Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10688223 | Journal of Cleaner Production | 2015 | 25 Pages |
Abstract
As China's economy has grown rapidly in recent years, China's environmental pollution has become increasingly significant. Among all the traditional pollutants, sulfur dioxide has been monitored by the Chinese government since the 1990s. In this study, China's city-level panel data between 2002 and 2012 are utilized to investigate the existence of convergence in per capita sulfur dioxide emissions across Chinese cities. The conventional estimation methods for β-convergence suffer from an endogeneity problem and therefore produce biased results. To address the endogeneity problem and allow for dynamics, dynamic panel data estimators are utilized, and the static estimation results are conducted as a robustness check. In addition, the influential factors for convergence are examined. The empirical results indicate that, in the chosen sample period, there were absolute and conditional convergences in per capita sulfur dioxide emissions across cities within the whole nation as well as in the eastern, western and central regions of China. Because per capita Gross Domestic Product and the ratio of secondary industry to Gross Domestic Product are both positively related with per capita sulfur dioxide emissions, higher income per capita and greater importance of the secondary industry would cause the convergence speed to be lower. Therefore, the most straightforward policy implication for the empirical results is that the policies for controlling sulfur dioxide emissions should be regionally differentiated: for cities with high sulfur dioxide emissions per capita, the regulation could be tight, as the emissions would decrease faster because convergence exists; however, for cities with low sulfur dioxide emissions per capita, the target for reducing emissions should not be excessively aggressive.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Yu Hao, Qianxue Zhang, Ming Zhong, Baihe Li,