The spatial dynamic relationship between haze pollution and economic growth: new evidence from 285 prefecture-level cities in China

Abstract

Numerous studies have examined the correlation between haze pollution and economic growth, but few have focused on the bilateral causality and spatial dependence of the relationship simultaneously. With a recent, large sample of 285 Chinese prefecture-level cities over the period from 1998 to 2016, the dynamic relationship between economic growth and PM_2.5 concentration was investigated in China and different geographical regions via the generalized spatial three-stage least squares method (GS3SLS). In addition, the spatial spillover effects of PM_2.5 concentration and economic growth were explored, and the socioeconomic factors of the feedback effects of haze pollution on economic growth were identified. The results indicate the following: (1) There is an inverted U-shaped relationship between economic growth and haze pollution, and haze pollution can, in turn, inhibit economic growth; (2) haze pollution and economic growth have an obvious spatial spillover effect that closely links neighboring areas, such that haze pollution has a clear and significant positive spatial spillover, and the spatial effect of economic growth depends on the regional characteristics; (3) environmental regulation has significance in explaining the inhibitory effect of haze pollution on economic growth; and (4) heterogeneity by geographical region and urban agglomeration is observed in the relationship between haze pollution and economic growth. Therefore, it is urgent to control haze pollution and avoid oversimplified and crude environmental regulation measures that may damage economic growth. Multiregional joint governance, systematic environmental regulation and technical environmental regulation should be strengthened.

Keywords:

• economic growth
• PM_2.5
• spatial simultaneous equation model
• environmental regulation

Acknowledgements

The authors are also very grateful to five anonymous reviewers and Managing Editor Dr. Neil Powe for their insightful comments that helped us sufficiently improve the quality of this paper. The usual disclaimer applies.

Supplemental data

Supplemental data for this article can be accessed here.

Notes

1 Data source: National Bureau of Statistics of China, http://www.stats.gov.cn/.

2 Data source: China Economic and Social Development Statistical Database, https://data.cnki.net/Yearbook/.

3 A brief introduction to the method of GS3SLS can be found in Appendix B (online supplemental data).

4 Data source: NASA, Socioeconomic data and applications center (SEDAC), https://sedac.ciesin.columbia.edu/.

5 For a detailed discussion of the reasons for choosing the control variables in the haze pollution equation, please refer to the interpretations in Appendix C (online supplemental data).

6 Data source: National Meteorological Center, http://data.cma.cn/site

7 The estimation results analysis of control variables in the haze pollution equation of single equation, SEM and spatial SEM are also given in Appendix D (online supplemental data).

8 The methods and results of the robustness test are reported in Appendix E (online supplemental data).

Additional information

Funding

The authors acknowledge financial support from the National Natural Science Foundation of China (71761137001, 72073010, 71403015, 71521002), the key research program of the Beijing Social Science Foundation (17JDYJA009), the Beijing Natural Science Foundation (9162013), the National Key Research and Development Program of China (2016YFA0602801, 2016YFA0602603), and the Special Fund for Joint Development Program of the Beijing Municipal Commission of Education.