Cao, Jing

Submitted
Jing Cao, Mun S. Ho, and Wenhao Hu. Submitted. “Energy Consumption of Urban Households in China.” China Economic Review.
Jing Cao, Mun S. Ho, Wenhao Hu, and Dale Jorgenson. Submitted. “Household Consumption in China.” China Economic Review.
Jing Cao, Mun S. Ho, Wenhao Hu, and Dale W. Jorgenson. Submitted. “Urban Household Consumption in China.” European Economic Review.
Jing Cao, Mun S. Ho, Wenhao Hu, Dale W. Jorgenson, and Qiong Zhang. Submitted. “Welfare and Inequality Measures for China Based on Consumption.” International Economic Review.
2019
Jing Cao, Mun S. Ho, Dale W. Jorgenson, and Chris P. Nielsen. 2019. “China’s Emissions Trading System and an ETS-Carbon Tax Hybrid.” Energy Economics, 81, Pp. 741-753. Publisher's VersionAbstract
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.
Appendix
Jing Cao, Mun Sing Ho, Yating Li, Richard G. Newell, and William A. Pizer. 2019. “Chinese residential electricity consumption estimation and forecast using micro-data.” Resource and Energy Economics, 56, Pp. 6-27. Publisher's VersionAbstract
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.
2018
Govinda R. Timilsina, Jing Cao, and Mun S. Ho. 2018. “Carbon Tax for Achieving China's NDC: Simulations of Some Design Features Using a CGE Model.” Climate Change Economics, 9, 3. Publisher's VersionAbstract
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.
Xiaolin Guo, Mun Sing Ho, Liangzhi You, Jing Cao, Yu Fang, Taotao Tu, and Yang Hong. 2018. “Industrial Water Pollution Discharge Taxes in China: A Multi-Sector Dynamic Analysis.” Water, 10, 12, Pp. 1742. Publisher's VersionAbstract
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.
water-10-01742-v2.pdf
2017
Nan Zhong, Jing Cao, and Yuzhu Wang. 2017. “Traffic congestion, ambient air pollution and health: Evidence from driving restrictions in Beijing.” Journal of the Association of Environmental and Resource Economists, 4, 3, Pp. 821–856. Publisher's VersionAbstract

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. 

2016
Jing Cao, Mun S. Ho, and Huifang Liang. 2016. “Household energy demand in urban China: Accounting for regional prices and rapid economic change.” The Energy Journal, 37. Publisher's VersionAbstract

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.

2013
Jing Cao, Mun S Ho, and Dale W Jorgenson. 2013. “The Economics of Environmental Policies in China.” In Clearer Skies Over China: Reconciling Air Quality, Climate, and Economic Goals, Pp. 329-372. Cambridge, MA: MIT Press. Publisher's Version
Chris P Nielsen, Mun S Ho, Jing Cao, Yu Lei, Yuxuan Wang, and Yu Zhao. 2013. “Summary: Carbon Taxes for 2013-2020.” In Clearer Skies Over China: Reconciling Air Quality, Climate, and Economic Goals, Pp. 103-157. Cambridge, MA: MIT Press. Publisher's Version
Chris P Nielsen, Mun S Ho, Yu Zhao, Yuxuan Wang, Yu Lei, and Jing Cao. 2013. “Summary: Sulfur Mandates and Carbon Taxes for 2006-2010.” In Clearer Skies Over China: Reconciling Air Quality, Climate, and Economic Goals, Pp. 59-102. Cambridge, MA: MIT Press. Publisher's Version
2012
Jing Cao, Mun S Ho, and Dale W Jorgenson. 2012. “An integrated assessment of the economic costs and environmental benefits of pollution and climate control.” In The Chinese Economy: A New Transition, edited by Masahiko Aoki. London: Palgrave Macmillan. Publisher's Version
2009
Jing Cao, Richard Garbaccio, and Mun S Ho. 2009. “China's 11th Five-Year Plan and the environment: Reducing SO2 emissions.” Review of Environmental Economics and Policy, 3, 2, Pp. 189-208. Publisher's Version
Jing Cao, Mun S Ho, Dale W Jorgenson, Rouen Ren, Linlin Sun, and Ximing Yue. 2009. “Industrial and aggregate measures of productivity growth in China, 1982-2000.” Review of Income Wealth , 55, s1, Pp. 485-513. Publisher's Version
Jing Cao, Mun S Ho, and Dale W Jorgenson. 2009. “The local and global benefits of green tax policies in China.” Review of Environmental Economics and Policy, 3, 2, Pp. 231-250. Publisher's Version