Abstract
Although performance targets imposed by high levels of governments are an important vertical result-oriented influence on local governments’ policy adoption, no studies have examined the impacts of performance targets on policy processes. Using the adoption of atmospheric pollutant emission standards by Chinese provinces in the 2000 to 2015 period, this study runs spatial autoregression models to investigate the effects of mandatory performance targets on environmental policy adoption processes in China. The results show that top-down performance targets can drive the adoption of atmospheric pollutant emission standards in Chinese provinces. Furthermore, the influence of vertical environmental performance targets on the adoption of local atmospheric pollutant emission standards is stronger in Chinese provinces that have adopted more similar standards in the previous period. The results show that top-down performance management shapes the policy adoption processes and policy adoption of Chinese provinces under vertical result-oriented performance target pressure is path-dependent.
Disclosure statement
The authors declare no conflict of interest.
Notes
1 Generally, previous studies have used a dummy variable (e.g., years before the 11th Five-year Plan period are assigned 0, and otherwise, 1) to measure the top-down environmental target responsibility system launched in 2006 (Liang and Langbein Citation2015; Liang and Langbein Citation2019). We acknowledge that our measure, which is based on the specific pollutant emission reduction targets in each Five-year Plan, has some drawbacks; specifically, these targets are constant for the entire Five-year Plan period. Unfortunately, the central government of China does not break its Five-year Plan targets down by year. However, our current method still has advantages over the simple dummy variable used in previous research because of the variations in the performance targets between provinces.
2 The six Inspection Bureaus are the North China Inspection Bureau (monitoring Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, and Henan), the Northeast China Inspection Bureau (monitoring Liaoning, Jilin, and Heilongjiang), the East China Inspection Bureau (monitoring Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong), the South China Inspection Bureau (monitoring Hubei, Hunan, Guangdong, Guangxi, and Hainan), the Southwest Inspection Bureau (monitoring Chongqing, Sichuan, Guizhou, Yunnan, and Tibet), and the Northwest Inspection Bureau (monitoring Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang).
3 We thank one reviewer for his/her great suggestion to use air pollution petitioning to measure this variable more accurately. Unfortunately, the China Environment Yearbooks have not separated air pollution petitions from other types of petitions since 2011.
4 One reviewer commented that the neighboring effect may to some extent coincide with influences from counterparts supervised by the same MEP Inspection Bureau. When we rerun these models by separately controlling one of these two factors, the main findings related to our theoretical hypotheses remain similar to those reported for Models 1–6 in .
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Pan Zhang
Pan Zhang ([email protected]) earned his PhD degree at the School of Public Policy and Administration at Xi’an Jiaotong University. He is currently an assistant professor at the School of International and Public Affairs at Shanghai Jiao Tong University. He also serves as a research fellow at the China Institute for Urban Governance at Shanghai Jiao Tong University and a research fellow at the Center for Chinese Local Governance Innovations at Xi’an Jiaotong University. His research interests include policy process and environmental policy.
Jiannan Wu
Jiannan Wu ([email protected]) is a distinguished professor at the School of International and Public Affairs, Shanghai Jiao Tong University, China. He currently serves as Executive Vice Director of the China Institute for Urban Governance and Director of Center for Reform, Innovation and Governance at Shanghai Jiao Tong University. His research interests include innovation, performance management, and urban governance.