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Does government information transparency contribute to pollution abatement? Evidence from 264 Chinese cities

  • Research in Environmental Governance and Innovation
  • Published:
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Abstract

Government information transparency is taken as a prominent instrument of environmental regulation in Chinese cities, especially in the current digital age. However, polluters’ strategic emission and production behaviors across cities, confronted with changing information disclosure level, might make the policy’s effect unexpected in practice. While many existing studies have explored the impact of institutions on pollution, government information disclosure only attracted little attention from empirical studies. Using the method framework of the spatial Durbin model, this study empirically investigates the impact of government information transparency on sulfur dioxide emissions with samples of 264 Chinese cities from 2005 to 2012. We find that a city’s government information transparency negatively relates to its local emission level of sulfur dioxide. Moreover, a city’s sulfur dioxide emissions positively relate to its neighboring cities’ government information transparency levels. The further calculations of marginal effects show that the average of such a direct and local impact of government information transparency outweighs the average indirect effect a city receives from its neighboring cities, making government information transparency benefits to pollution abatement in total.

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Notes

  1. For instance, the two control zones policy is implemented in 1998, and mandatory provincial targets of emission reduction are set in the Eleventh Five-Year Plan that started from 2005.

  2. For data and method of GIDI, see http://www.mofcom.gov.cn/article/zt_jxpg2016/.

  3. We thank an anonymous referee for inquiry about multi-collinearity problem. The correlation coefficients of all independent variables are below 0.7 (most are below 0.5), indicating no high correlation existed.

  4. We acknowledge an anonymous referee for suggestion on robustness check. In this paper, we tried to replace the dependent variable with the total amount of SO2 emissions (lnso2), use alternative proxy for FDI variable, and control time trends or year dummy variables. The main results are shown to be robust.

  5. We thank two anonymous referees for their suggestion about the result discussions.

  6. In Appendix, results of a spatial autoregressive model show that government information transparency of a city is positively related to that of its neighbors.

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Acknowledgments

We would like to thank the senior experimentalist Mr. Zengguang Zhong of MOE Key Laboratory of Econometrics, School of Economics at Xiamen University who kindly provides data analysis and calculation support.

Funding

This paper is supported by the National Natural Science Foundation of China (No. 72003145, 71603148, 71673230, 71303199), China Postdoctoral Science Foundation (Grant no. 2020M683437), and the Fundamental Research Funds for the Central Universities at Xiamen University (Grand no. 20720191006).

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Authors and Affiliations

  1. School of Economics and Finance, Xi’an Jiaotong University, Xi’an, 710049, China

    Liying Song, Jun Jing & Zheming Yan

  2. China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China

    Chuanwang Sun

  3. MOE Key Laboratory of Econometrics, School of Economics, Xiamen University, Xiamen, 361005, China

    Chuanwang Sun

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  1. Liying Song
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Contributions

Liying Song:Writing – review & editing. Jun Jing: Data curation, Writing – original draft. Zheming Yan: Data curation, Validation, Writing – original draft, Writing – review & editing. Chuanwang Sun: Conceptualization, Methodology, Software.

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Correspondence to Zheming Yan or Chuanwang Sun.

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Data are available from the authors upon reasonable request and with permission of CSMAR (China Stock Market & Accounting Research), CEIC, and CSTC (China Software Testing Center).

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Appendix 1

Appendix 1

To investigate the institutional spillover effect, we simply apply a spatial autoregressive (SAR) model. From Table 4, the estimated parameter ρ is significant and positive, indicating that a city’s improvement in government information transparency is positively related to its neighbors’ levels of government information transparency.

Table 4 Results of institutional spillover (explained variable: inftrans)
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Song, L., Jing, J., Yan, Z. et al. Does government information transparency contribute to pollution abatement? Evidence from 264 Chinese cities. Environ Sci Pollut Res 29, 12853–12863 (2022). https://doi.org/10.1007/s11356-020-12240-7

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