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Environmental policy innovation in China and examining its dynamic relations with air pollution and economic growth using SEM panel data

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Abstract

Along with monitoring air pollution level and rapid economic growth in China, the government has paid attention to the environmental policy innovation (EPI) capacity of local governments. However, scholarly research has not yet clarified the ability of local governments in EPI and its related drivers and impacts. This study explores the dynamic relations between EPI, air pollution, and the economy for the first time, using the simultaneous equation model (SEM) in China during 2006–2015 across 30 provinces. To calculate EPI, this study introduces the comprehensive concept of policy innovation, consisting of invention, diffusion, and evaluation. The results show that EPI is strongly promoted by air pollution; however, promoting EPI alone cannot decrease air pollution. These results would vary among eastern and western provinces. Economic growth has a significant positive effect on EPI and can significantly reduce air pollution. This study suggests policymakers strengthen EPI in order to achieve a balance between air pollution and economic growth.

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Notes

  1. SEM, using maximum likelihood (ML) estimation, provides less biased and more efficient results compared to the generalized method of moments (GMM) (Allison et al. 2017).

  2. EPI is the unobserved endogenous variable which is defined as a latent variable with three measurements.

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Acknowledgments

The authors express sincere gratitude to three anonymous reviewers for their helpful comments to improve the quality of this paper.

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  1. School of Management, University of Science and Technology of China, Hefei, 230026, China

    Mohaddeseh Azimi, Feng Feng & Chongyang Zhou

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  1. Mohaddeseh Azimi
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Correspondence to Mohaddeseh Azimi or Feng Feng.

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Azimi, M., Feng, F. & Zhou, C. Environmental policy innovation in China and examining its dynamic relations with air pollution and economic growth using SEM panel data. Environ Sci Pollut Res 27, 9987–9998 (2020). https://doi.org/10.1007/s11356-020-07644-4

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