Revisiting the environmental Kuznets curve of PM2.5 concentration: evidence from prefecture-level and above cities of China

Wang, Yongpei; Komonpipat, Supak

doi:10.1007/s11356-020-07621-x

Cited by 17 publications

(15 citation statements)

References 37 publications

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“…2019b ; Shen et al. 2020 ; Wang and Komonpipat 2020 ). The lack of comprehensive research on environmental inequality in China, including theories and national empirical studies of SES and ambient air pollution, are important gaps in the literature.…”

Section: Introductionmentioning

confidence: 99%

Ambient Air Pollution and Socioeconomic Status in China

Wang

et al. 2022

Environ Health Perspect

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Section: Introductionmentioning

confidence: 99%

Ambient Air Pollution and Socioeconomic Status in China

Wang

et al. 2022

Environ Health Perspect

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“…Our finding was supported by a prior paper reported that the risk of PM 2.5 exposure was declining in most western developed countries; in contrast, the risk of PM 2.5 exposure was prominent in developing countries ( 44 ). Since 2013, the Chinese government has carried out a pilot project to monitor PM 2.5 concentration in 33 major cities and implemented an air quality standard for PM 2.5 concentration in 2016 ( 45 ). In 2019, the PM 2.5 concentration in 22 of 31 major cities in mainland China exceeded the annual limitation concentration for China (35.0 μg/m 3 ) and 31 of them exceeded the WHO recommendation (10.0 μg/m 3 ) ( 46 ).…”

Section: Discussionmentioning

confidence: 99%

The Association Between Long-Term Exposure to Particulate Matter and Incidence of Hypertension Among Chinese Elderly: A Retrospective Cohort Study

Zhou

Chen

Zhou

et al. 2022

Front. Cardiovasc. Med.

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Background and Objectives: Studies that investigate the links between particulate matter ≤2. 5 μm (PM2.5) and hypertension among the elderly population, especially those including aged over 80 years, are limited. Therefore, we aimed to examine the association between PM2.5 exposure and the risk of hypertension incidence among Chinese elderly.Methods: This prospective cohort study used 2008, 2011, 2014, and 2018 wave data from a public database, the Chinese Longitudinal Healthy Longevity Survey, a national survey investigating the health of those aged over 65 years in China. We enrolled cohort participants who were free of hypertension at baseline (2008) from 706 counties (districts) and followed up in the 2011, 2014, and 2018 survey waves. The annual PM2.5 concentration of 706 counties (districts) units was derived from the Atmospheric Composition Analysis Group database as the exposure variable, and exposure to PM2.5 was defined as 1-year average of PM2.5 concentration before hypertension event occurrence or last interview (only for censoring). A Cox proportional hazards model with penalized spline was used to examine the non-linear association between PM2.5 concentration and hypertension risk. A random-effects Cox proportional hazards model was built to explore the relationship between each 1 μg/m3, 10 μg/m3 and quartile increment in PM2.5 concentration and hypertension incidence after adjusting for confounding variables. The modification effects of the different characteristics of the respondents were also explored.Results: A total of 7,432 participants aged 65–116 years were enrolled at baseline. The median of PM2.5 exposure concentration of all the participants was 52.7 (inter-quartile range, IQR = 29.1) μg/m3. Overall, the non-linear association between PM2.5 and hypertension incidence risk indicated that there was no safe threshold for PM2.5 exposure. The higher PM2.5 exposure, the greater risk for hypertension incidence. Each 1 μg/m3 [adjusted hazard ratio (AHR): 1.01; 95% CI: 1.01–1.02] and 10 μg/m3 (AHR: 1.12; 95% CI: 1.09–1.16) increments in PM2.5, were associated with the incidence of hypertension after adjusting for potential confounding variables. Compared to first quartile (Q1) exposure, the adjusted HRs of hypertension incidence for the Q2, Q3 and Q4 exposure of PM2.5 were 1.31 (95% CI: 1.13–1.51), 1.35 (95% CI: 1.15–1.60), and 1.83 (95% CI: 1.53–2.17), respectively. The effects appear to be stronger among those without a pension, living in a rural setting, and located in central/western regions.Conclusion: We found no safe threshold for PM2.5 exposure related to hypertension risk, and more rigorous approaches for PM2.5 control were needed. The elderly without a pension, living in rural and setting in the central/western regions may be more vulnerable to the effects of PM2.5 exposure.

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“…To explore the impact that environmental pollution has on economic growth, Krueger (1991, 1995) proposed multiple innovations; however, there are divergent and varying perspectives on whether the Kuznets effect is an inverted U-or N-type (Stern & Zha, 2016;Egbetokun et al, 2020;Hao et al, 2018;Wang & Komonpipat, 2020). This disagreement may be due to inconsistent data (with respect to inconsistent years) used by previous studies, as well as statistical bias and a lack of regional heterogeneity inclusion.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similar to previous studies (Yan et al, 2020;Ouyang et al, 2019;Liu et al, 2019;Xu et al, 2019;Wang & Komonpipat, 2020), this study also compensated for a set of urban fixed effects variables in the benchmark regression model to minimize bias caused by the omitted variables. Specifically, the population density variable was measured by the number of people per unit area; the infrastructure variable was measured by the number of paved roads per capita, the number of operating buses at the end of the year, and the number of hospitals; the local STI (science, technology, and innovation) development variable was measured by the number of patent applications; and the urbanization variable was measured by the proportion of the urban population.…”

Section: Model Settingmentioning

confidence: 99%

Economic Growth, Air Pollution, and Government Environmental Regulation: Evidence From 287 Prefecture-Level Cities in China

Chen

Gao

et al. 2021

Technological and Economic Development of Economy

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Air pollution control is crucial for promoting the modernization of governance systems and efficiency. To address the subjective contrived factors and errors in the gross domestic product (GDP) data in traditional statistical almanacs, our study aims to construct a panel data model of 287 prefecture-level cities for the period from 1998–2016 (using objective nighttime light data). We also used government work report words related to environmental regulation to characterize the constraints of government environmental regulations. For this purpose, we used instrumental variables (to explore the relationship and interaction between air pollution and economic growth) and a model setting, with which we carried out regression analysis and robustness tests; the findings were validated using a transmission mechanism hypothesis. We found that that economic growth and air pollution positively influence each other and government environmental regulations significantly reduce air pollution. We also found that to achieve high economic development, environmental pollution must be controlled to avoid further damage to human and material capital. Furthermore, government environmental regulations can help improve the environmental comfort level and economic development quality.

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Revisiting the environmental Kuznets curve of PM2.5 concentration: evidence from prefecture-level and above cities of China

Cited by 17 publications

References 37 publications

Ambient Air Pollution and Socioeconomic Status in China

Ambient Air Pollution and Socioeconomic Status in China

The Association Between Long-Term Exposure to Particulate Matter and Incidence of Hypertension Among Chinese Elderly: A Retrospective Cohort Study

Economic Growth, Air Pollution, and Government Environmental Regulation: Evidence From 287 Prefecture-Level Cities in China

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