are taken from projections made by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat<sup>23</sup> and in the central population scenario we adjust them to incorporate a looser child policy. The results are plotted in Figure A5.

The composition of the work force changes over time with a bigger portion of educated workers, bigger or smaller portion of more experienced workers, and an older average age. This quality of labor _L_ input index,<sup>_q_</sup> _t_<sup>, in estimated in Cao et al. (2009) to have grown at 0.9% per year for the period 1983-2000.</sup> Given the expectation of continued higher educational attainment in the future we assume that China's aggregate labor quality continue to rise, but at a diminishing rate. By 2040 the quality index is assumed to grow at only 0.2% per year. For comparison, the U.S. labor quality growth peaked at 0.5% during the 1960s, and fell to 0.3% per year during 1995-2000 (Jorgenson, Ho and Stiroh, 2005, Table 6.5).

Total labor hours depend also on the participation rate and annual working hours. There is no comprehensive data on the number of hours worked and based on comparisons to other countries we project it to rise due to improvements in the functioning of the labor market -- lower underemployment, seasonal unemployment and other labor market frictions. We assume that hours worked per capita rises at 0.2% per year initially but slowing down over time. The results are plotted in Figure A6.

_I_ We allow for improvements in future capital “quality,” or composition, as represented by the  _t_ coefficient in (A73). Cao et al. (2009) note how the composition of the capital stock in China has shifted towards assets with shorter life, i.e. towards a smaller share of structures and a larger share of equipment such as computers. They explain how assets which have shorter useful lives generate higher annual capital services per dollar of capital stock, and hence is of a higher quality in the terminology of Jorgenson, Ho and Stiroh (2005). While the ratio of equipment to structures has moved in different directions over the past 30 years, we believe it will return to a more typical development trend of rising equipment ratios. We project that capital quality rises by 1.5% per year initially, then gradually decelerating. For land, the supply

> 23 The demographic projections are from the U.N. Population Division’s _World Population Prospects: 2012 Revision_ , downloaded from their web site, <u>http://esa.un.org/unpd/wpp/unpp/panel_population.htm</u> .

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of land for agriculture, oil mining and gas mining is simply set fixed for all periods equal to the base year value.

Tax rates are set equal to those for 2010 derived from the SAM. These are summarized in Table A4. For the government deficit,  _G_ , we set it at the base year 1.69% of GDP initially, declining steadily towards zero in the long run. These deficits are cumulated into the stocks of domestic and foreign debt, _Bt G_ * and<sup>_B_</sup> _t_ , assuming a constant division between domestic and foreign borrowing. Data for the stock of debt and interest paid on it comes from the China Statistical Yearbook (NBS 2012, Table 8-13), IMF’s _World Economic Outlook 2012_ online database and the 2010 Social Accounting Matrix. Government transfers, _G_ transfer_ , are set to rise in proportion with population and average wage. The nontax fees paid by enterprises are set to be a fixed share of GDP equal to the base year’s share (Table A4).

The current account balance was in a huge surplus in the mid-2000s but has since declined. There is no consensus about the future evolution of this variable, for simplicity, after setting it as a share of GDP at the observed sample period values, we set it to decline rapidly to zero. This _CAt_ deficit is also the assumed rate of borrowing from the world. Import prices, _PM_<sup>_i*_</sup> , are assumed fixed at the base year value for every period with one important exception. World oil price forecasts are taken from the U.S. Energy Information Administration and shown in Figure A7.<sup>24</sup> The model also requires projections of the export share; while this has been rising rapidly in the past, it fell with during the 2008 Global Financial Crisis and never recovered. Given the aim to rebalance the economy away from exports and investment towards consumption, we simply project a constant value for the share parameter.

The base year data for 2010 was constructed in 2013 since then, the macro variables for 2011-13 is now available; these include the GDP, investment and current account surplus. The current account surplus has fallen, and the unusually high share of investment in GDP has risen even more after 2010. We take these into account in setting the savings rate and current account balance as share of GDP for these years.

# _Parameters_

The rate of productivity growth is another factor that has a large effect on the base case growth rate of the economy but has little impact on the difference between cases. Total Factor Productivity growth at the industry level in the 1982-2000 period show a very wide range of performance as estimated by Cao et al. (2009), ranging from -10% to 5% per year. The Domar-weighted productivity growth for all industries was 2.7% for 1982-2000. To keep the base case as simple as possible we ignore this wide range of observed

> 24 The projections for crude oil prices are taken from the EIA’s _Annual Energy Outlook 2010_ , Table 12, which is available on their web page:  http://www.eia.gov/forecasts/aeo/.

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TFP growth, and in our projections of sector productivity terms in eqn. (A3) we initially set all the  _j_ 's to the same value, 0.018. These are then adjusted to match actual GDP growth rates in the initial years for which we have actual data.

The value share parameters of the production functions (  _Kj_ ,  _Lj_ , etc.) are set to the values in the 2010 IO table in the first year of the simulation. For future periods we change most of these parameters so that they gradually resemble the shares found in the US input output table for 1997. The exceptions to this are the coal inputs for all the sectors, this is set to converge to a value between current Chinese and US1997 shares.<sup>25</sup> The rate of reduction in energy use is set at a modest level relative to the rapid improvements in the recent Chinese history. We assume that the share of energy in industry output is reduced gradually to 60% of the 2005 levels in 40 years. This is conservative compared, for example, to the performance in the electric power industry during the 1990-99 period. In that time the thermal output grew 88% whereas coal input only rose 61%, a rate of improvement of some 1.5% per year.<sup>26</sup> _C_ The  _it_ parameters of the consumption function are set in a similar way. That is, for the first period they are equal to the shares in the 2010 Social Accounting Matrix, and for the future periods they gradually approach US 1997 shares except for coal. This implies a higher projected demand for private vehicles and gasoline than that assumed in most other models of China. The coefficients determining demand for _I G_ different types of investment goods (  _it_ ), and different types of government purchases (  _it_ ), are projected identically.

The import and export elasticities are set to the values in GTAP v4. The base share of exports and imports are taken from the SAM.

> 25 We have chosen to use U.S. patterns in our projections of these exogenous parameters because they seem to be a reasonable anchor.  While it is unlikely that China’s economy in 40 years time will mirror the U.S. economy of 1997, it is also unlikely to closely resemble any other economy.  Other projections, such as those by the World Bank (1994), use the input‐output tables of developed countries including the U.S.

> 26 China Energy Statistics Yearbook 1997‐1999, Tables 4‐5 and 4‐15.

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