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Identifying the driving factors of NO2 pollution of One Belt One Road countries: satellite observation technique and dynamic spatial panel data analysis.
Jiang, Lei; Zhou, Haifeng; He, Shixiong; Cui, Yuanzheng; Wang, Jionghua.
Affiliation
  • Jiang L; School of Economics, Zhejiang University of Finance & Economics, Hangzhou, 310018, China.
  • Zhou H; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China.
  • He S; School of Urban and Regional Science, Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, 200433, China.
  • Cui Y; College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China. yzcui@niglas.ac.cn.
  • Wang J; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, The Chinese Academy of Sciences, Nanjing, 210008, China. yzcui@niglas.ac.cn.
Environ Sci Pollut Res Int ; 28(16): 20393-20407, 2021 Apr.
Article in En | MEDLINE | ID: mdl-33405127
ABSTRACT
To recover the global economy, China in 2013 called for a new global strategy, namely, "One Belt and One Road Initiative" (BRI), which aims at reinforcing regional economic cooperation, enhancing regional collaboration of economic policy, and realizing the goal of rapid economic development of member countries. Accelerating industrialization not only has been recognized as an effective way to stimulate economic development, but also lead to the serious issue of environmental pollution, which challenges the environmental sustainability. In this study, we focus on the industrializing region as a study area to investigate the driving factors of environmental pollution. Technically, we utilized satellite observation technique to obtain NO2 columns data to denote environmental pollution and then applied dynamic spatial panel data models to evaluate what affects NO2 pollution levels. The findings are the following. (1) NO2 pollution exhibits significant and positive spatial autocorrelation, indicating spatial spillovers of NO2 pollution. (2) Lebanon, Bangladesh, Kyrgyzstan, and India experienced the largest increase of NO2 pollution while NO2 pollution in Singapore, Hungary, Greece, and Ukraine was substantially reduced. (3) The results of the dynamic spatial panel data models show that both the time dynamics effects and the spatial spillover effects are found to be significant and positive. In other words, both effects should be considered. Population is the foremost contributor to increase NO2 pollution while urbanization is an effective way to reduce pollution. An EKC relationship between NO2 pollution and per capita income was verified. Besides, industrialization, foreign direct investment, and trade openness have positive impacts on NO2 pollution.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Analysis / Nitrogen Dioxide Type of study: Prognostic_studies Country/Region as subject: Asia / Europa Language: En Journal: Environ Sci Pollut Res Int Journal subject: SAUDE AMBIENTAL / TOXICOLOGIA Year: 2021 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Data Analysis / Nitrogen Dioxide Type of study: Prognostic_studies Country/Region as subject: Asia / Europa Language: En Journal: Environ Sci Pollut Res Int Journal subject: SAUDE AMBIENTAL / TOXICOLOGIA Year: 2021 Document type: Article Affiliation country: China