Economic and Environmental Impacts of Carbon Pricing Policies in China

Abstract

Carbon policies are important not only for mitigating global climate change, but also for controlling local and regional air pollution. The large regional disparities in economic development and air pollution across China calls for regionally differentiated policies. Previous studies have not estimated the air quality co-benefits of unbalanced carbon policies across China based on the systematically analysis on factors that influence energy and air pollutant emissions. Here this thesis analyzes factors that influence energy use from 1953 to 2017 based on decomposition analysis using the Logarithmic Mean Divisia Index (LMDI). Estimates of the economic and social factors that contribute to provincial air pollutant emissions during the period 2005-2015, such as nitrogen oxides (NOX), particulate matter with diameter less than 2.5 μm (PM2.5), particulate matter with diameter less than 10 μm (PM10), sulfur dioxide (SO2), and volatile organic compounds (VOC), are based on spatial econometric regression models. A multi-regional dynamic computable general equilibrium (CGE) model covering 30 provinces is used for policy analysis. Based on this model, this thesis designs one national carbon policy (NP) which aims to achieve China’s 2030 national CO2 abatement target. Three regional policies, with the same policy stringency as the national policy, are applied to eastern China (EP), the Jiangsu-Shanghai-Zhejiang area (JSZP), and the Beijing-Tianjin-Hebei area (BTHP). Given regional disparities, this thesis also analyses four subnational differentiated policies that price carbon based on historical provincial per capita PM2.5 concentrations (S1), per capita GDP (S2), per capita CO2 emissions (S3), and energy use per unit GDP (S4). These differentiated carbon policies are aimed at achieving China’s 2030 national CO2 abatement targets, and their accumulated CO2 emissions from 2030 to 2050 are approximately equal to the uniform national policy. Additionally, the “extend response surface model technologies” (ERSM) is used to estimate regional PM2.5 concentrations as air quality co-benefits in 2050. The results show that the output effect played a significant role in increasing China’s energy use during 1953-2017, followed by the emission effect, the per capita III emission effect and the energy mix effect, while the energy intensity effect plays a significant role in reducing energy use. Air pollutant emissions across China have significant spatial dependence and strong spillover effects. With the exception of SO2, NOX, PM10, VOC, and PM2.5 exhibit an inverted U-shaped relationship with per capita GDP. Air pollutant emissions are positively associated with coal consumption. Industrial structure, FDI, and traffic intensity, have significant and positive effects on all emitted air pollutant emissions, while forest coverage has negative effects. Regional policies (EP, JSZP, and BTHP) are as effective in reducing CO2 emissions in their targeted regions as the national policy. However, they lead to an increase in CO2 emissions in untargeted regions (the so-called “emissions leakage”). The CO2 leakage rates, which depend on the policy spatial coverage, are 4%, 13%, and 65% for EP, JSZP, and BTHP, respectively, in 2050. Compared with CO2, changes in air pollutant emissions, exhibit a similar pattern under all policy scenarios, but the magnitude of change is significantly smaller. This is because carbon and air pollutant emissions are mainly related to fuel combustion. The higher-than-average carbon price over more polluted provinces in the S1 scenario results in twice as much reduction in CO2 and air pollutant emissions in 2050 as that in the NP scenario. In these strictly regulated provinces, reductions in PM2.5 concentrations under NP are 7%–11%, while S1 results in a larger decrease of 9%–18%. Moreover, S1 eliminates high PM2.5 exposure of over 45 µg/m3 which 12% people suffer from in NP. The S2-S4 policies also help to alleviate heavy PM2.5 pollution compared with NP, but they are not as effective as S1. Furthermore, S1 substantially reduces regional disparity in PM2.5 pollution and hence improves environmental equity relative to other policies. Transportation, thermal power, and some energy intensive industries are the three largest contributors to reductions of CO2 and air pollutant emissions. Regional carbon policy is effective in reducing CO2 and air pollutant emissions in the targeted regions, and extending the spatial coverage or increasing policy stringency can largely inhibit emissions leakage. Furthermore, subnational carbon policies differentiated according to PM2.5 concentrations are a promising policy instrument to mitigate severe pollution and promote environmental equity. IV Key Words: energy use; air pollution; spillover effect; emissions leakage; co-benefits; PM2.5; environmental equity; regional carbon policy; subnationally differentiated carbon policy; decomposition analysis; spatial econometrics; multi-regional CGE model.