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Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China

With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM(2.5) (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM(2.5) concentr...

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Detalles Bibliográficos
Autores principales: Zhao, Haoran, Guo, Sen, Zhao, Huiru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719022/
https://www.ncbi.nlm.nih.gov/pubmed/31443198
http://dx.doi.org/10.3390/ijerph16162926
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author Zhao, Haoran
Guo, Sen
Zhao, Huiru
author_facet Zhao, Haoran
Guo, Sen
Zhao, Huiru
author_sort Zhao, Haoran
collection PubMed
description With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM(2.5) (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM(2.5) concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM(2.5) concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM(2.5) concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM(2.5) concentrations. It was also found that a bi-directional Granger causal nexus exists between PM(2.5) concentrations and economic progress as well as between PM(2.5) concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles.
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spelling pubmed-67190222019-09-10 Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China Zhao, Haoran Guo, Sen Zhao, Huiru Int J Environ Res Public Health Article With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM(2.5) (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM(2.5) concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM(2.5) concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM(2.5) concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM(2.5) concentrations. It was also found that a bi-directional Granger causal nexus exists between PM(2.5) concentrations and economic progress as well as between PM(2.5) concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles. MDPI 2019-08-15 2019-08 /pmc/articles/PMC6719022/ /pubmed/31443198 http://dx.doi.org/10.3390/ijerph16162926 Text en © 2019 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Haoran
Guo, Sen
Zhao, Huiru
Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title_full Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title_fullStr Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title_full_unstemmed Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title_short Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM(2.5) Concentration: A Provincial Panel Data Model Analysis of China
title_sort quantifying the impacts of economic progress, economic structure, urbanization process, and number of vehicles on pm(2.5) concentration: a provincial panel data model analysis of china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719022/
https://www.ncbi.nlm.nih.gov/pubmed/31443198
http://dx.doi.org/10.3390/ijerph16162926
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