Does an anticorruption campaign deteriorate environmental quality? Evidence from China

Boying Li, Yu Hao, Chun Ping Chang*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    This study makes a first attempt to thoroughly investigate the relationship between corruption and environmental quality in China from 1995 to 2011 using province-level panel data with both linear and nonlinear approaches. First, a linear least squares dummy variable corrected estimator is utilized. Second, the panel smooth transition regression is then employed to examine a potential nonlinear relationship between corruption and environmental quality. Our main empirical evidence indicates that the relationship between corruption and CO2 emissions per capita varies by region in China. Although the anticorruption campaign significantly improves the environmental quality in western China, such an endeavor to some degree triggers pollutant emissions in eastern China and for all of China, whereas weak evidence is found in central region. These findings are also supported in our sensitivity analysis by using panel fixed effect model and a different air pollutant (i.e. SO2). Thus, corruption is a double-edged sword and the Chinese central government should customize different anticorruption policies for different regions, as those western China could further launch its own anticorruption campaign, while eastern China and all of China should keep a balance between the anticorruption campaign and environmental protection regulation. Our evidence from China offers insights for other transitional economies considering how to formulate an anticorruption policy interacting with environmental issues.

    Original languageEnglish
    Pages (from-to)67-94
    Number of pages28
    JournalEnergy and Environment
    Volume29
    Issue number1
    DOIs
    Publication statusPublished - 1 Feb 2018

    Keywords

    • CO emissions
    • China
    • Corruption
    • environmental quality
    • panel smooth transition regression

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