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Political incentives, Party Congress, and pollution cycle: empirical evidence from China

Published online by Cambridge University Press:  02 July 2020

Zhihua Tian
Affiliation:
School of Economics, Zhejiang University of Technology, Hangzhou, China
Yanfang Tian*
Affiliation:
School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai, China
*
*Corresponding author. E-mail: tianyanfang@outlook.com
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Abstract

The political incentives of local officials affect their preferences for policy options. This study examines the impact of the convening cycle of Provincial Communist Party Congresses (PCPCs) in China on pollution emission intensity. Based on the data of 281 cities and city officials from 2003 to 2014, the present study finds strong evidence of a political pollution cycle manifesting as significant increases in pollution emission intensity before PCPCs followed by visible decreases after PCPCs. PCPCs provide city officials with strong political incentives to pursue short-term economic performance before congresses, which leads to a surge in pollution emission intensity. The difference in pollution emission intensity before and after the PCPCs reveals the existence of such political incentives. The findings suggest that a significant relationship exists between the political incentives of city officials and environmental pollution. Therefore, the effective governance of environmental pollution must involve changing the incentive structure of city officials.

Information

Type
Research Article
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press
Figure 0

Table 1. Frequency distribution of city leader turnover during 2005–2014

Figure 1

Figure 1. SO2 emissions in China (2001–2014).Data source: China Statistical Yearbook.

Figure 2

Table 2. Definitions and descriptive statistics of variables

Figure 3

Table 3. Benchmark regression results

Figure 4

Table 4. Estimation of the impact of mayor turnover on pollution emission intensity

Figure 5

Table 5. Regression results of subsamples

Figure 6

Table 6. Robustness test results