Does central supervision enhance local environmental enforcement? Quasi-experimental evidence from China

Bing Zhang, Xiaolan Chen and Huanxiu Guo

Journal of Public Economics, 2018, vol. 164, issue C, 70-90

Abstract: This paper draws on a natural experiment generated by the National Specially Monitored Firms (NSMF) program in China to evaluate the effectiveness of central supervision at improving local environmental enforcement. We explore a unique firm-level Chinese Environmental Statistics dataset and utilize a regression discontinuity design to assess the impact of central supervision through the NSMF program on an industrial firm's chemical oxygen demand (COD) emissions. The results suggest that central supervision significantly reduces industrial COD emissions by at least 26.8%. These results highlight the substantial room for improvement in Chinese environmental regulations via central supervision. A more flexible environmental decentralization regime and comprehensive central supervision are thus recommended for future reforms.

Keywords: Environmental federalism; Central supervision; Water pollution; Regression discontinuity design; China (search for similar items in EconPapers)
JEL-codes: D22 O53 Q28 Q58 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (145)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047272718300914
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:164:y:2018:i:c:p:70-90

DOI: 10.1016/j.jpubeco.2018.05.009

Access Statistics for this article

Journal of Public Economics is currently edited by R. Boadway and J. Poterba

More articles in Journal of Public Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().