Retrospect and Outlook of Research on Regional Haze Pollution in China: A Systematic Literature Review
Abstract
:1. Introduction
2. The Connotations, Formation and Mechanism of Effects of Haze Pollution
2.1. Basis Research on Concepts, Attributes and Evolution Characteristics
2.1.1. Concept Definition
2.1.2. Attribute Characteristics
2.2. Measurement Indicators and Evaluation
2.3. Driving Factors and Analysis of Mechanisms of Haze Pollution
3. Spatial Characteristics and Dynamic Evolution of China’s Regional Haze Pollution
3.1. Evidence from the Beijing–Tianjin–Hebei Urban Agglomeration
3.2. Evidence from the Chengdu–Chongqing City Group
3.3. Evidence from the Pearl River Delta Urban Agglomeration
3.4. Evidence from the Yangtze River Delta Urban Agglomeration
3.5. Comparative Analysis and Discussion
3.5.1. Comparative Analysis
3.5.2. Summary and Discussion
4. Evolution Process and Policy Analysis on Haze Pollution Prevention and Control
4.1. Evolution Process and Progress
4.2. Governance Policy Analysis and Evalution
4.2.1. Short-Term Governance Policy Research
4.2.2. Long-Run Governance Policy Research
4.3. Research on Mechanisms for the Prevention and Control of Haze Pollution
4.4. Policy Research Overview on the Prevention and Control of Haze Pollution
5. Theoretical and Empirical Research Overview
5.1. Theoretical Basis and Analytical Framework
5.2. Empirical Methods and Quantitative Models
6. Conclusions and Future Research Outlook
6.1. Major Research Findings and Limitations
6.2. Future Direction and Discussions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbrev. | Explanation |
PM2.5 | Fine particulate matter |
PM10 | Inhalable particulate matter |
AQI | Air quality index |
API | Air pollution index |
SO2 | Sulphur dioxide |
CO2 | Carbon dioxide |
NOx | Nitrogen oxides |
O3 | Ozone |
VOCs | Volatile organic compounds |
EKC | Environmental Kuznets curve |
STIRPAT | Stochastic impacts by regression on population, affluence and technology |
GMM | Gaussian mixed model |
SAR | Spatial auto regression model |
SLM | Spatial lag model |
SDM | Spatial Dubin model |
SEM | Spatial error model |
GDP | Cross domestic product |
GIS | Geographic information system |
References
- Lelieveld, J.; Evans, J.S.; Fnais, M.; Giannadaki, D.; Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 2015, 525, 367–371. [Google Scholar] [CrossRef] [PubMed]
- Huang, J.; Pan, X.; Guo, X.; Li, G. Health impact of China’s Air Pollution Prevention and Control Action Plan: An analysis of national air quality monitoring and mortality data. Lancet Planet. Health 2018, 2, e313–e323. [Google Scholar] [CrossRef] [Green Version]
- Mackerron, G.; Mourato, S. Life satisfaction and air quality in London. Ecol. Econ. 2009, 68, 1441–1453. [Google Scholar] [CrossRef]
- Li, M.; Zhang, L. Haze in China: Current and future challenges. Environ. Pollut. 2014, 189, 85–86. [Google Scholar] [CrossRef]
- Shi, H.; Wang, Y.; Chen, J.; Huisingh, D. Preventing smog crises in China and globally. J. Clean. Prod. 2016, 112, 1261–1271. [Google Scholar] [CrossRef]
- Silva, R.A.; West, J.J.; Zhang, Y.; Anenberg, S.C.; Lamarque, J.; Shindell, D.; Collins, W.J.; Dalsoren, S.; Faluvegi, G.; Folberth, G.; et al. Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change. Environ. Res. Lett. 2013, 8, 034005. [Google Scholar] [CrossRef]
- Wang, C.; Jiang, H.; Pan, D.; Lu, X.; Wang, Y.; Zhang, S.; Xu, J. A key study on spatial source distribution of PM2.5 based on the airflow trajectory model. Int. J. Remote Sens. 2016, 37, 5864–5883. [Google Scholar] [CrossRef]
- Chen, S.Y.; Chen, D.K. Air pollution, government regulation and high-quality economic development. Econ. Res. J. 2018, 53, 20–34. [Google Scholar]
- Yang, L.; Han, L.; Chen, Z.; Zhou, J.; Wang, J. Growing trend of China’s contribution to haze research. Scientometrics 2015, 105, 525–535. [Google Scholar] [CrossRef]
- Li, C.X.; Wu, K.N.; Wu, J.Y. A bibliometric analysis of research on haze during 2000–2016. Environ. Sci. Pollut. Res. 2017, 24, 24733–24742. [Google Scholar] [CrossRef] [Green Version]
- Jiang, J.-J.; Ye, B.; Zhou, N.; Zhang, X. Decoupling analysis and environmental Kuznets curve modelling of provincial-level CO2 emissions and economic growth in China: A case study. J. Clean. Prod. 2019, 212, 1242–1255. [Google Scholar] [CrossRef]
- Qi, J. Study on Health Damage Assessment of Short-Term Heavy Air Pollution. Ph.D. Thesis, Tsinghua University, Beijing, China, 2019. [Google Scholar]
- Xiong, H.; Zhao, Z. The Correlation between Haze and Economic Growth: Bibliometric Analysis Based on Wos Database. Appl. Ecol. Environ. Res. 2020, 18, 59–75. [Google Scholar] [CrossRef]
- Zhao, H.; Cao, X.; Ma, T. A spatial econometric empirical research on the impact of industrial agglomeration on haze pollution in China. Air Qual. Atmos. Health 2020, 13, 1305–1312. [Google Scholar] [CrossRef]
- Heil, A.; Goldammer, J.G. Smoke-haze pollution: A review of the 1997 episode in Southeast Asia. Reg. Environ. Chang. 2001, 2, 24–37. [Google Scholar] [CrossRef]
- Elias, T.; Haeffelin, M.; Drobinski, P.; Gomes, L.; Rangognio, J.; Bergot, T.; Chazette, P.; Raut, J.; Colomb, M. Particulate contribution to extinction of visible radiation: Pollution, haze, and fog. Atmos. Res. 2009, 92, 443–454. [Google Scholar] [CrossRef]
- Wu, D.; Yu, Y.X.; Xia, J.R.; Cao, S. Hazy Pollution Research of China: A Review. Environ. Sci. Technol. 2014, 37, 295–304. [Google Scholar]
- Pandis, S.N.; Wexler, A.S.; Seinfeld, J.H. Dynamics of tropospheric aerosols. J. Phys. Chem. 1995, 99, 9646–9659. [Google Scholar] [CrossRef]
- Wang, L.; Xu, J.; Yang, J.; Zhao, X.; Wei, W.; Cheng, D.; Pan, X.; Su, J. Understanding haze pollution over the southern Hebei area of China using the CMAQ model. Atmos. Environ. 2012, 56, 69–79. [Google Scholar] [CrossRef]
- Fu, Q.; Zhuang, G.; Wang, J.; Xu, C.; Huang, K.; Li, J.; Hou, B.; Lu, T. Mechanism of formation of the heaviest pollution episode ever recorded in the Yangtze River Delta, China. Atmos. Environ. 2008, 42, 2023–2036. [Google Scholar] [CrossRef]
- Huang, R.J.; Zhang, Y.; Bozzetti, C.; Ho, K.; Cao, J.; Han, Y.; Daellenbach, K.; Slowik, J.; Platt, S.; Canonaco, F.; et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 2014, 514, 218–222. [Google Scholar] [CrossRef] [Green Version]
- Ye, B.M.; Ji, X.L.; Yang, H.Z.; Yao, X.; Chan, C.; Cadle, S.; Chan, T.; Mulawa, P. Concentration and chemical composition of PM2.5 in Shanghai for a 1-year period. Atmos. Environ. 2003, 37, 499–510. [Google Scholar] [CrossRef]
- Wang, Y.; Yao, L.; Wang, L.; Liu, Z.; Ji, D.; Tang, G.; Zhang, J.; Sun, Y.; Hu, B.; Xin, J. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci. 2013, 57, 14–25. [Google Scholar] [CrossRef]
- Amato, F.; Querol, X.; Johansson, C.; Nagl, C.; Alastuey, A. A review on the effectiveness of street sweeping, washing and dust suppressants as urban PM control methods. Sci. Total Environ. 2010, 408, 3070–3084. [Google Scholar] [CrossRef] [PubMed]
- Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Chen, L.W.; Trop, R.J.; Park, K.; Magliano, K.A. PM2.5 and PM10 mass measurements in California’s San Joaquin Valley. Aerosol Sci. Technol. 2006, 40, 796–810. [Google Scholar] [CrossRef] [Green Version]
- Park, S.; Jung, S.; Gong, B.; Cho, S.; Lee, S. Characteristics of PM2.5 Haze Episodes Revealed by Highly Time-Resolved Measurements at an Air Pollution Monitoring Supersite in Korea. Aerosol Air Qual. Res. 2013, 13, 957–976. [Google Scholar] [CrossRef]
- Wu, Y.; Zhu, C.; Feng, G.; Li, B. Mathematical modeling of Fog-Haze evolution. Chaos Solitons Fractals 2018, 107, 1–4. [Google Scholar] [CrossRef]
- Li, L.; Tang, D.; Kong, Y.; Yang, Y.; Liu, D. Spatial analysis of haze-fog pollution in China. Energy Environ. 2016, 27, 726–740. [Google Scholar] [CrossRef]
- Tang, D.; Li, L.; Yang, Y. Spatial Econometric Model Analysis of Foreign Direct Investment and Haze Pollution in China. Pol. J. Environ. Stud. 2016, 25, 317–324. [Google Scholar] [CrossRef]
- Shao, S.; Li, X.; Cao, J.H. China’s Economic Policy Choices for Governing Smog Pollution Based on Spatial Spillover Effects. Econ. Res. J. 2016, 51, 73–88. [Google Scholar]
- Wang, S.; Xu, Y. Environmental regulation and haze pollution decoupling effect—based on the perspective of enterprise investment perfeiences. China Ind. Econ. 2015, 4, 18–30. [Google Scholar]
- Ma, Y.-R.; Ji, Q.; Fan, Y. Spatial linkage analysis of the impact of regional economic activities on PM 2.5 pollution in China. J. Clean. Prod. 2016, 139, 1157–1167. [Google Scholar] [CrossRef]
- Yuan, M.; Huang, Y.; Shen, H.; Li, T. Effects of urban form on haze pollution in China: Spatial regression analysis based on PM2.5 remote sensing data. Appl. Geogr. 2018, 98, 215–223. [Google Scholar] [CrossRef]
- Du, Y.; Wan, Q.; Liu, H.; Liu, H.; Kapsar, K.; Peng, J. How does urbanization influence PM2.5 concentrations? Perspective of spillover effect of multi-dimensional urbanization impact. J. Clean. Prod. 2019, 220, 974–983. [Google Scholar] [CrossRef]
- Fan, J.; Rosenfeld, D.; Yang, Y.; Zhao, C.; Leung, L.; Li, Z. Substantial contribution of anthropogenic air pollution to catastrophic floods in Southwest China. Geophys. Res. Lett. 2015, 42, 6066–6075. [Google Scholar] [CrossRef]
- Lu, J.G. Air pollution: A systematic review of its psychological, economic, and social effects. Curr. Opin. Psychol. 2020, 32, 52–65. [Google Scholar] [CrossRef]
- Fang, K.; Wang, T.; He, J.; Wang, T.; Xie, X.; Tang, Y.; Shen, Y.; Xu, A. The distribution and drivers of PM2.5 in a rapidly urbanizing region: The Belt and Road Initiative in focus. Sci. Total Environ. 2020, 716, 137010. [Google Scholar] [CrossRef]
- Grossman, G.M.; Krueger, A.B. Economic-growth and the environment. Q. J. Econ. 1995, 110, 353–377. [Google Scholar] [CrossRef] [Green Version]
- Antweiler, W.; Copeland, B.R.; Taylor, M.S. Is free trade good for the environment? Am. Econ. Rev. 2001, 91, 877–908. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Feng, K.; Li, M. Identifying the main contributors of air pollution in Beijing. J. Clean. Prod. 2017, 163, S359–S365. [Google Scholar] [CrossRef]
- Li, L.; Liu, X.; Ge, J.; Chu, X.; Wang, J. Regional differences in spatial spillover and hysteresis effects: A theoretical and empirical study of environmental regulations on haze pollution in China. J. Clean. Prod. 2019, 230, 1096–1110. [Google Scholar] [CrossRef]
- He, G.; Liu, T.; Zhou, M. Straw burning, PM2.5, and death: Evidence from China. J. Dev. Econ. 2020, 145, 102468. [Google Scholar]
- Poon, J.P.H.; Casas, I.; He, C. The impact of energy, transport, and trade on air pollution in China. Eurasian Geogr. Econ. 2006, 47, 568–584. [Google Scholar] [CrossRef]
- Chen, Y.; Whalley, A. Green Infrastructure: The Effects of Urban Rail Transit on Air Quality. Am. Econ. J.-Econ. Policy 2012, 4, 58–97. [Google Scholar] [CrossRef] [Green Version]
- Zhu, W.; Wang, M.; Zhang, B. The effects of urbanization on PM2.5 concentrations in China’s Yangtze River Economic Belt: New evidence from spatial econometric analysis. J. Clean. Prod. 2019, 239, 118065. [Google Scholar] [CrossRef]
- Ren, Y.Y.; Zhang, G.L. Can city innovation dispel haze? Evidence from the perspective of spatial spillover. China Popul. Resour. Environ. 2020, 30, 111–120. [Google Scholar]
- Lin, B.Q.; Wang, B.Y.; Ma, J.J. A summary on research of environmental cost calculation. Ind. Saf. Environ. Prot. 2006, 5, 1–4. [Google Scholar]
- Chen, Y.; Ebenstein, A.; Greenstone, M.; Li, H. Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. USA 2013, 110, 12936–12941. [Google Scholar] [CrossRef]
- Li, C.; Ma, X.; Fu, T.; Guan, S. Does public concern over haze pollution matter? Evidence from Beijing-Tianjin-Hebei region, China. Sci. Total Environ. 2021, 755, 142397. [Google Scholar] [CrossRef]
- Gu, W.D. Research on the special formation mechanism of China’s haze pollution. Macroeconomics 2014, 6, 3–7. [Google Scholar]
- Zhang, H.; Wang, Y.; Hu, J.; Ying, Q.; Hu, X. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ. Res. 2015, 140, 242–254. [Google Scholar] [CrossRef]
- Wang, H.; Chen, H.; Liu, J. Arctic Sea Ice Decline Intensified Haze Pollution in Eastern China. Atmos. Ocean. Sci. Lett. 2015, 8, 1–9. [Google Scholar]
- Zhang, M.; Liu, X.; Ding, Y.; Wang, W. How does environmental regulation affect haze pollution governance?—An empirical test based on Chinese provincial panel data. Sci. Total Environ. 2019, 695, 133905. [Google Scholar] [CrossRef]
- Wu, W.; Zhang, M.; Ding, Y. Exploring the effect of economic and environment factors on PM2.5 concentration: A case study of the Beijing-Tianjin-Hebei region. J. Environ. Manag. 2020, 268, 110703. [Google Scholar] [CrossRef]
- Jiang, K.J.; Dai, C.Y.; He, C.M.; Zhu, S.L. Impact of air pollution prevention on economic development in China after 2013: Case study for Beijing-Tianjin-Hebei Region. Bull. Chin. Acad. Sci. 2020, 35, 732–741. [Google Scholar]
- Pan, H.F.; Wang, X.; Zhang, S.Y. Duration and spatial spillover effects of haze pollution—Evidence from Beijing-Tianjin-Hebei Region. China Soft Sci. 2015, 12, 134–143. [Google Scholar]
- Zhu, L.Y.; Li, T.; Ma, L.Y.; Liu, Z.L. The influence of industrial structure adjustment on haze pollution: An empirical study of Jing-Jin-Ji Region. Ecol. Econ. 2018, 34, 141–148. [Google Scholar]
- Shi, Y.P.; Liu, B.J.; Li, Y. The spillover effects of haze pollution in Beijing-Tianjin-Hebei Region. Econ. Manag. 2017, 31, 20–26. [Google Scholar]
- Wang, H.Z.; Du, L.W.; Lv, J.H. Spatial differentiation and dynamic association of haze pollution in urban agglomeration: An empirical analysis based on Beijing-Tianjin-Hebei Urban Agglomeration. Chin. J. Environ. Manag. 2020, 12, 80–86. [Google Scholar]
- Li, H.; Lu, J. Can regional integration control transboundary water pollution? A test from the Yangtze River economic belt. Environ. Sci. Pollut. Res. Int. 2020, 27, 28288–28305. [Google Scholar] [CrossRef]
- Wu, Y.; Chau, K.W.; Lu, W.; Shen, L.Y.; Shuai, C.Y.; Chen, J.D. Decoupling relationship between economic output and carbon emission in the Chinese construction industry. Environ. Impact Assess. Rev. 2018, 71, 60–69. [Google Scholar] [CrossRef]
- Liu, G.; Yang, Z.; Chen, B.; Zhang, Y.; Su, M.; Ulgiati, S. Prevention and control policy analysis for energy-related regional pollution management in China. Appl. Energy 2016, 166, 292–300. [Google Scholar] [CrossRef]
- Liu, H.J.; Sun, Y.N.; Chen, M.H. Dynamic correlation and causes of urban haze pollution. China Popul. Resour. Environ. 2017, 27, 74–81. [Google Scholar]
- Chen, L.; Zhang, X.; He, F.; Yuan, R. Regional green development level and its spatial relationship under the constraints of haze in China. J. Clean. Prod. 2019, 210, 376–387. [Google Scholar] [CrossRef]
- Xie, R.; Wei, D.; Han, F.; Lu, Y.; Fang, J.; Liu, Y.; Wang, J. The effect of traffic density on smog pollution: Evidence from Chinese cities. Technol. Forecast. Soc. Chang. 2019, 144, 421–427. [Google Scholar] [CrossRef]
- Sun, H.; Nie, F.F.; Shen, J.; Peng, L.S.; Yu, S.W. The air pollution, the spatial spill-over and the public health: A case from the pearl river delta in China. China Population. Resour. Environ. 2017, 27, 35–45. [Google Scholar]
- Wang, Z.B.; Liang, L.W.; Wang, X.J. Spatial-temporal evolution patterns and influencing factors of PM2.5 in Chinese urban agglomerations. Acta Geogr. Sin. 2019, 74, 2614–2630. [Google Scholar]
- Lv, C.M.; Li, Y. City Reducing Emission Difference and Joint Control of Air Pollution under Public Opinion Explosion on Haze. Econ. Geogr. 2017, 37, 148–154. [Google Scholar]
- Liu, X.T. How to Utilize the Haze-governance Effect of Industry Transfer? Empirical Study Based on Yangtze River Delta Area. Sci. Decis. Mak. 2018, 3, 83–94. [Google Scholar]
- Suo, L.M.; Kan, Y.Q.; Li, X. Institutional collective action, cooperation field differences and inter-government collaborative governance. Public Adm. Policy Rev. 2020, 9, 3–14. [Google Scholar]
- Liang, W.; Yang, M.; Zhang, Y.W. Will the increase of the urbanization rate inevitably exacerbate haze pollution? A discussion of the spatial spillover effects of urbanization and haze pollution. Geogr. Res. 2017, 36, 1947–1958. [Google Scholar]
- Du, G.; Liu, S.; Lei, N.; Huang, Y. A test of environmental Kuznets curve for haze pollution in China: Evidence from the penal data of 27 capital cities. J. Clean. Prod. 2018, 205, 821–827. [Google Scholar] [CrossRef]
- Li, H.; Yang, S.; Zhang, J.; Qian, Y. Coal-based synthetic natural gas (SNG) for municipal heating in China: Analysis of haze pollutants and greenhouse gases (GHGs) emissions. J. Clean. Prod. 2016, 112, 1350–1359. [Google Scholar] [CrossRef]
- Zhang, X.; Geng, Y.; Shao, S.; Song, X.; Fan, M.; Yang, L.; Song, J. Decoupling PM2.5 emissions and economic growth in China over 1998-2016: A regional investment perspective. Sci. Total Environ. 2020, 714, 136841. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.B.; Yu, B. Grey correlation analysis of PM2.5 in Beijing based on social factors. Environ. Prot. 2020, 48, 60–66. [Google Scholar]
- Hu, M.; He, L.Y.; Zhang, Y.H.; Wang, M.; Kim, Y.P.; Moon, K.C. Seasonal variation of ionic species in fine particles at Qingdao, China. Atmos. Environ. 2002, 36, 5853–5859. [Google Scholar] [CrossRef]
- Wu, S.H.; Shi, K.; Liu, C.Q.; Li, S.C.; Huang, Y.; Ying, H. Fractal feature and DFA analysis of PM10 evolution in a typical fog-haze episode in Chengdu. J. Saf. Environ. 2014, 14, 285–291. [Google Scholar]
- Zheng, Z.; Xu, G.; Yang, Y.; Wang, Y.; Li, Q. Statistical characteristics and the urban spillover effect of haze pollution in the circum-Beijing region. Atmos. Pollut. Res. 2018, 9, 1062–1071. [Google Scholar] [CrossRef]
- Wang, S.; Hao, J. Air quality management in China: Issues, challenges, and options. J. Environ. Sci. 2012, 24, 2–13. [Google Scholar] [CrossRef]
- Jia, S.; Liu, X.; Yan, G. Effect of APCF policy on the haze pollution in China: A system dynamics approach. Energy Policy 2019, 125, 33–44. [Google Scholar] [CrossRef]
- An, Z.; Huang, R.J.; Zhang, R.; Tie, X.; Li, G.; Cao, J.; Zhou, W.; Shi, Z.; Han, Y.; Gu, Z.; et al. Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes. Proc. Natl. Acad. Sci. USA 2019, 116, 8657–8666. [Google Scholar] [CrossRef] [Green Version]
- Tao, M.H.; Chen, L.F.; Xiong, X.Z.; Zhang, M.G.; Ma, P.F.; Tao, J.H.; Wang, Z.F. Formation process of the widespread extreme haze pollution over northern China in January 2013: Implications for regional air quality and climate. Atmos. Environ. 2014, 98, 417–425. [Google Scholar] [CrossRef]
- Fu, S.; Ma, Z.; Peng, J. “Political blue sky” in fog and haze governance: Evidence from the local major international events in China. Environ. Sci. Pollut. Res. Int. 2021, 28, 775–788. [Google Scholar] [CrossRef]
- Han, S.; Wu, J.; Zhang, Y.; Cai, Z.; Feng, Y.; Yao, Q.; Li, X.; Liu, Y.; Zhang, M. Characteristics and formation mechanism of a winter haze–fog episode in Tianjin, China. Atmos. Environ. 2014, 98, 323–330. [Google Scholar] [CrossRef]
- Landrigan, P.J.; Fuller, R.; Acosta, N.J.; Adeyi, O.; Arnold, R.; Basu, N.; Balde, A.; Bertollini, R.; O’Reilly, S.; Boufford, J.; et al. The Lancet Commission on pollution and health. Lancet 2018, 391, 462–512. [Google Scholar] [CrossRef] [Green Version]
- Janhäll, S. Review on urban vegetation and particle air pollution—Deposition and dispersion. Atmos. Environ. 2015, 105, 130–137. [Google Scholar] [CrossRef]
- Jin, Y.; Andersson, H.; Zhang, S. Air Pollution Control Policies in China: A Retrospective and Prospects. Int. J. Environ. Res. Public Health 2016, 13, 1219. [Google Scholar] [CrossRef] [Green Version]
- Lu, Y.; Wang, Y.; Zuo, J.; Jiang, D.; Rameezdeen, R. Characteristics of public concern on haze in China and its relationship with air quality in urban areas. Sci. Total Environ. 2018, 637–638, 1597–1606. [Google Scholar] [CrossRef]
- Schieweck, A.; Uhde, E.; Salthammer, T.; Salthammer, L.; Morawska, L.; Mazaheri, M.; Kumar, P. Smart homes and the control of indoor air quality. Renew. Sustain. Energy Rev. 2018, 94, 705–718. [Google Scholar] [CrossRef]
- Zhou, Q.; Zhang, X.; Shao, Q.; Wang, X. The non-linear effect of environmental regulation on haze pollution: Empirical evidence for 277 Chinese cities during 2002–2010. J. Environ. Manag. 2019, 248, 109274. [Google Scholar] [CrossRef]
- Wei, N.; Meng, Q.G. Mechanism and institutional logic of cross-regional collaborative governance of air pollution—Based on the cooperative practice of Jing-Jin-Ji region. China Soft Sci. 2018, 10, 79–92. [Google Scholar]
- Xie, Y.; Dai, H.; Dong, H.; Hanaoka, T.; Masui, T. Economic Impacts from PM2.5 Pollution-Related Health Effects in China: A Provincial-Level Analysis. Env. Sci Technol 2016, 50, 4836–4843. [Google Scholar] [CrossRef] [PubMed]
- Mol, A.P.J.; Carter, N.T. China’s environmental governance in transition. Environ. Politics 2006, 15, 149–170. [Google Scholar] [CrossRef]
- Caruson, K.; Macmanus, S.A. Disaster vulnerabilities—How strong a push toward regionalism and intergovernmental cooperation? Am. Rev. Public Adm. 2008, 38, 286–306. [Google Scholar] [CrossRef]
- Ye, Y.; Ye, S.; Yu, H. Can Industrial Collaborative Agglomeration Reduce Haze Pollution? City-Level Empirical Evidence from China. Int. J. Environ. Res. Public Health 2021, 18, 1566. [Google Scholar] [CrossRef]
- Li, H.; Zhang, M.; Li, C.; Li, M. Study on the spatial correlation structure and synergistic governance development of the haze emission in China. Environ. Sci. Pollut. Res. Int. 2019, 26, 12136–12149. [Google Scholar] [CrossRef]
- Li, Y.; Zhao, W.; Fu, J.; Liu, Z.; Li, C.; Zhang, J.; He, C.; Wang, K. Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory. Sustainability 2020, 12, 2019. [Google Scholar] [CrossRef] [Green Version]
- Ji, Z.L. Research on regional government collaborative governance—A case study of the Yangtze Delta Area. Ph.D. Thesis, Shanghai Jiao Tong University, Shanghai, China, 2012. [Google Scholar]
- Zeng, W.H. Regulation on trans-boundary water pollution: A study on inter-judiciary River-basin pollution in Chia. China Econ. Q. 2008, 2, 447–464. [Google Scholar]
- Li, J.; Ye, S. Regional policy synergy and haze governance-empirical evidence from 281 prefecture-level cities in China. Environ. Sci. Pollut. Res. Int. 2021, 28, 10763–10779. [Google Scholar] [CrossRef]
- Querol, X.; Zhuang, X.G.; Alastuey, A.; Viana, M.; Lv, W.; Wang, Y.; Lopez, A.; Zhu, Z.; Wei, H.; Xu, S. Speciation and sources of atmospheric aerosols in a highly industrialized emerging mega-city in Central China. J. Environ. Monit. Assess. 2006, 8, 1049–1059. [Google Scholar] [CrossRef]
- Wei, F.; Teng, E.; Wu, G.; Hu, W.; Wilson, W.; Chapman, R.; Pau, J.; Zhang, J. Ambient concentrations and elemental compositions of PM10 and PM2.5 in four Chinese cities. Environ. Sci. Technol. 1999, 33, 4188–4193. [Google Scholar] [CrossRef]
- Liang, X.; Li, S.; Zhang, S.; Huang, H.; Chen, S. PM2.5data reliability, consistency, and air quality assessment in five Chinese cities. J. Geophys. Res. Atmos. 2016, 121, 10220–10236. [Google Scholar] [CrossRef]
- Chen, S.Y.; Lin, B.Q. The summary of the 1st forum for China’s energy, environment and climate change economics. Econ. Res. J. 2019, 54, 203–208. [Google Scholar]
- Shao, S.; Zhang, K.; Dou, J.M. Effect of economic agglomeration on energy saving and emission reduction: Theory and empirical evidence from China. Manag. World 2019, 35, 36–60. [Google Scholar]
- Lee, J.S.H.; Jaafar, Z.; Tan, A.K.J.; Carrasco, L.R.; Ewing, J.J.; Bickford, D.P.; Webb, E.L.; Koh, L.P. Toward clearer skies: Challenges in regulating transboundary haze in Southeast Asia. Environ. Sci. Policy 2016, 55, 87–95. [Google Scholar] [CrossRef]
- Ma, Z.; Hu, X.; Sayer, A.M.; Levy, R.; Zhang, Q.; Xue, Y.; Tong, S.L.; Bi, J.; Huang, L.; Liu, Y. Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013. Environ. Health Perspect. 2016, 124, 184–192. [Google Scholar] [CrossRef] [Green Version]
- Ding, Y.; Wu, P.; Liu, Y.; Song, Y. Environmental and Dynamic Conditions for the Occurrence of Persistent Haze Events in North China. Engineering 2017, 3, 266–271. [Google Scholar] [CrossRef]
- Fu, H.; Chen, J. Formation, features and controlling strategies of severe haze-fog pollutions in China. Sci. Total Environ. 2017, 578, 121–138. [Google Scholar] [CrossRef]
- Gao, J.; Woodward, A.; Vardoulakis, S.; Kovats, S.; Wilkinson, P.; Li, L.; Xu, L.; Li, J.; Yang, J.; Li, J.; et al. Haze, public health and mitigation measures in China: A review of the current evidence for further policy response. Sci. Total Environ. 2017, 578, 148–157. [Google Scholar] [CrossRef]
- Diao, B.; Ding, L.; Zhang, Q.; Na, J.; Cheng, J. Impact of Urbanization on PM2.5-Related Health and Economic Loss in China 338 Cities. Int. J. Environ. Res. Public Health 2020, 17, 990. [Google Scholar] [CrossRef] [Green Version]
- Klausbruckner, C.; Annegarn, H.; Henneman, L.R.F.; Rafaj, P. A policy review of synergies and trade-offs in South African climate change mitigation and air pollution control strategies. Environ. Sci. Policy 2016, 57, 70–78. [Google Scholar] [CrossRef]
- Cui, X.; Wang, X.; Yang, L.; Chen, B.; Chen, J.; Andersson, A.; Gustafssoon, O. Radiative absorption enhancement from coatings on black carbon aerosols. Sci. Total Environ. 2016, 551, 51–56. [Google Scholar] [CrossRef]
- Kazem, A.A.; Chaichan, M.T.; Kazem, H.A. Dust effect on photovoltaic utilization in Iraq: Review article. Renew. Sustain. Energy Rev. 2014, 37, 734–749. [Google Scholar] [CrossRef]
- Hyslop, N.P. Impaired visibility: The air pollution people see. Atmos. Environ. 2009, 43, 182–195. [Google Scholar] [CrossRef]
- Ataguba, O.A.; Ataguba, J.E. Social determinants of health: The role of effective communication in the COVID-19 pandemic in developing countries. Glob. Health Action 2020, 13, 1788263. [Google Scholar] [CrossRef]
- Ahmad, N.A.; Ismail, N.W.; Ahmad Sidique, S.F.; Mazlan, N. Air pollution effects on adult mortality rate in developing countries. Environ. Sci. Pollut. Res. Int. 2021, 28, 8709–8721. [Google Scholar] [CrossRef]
- Ye, B.; Jiang, J.; Liu, J.; Zheng, Y.; Zhou, N. Research on quantitative assessment of climate change risk at an urban scale: Review of recent progress and outlook of future direction. Renew. Sustain. Energy Rev. 2021, 135, 110415. [Google Scholar] [CrossRef]
- Hooftman, N.; Messagie, M.; Van Mierlo, J.; Coosemans, T. A review of the European passenger car regulations—Real driving emissions vs local air quality. Renew. Sustain. Energy Rev. 2018, 86, 1–21. [Google Scholar] [CrossRef]
- Liu, Y.J.; Dong, F. Haze pollution and corruption: A perspective of mediating and moderating roles. J. Clean. Prod. 2021, 279, 123550. [Google Scholar] [CrossRef]
- Wu, X.; Chen, Y.; Guo, J.; Cao, G. Inputs optimization to reduce the undesirable outputs by environmental hazards: A DEA model with data of PM2.5 in China. Nat. Hazards 2017, 90, 1–25. [Google Scholar] [CrossRef]
- Fan, F.; Cao, D.; Ma, N. Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities. Int. J. Environ. Res. Public Health 2020, 17, 6095. [Google Scholar] [CrossRef]
Category | Indicator Content | Introduction | Advantages | Limitations | Applicable Scope |
---|---|---|---|---|---|
Emission statistics | industrial pollutant emissions | independent statistics on corporate emissions | better continuity and availability | the coverage is not comprehensive, and the statistical caliber varies greatly | industry level |
Surface pollutant monitoring | particulate matter | environmental monitoring data on the concentration of ground pollutants | better effectiveness and timeliness | affected by the distribution of monitoring points, human operation error interference | national/ provincial |
Satellite remote sensing monitoring | optical depth/ concentration | satellite remote sensing monitoring and pollutants data conversion | wide monitoring range, high reliability of full-caliber statistical data less statistical error | affected by meteorological conditions and atmospheric environmental conditions; the identification of subdivided pollutants is low | regional/ global |
Comprehensive air index | air quality index/air pollution index | comprehensive data weighted based on air quality standards and the impact of pollutants | high-frequency data, the same statistical caliber | affected by air quality standards and personal error | national/ city level |
Others | days of up-to-standard weather | statistical data for the overall judgment of annual air quality | comprehensively reflect the city’s annual air quality status | difficult to unify statistical standards | city level |
home indoor survey | household survey data based on indoor air pollution | truly and effectively reflect the situation of indoor air pollutants | affected by subjective emotions, interference by personal error | city level |
No. | Policy Name | Release Year | Release Department |
---|---|---|---|
1 | Law of the People’s Republic of China on the prevention and control of atmospheric pollution | 1988, 2016, 2018 | The National People’s Congress |
2 | Guidance on promoting the joint prevention and control of air pollution and improving regional air quality | 2010 | The State Council |
3 | The air pollution prevention and control plan | 2013 | The State Council |
4 | The clean air action plan in Shanghai (2013–2017) | 2013 | Shanghai Municipality |
5 | Municipal air pollution prevention and control regulations | 2014 | Beijing Municipality |
6 | Measures for the prevention and control of haze pollution in Sichuan Province | 2015 | Sichuan Province |
7 | Notice on the implementation plan of full-compliance emissions for industrial pollution sources | 2016 | Ministry of Ecology and Environment |
8 | Work program for air pollution prevention and control around Beijing–Tianjin–Hebei and the surrounding urban agglomeration | 2017 | Cooperative group of air pollution prevention and control |
9 | The three-year action plan for winning the “blue sky defense war” | 2018 | The State Council |
10 | The comprehensive control action plan for air pollution in autumn and winter around the Beijing–Tianjin–Hebei urban agglomeration | 2018 | Ministry of Ecology and Environment |
11 | The linkage support work plan for winning the “blue sky defense war” | 2020 | Sichuan Province and Chongqing Municipality |
Category of Theoretical Basis | Core Point of Views | Main Features | Applicable Scope | |
---|---|---|---|---|
Environmental Regulation theory | Porter Hypothesis | Environmental regulations can improve the production efficiency and competitiveness of enterprises with innovation compensation effects, and achieve a win-win situation for environmental protection and economic growth. | The strong Porter hypothesis, weak Porter hypothesis, narrow Porter hypothesis | Enterprise environmental technology innovation capability |
Race-to-the-Bottom Hypothesis | Developing countries or regions with strict environmental regulations tend to lower environmental regulatory standards to attract the participation and entry of external enterprises to stimulate economic growth and enhance industrial competitiveness | Race-to-the-bottom or race-to-the top | The impact of industrial structure changes | |
Pollution Haven Hypothesis | Pollution-intensive multinational companies move to countries with loose environmental regulations with the large-scale transfer of pollutants to circumvent the domestic strict environmental regulations. | Phenomenon of pollution refuge or pollution paradise | Foreign direct investment analysis | |
Resource Curse theory | Abundant natural resources may be a curse of economic development, rather than an advantage. Most countries with rich natural resources grow more slowly than those with scarce resources. | Dutch disease phenomenon | Rich in natural resources or endowments | |
Stakeholder theory | Stakeholders influence the company’s long-term strategic goals, and it is necessary to clearly incorporate the interests of stakeholders into the company’s strategic decision-making in order to efficiently allocate and manage a lack of resources. | Enterprise dependence, strategic management and ownership distribution | Multi-subject participation and coordination in environmental governance | |
Benefit-cost analysis | The main body engaged in economic activities tends to acquire profit maximization with the maximum benefit and the minimum cost. The reason people want to carry out cost-benefit analyses is to obtain the maximum benefit with the least input. | Pursue the maximization of utility; the characteristics are self-interest, economy and calculation | Consider the gains and losses of specific economic actions in terms of economic value | |
Synergy theory | Taking the principle of self-organization as the core, emphasizing that the internal subsystems of the system form a certain structure and function according to certain rules, and mainly study the issue of multi-center subjects participating in the governance process. | System environment, collaborative process and synergistic effect | Diversified governance bodies, self-organization and common rules | |
Environmental Kuznets Curve | There is a certain regularity hypothesis relationship between economic growth and environmental quality, showing an inverted U-shaped change in the long run. | Scale effect, structure effect, technical effect | The long-term impact of the environment | |
System Science theory | Only when the various elements in the system reach a state of harmonious coexistence, can the system be called a coordinated development system. | The relationship between various elements in the system and its dynamic changes | Energy–economy–environment system | |
Externality theory | Externalities exist when a consumption or production activity has an indirect effect on other consumption or production activities that is not reflected in the market price | External cost, external effect or spillover effect | Perfectly competitive economic activity | |
Cooperative game theory | The interests of both parties in the game have increased, or at least the interests of one side have increased, while the interests of the other side are not harmed, so the interests of the entire society have increased | Divided into cooperative game and non-cooperative game theory, according to whether a binding agreement can be reached | Problem of income distribution |
Study Method or Model | Introduction | Main Features | Applicable Scope |
---|---|---|---|
Multiple linear regression model | A dependent variable is affected by multiple independent variables, and the relationship between each independent variable and the dependent variable is linear. | Correlation analysis is used to measure the strength of the association between several variables. | Identifies the correlation and causality between multiple variables. |
Spatial econometric regression model | This method is mainly used to study problems with spatial dependence, adding spatial interaction effects on the basis of time and individual effects, including the spatial lag model, the spatial error model and the spatial Durbin model, which are widely used as optimal models. | A spatial autocorrelation test needs to be performed on spatial data; the spatial weight value includes three methods of adjacency, geographic distance and economic distance, and the appropriate weight value method needs to be selected. | It is suitable for panel data and is mostly used to analyze the direct and indirect effects of variables. |
Geographically weighted regression model | This method incorporates the spatial correlation into the regression model, uses the relevant information from neighboring regions to estimate the local regression parameters, and realizes that the coefficients of the regression model in different regions change with spatial changes. | It is a cross-sectional data model; the value of the spatial weight includes the distance threshold method, the inverse distance method, the Gaussian function method, etc.; through the regression of each area one by one, the parameter matrix of all areas to be estimated is obtained. | The time series of the sample data is short and the cross-sections vary greatly. |
Spatial simultaneous equation model | This method combines multiple regression equations to explore the impact and spatial effects of various factors on the research object. It is usually estimated using the two-stage or three-stage least squares method. | Reflecting the interactive influence and spatial effects among various variables, the method of selecting spatial weights is similar to that of spatial measurement models. | The interaction between spatial effects and variables. Response mechanism. |
Evolutionary game model | This method combines game theory analysis and dynamic evolution process analysis, which emphasizes a dynamic equilibrium. | Analyzing the factors affecting the formation of social habits, norms, institutions or institutions and explain their formation process | Assumes that participants are completely rational and have consistent preferences. |
Data envelopment analysis model | Quantitative analysis method, used to evaluate the relative effectiveness of comparable units of the same type. | A method of linear programming with multiple input indicators and multiple output indicators. | Affected by the selection of cost indicators and benefit indicators. |
Computable general equilibrium model | This method can be used to analyze the economic impact of many variables and address regional issues within and between countries through multi-regional analysis. | It has the characteristics of computability, generality and balance | Real economic data are used as an input and the equilibrium solution is calculated. |
Multi-area input-output model | This method can comprehensively analyze the quantitative dependence between inputs and outputs in economic activities. | It is composed of two parts: the input–output table and the mathematical equations established according to the balance relationship of the input–output table. | Analyzing and examining the quantitative dependence between product production and consumption in various sectors of the national economy |
Spatial auto-correlation analysis | This method is used to measure the spatial distribution characteristics of physical or ecological variables and their influence on the field. | Global autocorrelation coefficient and local autocorrelation coefficient. | Correlation analysis can detect whether there is a correlation between changes in two phenomena (statistics). |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, L.; Deng, P.; Wang, J.; Wang, Z.; Sun, J. Retrospect and Outlook of Research on Regional Haze Pollution in China: A Systematic Literature Review. Int. J. Environ. Res. Public Health 2021, 18, 11495. https://doi.org/10.3390/ijerph182111495
Li L, Deng P, Wang J, Wang Z, Sun J. Retrospect and Outlook of Research on Regional Haze Pollution in China: A Systematic Literature Review. International Journal of Environmental Research and Public Health. 2021; 18(21):11495. https://doi.org/10.3390/ijerph182111495
Chicago/Turabian StyleLi, Li, Peng Deng, Jun Wang, Zixuan Wang, and Junwei Sun. 2021. "Retrospect and Outlook of Research on Regional Haze Pollution in China: A Systematic Literature Review" International Journal of Environmental Research and Public Health 18, no. 21: 11495. https://doi.org/10.3390/ijerph182111495