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Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model

Green innovation has become the goal for promoting the transformation and upgrading heavy pollution industries in the context of high-quality development, and the key factor for the success of green innovation is increasing the green innovation efficiency of heavy pollution industries. To understand...

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Detalles Bibliográficos
Autores principales: Fu, Chun, Li, Yanfang, Zhang, Jing, Min, Weiqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522312/
https://www.ncbi.nlm.nih.gov/pubmed/36174019
http://dx.doi.org/10.1371/journal.pone.0274875
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author Fu, Chun
Li, Yanfang
Zhang, Jing
Min, Weiqi
author_facet Fu, Chun
Li, Yanfang
Zhang, Jing
Min, Weiqi
author_sort Fu, Chun
collection PubMed
description Green innovation has become the goal for promoting the transformation and upgrading heavy pollution industries in the context of high-quality development, and the key factor for the success of green innovation is increasing the green innovation efficiency of heavy pollution industries. To understand the current situation of China’s industrial innovation and get out of the dilemma, we use non-expected Slacks-based model (SBM) to measure green innovation efficiency in Chinese industry, Lasso regression to screen the influencing factors of heavy pollution industries, tobit regression to study the influence degree and direction of different influencing factors on green innovation efficiency of heavy pollution industry. The results show that: (1) The green innovation efficiency of the 16 heavily polluting industries studied in this paper is generally low; (2) Coordination, green and openness all have a positive impact on the green innovation efficiency of the industry. (3) A certain degree of government scientific research support is conducive to improving the efficiency of industrial green innovation and exceeding the limit will have a restraining effect on enterprise innovation. According to the results, we put forward the corresponding policy implications.
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spelling pubmed-95223122022-09-30 Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model Fu, Chun Li, Yanfang Zhang, Jing Min, Weiqi PLoS One Research Article Green innovation has become the goal for promoting the transformation and upgrading heavy pollution industries in the context of high-quality development, and the key factor for the success of green innovation is increasing the green innovation efficiency of heavy pollution industries. To understand the current situation of China’s industrial innovation and get out of the dilemma, we use non-expected Slacks-based model (SBM) to measure green innovation efficiency in Chinese industry, Lasso regression to screen the influencing factors of heavy pollution industries, tobit regression to study the influence degree and direction of different influencing factors on green innovation efficiency of heavy pollution industry. The results show that: (1) The green innovation efficiency of the 16 heavily polluting industries studied in this paper is generally low; (2) Coordination, green and openness all have a positive impact on the green innovation efficiency of the industry. (3) A certain degree of government scientific research support is conducive to improving the efficiency of industrial green innovation and exceeding the limit will have a restraining effect on enterprise innovation. According to the results, we put forward the corresponding policy implications. Public Library of Science 2022-09-29 /pmc/articles/PMC9522312/ /pubmed/36174019 http://dx.doi.org/10.1371/journal.pone.0274875 Text en © 2022 Fu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fu, Chun
Li, Yanfang
Zhang, Jing
Min, Weiqi
Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title_full Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title_fullStr Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title_full_unstemmed Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title_short Efficiency evaluation of green innovation of China’s heavy pollution industries based on SBM-Lasso-Tobit model
title_sort efficiency evaluation of green innovation of china’s heavy pollution industries based on sbm-lasso-tobit model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522312/
https://www.ncbi.nlm.nih.gov/pubmed/36174019
http://dx.doi.org/10.1371/journal.pone.0274875
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