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The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis

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  • Beidi Diao

    (School of Economics and Management, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China)

  • Lei Ding

    (School of International Business & Languages, Ningbo Polytechnic, 1069 Xinda Road, Ningbo 315800, China)

  • Panda Su

    (School of Public Administration, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China)

  • Jinhua Cheng

    (School of Economics and Management, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China)

Abstract

While the progress of China’s industrialization and urbanization has made great strides, atmospheric pollution has become the norm, with a wide range of influence and difficult governance. While many previous works on NOx pollution have been developed from the perspectives of natural science and technology, few studies have been conducted from social-economic points of view, and regional differences have not been given adequate attention in driving force models. This paper adopts China’s provincial panel data from 2006 to 2015, an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model, and spatial econometric models to investigate the socio-economic influential factors and spatial-temporal patterns of NOx emissions. According to the spatial correlation analysis results, the provincial NOx emission changes not only affected the provinces themselves, but also neighboring regions. Spatial econometric analysis shows that the spatial effect largely contributes to NOx emissions. The other explanatory variables all have positive impacts on NOx emissions, except for the vehicular indicator (which did not pass the significance test). As shown through the estimated consequences of direct and indirect effects, the indicators have significant positive effects on their own areas, and exacerbate NOx pollution. In terms of indirect effects, only three factors passed the significant test. An increase in gross domestic product (GDP) and energy consumption will exacerbate adjacent NOx pollution. Finally, a series of socio-economic measures and regional cooperation policies should be applied to improve the current air environment in China.

Suggested Citation

  • Beidi Diao & Lei Ding & Panda Su & Jinhua Cheng, 2018. "The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis," IJERPH, MDPI, vol. 15(7), pages 1-19, July.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1405-:d:156123
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    1. Zhang, Xin & Zhang, Xiaobo & Chen, Xi, 2017. "Valuing Air Quality Using Happiness Data: The Case of China," Ecological Economics, Elsevier, vol. 137(C), pages 29-36.
    2. Raskin, Paul D., 1995. "Methods for estimating the population contribution to environmental change," Ecological Economics, Elsevier, vol. 15(3), pages 225-233, December.
    3. Yu Qin & Hongjia Zhu, 2018. "Run away? Air pollution and emigration interests in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(1), pages 235-266, January.
    4. Noailly, Joëlle & Ryfisch, David, 2015. "Multinational firms and the internationalization of green R&D: A review of the evidence and policy implications," Energy Policy, Elsevier, vol. 83(C), pages 218-228.
    5. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    6. Chuanglin Fang & Haimeng Liu & Guangdong Li & Dongqi Sun & Zhuang Miao, 2015. "Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models," Sustainability, MDPI, vol. 7(11), pages 1-23, November.
    7. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    8. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
    9. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    10. Zilio, Mariana & Recalde, Marina, 2011. "GDP and environment pressure: The role of energy in Latin America and the Caribbean," Energy Policy, Elsevier, vol. 39(12), pages 7941-7949.
    11. Maddison, David, 2006. "Environmental Kuznets curves: A spatial econometric approach," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 218-230, March.
    12. Richard York & Eugene A. Rosa & Thomas Dietz, 2002. "Bridging Environmental Science with Environmental Policy: Plasticity of Population, Affluence, and Technology," Social Science Quarterly, Southwestern Social Science Association, vol. 83(1), pages 18-34, March.
    13. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    14. Dinda, Soumyananda, 2004. "Environmental Kuznets Curve Hypothesis: A Survey," Ecological Economics, Elsevier, vol. 49(4), pages 431-455, August.
    15. Andreas Richter & John P. Burrows & Hendrik Nüß & Claire Granier & Ulrike Niemeier, 2005. "Increase in tropospheric nitrogen dioxide over China observed from space," Nature, Nature, vol. 437(7055), pages 129-132, September.
    16. Hosseini, Hossein Mirshojaeian & Kaneko, Shinji, 2013. "Can environmental quality spread through institutions?," Energy Policy, Elsevier, vol. 56(C), pages 312-321.
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