Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Methodology
2.2.1. Dagum Gini Coefficient and Its Decomposition
2.2.2. Kernel Density Estimation
2.2.3. Spatial Autocorrelation Analysis
- (1)
- Global spatial autocorrelation
- (2)
- Local spatial autocorrelation
2.2.4. Spatial Quantile Regression Model
2.3. Data and Data Source
2.3.1. Data of Annual Average PM2.5 Concentration
2.3.2. Data of Driving Factors
- (1)
- Economic development level (PGDP): Some studies believed that the energy consumption and pollutant emissions caused by economic development had aggravated the environmental pollution situation [85]; Nevertheless, some other studies confirmed that economic development indirectly alleviated environmental pollution by promoting the optimization and use of advanced environmental protection technologies [86]. To verify the impact of economic development on PM2.5, the economic development level expressed by per capita GDP is included as an explanatory variable.
- (2)
- (3)
- Population density (PD): PD was measured by the population per square kilometer. It has a dual impact on the environment [33,40]. For one thing, higher population density would lead to more domestic garbage and sewage, which positively affects PM2.5; For another, higher population density negatively affects PM2.5 through scale effect and agglomeration effect, so the impact of PD on PM2.5 is uncertain.
- (4)
- Technology level (TEC): TEC, expressed by science and technology expenditure, is a vital factor in mitigating haze pollution [89]. It can not only improve energy efficiency and productivity and prevent haze pollution from the source, but also strengthen pollution control and alleviate haze pollution from the terminal. Hence, the coefficient is expected to be positive.
- (5)
- Financial expenditure scale (FES): FES was denoted by the proportion of fiscal expenditure in GDP. Financial expenditure, especially environmental protection expenditure, provides special funds for preventing and controlling environmental pollution and improving environmental quality [90]. Therefore, FES can play a crucial role in haze pollution mitigation.
- (6)
- Greening level (GL): Greening level was defined as the green coverage rate in built-up. GL can effectively purify sewage, reduce noise, and absorb pollutants and radioactive materials [82]. Hence, high green coverage is a significant way to purify the air and mitigate PM2.5 pollution.
- (7)
- Intensity of public transportation (TN): TN was measured by Annual public bus (tram) passenger traffic. Studies showed that TN hindered PM2.5 pollution by alleviating the pressure of energy use and reducing air pollutant emissions [91]. Thus, the impact of TN on PM2.5 is expected to be negative.
3. Results and Discussion
3.1. Temporal Variation Characteristics
3.2. Spatial Distribution Characteristics
3.3. Spatial Correlation Analysis
3.4. Regional Difference and Its Decomposition
3.5. Dynamic Evolution Characteristics
3.6. Driving Factors
- (1)
- The level of economic development (PGDP) had a negative effect on PM2.5 at the 10th and 90th quantiles while having a positive impact at other quantiles. This result indicates that in cities with low PM2.5 concentration, economic development is easier to mitigate haze pollution. In cities with medium PM2.5 concentration, the rapid industrialization process leads to excessive energy consumption and increasing industrial pollutants, thereby aggravating PM2.5 pollution. For cities with high PM2.5 concentration, the pressure of haze governance and the urgent requirement of ecological civilization construction force the local governments to take a series of PM2.5 mitigation measures, including increasing investment in environmental protection, guiding technological innovation, strengthening environmental protection law enforcement, etc. This reduces the PM2.5 concentration within the region and triggers the spillover and diffusion of pollution control technologies between regions, inhibiting PM2.5 pollution in adjacent regions.
- (2)
- At different quantiles, the estimated coefficients of industrial service level (IS) were negative and passed the 1% significance level test, indicating that the tertiary industry has played a crucial role in alleviating PM2.5 pollution in local and neighboring areas. Similar conclusions were drawn by Zhu et al. [92]: The rapid development of the tertiary industry has shifted the production factors originally attached to the industrial chain with high energy consumption and low efficiency to the industrial chain with high efficiency and low consumption, thereby improving the effective utilization rate of resources and environment and alleviating the PM2.5 pollution. At the same time, developing an environmentally friendly tertiary industry in a particular area is conducive to promoting environmental protection technology cooperation and sharing advanced governance concepts with neighboring areas, suppressing PM2.5 pollution in neighboring areas.
- (3)
- The coefficients of population density (PD) were significantly positive across different quantiles at the significance level of 1%, which implies that the positive impact of population density on PM2.5 is more significant than the negative impact. Thus, PD exacerbates PM2.5 pollution status. This result has been confirmed by previous studies [93,94]: A great demand for production, living, and infrastructure caused by high population density easily triggers a series of problems, such as excessive consumption of energy and resources, a massive discharge of pollutants, and insufficient carrying capacity of the urban environment, which aggravates the PM2.5 pollution situation. Moreover, under atmospheric circulation, atmospheric chemistry, and other natural factors, PD easily worsens the PM2.5 pollution level in adjacent areas while causing PM2.5 pollution in a certain area.
- (4)
- The estimated coefficients of technology level (TEC) were negative and significant at a 1% significance level except for the 75th quantile level. These results suggest that the improvement of technology level is more conducive to promoting clean production at the source of enterprises, improving energy efficiency and resource utilization rate [95], thus inhibiting PM2.5 pollution in local and adjacent areas. In addition, the negative effects of TEC were more significant at the low and high quantiles than at the middle quantile. The reason may be that the improvement of TEC can effectively ameliorate PM2.5 pollution through innovative technology and equipment, and it may also promote the rapid development of the economy, leading to a large amount of pollutants emission and causing PM2.5 pollution. Therefore, the dual effect of technology level may lead to the insignificant negative effect of technology level on PM2.5 at the 75th quantile.
- (5)
- The financial expenditure scale (FES) adversely affected PM2.5, and all estimated coefficients were significantly negative at the 1% significance level, which was consistent with our expectations. As mentioned previously, the exclusive funds provided by the fiscal expenditure play a substantial role in haze control and prevention in local and neighboring areas. On the one hand, it can directly control PM2.5 pollution; On the other hand, it can induce enterprises to adopt environmentally friendly production technologies and pollution control technologies through scientific research and policy incentives, indirectly realizing the prevention and control of PM2.5 pollution. In addition, FES also has a spillover effect. While promoting the development of special haze control activities in a certain area, it also provides a demonstration role for neighboring areas and promotes the improvement of air quality in neighboring areas. Hence, improving the financial expenditure scale is a crucial pathway to effectively mitigate PM2.5 pollution. The cities in the YREB should pay more attention to the level and efficiency of fiscal expenditure.
- (6)
- The estimated coefficient of greening level (GL) was positive in the low and high quantiles, but negative in the middle quantiles. The reason may be that the areas in the middle quantiles attach more importance to the coordination of economic development and environmental protection, formulate a relatively complete set of environmental protection schemes, and actively promote the construction of urban greening levels. As a result, the GL can better enhance the regional ecological carrying capacity and environmental self-purification ability and exert its powerful functions in dust removal, toxic and harmful gas filtration, and air purification, thus suppressing PM2.5 pollution. At the same time, the improvement of GL in a certain area can drive green level management and construction in neighboring areas and play a certain role in controlling PM2.5 pollution in neighboring areas. However, at the low quantiles of PM2.5 concentration, due to the public’s lack of attention to air quality and the problems of management and maintenance in the greening process, the suppression effect of GL on PM2.5 pollution has not yet appeared in local and adjacent areas. At high quantiles, the ability of green plants to absorb PM2.5 and purify the air is very limited. Under large-scale pollution emissions, relying solely on GL to control PM2.5 pollution is only a drop in the bucket, so GL cannot effectively reduce PM2.5 pollution in local and adjacent areas.
- (7)
- Intensity of public transportation (TN) has a negative correlation with PM2.5 in the 10th–50th quantiles while a positive correlation with PM2.5 in the 75th–90th quantiles. In the 10th–50th quantiles of PM2.5, TN is more conducive to human capital agglomeration. Human capital agglomeration can drive the flow of knowledge and technology to central cities and promote the application of clean and environmental protection technologies, thus improving the air quality in central cities. Meanwhile, it can also mitigate the PM2.5 pollution situation in neighboring areas through the spillover effect of knowledge and technology. However, the transportation infrastructure industry is not a smokeless industry, and the increase in public transportation intensity often leads to more emissions of carbon dioxide and nitrogen oxides. In addition, it is rather difficult to control PM2.5 pollution at high quantiles, so TN cannot hinder the PM2.5 pollution in the local and neighboring areas.
4. Discussion
4.1. Suggestions for PM2.5 Mitigation Measures
4.2. Future Research Direction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Chen, Y.; Zhu, Z.; Cheng, S. Industrial agglomeration and haze pollution: Evidence from China. Sci. Total Environ. 2022, 845, 157392. [Google Scholar] [CrossRef]
- Liu, H.M.; Fang, C.L.; Zhang, X.L.; Wang, Z.Y.; Bao, C.; Li, F.Z. The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach. J. Clean. Prod. 2017, 165, 323–333. [Google Scholar] [CrossRef]
- Zhao, Q.; Yuan, C.H. Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations. Sustainability 2020, 12, 1607. [Google Scholar] [CrossRef]
- Zhao, W.C.; Cheng, J.P.; Li, D.L.; Duan, Y.S.; Wei, H.P.; Ji, R.X.; Wang, W.H. Urban ambient air quality investigation and health risk assessment during haze and non–haze periods in Shanghai, China. Atmos. Pollut. Res. 2013, 4, 275–281. [Google Scholar] [CrossRef]
- Ding, Y.; Zhang, M.; Qian, X.; Li, C.; Chen, S.; Wang, W. Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China. J. Clean. Prod. 2019, 211, 1480–1490. [Google Scholar] [CrossRef]
- Li, M.; Zhang, L. Haze in China: Current and future challenges. Environ. Pollut. 2014, 189, 85–86. [Google Scholar] [CrossRef]
- Xu, B.; Lin, B.Q. What cause large regional differences in PM2.5 pollutions in China? Evidence from quantile regression model. J. Clean. Prod. 2018, 174, 447–461. [Google Scholar] [CrossRef]
- Zhang, B.B.; Wu, B.B.; Liu, J. PM2.5 pollution-related health effects and willingness to pay for improved air quality: Evidence from China’s prefecture-level cities. J. Clean. Prod. 2020, 273, 122876. [Google Scholar] [CrossRef]
- Hao, Y.; Peng, H.; Temulun, T.; Liu, L.Q.; Mao, J.; Lu, Z.N.; Chen, H. How harmful is air pollution to economic development? New evidence from PM2.5 concentrations of Chinese cities. J. Clean. Prod. 2018, 172, 743–757. [Google Scholar] [CrossRef]
- Liu, G.X.; Dong, X.C.; Kong, Z.Y.; Dong, K.Y. Does national air quality monitoring reduce local air pollution? The case of PM2.5 for China. J. Environ. Manag. 2021, 296, 113232. [Google Scholar] [CrossRef]
- Shi, K.F.; Wu, Y.Z.; Li, L.Y. Quantifying and evaluating the effect of urban expansion on the fine particulate matter (PM2.5) emissions from fossil fuel combustion in China. Ecol. Indic. 2021, 125, 107541. [Google Scholar] [CrossRef]
- Lv, Q.; Chai, Z. Highly efficient and clean utilization of fossil energy under carbon peak and neutrality targets. Bull. Chin. Acad. Sci. 2022, 37, 541–548. [Google Scholar]
- Wang, Y.; Hu, J.; Zhu, J.; Li, J.; Qin, M.; Liao, H.; Chen, K.; Wang, M. Health burden and economic impacts attributed to PM2.5 and O3 in China from 2010 to 2050 under different representative concentration pathway scenarios. Resour. Conserv. Recycl. 2021, 173, 105731. [Google Scholar] [CrossRef]
- Heck, T.; Hirschberg, S. China: Economic impacts of air pollution in the country. Encycl. Environ. Health 2011, 1, 625–640. [Google Scholar]
- Liu, H.M.; Fang, C.L.; Huang, J.J.; Zhu, X.D.; Zhou, Y.; Wang, Z.B.; Zhang, Q. The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration. Acta Geogr. Sin. 2018, 73, 177–191. [Google Scholar]
- Zhang, S.L.; Wang, Y.H.; Li, Y.; Zhang, P.F. Spatial distribution of haze pollution and its influencing factors. China Popul. Resour. Environ. 2017, 27, 15–22. [Google Scholar]
- Xie, Q.C.; Xu, X.; Liu, X.Q. Is there an EKC between economic growth and smog pollution in China? New evidence from semiparametric spatial autoregressive models. J. Clean. Prod. 2019, 220, 873–883. [Google Scholar] [CrossRef]
- Martini, F.; Hasenkopf, C.A.; Roberts, D.C. Statistical analysis of PM2.5 observations from diplomatic facilities in China. Atmos. Environ. 2015, 110, 174–185. [Google Scholar] [CrossRef]
- Shen, Y.; Zhang, L.; Fang, X.; Ji, H.; Li, X.; Zhao, Z. Spatiotemporal patterns of recent PM2.5 concentrations over typical urban agglomerations in China. Sci. Total Environ. 2019, 655, 13–26. [Google Scholar] [CrossRef]
- Jiang, W.; Gao, W.D.; Gao, X.M.; Ma, M.C.; Zhou, M.M.; Du, K.; Ma, X. Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019. J. Environ. Manag. 2021, 296, 113172. [Google Scholar] [CrossRef]
- Li, L.; Qian, J.; Ou, C.Q.; Zhou, Y.X.; Guo, C.; Guo, Y. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011. Environ. Pollut. 2014, 190, 75–81. [Google Scholar] [CrossRef]
- Miao, Y.; Guo, J.; Liu, S.; Liu, H.; Zhang, G.; Yan, Y.; He, J. Relay transport of aerosols to Beijing-Tianjin-Hebei region by multi-scale atmospheric circulations. Atmos. Environ. 2017, 165, 35–45. [Google Scholar] [CrossRef]
- Lu, D.; Xu, J.; Yang, D.; Zhao, J. Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014. Atmos. Pollut. Res. 2017, 8, 1151–1159. [Google Scholar] [CrossRef]
- Wang, Y.C.; Liu, C.U.; Wang, Q.Y.; Qin, Q.D.; Ren, H.H.; Cao, J.J. Impacts of natural and socioeconomic factors on PM2.5 from 2014 to 2017. J. Environ. Manag. 2021, 284, 112071. [Google Scholar] [CrossRef]
- Sabetghadam, S.; Ahmadi-Givi, F. Relationship of extinction coefficient, air pollution, and meteorological parameters in an urban area during 2007 to 2009. Environ. Sci. Pollut. Res. 2014, 21, 538–547. [Google Scholar] [CrossRef]
- Pateraki, S.; Asimakopoulos, D.N.; Flocas, H.A.; Maggos, T.; Vasilakos, C. The role of meteorology on different sized aerosol fractions (PM10, PM2.5, PM2.5–10). Sci. Total Environ. 2012, 419, 124–135. [Google Scholar] [CrossRef]
- Yang, Y.R.; Liu, X.G.; Qu, Y.; Wang, J.L.; An, J.L.; Zhang, Y.; Zhang, F. Formation mechanism of continuous extreme haze episodes in the megacity Beijing, China, in January 2013. Atmos. Res. 2015, 155, 192–203. [Google Scholar] [CrossRef]
- Vieira-Filho, M.S.; Lehmann, C.; Fornaro, A.J.A.E. Influence of local sources and topography on air quality and rainwater composition in Cubato and So Paulo, Brazil. Atmos. Environ. 2015, 101, 200–208. [Google Scholar] [CrossRef]
- Alvarez, H.B.; Sosa, E.R.; Alvarez, P.S.; Krupa, S. Air quality standards for particulate matter (PM) at high altitude cities. Environ. Pollut. 2013, 173, 255–256. [Google Scholar] [CrossRef]
- Han, L.J.; Zhou, W.Q.; Pickett, S.T.A.; Li, W.F.; Li, L. An optimum city size? The scaling relationship for urban population and fine particulate (PM2.5) concentration. Environ. Pollut. 2016, 208, 96–101. [Google Scholar] [CrossRef]
- Hixson, M.; Mahmud, A.; Hu, J.; Kleeman, M.J. Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley, USA. J. Air Waste Manag Assoc 2012, 62, 566–575. [Google Scholar] [CrossRef]
- Han, L.; Zhou, W.; Li, W.; Li, L. Impact of urbanization level on urban air quality: A case of fine particles (PM2.5) in Chinese cities. Environ. Pollut. 2014, 194, 163–170. [Google Scholar] [CrossRef] [PubMed]
- Lou, C.R.; Liu, H.Y.; Li, Y.F.; Li, Y.L. Socioeconomic drivers of PM2.5 in the accumulation phase of air pollution episodes in the Yangtze River Delta of China. Int. J. Environ. Res. Public Health 2016, 13, 928. [Google Scholar] [CrossRef]
- Grossman, G.M.; Krueger, A.B. Economic growth and the environment. Nber Work. Pap. 2001, 110, 277–284. [Google Scholar]
- Ma, Y.R.; Ji, Q.; Fan, Y. Spatial linkage analysis of the impact of regional economic activities on PM2.5 pollution in China. J. Clean. Prod. 2016, 139, 1157–1167. [Google Scholar] [CrossRef]
- Cheng, Z.; Li, L.; Liu, J. Identifying the spatial effects and driving factors of urban PM2.5 pollution in China. Ecol. Indic. 2017, 82, 61–75. [Google Scholar] [CrossRef]
- Zhu, W.W.; Wang, M.C.; Zhang, B.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]
- Lin, X.; Wang, D. Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China. J. Geogr. Sci. 2016, 26, 1533–1549. [Google Scholar] [CrossRef]
- Wu, Q.; Guo, R.; Luo, J.; Chen, C. Spatiotemporal evolution and the driving factors of PM2.5 in Chinese urban agglomerations between 2000 and 2017. Ecol. Indic. 2021, 125, 107491. [Google Scholar] [CrossRef]
- Zhao, X.L.; Zhou, W.Q.; Han, L.J.; Locke, D. Spatiotemporal variation in PM2.5 concentrations and their relationship with socioeconomic factors in China’s major cities. Environ. Int. 2019, 133, 105145. [Google Scholar] [CrossRef]
- Wang, Y.P.; Komonpipat, S. Revisiting the environmental Kuznets curve of PM2.5 concentration: Evidence from prefecture-level and above cities of China. Environ. Sci. Pollut. Res. 2020, 27, 9336–9348. [Google Scholar] [CrossRef] [PubMed]
- Dietz, T.; Rosa, E.A. Effects of population and affluence on CO2 emissions. Proc. Natl. Acad. Sci. USA 1997, 94, 175–179. [Google Scholar] [CrossRef] [PubMed]
- Wang, M.W.; Che, Y.; Yang, K.; Wang, M.; Xiong, L.J.; Huang, Y.C. A local-scale low-carbon plan based on the STIRPAT model and the scenario method: The case of Minhang District, Shanghai, China. Energy Policy 2011, 39, 6981–6990. [Google Scholar] [CrossRef]
- Xu, B.; Lin, B.Q. How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models. Energy Econ. 2015, 48, 188–202. [Google Scholar] [CrossRef]
- Xu, B.; Luo, L.; Lin, B. A dynamic analysis of air pollution emissions in China: Evidence from nonparametric additive regression models. Ecol. Indic. 2016, 63, 346–358. [Google Scholar] [CrossRef]
- Brunsdon, C.; Fotheringham, A.S.; Charlton, M.E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 1996, 28, 281–298. [Google Scholar] [CrossRef]
- Wang, J.F.; Zhang, T.L.; Fu, B.J. A measure of spatial stratified heterogeneity. Ecol. Indic. 2016, 67, 250–256. [Google Scholar] [CrossRef]
- Anselin, L. Spatial Econometrics: Methods and Models; Kluwer Academic: Boston, MA, USA, 1988. [Google Scholar]
- Anselin, L.; Florax, R.; Ray, S. Advanced in Spatial Econometrics: Methodology, Tools and Applications; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
- Elhorst, J.P. Specification and estimation of spatial panel data models. Int. Reg. Sci. Rev. 2003, 26, 244–268. [Google Scholar] [CrossRef]
- Tu, J.; Xia, Z.G. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Sci. Total Environ. 2008, 407, 358–378. [Google Scholar] [CrossRef]
- Wang, S.J.; Gao, S.; Chen, J. Spatial heterogeneity of driving factors of urban haze pollution in China based on GWR model. Geogr. Reasearch 2020, 39, 651–668. [Google Scholar]
- Bai, L.; Jiang, L.; Yang, D.Y.; Liu, Y.B. Quantifying the spatial heterogeneity influences of natural and socioeconomic factors and their interactions on air pollution using the geographical detector method: A case study of the Yangtze River Economic Belt, China. J. Clean. Prod. 2019, 232, 692–704. [Google Scholar] [CrossRef]
- Wang, J.F.; Li, X.H.; Christakos, G.; Liao, Y.L.; Zhang, T.; Gu, X.; Zheng, X.Y. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. Int. J. Geogr. Inf. Sci. 2010, 24, 107–127. [Google Scholar] [CrossRef]
- Yang, D.; Wang, X.; Xu, J.; Xu, C.; Lu, D.; Ye, C.; Wang, Z.; Bai, L. Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China. Environ. Pollut. 2018, 241, 475–483. [Google Scholar] [CrossRef]
- Liu, X.J.; Xia, S.Y.; Yang, Y.; Wu, J.F.; Zhou, Y.N.; Ren, Y.W. Spatiotemporal dynamics and impacts of socioeconomic and natural conditions on PM2.5 in the Yangtze River Economic Belt. Environ. Pollut. 2020, 263, 114569. [Google Scholar] [CrossRef]
- Wang, K.L.; Pang, S.Q.; Ding, L.L.; Miao, Z. Combining the biennial Malmquist-Luenberger index and panel quantile regression to analyze the green total factor productivity of the industrial sector in China. Sci. Total Environ. 2020, 739, 140280. [Google Scholar] [CrossRef]
- Tang, L.W.; He, G. How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China. Energy 2021, 235, 121375. [Google Scholar] [CrossRef]
- Hu, S.L.; Zeng, G.; Cao, X.Z.; Yuan, H.X.; Chen, B. Does technological innovation promote green development? A case study of the Yangtze River Economic Belt in China. Int. J. Environ. Res. Public Health 2021, 18, 6111. [Google Scholar] [CrossRef]
- Li, Y.; Shao, H.; Jiang, N.; Shi, G.; Cheng, X. The evolution of the urban spatial pattern in the Yangtze River Economic Belt: Based on multi-source remote sensing data. Sustainability 2018, 10, 2733. [Google Scholar] [CrossRef]
- Mao, M.; Sun, H.F.; Zhang, X.L. Air pollution characteristics and health risks in the Yangtze River Economic Belt, China during winter. Int. J. Environ. Res. Public Health 2020, 17, 9172. [Google Scholar] [CrossRef] [PubMed]
- Yan, J.W.; Tao, F.; Zhang, S.Q.; Lin, S.; Zhou, T. Spatiotemporal distribution characteristics and driving forces of PM2.5 in three urban agglomerations of the Yangtze River Economic Belt. Int. J. Environ. Res. Public Health 2021, 18, 2222. [Google Scholar] [CrossRef]
- Zhang, X.; Feng, T.; Zhao, S.Y.; Yang, G.; Zhang, Q.; Qin, G.R.; Liu, L.; Long, X.; Sun, W.W.; Gao, C.; et al. Elucidating the impacts of rapid urban expansion on air quality in the Yangtze River Delta, China. Sci. Total Environ. 2021, 799, 149426. [Google Scholar] [CrossRef] [PubMed]
- Dagum, C. A new approach to the decomposition of the Gini income inequality ratio. Empir. Econ. 1997, 22, 515–531. [Google Scholar] [CrossRef]
- Han, H.B.; Ding, T.; Nie, L.; Hao, Z.Z. Agricultural eco-efficiency loss under technology heterogeneity given regional differences in China. J. Clean. Prod. 2019, 250, 119511. [Google Scholar] [CrossRef]
- Kumar, S.; Russell, R.R. Technological change, technological catch-up, and capital deepening: Relative contributions to growth and convergence. Am. Econ. Rev. 2002, 92, 527–548. [Google Scholar] [CrossRef]
- Plackett, R.L. An Introduction to the Theory of Statistics; Oliver and Boyd: Edinburgh, UK, 1971. [Google Scholar]
- Tan, S.K.; Hu, B.X.; Kuang, B.; Zhou, M. Regional differences and dynamic evolution of urban land green use efficiency within the Yangtze River Delta, China. Land Use Policy 2021, 106, 105449. [Google Scholar] [CrossRef]
- Katkovnik, V.; Shmulevich, I. Kernel density estimation with adaptive varying window size. Pattern Recognit. Lett. 2002, 23, 1641–1648. [Google Scholar] [CrossRef]
- Silverman, B.W. Density Estimation for Statistics and Data Analysis; Chapman and Hall: New York, NY, USA, 1986. [Google Scholar]
- Quah, D. Galton’s fallacy and tests of the convergence hypothesis. Scand. J. Econ. 1993, 95, 427–443. [Google Scholar] [CrossRef]
- Tobler, W.R. A computer movie simulating urban growth in the detroit region. Econ. Geogr. 1970, 46, 234–240. [Google Scholar] [CrossRef]
- Moran, P.A.P. The interpretation of statistical maps, Journal of the Royal Statistical Society. J. R. Stat. Soc. 1948, 10, 243–251. [Google Scholar]
- Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef]
- Ord, J.K.; Getis, A. Local spatial autocorrelation statistics: Distributional issues and an application. Geogr. Anal. 1995, 27, 286–306. [Google Scholar] [CrossRef]
- Koenker, R.; Bassett, J.G. Regression quantiles. Econometrica 1978, 46, 33–50. [Google Scholar] [CrossRef]
- Koenker, R. Quantile regression for longitudinal data. J. Multivar. Anal. 2004, 91, 74–89. [Google Scholar] [CrossRef]
- Chernozhukov, V.; Hansen, C. Instrumental quantile regression inference for structural and treatment effect models. J. Econom. 2006, 132, 491–525. [Google Scholar] [CrossRef]
- Su, L.; Yang, Z. Instrumental variable quantile estimation of spatial autoregressive models. Work. Pap. 2007, 22476. [Google Scholar]
- Peng, J.; Chen, S.; Lu, H.L.; Liu, Y.X.; Wu, J.S. Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011. Remote Sens. Environ. 2016, 174, 109–121. [Google Scholar] [CrossRef]
- Xu, Y.Z.; Fan, X.M.; Zhang, Z.Q.; Zhang, R.J. Trade liberalization and haze pollution: Evidence from China. Ecol. Indic. 2020, 109, 105825. [Google Scholar] [CrossRef]
- Shi, T.; Zhang, W.; Zhou, Q.; Wang, K. Industrial structure, urban governance and haze pollution: Spatiotemporal evidence from China. Sci. Total Environ. 2020, 742, 139228. [Google Scholar] [CrossRef]
- 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]
- Zhou, J.; Lan, H.L.; Zhao, C.; Zhou, J.P. Haze Pollution Levels, Spatial Spillover Influence, and Impacts of the Digital Economy: Empirical Evidence from China. Sustainability 2021, 13, 9076. [Google Scholar] [CrossRef]
- Li, G.D.; Fang, C.L.; Wang, S.J.; Sun, S. The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China. Environ. Sci. Technol. 2016, 50, 11452–11459. [Google Scholar] [CrossRef] [PubMed]
- Zoundi, Z. CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renew. Sustain. Energy Rev. 2017, 72, 1067–1075. [Google Scholar] [CrossRef]
- Hao, Y.; Zheng, S.Q.; Zhao, M.Y.; Wu, H.T.; Guo, Y.X.; Li, Y.W. Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China-New evidence using the dynamic threshold panel model. Energy Rep. 2020, 6, 28–39. [Google Scholar] [CrossRef]
- Wang, L.; Jiang, S.M.; Xu, H. Reexamining the impact of industrial structure on haze pollution based on the Yangtze River Delta. Atmosphere 2021, 12, 613. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, W.; Liang, L.W.; Wang, D.P.; Cui, X.H.; Wei, W.D. Spatial-temporal pattern evolution and driving factors of China’s energy efficiency under low-carbon economy. Sci. Total Environ. 2020, 739, 140197. [Google Scholar] [CrossRef]
- Jiang, L.; Zhou, H.F.; He, S.X. The role of governments in mitigating SO2 pollution in China: A perspective of fiscal expenditure. Environ. Sci. Pollut. Res. 2020, 27, 33951–33964. [Google Scholar] [CrossRef]
- Hassan, S.T.; Zhu, B.Z.; Lee, C.C.; Ahmad, P.; Sadiq, M. Asymmetric impacts of public service “transportation” on the environmental pollution in China. Environ. Impact Assess. Rev. 2021, 91, 106660. [Google Scholar] [CrossRef]
- Zhu, B.; Zhang, M.; Zhou, Y.; Wang, P.; Xie, R. Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: A novel integrated approach. Energy Policy 2019, 134, 110946. [Google Scholar] [CrossRef]
- Luo, Z.; Wan, G.; Wang, C.; Zhang, X. Urban pollution and road infrastructure: A case study of China. China Econ. Rev. 2018, 49, 171–183. [Google Scholar] [CrossRef]
- Zheng, S.; Kahn, M.E. Understanding China’s urban pollution dynamics. Oper. Res. Manag. Sci. 2013, 51, 731–772. [Google Scholar] [CrossRef] [Green Version]
- Yang, Y.; Cai, W.; Wang, C. Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces. Appl. Energy 2014, 131, 117–127. [Google Scholar]
Variables | Meaning | Unit |
---|---|---|
PM2.5 | The average concentration of PM2.5 | mg/m3 |
The level of economic development (PGDP) | Per capita GDP | 104 yuan |
Industrial structure (IS) | The proportion of tertiary industry output value in GDP | % |
Population density (PD) | Population per square kilometer | persons/square kilometer |
Technology level (TEC) | Expenditure for science and technology | 108 yuan |
Financial expenditure scale (FES) | The proportion of fiscal expenditure in GDP | % |
Greening level (GL) | Green coverage rate in a built-up area | % |
The intensity of public transportation (TN) | Annual public bus (tram) passenger traffic | 108 persons |
Variables | Mean | Std. Dev | Min | Max | Obs. |
---|---|---|---|---|---|
PM2.5 | 48.1816 | 15.7575 | 8.0915 | 101.1900 | 1728 |
PGDP | 3.5348 | 3.0292 | 0.0099 | 19.9017 | 1728 |
IS | 38.0672 | 8.1988 | 20.6600 | 77.4900 | 1728 |
PD | 481.6207 | 294.8146 | 52.7300 | 2305.6300 | 1728 |
TEC | 80,839.2100 | 258,354.8000 | 77.0000 | 4,263,655.0000 | 1728 |
FES | 16.4643 | 8.3178 | 4.9095 | 68.7608 | 1728 |
GL | 37.5647 | 8.6913 | 0.3600 | 93.8100 | 1728 |
TN | 2.0277 | 3.8147 | 0.0000 | 28.3800 | 1728 |
Year | Moran’s I | Z-Score | Year | Moran’s I | Z-Score |
---|---|---|---|---|---|
2003 | 0.176 *** | 14.659 | 2011 | 0.169 *** | 14.084 |
2004 | 0.173 *** | 14.492 | 2012 | 0.162 *** | 13.635 |
2005 | 0.169 *** | 14.167 | 2013 | 0.177 *** | 14.714 |
2006 | 0.17 2 *** | 14.454 | 2014 | 0.211 *** | 17.453 |
2007 | 0.196 *** | 16.285 | 2015 | 0.240 *** | 19.677 |
2008 | 0.175 *** | 14.618 | 2016 | 0.205 *** | 16.960 |
2009 | 0.206 *** | 17.063 | 2017 | 0.240 *** | 19.769 |
2010 | 0.178 *** | 14.789 | 2018 | 0.254 *** | 20.837 |
Variables | Spatial Durbin Model | Spatial Quantile Regression | ||||
---|---|---|---|---|---|---|
τ = 10 | τ = 25 | τ = 50 | τ = 75 | τ = 90 | ||
WY | 1.6962 *** | 0.8839 *** | 0.7802 *** | 0.7635 *** | 0.7834 *** | 0.7055 *** |
(0.0219) | (0.0255) | (0.0198) | (0.0134) | (0.0142) | (0.0172) | |
PGDP | −0.8459 *** | −0.0632 | 0.0050 | 0.2144 ** | 0.0975 | −0.1355 |
(0.1524) | (0.1946) | (0.1335) | (0.1010) | (0.1670) | (0.1336) | |
IS | −0.1979 *** | −0.1269 ** | −0.1343 *** | −0.1278 *** | −0.0763 * | −0.1226 ** |
(0.0358) | (0.0582) | (0.0284) | (0.0226) | (0.0416) | (0.0573) | |
PD | 0.0181 *** | 0.0163 *** | 0.0102 *** | 0.0108 *** | 0.0122 *** | 0.0105 *** |
(0.0011) | (0.0019) | (0.0015) | (0.0011) | (0.0015) | (0.0011) | |
TEC | −0.0185 | −0.0278 *** | −0.0457 ** | −0.0366 *** | −0.0396 | −0.0302 *** |
(0.0120) | (0.0101) | (0.0189) | (0.0076) | (0.0259) | (0.0079) | |
FES | −0.5255 *** | −0.3226 *** | −0.3394 *** | −0.2425 *** | −0.2475 *** | −0.3646 *** |
(0.0358) | (0.0520) | (0.0406) | (0.0309) | (0.0328) | (0.0371) | |
GL | −0.0115 | 0.0193 | −0.0124 | −0.0081 | 0.0001 | 0.0128 |
(0.0268) | (0.0431) | (0.0258) | (0.0182) | (0.0217) | (0.0315) | |
TN | −0.4326 *** | −0.4603 *** | −0.0406 | −0.0395 | 0.0520 | 0.1141 |
(0.0957) | (0.1490) | (0.1171) | (0.0586) | (0.1149) | (0.1466) | |
Cons | 54.1386 *** | 41.7269 *** | 44.1979 *** | 54.1068 *** | 55.1763 *** | |
(0.1810) | (0.2582) | (0.1122) | (0.2103) | (0.2213) | ||
Obs | 1728 | 1728 | 1728 | 1728 | 1728 | 1728 |
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Wang, W.; Wang, Y. Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt. Sustainability 2023, 15, 3381. https://doi.org/10.3390/su15043381
Wang W, Wang Y. Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt. Sustainability. 2023; 15(4):3381. https://doi.org/10.3390/su15043381
Chicago/Turabian StyleWang, Weiguang, and Yangyang Wang. 2023. "Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt" Sustainability 15, no. 4: 3381. https://doi.org/10.3390/su15043381