4.7 Article

Transformation of pollution control and green development: Evidence from China's chemical industry

期刊

JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 275, 期 -, 页码 -

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2020.111246

关键词

Air pollution prevention and control action plan; Green total factor productivity; Meta-frontier method; Global Malmquist index; Difference-in-differences model; Chemical sub-industries

资金

  1. National Natural Science Foundation of China [71673304, 71633006, 71874207, 71373283]
  2. Postgraduate Innovation Research Major Project in Hunan Province [CX 2018B046]
  3. Fundamental Research Funds for the Central Universities of Central South University [2019zzts208]

向作者/读者索取更多资源

The Air Pollution Prevention and Control Action Plan (APCP) is an important pollution control policy formulated by the Chinese government to promote green development. However, there are few studies of its impact. Based on a province-level panel dataset from 2007 to 2017, we use a three-hierarchy meta-frontier slack-based data envelopment analysis (SBM-DEA) model and the global Malmquist (GM) index to estimate the green total factor productivity (GTFP) of China's chemical sub-industries. Next, we use the difference-in-differences (DID) method to explore the effect of the implementation of the APCP in 2013 on the GTFP of China's chemical industry. We find an increasing trend in the GTFP of China's chemical industry between 2007 and 2017. A decomposition of the change in GTFP shows that technical progress was the main driving factor and management inefficiency was the main inhibitory factor. The APCP had a significant positive effect on GTFP. Overall, in the post-implementation period, the increase in the GTFP of the treatment group was approximately 7.9% greater than in the control group. The APCP also significantly improved the technical efficiency change index. A dynamic effect analysis shows that the positive effect of the APCP on GTFP had an inverted U-shaped trend. A series of robustness tests, including counterfactual, re-grouping, and quasi-DID tests, suggest that our findings are reliable. In addition, changes in exports, capital deepening, and energy prices all significantly improved the GTFP of China's chemical industry.

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