Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Cheng, Kangkang
- Spatial Overflow Effect of Haze Pollution in China and Its Influencing Factors
Abstract Views :108 |
PDF Views:0
Authors
Affiliations
1 Wuxi Institute of Technology, Wuxi, Jiangsu, 214021, CN
1 Wuxi Institute of Technology, Wuxi, Jiangsu, 214021, CN
Source
Nature Environment and Pollution Technology, Vol 15, No 4 (2016), Pagination: 1409-1416Abstract
The influencing factors of haze pollution aggravation were explored to further analyse the spatial distribution and overflow effect of haze pollution in China. The global Moran's I of haze pollution distribution was estimated based on the panel data of 30 provinces (including cities and municipalities) in China from 2003 to 2013, and the spatial autocorrelation of haze pollution in these 30 provinces was analysed. An index system of social and economic variables that influence haze pollution in China, which covers economy, population and policy, was established. Subsequently, the spatial correlation of haze pollution in China and its corresponding influencing factors were explored based on the extreme bounds analysis model. An empirical study was then conducted, which found that the global Moran's I fluctuated between 0.4 and 0.5 and achieved a 1% significance level, thereby indicating that haze pollution demonstrated strong spatial correlation. The robustness testing coefficient of the overflow effect (ρ) is relatively large, which shows that haze pollution exhibits a robust spatial overflow effect. Haze pollution in one region is frequently significantly influenced by haze pollution in adjacent regions. Haze concentration occurs in the Beijing-Tianjin-Hebei, Yangtze River Delta and mid-east regions. Industrial structure, energy consumption structure, urban construction architecture, population dimension and car ownership have an antiinterference robustness effect on haze pollution. Conclusions of this study are not only significant for understanding the spatial distribution and spatial overflow effect of haze pollution in China and for identifying its main influencing factors, but can also provide references for the government to formulate haze control policies and enhance joint control of haze-affected regions.Keywords
Haze Pollution, Spatial overflow Effect, Influencing Factors.References
- Ang, J.B. 2008. Economic development, pollutant emissions and energy consumption in Malaysia. Journal of Policy Modeling, 30(2): 271-278.
- Anselin, L. 2001. Spatial effects in econometric practice in environmental and resource economics. American Journal of Agricultural Economics, 83(3): 705-710.
- Chen, L.W.A., Watson, J.G. and Chow, J.C. et al. 2010. Chemical mass balance source apportionment for combined PM 2.5 measurements from US non-urban and urban long-term networks. Atmospheric Environment, 44(38): 4908-4918.
- Coondoo, D. and Dinda, S. 2002. Causality between income and emission: a country group-specific econometric analysis. Ecological Economics, 40(3): 351-367.
- Engel-Cox, J. A., Holloman, C.H. and Coutant, B.W. et al. 2004. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment, 38(16): 2495-2509.
- Ghosh, S. and Yamarik, S. 2004. Are regional trading arrangements trade creating: an application of extreme bounds analysis. Journal of International Economics, 63(2): 369-395.
- Hosseini, H. M. and Rahbar, F. 2011. Spatial environmental Kuznets Curve for Asian countries: study of CO2 and PM2. 5. Journal of Environmental Studies, 37(9): 1-3.
- Kim, E., Larson, T. V. and Hopke, P. K. et al. 2003. Source identification of PM 2.5 in an arid northwest US city by positive matrix factorization. Atmospheric Research, 66(4): 291-305.
- Levine, R. and Renelt, D. 1992. A sensitivity analysis of cross-country growth regressions. The American economic review, 82(4): 942963.
- Li, Q., Song, J. and Wang, E. et al. 2014. Economic growth and pollutant emissions in China: a spatial econometric analysis. Stochastic Environmental Research and Risk Assessment, 28(2): 429-442.
- Maddison, D. 2006. Environmental Kuznets curves: a spatial econometric approach. Journal of Environmental Economics and management, 51(2): 218-230.
- Poon, J.P.H., Casas, I. and He, C. 2006. The impact of energy, transport, and trade on air pollution in china. Eurasian Geography and Economics, 47(5): 568-584.
- Rupasingha, A., Goetz, S. J. and Debertin, D. L. et al. 2004. The environmental Kuznets curve for US counties: A spatial econometric analysis with extensions. Papers in Regional Science, 83(2): 407-424.
- Soytas, U., Sari, R. and Ewing, B. T. 2007. Energy consumption, income, and carbon emissions in the United States. Ecological Economics, 62(3): 482-489.
- Watson, J. G., Chow, J. C. and Houck, J.E. 2001. PM 2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere, 43(8): 1141-1151.
- Zhao, P., Feng, Y., Zhu, T. et al. 2006. Characterizations of resuspended dust in six cities of North China. Atmospheric Environment, 40(30): 5807-5814.