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Estimating the Economic Impact of Intensifying Environmental Regulation in China

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Abstract

Faced with serious environmental degradation, China under Xi Jinping has pursued a massive and sustained campaign against pollution, especially air pollution, since 2013. How much of a burden has the campaign-style escalation in environmental enforcement had on manufacturing firms? Using firm-level environmental supervision records, we directly measure the environmental regulation intensity. We combine a regression discontinuity design based on Qinling–Huaihe winter heating policy with a first difference approach to estimate the causal effect of environmental regulatory enforcement on firm performance. We find that, for high air-polluting manufacturing firms, a 1% increase in the probability of being penalized for environmental violations lowers their total factor productivity by 2.5%. We also find that the campaign-style environmental enforcement has affected larger enterprises and state-owned enterprises less while deterring the entry of new firms. While we appreciate the importance of improving air quality, our research offers a more well-rounded understanding of China’s environmental enforcement initiatives and especially the costs of such enforcement on industry. Our findings suggest that the reward and punishment of local officials and of firms need to be sensitive to the costs of adjustment.

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Notes

  1. President Hu Jintao stipulated that the building of ecological civilization would henceforth be integrated with economic, political, cultural, social construction in China’s continuing quest for modernization and national rejuvenation in 2012.

  2. Article Seven, Air Ten Articles, 2013.

  3. http://www.china-embassy.org/eng/zt/18th_CPC_National_Congress_Eng/t992917.html

  4. http://china.caixin.com/2013-11-11/100603285.html

  5. http://www.xinhuanet.com//politics/2017-11/17/c_1121968691.htm

  6. An extreme smog event.

  7. http://www.sustainabletransport.org/wp-content/uploads/2017/08/National-Action-Plan-of-Air-Pollution-Control.pdf

  8. https://www.cecc.gov/resources/legal-provisions/measures-on-open-environmental-information-trial-cecc-full-translation

  9. CIED 2015 is the latest available data for researchers.

  10. This standard was 5 million RMB in 2010. To make our sample being comparable, we exclude the firms that have sales between 5 million RMB and 20 million RMB in 2010.

  11. We classify each firm as high air-polluting firm if their average industrial waste gas emission exceeds 6000 m3. The results remain robust when we use the other thresholds.

  12. Note that the regulation intensity firm i faces is constructed at the city(m)-by-sector(j) level.

  13. We geocode the firms’ latitude and longitude based on their name and the location, and calculate the closest geographic distance between each firm and the Qinling-Huaihe winter heating line.

  14. Note that we do not report the IV results for the firms’ in low air polluting industries, since the first stage results reported in Table 10 is weak: winter heating line will not cause a lot of change in regulation intensity since 2013 on these firms.

  15. It is constructed at the city by sector level.

  16. If we look at the results in Column (2), this number is 0.09/0.39 = 23%.

  17. If we use the results in column (2), the effect of environmental regulation on state-owned enterprises will be –2.85% + 2.81% = 0.049%

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Acknowledgements

We wish to thank the Institute of Public & Environmental Affairs (IPE) for assistance with the regulatory enforcement data. We are indebted to Maureen Cropper for her guidance and support throughout the project. We thank Zhansheng Cao, Qifeng Chen, and Jun Ma for their hospitality during our research in IPE. We thank Ziyang Chen, Sebastian Galiani, Koichiro Ito, Ginger Jin, Xiuyan Liu, Bin Qiu, Yana Gallen, and seminar and conference participants at AERE-MEA and Southeast University for valuable comments.

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Authors and Affiliations

  1. University of Pittsburgh, 4700 Wesley W. Posvar Hall, 230 South Bouquet Street, Pittsburgh, PA, 15260, USA

    Jiangnan Zeng

  2. University of Maryland, 3114 Tydings Hall, 7343 Preinkert Dr., College Park, MD, 20742, USA

    Qiyao Zhou

  3. University of Chicago, 5828 S. University Avenue, Chicago, IL, 60637, USA

    Dali Yang

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  1. Jiangnan Zeng
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Correspondence to Jiangnan Zeng.

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Appendix

Appendix

1.1 ITT Effect of Environmental Regulation on City-Level Variables

We exploit the ITT effect of environmental regulation on some city-level indicators under the framework of Eq. (2). The dependent variable is the change of the city-level outcomes between the average level from 2013 to 2015 and its average level from 2010 to 2012, i.e., \({\overline{var} }_{m,13-15}-{\overline{Var} }_{m,10-12}\).

Table

Table 8 ITT effect of environmental regulation on other city-level variables
Full size table

8 reports the ITT effect on the change of the share of manufacturing sector (column 1), the share of service sector (column 2), the change of GDP growth rate (column 3), the change of sulfur dioxide emission (column 4), and the change of waste water emission (column 5). The intensifying environmental regulation in the north since 2013 lowers the share of manufacturing sectors in the northern provinces by 2.7 percentage points, and consequently, increases the share of the service sectors by 2.2 percentage points. While it has moderate negative impact on the GDP growth rate (0.5 percentage points) and is statistically insignificant. The results in the first three columns echo the main results and provide some suggestive evidence of the structural transformation effect of environmental regulation. The results in column (4) indicate that the intensifying environmental regulation reduces the sulfur dioxide emission by 13%, though imprecisely estimated (p-value = 0.18). The results in column (5) serve as a falsification test: winter heating has a small impact on the change of waste water emission.

1.2 Winter Heating and Environmental Regulation in Low Air-Polluting Industries

As a robustness check of the main results reported in Table 4, we estimate the ITT effect of environmental regulation on firms in low air-polluting industries (Table

Table 9 ITT effect of environmental regulation on firms in low air-polluting industries
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9) and the estimated effect of winter heating policy on air environmental regulation intensity (Table

Table 10 Winter heating and air regulation intensity in low air-polluting industries
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10).

1.3 Sensitivity Checks

We categorize the firm level environmental supervision records into three types: air pollution, water pollution, and land pollution. Then we separately test the effect of Qinling–Huaihe winter heating policy on the change of environmental regulation intensity with firms having water pollution records and firms having land pollution records. Equation (1) is estimated with the dependent variable being changed from Δ air regulation intensity to Δ land regulation intensity and Δ water regulation intensity. The results are reported in Table

Table 11 Sensitivity checks
Full size table

11.

Table 11 shows that almost all estimated coefficients are insignificant, and the magnitude is also small. The results are consistent with panel (b) and (c) in Fig. 5, where we do not find discontinuity in the change of solid and water regulation intensity.

1.4 The Distribution of the Polluting Firms Across Winter-Heating Line

In this section, we test whether the distribution of the polluting firms are different across the Qinling–Huaihe winter heating line during the pre-treatment periods (i.e., 2010–2012). The dependent variable is a dummy equals 1 if the firm is in the polluting industry. The results are reported in Table

Table 12 Winter-heating line and polluting firms in pre-treatment periods
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12. The winter heating line does not have a significant impact on the distribution of the polluting firms during the pre-treatment periods (i.e., 2010–2012).

1.5 Industrial Sectors and Regulation Intensity

As a cross-validity test of the measure of environmental regulation intensity, we test whether firms in polluting industries are being monitored and penalized more. The results are reported in Table

Table 13 Whether firms in high air-polluting industries being penalized more?
Full size table

13. The dependent variable is a firm-by-year-level dummy equaling 1 if the firm is being monitored and penalized. The independent variable is a dummy, equals 1 if the firm is in a high air-polluting industry. We classify industries as high air-polluting if their per-firm industrial waste gas emission exceeds 6000 m3. The detailed list of high air-polluting industry can be seen in Table 14.

Table 14 Air-polluting industry
Full size table

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Zeng, J., Zhou, Q. & Yang, D. Estimating the Economic Impact of Intensifying Environmental Regulation in China. Environ Resource Econ 86, 147–172 (2023). https://doi.org/10.1007/s10640-023-00791-5

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