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Understanding government pollution control actions and well-being: Evidence from micro individuals

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Abstract

The Chinese government took many measures to improve air quality in recent years, and the Atmosphere Ten Articles Plan (ATAP) was one of the most significant air pollution control policies. We used the staggered difference-in-differences method to determine the causal link between air quality improvement and people's subjective well-being (SWB) by utilizing the quasi-natural experiment of different Chinese cities implementing the ATAP. We found that ATAP implementations significantly improved people’s SWB, as supported by multiple robustness tests, such as excluding spillover effects and self-selection effects and placebo tests. Moreover, potential mechanisms by which ATAP implementations affected people’s SWB included improving physical and mental health status, alleviating emotional problems, reducing insomnia, and promoting exercise and outdoor activities. Furthermore, heterogeneity effect results suggested that ATAP implementations were more beneficial to some socially vulnerable groups and this SWB improvement effect was greater for residents living in urban areas. These findings added to the body of literature on how environmental policies affect people's welfare and had important implications for better air quality improvement policies in China and other developing countries.

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Fig. 1

Source: China Ecological and Environmental Status Bulletin (2013–2018) and Atmospheric Composition Analysis Group of Dalhousie University

Fig. 2

Source: China Ecological and Environmental Status Bulletin

Fig. 3

Source: China Ecological and Environmental Status Bulletin, daily city air quality data published by the Ministry of Ecology and Environmental of China, and Atmospheric Composition Analysis Group of Dalhousie University

Fig. 4

Source: China Ecological and Environmental Status Bulletin

Fig. 5

Similar content being viewed by others

Data availability

The authors do not have permission to share data.

Notes

  1. The ATAP is also known as the “Air Pollution Prevention and Control Action Plan” in China (Luo & Li 2018; Yu et al. 2022).

  2. In this paper, cities are used to refer to administrative units like prefecture-level cities and provincial municipalities. For most cities in China, they include both the urban areas and rural areas under their jurisdiction.

  3. This is the reason why this plan is called the “Atmosphere Ten Articles.”

  4. The 2018 China Ecological and Environmental Status Bulletin no longer counted 74 main cities, but 161 cities instead. Thus, we manually counted the average PM2.5 concentrations for 74 cities in 2018. These 74 main cities are the first batch of key monitoring cities, which cover province-level municipalities, sub-provincial cities, provincial capitals, planned cities, and regional key cities (like the BTH city cluster). The second batch of 87 cities were further included in the scope of key monitoring.

  5. The SWB and happiness are synonymous expressions in this paper.

  6. These 16 cities were implemented the ATAP earlier than September 2013, because the air pollution control program had already started in the “12th Five-Year Plan for Prevention and Control of Air Pollution in Key Regions,” which is considered as the predecessor of the ATAP. Therefore, according to Luo and Li (2018), these cities' actions for implementing the ATAP predated the release of the State Council of China. In the analysis below, we removed these cities for a robustness test.

  7. In the later robustness tests, we further confirmed the validity of the treatment and control groups by excluding the spillover and self-selection effects.

  8. One of the main advantages of assigning the ATAP in this way is that it allows for better parallel trend tests. Of course, this relatively crude setup also faces risks; therefore, in the empirical analysis, we perform two robustness tests to confirm that this setup method has no great impact on our main results. In addition, since the CLDS investigation event is in July or August, this setting does not cause a time mismatch between ATAP and people SWB.

  9. Average PM2.5 concentration data for each city is from the Washington University in St. Louis.

  10. The estimation results for the other values of SWB are shown in Table 10 in the Appendix.

  11. In this case, only those cities that happen to implement ATAP in that year will be treated as set to 1 (full treatment). For example, if a city implemented ATAP in July 2013, the treatment variable for this city in 2013 would be 0.5.

  12. Industrial structure is measured by the share of tertiary industry in the secondary industry; financial development is determined by the ratio of total deposits and loans to GDP. Housing prices data are obtained from https://www.gotohui.com, and the China City Statistical Yearbook is where the remaining macro variables.

  13. We used the respondents' place of residence (urban or rural) as a criterion for classification.

  14. In the full sample, the mean age was similarly close to 45 years old.

  15. Since the normal value of BMI for individuals is between 20 and 25, we excluded samples with BMI less than 20 (too thin to be healthy); thus, the larger the value of BMI, the fatter (unhealthier) respondents are.

  16. For example, if the respondent does not match any of items for emotional stability, set to 0. If one of them matches, set to 1, and if all of them match, set to 9.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 72304102), the Ministry of education of Humanities and Social Science project (No. 23YJCZH110), and the Philosophy and Social Science Program Discipline Co-construction Project of Guangdong Province (No. GD23XYJ68).

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  1. College of Economics and Management, South China Agricultural University, No. 483, Wushan Road, Tianhe District, Guangzhou, Guangdong, China

    Chunkai Zhao

  2. College of Economics and Management, Southwest University, No. 2, Tiansheng Road, Beibei District, Chongqing, China

    Xing Li

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  1. Chunkai Zhao
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Appendix

Appendix

Tables 9, 10, 11, 12, 13 and 14

Table 9 Variable definitions
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Table 10 Marginal effects of SWB in column (4) of Table 2
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Table 11 Robustness checks: alternative specifications
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Table 12 Robustness checks: placebo tests
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Table 13 Robustness checks: considering self-selection effects and expectation effects
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Table 14 Robustness checks: excluding other policy interference
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Zhao, C., Li, X. Understanding government pollution control actions and well-being: Evidence from micro individuals. Air Qual Atmos Health (2024). https://doi.org/10.1007/s11869-024-01551-0

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