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How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach

This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China’s heavy industry, based on 2005–2019 panel data. The main findings are as follows: (...

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Detalles Bibliográficos
Autor principal: Xu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566165/
https://www.ncbi.nlm.nih.gov/pubmed/36232164
http://dx.doi.org/10.3390/ijerph191912865
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author Xu, Bin
author_facet Xu, Bin
author_sort Xu, Bin
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description This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China’s heavy industry, based on 2005–2019 panel data. The main findings are as follows: (1) incentive-based environmental regulations have the greater impact on the carbon intensity in Jiangsu, Shandong, Zhejiang, Henan, Liaoning, and Shaanxi, because these provinces invest more in environmental governance and levy higher resource taxes; (2) the impact of mandatory environmental regulations on carbon intensity in Beijing, Tianjin, and Guangdong provinces is smaller, since these three provinces have the fewest enacted environmental laws and rely mainly on market incentives; (3) conversely, foreign direct investment has contributed most to carbon intensity reduction in Tianjin, Beijing, and Guangdong provinces, because these three have attracted more technologically advanced foreign-funded enterprises; (4) technological progress contributes more to the carbon intensity in the low quantile provinces, because these provinces have more patented technologies; (5) the carbon intensity of Shaanxi, Shanxi, and Inner Mongolia provinces is most affected by energy consumption structures because of their over-reliance on highly polluting coal.
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spelling pubmed-95661652022-10-15 How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach Xu, Bin Int J Environ Res Public Health Article This decoupling between carbon dioxide emissions and the heavy industry is one of the main topics of government managers. This paper uses the quantile regression approach to investigate the carbon intensity of China’s heavy industry, based on 2005–2019 panel data. The main findings are as follows: (1) incentive-based environmental regulations have the greater impact on the carbon intensity in Jiangsu, Shandong, Zhejiang, Henan, Liaoning, and Shaanxi, because these provinces invest more in environmental governance and levy higher resource taxes; (2) the impact of mandatory environmental regulations on carbon intensity in Beijing, Tianjin, and Guangdong provinces is smaller, since these three provinces have the fewest enacted environmental laws and rely mainly on market incentives; (3) conversely, foreign direct investment has contributed most to carbon intensity reduction in Tianjin, Beijing, and Guangdong provinces, because these three have attracted more technologically advanced foreign-funded enterprises; (4) technological progress contributes more to the carbon intensity in the low quantile provinces, because these provinces have more patented technologies; (5) the carbon intensity of Shaanxi, Shanxi, and Inner Mongolia provinces is most affected by energy consumption structures because of their over-reliance on highly polluting coal. MDPI 2022-10-08 /pmc/articles/PMC9566165/ /pubmed/36232164 http://dx.doi.org/10.3390/ijerph191912865 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Bin
How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title_full How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title_fullStr How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title_full_unstemmed How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title_short How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach
title_sort how to efficiently reduce the carbon intensity of the heavy industry in china? using quantile regression approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566165/
https://www.ncbi.nlm.nih.gov/pubmed/36232164
http://dx.doi.org/10.3390/ijerph191912865
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