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Is More Always Better? Government Attention and Environmental Governance Efficiency: Empirical Evidence from China

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  • Fengyu Wang

    (College of Business, Xuzhou University of Technology, Xuzhou 221018, China)

  • Mi Zhou

    (College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China)

  • Huansheng Yu

    (College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

In recent years, the thorough implementation of China’s green development concept has compelled local governments to devote more attention to environmental issues. This study aimed to verify whether increased government environmental attention (GEA) can sustainably ensure the implementation of environmental governance, particularly air pollution control. Using government work reports (GWRs) from local governments, this study employed machine learning methods to identify and quantify the attitudes of government officials as expressed in policy texts. A weighted dictionary method was used to quantify GEA from 2011 to 2016. The results of spatial econometric models indicated that air pollution exhibited positive spatial clustering effects across different regions, with the Yangtze River Delta and the Beijing–Tianjin–Hebei region being classified as high–high areas, while the western regions were classified as low–low areas. Baseline regression results showed that increased GEA can improve the effectiveness of pollution control, but excessive attention leads to a decline in governance efficiency. Overall, this study helps explain the unsustainability of campaign-style environmental governance and provides guidance for local governments on the rational allocation of attention when addressing environmental issues.

Suggested Citation

  • Fengyu Wang & Mi Zhou & Huansheng Yu, 2024. "Is More Always Better? Government Attention and Environmental Governance Efficiency: Empirical Evidence from China," Sustainability, MDPI, vol. 16(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7146-:d:1460070
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    References listed on IDEAS

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