Cao, Jing

We present evidence on the distortions that arise from imperfect regulations compared with market allocation mechanisms. Using a triple difference strategy, we evaluate the effectiveness of the Energy-Saving Generation Dispatch reform in China, which aims to allocate more generating hours to power plants with higher energy efficiency. We find that the new dispatch rule improved resource allocation within provinces compared with the previous equal-share dispatch rule. However, despite these improvements, the reform fell short of its intended goals because of the failure to strictly implement the merit order based on real-time coal consumption rates. We demonstrate how the lack of compensation for losers, technical requirements for grid stability, the existence of multiple goals, and information costs contribute to imperfect regulation.

Climate actions have focused on CO2 mitigation and only some studies of China consider non-CO2 greenhouse gases (GHGs), which account for nearly 18% of gross GHG emissions. The economy-wide impact of mitigation covering CO2 and non-CO2 GHGs in China, has not been comprehensively studied and we develop a multi-sector dynamic model to compare the impact of CO2-only mitigation with a multi-GHG mitigation policy that also price non-CO2 GHGs. We find that the multi-GHG approach significantly reduces the marginal abatement cost and economic loss to reach the same level of GHG emissions (measures as 100 year global warming potential) compared to a CO2-only scenario. By 2060, multi-gas mitigation can reduce the tax rate by 15.44% and improve real gross domestic product (GDP) by 0.41%. The aggregate gain brought by multi-GHG mitigation are robust to various pathways and but vary across periods and sectors.

This paper examines the effects of biomass power plants (BPPs) on farmers’ use of agricultural fires for land clearance in China from 2001 to 2019. We show that the entry of BPPs leads to a significant reduction of agricultural fires by 14%. Farmers near BPPs display stronger responses, leading to a more significant reduction in straw burning, especially during high agricultural fire seasons. The notable decline in agricultural fires is likely driven by economic incentives provided by BPPs to farmers for collecting crop straw from their land. Additionally, straw-burning bans have limited effectiveness in reducing total agricultural fires, but they appear to reduce straw burning during nighttime, when the monitoring of agricultural fires is easier. We also provide evidence that local air quality has markedly improved, resulting in substantial health benefits that surpass the social benefits of reducing carbon emissions.

Facing the dual challenges of climate change and air pollution, China has made great efforts to explore the co-control strategies for the both. We assessed the benefits of carbon and pollution control policies on air quality and human health, with an integrated framework combining an energy-economic model, an air quality model and a concentration–response model. With a base year 2015, seven combined scenarios were developed for 2030 based on three energy scenarios and three end-of-pipe control ones. Policy-specific benefits were then evaluated, indicated by the reduced emissions, surface concentrations of major pollutants, and premature deaths between scenarios. Compared to the 2030 baseline scenario, the nationwide PM2.5- and O3-related mortality was expected to decline 23% or 289 (95% confidence interval: 220–360) thousand in the most stringent scenario, and three quarters of the avoided deaths were attributed to the end-of-pipe control measures. Provinces in heavily polluted and densely populated regions would benefit more from carbon and pollution control strategies. The population fractions with PM2.5 exposure under the national air quality standard (35 μg/m3) and WHO guideline (10 μg/m3) would be doubled from 2015 to 2030 (the most stringent scenario), while still very few people would live in areas with the WHO guideline achieved for O3 (100 μg/m3). Increased health impact of O3 suggested a great significance of joint control of PM2.5 and O3 in future policy-making.

We conduct a multi-model comparison of a carbon tax policy in China to examine how different models simulate the impacts in both near-term 2020, medium-term 2030, and distant future 2050. Though Top-down computable general equilibrium(CGE) models have been applied frequently on climate or other environmental/energy policies to assess emission reduction, energy use and economy-wide general equilibrium outcomes in China, the results often vary greatly across models, making it challenging to derive policies. We compare 8 China CGE models with different characteristics to examine how they estimate the effects of a plausible range of carbon tax scenarios – low, medium and high carbon taxes.. To make them comparable we impose the same population growth, the same GDP growth path and world energy price shocks. We find that the 2030 NDC target for China are easily met in all models, but the 2060 carbon neutrality goal cannot be achieved even with our highest carbon tax rates. Through this carbon tax comparison, we find all 8 CGE models differ substantially in terms of impacts on the macroeconomy, aggregate prices, energy use and carbon reductions, as well as industry level output and price effects. We discuss the reasons for the divergent simulation results including differences in model structure, substitution parameters, baseline renewable penetration and methods of revenue recycling.

This paper provides retrospective firm-level evidence on the effectiveness of China’s carbon market pilots in reducing emissions in the electricity sector. We show that the carbon emission trading system (ETS) has no effect on changing coal efficiency of regulated coal- fired power plants. Although we find a significant reduction in coal consumption associated with ETS participation, this reduction was achieved by reducing electricity production. The output contraction in the treated plants is not due to their optimizing behavior but is likely driven by government decisions, because the impacts of emission permits on marginal costs are small relative to the controlled electricity prices and the reduction is associated with financial losses. In addition, we find no evidence of carbon leakage to other provinces, but a significant increase in the production of non-coal-fired power plants in the ETS regions. 

Multi-sector general equilibrium models are used to simulate the effects of environmental policies on industry output and consumption at disaggregated levels. The specification of household demand in such models often use simpler forms such as CES or Linear Expenditure Systems since there are few estimates of more flexible systems. We estimate a 2-stage translog utility function that explicitly accounts for detailed energy expenditures to allow us to capture the price and income effects more accurately than these simpler forms. We incorporate this into a China growth model to simulate the effects of a carbon price to achieve the government targets for the Climate Change (Paris) agreements.

Policy simulation results of Computable General Equilibrium (CGE) models largely hinge on the choices of substitution elasticities among key input factors. Currently, most CGE models rely on the common elasticities estimated from aggregated data, such as the GTAP model elasticity parameters. Using firm level data, we apply the control function method to estimate CES production functions with capital, labor and energy inputs and find significant heterogeneity in substitution elasticities across different industries. Our capital-labor substitution elasticities are much lower than the GTAP values while our energy elasticities are higher. We then incorporate these estimated elasticities into a CGE model to simulate China's carbon pricing policies and compare with the results using GTAP parameters. Our less elastic K-L substitution lead to lower base case GDP growth, but our more elastic energy substitution lead to lower coal use and carbon emissions. In the carbon tax policy exercises, we find that our elasticities lead to easier reductions in coal use and carbon emissions.

The falling projections of working-age population in China has led to predictions of much slower economic growth. We consider three mechanisms that could contribute to higher effective labor supply growth – further improvement in educational attainment due to cohort replacement and rising college enrollment, improvement in aggregate labor quality due to urbanization, and higher labor force participation due to later retirement. We find that these factors result in a projected growth rate of effective labor input of 0.40% for 2015-2030 compared to -0.60% for working age population. As a result, the projected growth rate of GDP will be 5.80% for 2015-2030 compared to 5.23% if these factors are ignored.

There are few comprehensive studies of household consumption in China due to data restrictions. This prevents the calculation of inequality indices based on consumption. Secondly, this makes a comprehensive analysis of policies that affect consumption difficult; economy-wide models used for analysis often have to employ simple consumption forms with unit income elasticities. We estimate a translog demand system distinguished by demographic characteristics, giving price and income elasticities that should be useful for policy analysis. We estimate separate functions for urban and rural households using household expenditure data and detailed commodity prices (1995-2006). This allows future analysis of social welfare and inequality based on consumption to supplement existing studies based on income. To illustrate an application of the model, we project consumption composition based on projected prices, incomes and demographic changes – aging, education improvement and urbanization.

China is introducing a national carbon emission trading system (ETS), with details yet to be finalized. The ETS is expected to cover only the major emitters but it is often argued that a more comprehensive system will achieve the emission goals at lower cost. We first examine an ETS that covers both electricity and cement sectors and consider an ambitious cap starting in 2017 that will meet the official objective to reduce the carbon-GDP intensity by 60-65% by 2030 compared to 2005 levels. The two ETS-covered industries are compensated with an output-based subsidy to represent the intention to give free permits to the covered enterprises. We then consider a hybrid system where the non-ETS sectors pay a carbon tax and share in the CO₂ reduction burden. Our simulations indicate that hybrid systems will achieve the same CO₂ goals with lower permit prices and GDP losses. We also show how auctioning of the permits improves the efficiency of the ETS and the hybrid systems. Finally, we find that these CO₂ control policies are progressive in that higher incomes households bear a bigger burden.

Based on econometric estimation using data from the Chinese Urban Household Survey, we develop a preferred forecast range of 85–143 percent growth in residential per capita electricity demand over 2009–2025. Our analysis suggests that per capita income growth drives a 43% increase, with the remainder due to an unexplained time trend. Roughly one-third of the income-driven demand comes from increases in the stock of specific major appliances, particularly AC units. The other two-thirds comes from non-specific sources of income-driven growth and is based on an estimated income elasticity that falls from 0.28 to 0.11 as income rises. While the stock of refrigerators is not projected to increase, we find that they contribute nearly 20 percent of household electricity demand. Alternative plausible time trend assumptions are responsible for the wide range of 85–143 percent. Meanwhile we estimate a price elasticity of demand of −0.7. These estimates point to carbon pricing and appliance efficiency policies that could substantially reduce demand.

We estimate China urban household energy demand as part of a complete system of consumption demand so that it can be used in economy-wide models. This allows us to derive cross-price elasticities unlike studies which focus on one type of energy. We implement a two-stage approach and explicitly account for electricity, domestic fuels and transportation demand in the first stage and gasoline, coal, LPG and gas demand in the second stage. We find income inelastic demand for electricity and home energy, but the elasticity is higher than estimates in the rich countries. Demand for total transportation is income elastic. The price elasticity for electricity is estimated to be −0.5 and in the range of other estimates for China, and similar to long-run elasticities estimated for the U.S.

China has set a goal of reducing its CO₂ intensity of GDP by 60–65% from the 2005 level in 2030 as its nationally determined contribution (NDC) under the Paris Climate Change Agreement. While the government is considering series of market and nonmarket measures to achieve its target, this study assesses the economic consequences if the target were to meet through a market mechanism, carbon tax. We used a dynamic computable general equilibrium model of China for the analysis. The study shows that the level of carbon tax to achieve the NDC target would be different depending on its design features. An increasing carbon tax that starts at a small rate in 2015 and rises to a level to meet the NDC target in 2030 would cause smaller GDP loss than the carbon tax with a constant rate would do. The GDP loss due to the carbon tax would be smaller when the tax revenue is utilized to cut existing distortionary taxes than when it is transferred to households as a lump-sum rebate.

We explore how water pollution policy reforms in China could reduce industrial wastewater pollution with minimum adverse impact on GDP growth. We use a multi-sector dynamic Computable General Equilibrium (CGE) model, jointly developed by Harvard University and Tsinghua University, to examine the long-term impact of pollution taxes. A firm-level dataset of wastewater and COD discharge is compiled and aggregated to provide COD-intensities for 22 industrial sectors. We simulated the impact of 4 different sets of Pigovian taxes on the output of these industrial sectors, where the tax rate depends on the COD-output intensity. In the baseline low rate of COD tax, COD discharge is projected to rise from 36 million tons in 2018 to 48 million in 2030, while GDP grows at 6.9% per year. We find that raising the COD tax by 8 times will lower COD discharge by 1.6% by 2030, while a high 20-times tax will cut it by 4.0%. The most COD-intensive sectors—textile goods, apparel, and food products—have the biggest reduction in output and emissions. The additional tax revenue is recycled by cutting existing taxes, including taxes on profits, leading to higher investment. This shift from consumption to investment leads to a slightly higher GDP over time.

Vehicles have recently overtaken coal to become the largest source of air pollution in urban China. Research on mobile sources of pollution has foundered due both to inaccessibility of Chinese data on health outcomes and strong identifying assumptions. To address these, we collect daily ambulance call data from the Beijing Emergency Medical Center and combine them with an idiosyncratic feature of a driving restriction policy in Beijing that references the last digit of vehicles’ license plate numbers. Because the number 4 is considered unlucky by many in China, it tends to be avoided on license plates. As a result, days on which the policy restricts license plates ending in 4 unintentionally allow more vehicles in Beijing. Leveraging this variation, we find that traffic congestion is indeed 22% higher on days banning 4 and that 24-hour average concentration of NO₂ is 12% higher. Correspondingly, these short term increases in pollution increase ambulance calls by 12% and 3% for fever and heart related symptoms, while no effects are found for injuries. These findings suggest that traffic congestion has substantial health externalities in China but that they are also responsive to policy. 

Understanding the rapidly rising demand for energy in China is essential to efforts to reduce the country's energy use and environmental damage. In response to rising incomes and changing prices and demographics, household use of various fuels, electricity and gasoline has changed dramatically in China. In this paper, we estimate both income and price elasticities for various energy types using Chinese urban household micro-data collected by National bureau of Statistics, by applying a two-stage budgeting AIDS model. We find that total energy is price and income inelastic for all income groups after accounting for demographic and regional effects. Our estimated electricity price elasticity ranges from - 0.49 to -0.57, gas price elasticity ranges from -0.46 to -0.94, and gasoline price elasticity ranges from -0.85 to -0.94. Income elasticity for various energy types range from 0.57 to 0.94. Demand for coal is most price and income elastic among the poor, whereas gasoline demand is elastic for the rich.