Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods

With the increasingly obvious restriction of the ecological environment on economic development, environmental regulations are widely used to achieve “green production,” that is, to improve green total factor productivity (GTFP). First, through the econometric model, it can be concluded that command...

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Autores principales:	Junyi Feng, Jianjun Yan, Xia Tao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	environmental regulations green total factor productivity LASSO model threshold model mediation effect model Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/db32e9965b0f474ca2bd3d8abe80a561
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id	oai:doaj.org-article:db32e9965b0f474ca2bd3d8abe80a561
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spelling	oai:doaj.org-article:db32e9965b0f474ca2bd3d8abe80a5612021-11-18T09:40:28ZExposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods2296-665X10.3389/fenvs.2021.779358https://doaj.org/article/db32e9965b0f474ca2bd3d8abe80a5612021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenvs.2021.779358/fullhttps://doaj.org/toc/2296-665XWith the increasingly obvious restriction of the ecological environment on economic development, environmental regulations are widely used to achieve “green production,” that is, to improve green total factor productivity (GTFP). First, through the econometric model, it can be concluded that command-based environmental regulations could improve GTFP, while market-based environmental regulations have no significant impact on GTFP. Unlike traditional econometric models, machine learning has no specific data requirements and research assumptions. We use Lasso regression to verify the above results by obtaining the optimal tuning parameter. Furthermore, considering that the leap of China’s economy is inseparable from foreign direct investment (FDI), we use FDI as a threshold variable. The threshold model results showe that when the intensity of FDI in China ranges between 1.2492 and 1.588, both types of environmental regulations can significantly promote GTFP. These conclusions passed the robustness test. Given the differences in economy and resource endowment among different regions in China, a regional heterogeneity test is conducted. The results show that the current environmental regulations in eastern and central China have no significant impact on GTFP. However, when the intensity of FDI in central China is greater than 3.6868, environmental regulations have a significant promoting effect on GTFP. In western China, when FDI intensity ranges between 1.3950 and 1.5880, market-based environmental regulations can significantly promote GTFP. Further, the path test of the mediation effect model reveals that command-based environmental regulations reduce GTFP by reducing FDI. The above conclusions provide empirical data for the intensity of FDI in different regions of China to improve GTFP.Junyi FengJianjun YanXia TaoFrontiers Media S.A.articleenvironmental regulationsgreen total factor productivityLASSO modelthreshold modelmediation effect modelEnvironmental sciencesGE1-350ENFrontiers in Environmental Science, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	environmental regulations green total factor productivity LASSO model threshold model mediation effect model Environmental sciences GE1-350
spellingShingle	environmental regulations green total factor productivity LASSO model threshold model mediation effect model Environmental sciences GE1-350 Junyi Feng Jianjun Yan Xia Tao Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
description	With the increasingly obvious restriction of the ecological environment on economic development, environmental regulations are widely used to achieve “green production,” that is, to improve green total factor productivity (GTFP). First, through the econometric model, it can be concluded that command-based environmental regulations could improve GTFP, while market-based environmental regulations have no significant impact on GTFP. Unlike traditional econometric models, machine learning has no specific data requirements and research assumptions. We use Lasso regression to verify the above results by obtaining the optimal tuning parameter. Furthermore, considering that the leap of China’s economy is inseparable from foreign direct investment (FDI), we use FDI as a threshold variable. The threshold model results showe that when the intensity of FDI in China ranges between 1.2492 and 1.588, both types of environmental regulations can significantly promote GTFP. These conclusions passed the robustness test. Given the differences in economy and resource endowment among different regions in China, a regional heterogeneity test is conducted. The results show that the current environmental regulations in eastern and central China have no significant impact on GTFP. However, when the intensity of FDI in central China is greater than 3.6868, environmental regulations have a significant promoting effect on GTFP. In western China, when FDI intensity ranges between 1.3950 and 1.5880, market-based environmental regulations can significantly promote GTFP. Further, the path test of the mediation effect model reveals that command-based environmental regulations reduce GTFP by reducing FDI. The above conclusions provide empirical data for the intensity of FDI in different regions of China to improve GTFP.
format	article
author	Junyi Feng Jianjun Yan Xia Tao
author_facet	Junyi Feng Jianjun Yan Xia Tao
author_sort	Junyi Feng
title	Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
title_short	Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
title_full	Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
title_fullStr	Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
title_full_unstemmed	Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods
title_sort	exposing the effects of environmental regulations on china’s green total factor productivity: results from econometrics analysis and machine learning methods
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/db32e9965b0f474ca2bd3d8abe80a561
work_keys_str_mv	AT junyifeng exposingtheeffectsofenvironmentalregulationsonchinasgreentotalfactorproductivityresultsfromeconometricsanalysisandmachinelearningmethods AT jianjunyan exposingtheeffectsofenvironmentalregulationsonchinasgreentotalfactorproductivityresultsfromeconometricsanalysisandmachinelearningmethods AT xiatao exposingtheeffectsofenvironmentalregulationsonchinasgreentotalfactorproductivityresultsfromeconometricsanalysisandmachinelearningmethods
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Exposing the Effects of Environmental Regulations on China’s Green Total Factor Productivity: Results From Econometrics Analysis and Machine Learning Methods

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