Industrial eco-efficiency and its determinants in China: A two-stage approach

China has undergone momentous changes and achieved remarkable economic progress since its economic reform and opening-up in 1978. However, the consequent resource depletion and environmental degradation have seriously restricted China’s potential for sustainable industrial development. As a practica...

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Autores principales:	Ken'ichi Matsumoto, Yueyang Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Industrial eco-efficiency Data envelopment analysis Random-effects Tobit regression Determinants Provincial-level analysis China Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/79c9f1f1c0764507992e4f685fc08cb8
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spelling	oai:doaj.org-article:79c9f1f1c0764507992e4f685fc08cb82021-12-01T04:58:48ZIndustrial eco-efficiency and its determinants in China: A two-stage approach1470-160X10.1016/j.ecolind.2021.108072https://doaj.org/article/79c9f1f1c0764507992e4f685fc08cb82021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21007378https://doaj.org/toc/1470-160XChina has undergone momentous changes and achieved remarkable economic progress since its economic reform and opening-up in 1978. However, the consequent resource depletion and environmental degradation have seriously restricted China’s potential for sustainable industrial development. As a practical tool contributing to sustainable development, the concept of eco-efficiency is considered increasingly important for reducing the trend of resource exhaustion and environmental degradation. This study first evaluated industrial eco-efficiency in 30 Chinese provinces during 2005–2015 using data envelopment analysis (DEA), and then identified the determinants of the resulting eco-efficiency scores using random-effects Tobit regression analysis. The DEA results showed that although China’s overall industrial eco-efficiency trend was upward, there were great disparities between provinces. Provinces with high industrial eco-efficiency were mainly distributed across the eastern region, while those in the often economically less developed western region had lower industrial eco-efficiency due to technological deficits and weak environmental policies. The Tobit regression results indicated that internal research and development expenditure in industrial enterprises, per capita gross regional product, and investment in wastewater treatment had positive effects on provincial industrial eco-efficiency. By contrast, the proportion of state-owned enterprises and investment in waste gas treatment had negative impacts. These findings provide valuable insights that can help provinces with low industrial eco-efficiency to pursue high-quality, green development.Ken'ichi MatsumotoYueyang ChenElsevierarticleIndustrial eco-efficiencyData envelopment analysisRandom-effects Tobit regressionDeterminantsProvincial-level analysisChinaEcologyQH540-549.5ENEcological Indicators, Vol 130, Iss , Pp 108072- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Industrial eco-efficiency Data envelopment analysis Random-effects Tobit regression Determinants Provincial-level analysis China Ecology QH540-549.5
spellingShingle	Industrial eco-efficiency Data envelopment analysis Random-effects Tobit regression Determinants Provincial-level analysis China Ecology QH540-549.5 Ken'ichi Matsumoto Yueyang Chen Industrial eco-efficiency and its determinants in China: A two-stage approach
description	China has undergone momentous changes and achieved remarkable economic progress since its economic reform and opening-up in 1978. However, the consequent resource depletion and environmental degradation have seriously restricted China’s potential for sustainable industrial development. As a practical tool contributing to sustainable development, the concept of eco-efficiency is considered increasingly important for reducing the trend of resource exhaustion and environmental degradation. This study first evaluated industrial eco-efficiency in 30 Chinese provinces during 2005–2015 using data envelopment analysis (DEA), and then identified the determinants of the resulting eco-efficiency scores using random-effects Tobit regression analysis. The DEA results showed that although China’s overall industrial eco-efficiency trend was upward, there were great disparities between provinces. Provinces with high industrial eco-efficiency were mainly distributed across the eastern region, while those in the often economically less developed western region had lower industrial eco-efficiency due to technological deficits and weak environmental policies. The Tobit regression results indicated that internal research and development expenditure in industrial enterprises, per capita gross regional product, and investment in wastewater treatment had positive effects on provincial industrial eco-efficiency. By contrast, the proportion of state-owned enterprises and investment in waste gas treatment had negative impacts. These findings provide valuable insights that can help provinces with low industrial eco-efficiency to pursue high-quality, green development.
format	article
author	Ken'ichi Matsumoto Yueyang Chen
author_facet	Ken'ichi Matsumoto Yueyang Chen
author_sort	Ken'ichi Matsumoto
title	Industrial eco-efficiency and its determinants in China: A two-stage approach
title_short	Industrial eco-efficiency and its determinants in China: A two-stage approach
title_full	Industrial eco-efficiency and its determinants in China: A two-stage approach
title_fullStr	Industrial eco-efficiency and its determinants in China: A two-stage approach
title_full_unstemmed	Industrial eco-efficiency and its determinants in China: A two-stage approach
title_sort	industrial eco-efficiency and its determinants in china: a two-stage approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/79c9f1f1c0764507992e4f685fc08cb8
work_keys_str_mv	AT kenichimatsumoto industrialecoefficiencyanditsdeterminantsinchinaatwostageapproach AT yueyangchen industrialecoefficiencyanditsdeterminantsinchinaatwostageapproach
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Industrial eco-efficiency and its determinants in China: A two-stage approach

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