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Health Benefits from Improved Air Quality: Evidence from Pollution Regulations in China’s “\(2{+}26\)” Cities

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Abstract

This study assesses the health benefits of better air quality by examining the causal impact of China’s stringent “\(2{+}26\)” regional air pollution control policy on local air quality and population health. Employing a spatial regression discontinuity design that capitalizes on the policy’s location-specific features, we present compelling evidence that the \(2{+}26\) policy results in an average reduction of 12.2 units in the local Air Quality Index (AQI) and a 47.0% decrease in per capita medical expenditure from 2014 to 2018. A one-unit reduction in AQI corresponds to a 0.88% reduction in per capita annual medical spending, equivalent to RMB 30.2 (US$4.6). These health gains stem from reduced chronic disease prevalence and improved subjective well-being. Nationally, air quality improvement during 2014–2018 could save RMB 674 billion (US$104 billion) annually in national direct medical costs, constituting 11.6% of national medical expenditure in 2018. Our findings underscore the substantial health and welfare gains achievable through pollution controls in developing countries.

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Notes

  1. See Brunekreef and Holgate (2002) and Fuller et al. (2022) for reviews on studies of health costs of air pollution and Aguilar-Gomez et al. (2022) for a review on the “non-health” costs.

  2. See, for example, Chay and Greenstone (2003), Currie and Neidell (2005), Currie and Walker (2011), Chen et al. (2013), Knittel et al. (2016), Clay et al. (2016), Ebenstein et al. (2017), Anderson (2020), He et al. (2016), Tanaka (2015), Fan et al. (2020).

  3. To prevent potential data tampering, the modern air pollution monitoring system gathers air quality data automatically and transmits it directly to the cloud-based data center managed by the central regulatory authority, the Ministry of Environmental Protection (MEP). Hourly air pollutant records are accessible in real-time through the MEP’s official website. This mechanism guarantees transparency and reliability of air pollution data. See more details in Barwick et al. (2019).

  4. For reference, the WHO considers daily AQI levels above 200 as very unhealthy and above 300 as hazardous.

  5. Consistently, Tu et al. (2020) show that individual’s willingness to pay for clean air increased by 25% after viewing a documentary on China’s air pollution titled “Under the Dome” released in 2015.

  6. There is also an increasing body of epidemiological research in developing countries (Borja-Aburto et al. 1997; Zhang et al. 2011; Yin et al. 2017; Guo et al. 2014; Kan and Gu 2011). These studies typically rely on time series variation in air pollution while controlling for weather conditions and sometimes region fixed effects, which address time-invariant unobservable socioeconomic or sub-regional factors. However, this approach may still be susceptible to omitted variable bias originating from unobserved, time-varying factors that impact both air pollution and population health. See Brewer et al. (2023) for a recent review.

  7. Estimates of the statistical value of life (SVL) exhibit significant variation across regions and time in developing countries. For example, in China, estimates on SVL range from US$15,000 to US$614,805 (Hammitt and Zhou 2006; Hoffmann et al. 2017; Guo and Hammitt 2009). This disparity in estimates of SVL poses a significant challenge for conducting cost-effectiveness evaluations of environmental regulations using the benefit-transfer approach in developing countries.

  8. The \(2{+}26\) cities include the two municipalities of Beijing and Tianjin and 26 prefecture-level cities: Shijiazhuang, Tangshan, Langfang, Baoding, Cangzhou, Hengshui, Xingtai, Handan in Hebei Province, Taiyuan, Yangquan, Changzhi, Jincheng in Shanxi Province, Jinan, Zibo, Jining, Dezhou, Liaocheng, Binzhou, Heze in Shandong Province and Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo, and Puyang in Henan Province. See the official announcement at https://english.mee.gov.cn/News_service/infocus/201309/t20130924_260707.shtml (in Chinese).

  9. See a technical report on the computation of the AQI at https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf.

  10. Specifically, we computed county-level pollution variables by calculating the weighted average of station-level variables within a 50 km radius around the centroid of each county. If a county had no monitoring station within its 50-km radius, we find the nearest air monitoring station for the county and assigned the air pollution data of this station to the county. Alternative radii are adopted and the results are similar.

  11. The real-time reporting-and-Conflict of interest system was implemented progressively across all prefecture-level cities in three waves (Barwick et al. 2019; Xie et al. 2023). Cities on the North China Plain had largely completed the implementation by 2014.

  12. These excluded counties differ significantly in elevation, terrain ruggedness, soil fertility, rainfall, ethnic composition, and preexisting settlement patterns compared to the rest of cities on the North China Plain.

  13. The United States has a population of 307 million in 2010.

  14. We adopt a linear function of distance in our baseline, and use second and third order polynomials as robustness checks. In addition, we also estimate Eq. 1 nonparametrically with local linear regression and the data-driven optimal bandwidth proposed by Calonico et al. (2014) as a robustness check.

  15. We do not have reliable annual county-level economic data, such as the county-level per capita GDP. In all our analysis, we use a city-level variable if the county-level variable is not available. In CFPS survey data, each city has at most one county surveyed.

  16. In geographic RD design with a long boundary line, regions across different segments of the boundary line may differ in unobservable ways. Following the practice of Dell (2010), we control for boundary segment fixed effects so that we exploit variation between counties along the same segment of the policy boundary.

  17. Specifically, Panel A includes a cubic polynomial in the Euclidean distance from the counties’ center to the nearest point on the policy boundary. Panel B includes a cubic polynomial for both the latitude and longitude of the counties. Panel C includes a cubic polynomial in the Euclidean distance from the counties’ center to Beijing.

  18. We acknowledge that the \(2{+}26\) policy’s impact on local pollution levels may differ from its impact on pollution emissions (which we cannot directly measure) due to pollution transmission and spillovers. Throughout our analysis, We focus on the air pollution level rather than pollution emissions because the former is the appropriate measure for pollution exposure that the local population experiences.

  19. We adopt a 1-to-1.19 conversion rate to relate 1-unit in AQI to 1.19 units in PM2.5. This conversion is based on our finding that the \(2{+}26\) policy has resulted in a 15.1 units reduction in AQI (Appendix Table A3) and a 17.9 units reduction in PM2.5 during the same period (Appendix Table A4).

  20. We adopt a 1-to-1.49 conversion rate to relate a 1-unit reduction in AQI to 1.49 \(\upmu g/m^3\) reduction of PM10. This conversion is based on our finding that the \(2{+}26\) policy has resulted in a 15.1 unit reduction of AQI along with \(22.5\,\upmu \text{g/m}^3\) reduction in PM10 during the same period (Appendix Table A5).

  21. We exercise caution here due to concerns of potential data tampering in the air pollution data prior to 2013, as highlighted by previous studies (Greenstone et al. 2020; Ghanem and Zhang 2014).

  22. In China, centralized winter heating is available to all cities located to the north of the Qinling–Huaihe Line, which corresponds roughly to the 33rd parallel. We, therefore, limit the sample to cities located to the North of Qinling–Huaihe Line.

  23. Please see the official document on pollution reduction targets at https://www.gov.cn/gzdt/2014-01/07/content_2561650.htm (in Chinese).

  24. Indeed, some \(2{+}26\) cities alongthe policy boundary, like Linyi, are well-industrialized and rank high in national city emission lists.

  25. We do not include income, employment status, and a set of variables that could be affected by pollution exposure to avoid the concern of bad controls.

  26. In more general DID setting, such as with staggered treatment timing and heterogeneous treatment effects, the DID estimator would assign different weights to different pairs of treatment-control units depending on their relative timing of treatment (Goodman-Bacon 2021; Sun and Abraham 2021).

  27. We obtain satellite-based PM2.5 data from the aerosol optical depth (AOD) captured by NASA’s Terra satellite using the MERRA-2 module. The annual concentration of PM2.5 at the grid level is calculated following the algorithm by Buchard et al. (2016) and then averaged from grid level to county level. The similarity between the satellite-based PM2.5 and ground-monitor-based PM2.5 is illustrated in Appendix Fig. B15.

  28. It is worth noting that satellite-based PM2.5 is derived from atmospheric variations in air pollution, whereas ground-monitor-based PM2.5 measures pollution exposure at ground level. In addition, satellite-based PM2.5 is calibrated based on ground-monitor-based PM2.5. It is thus more prone to measurement errors before 2014 when the ground-monitor-based PM2.5 data are limited. As a result, while the two datasets exhibit similarity at the national level, there may be large differences at the city level between satellite-based PM2.5 and monitor-station-based PM2.5 data, especially before 2014.

  29. In the literature, it is common to assume a linear dose-response relationship between air pollution and mortality and between air pollution and log medical costs in the welfare analysis. See, for example, Barwick et al. (2018), Deryugina et al. (2019), Chen et al. (2013), Ebenstein et al. (2017).

  30. We calculate the national average AQI based on data from 180 prefecture-level cities which have consistently reported AQI data from 2014 to 2018. Additionally, other cities have gradually installed modern air pollution monitoring stations and began monitoring local air pollution levels between 2014 and 2016. These 180 cities account for over 84% of the Chinese population. We compute the national average concentration of AQI using the city population as weights.

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Funding

We express our sincere gratitude to the editor and anonymous reviewers for their invaluable feedback and suggestions. Any errors are our own. We acknowledge financial support by the National Natural Science Foundation of China (No. 72203004), the National Social Science Foundation of China (No. 19CJY029), and the Research Seed Fund of School of Economics, Peking University. We declare no conflict of interest. Xie, Wang, and Yuan are joint first authors

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  1. School of Economics, Central University of Finance and Economics, Beijing, China

    Tingting Xie

  2. Policy Research Center for Environment and Economy, Ministry of Ecological Environment, Beijing, China

    Yong Wang

  3. School of Economics, Institute for Global Health and Development, Peking University, Beijing, China

    Ye Yuan

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  1. Tingting Xie
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Xie, T., Wang, Y. & Yuan, Y. Health Benefits from Improved Air Quality: Evidence from Pollution Regulations in China’s “\(2{+}26\)” Cities. Environ Resource Econ (2024). https://doi.org/10.1007/s10640-024-00860-3

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