Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 556

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Open access
Zhang, Bing, Chen, Xiaolan, Guo, Huanxiu·2018·Journal of Public Economics

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jpubeco.2018.05.009

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Metadata

Authors
Zhang, B., Chen, X., & Guo, H.
Venue
Journal of Public Economics
Publisher
Elsevier BV
Published
2018-08
Volume
164
Pages
70-90
ISSN
0047-2727 (ISSN-L)
OpenAlex
W2806819555
Cited by
556
References
3
FWCI
41.06
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Does central supervision enhance local environmental enforcement? Quasi-experimental evidence from China. https://app.cochat.ai/papers/10.1016/j.jpubeco.2018.05.009
The paper
Zhang, B., Chen, X., & Guo, H. (2018). Does central supervision enhance local environmental enforcement? Quasi-experimental evidence from China. Journal of Public Economics. https://doi.org/10.1016/j.jpubeco.2018.05.009

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.