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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9566165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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