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Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces

In the new stage of global economic development, we hope to achieve both economic development and environmental improvement through green technology innovation. How to effectively obtain the support of technology and finance to green technology innovation is an issue worth studying. This paper const...

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Autores principales: Liu, Yang, Yang, Yanlin, Zheng, Shuang, Xiao, Lei, Gao, Hongjie, Lu, Hechen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029450/
https://www.ncbi.nlm.nih.gov/pubmed/35457630
http://dx.doi.org/10.3390/ijerph19084764
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author Liu, Yang
Yang, Yanlin
Zheng, Shuang
Xiao, Lei
Gao, Hongjie
Lu, Hechen
author_facet Liu, Yang
Yang, Yanlin
Zheng, Shuang
Xiao, Lei
Gao, Hongjie
Lu, Hechen
author_sort Liu, Yang
collection PubMed
description In the new stage of global economic development, we hope to achieve both economic development and environmental improvement through green technology innovation. How to effectively obtain the support of technology and finance to green technology innovation is an issue worth studying. This paper constructed an improved super-SBM-DEA efficiency measurement model and combined it with the window analysis method to measure the green technology innovation efficiency (GTIE) of Chinese provinces from 2006 to 2018. Then, based on the PVAR model, the impulse response function and Monte Carlo simulation were used to study the dynamic impact of various variables of technology and finance on GTIE. Finally, the variance decomposition was used to explore the contribution degree of each variable of technology and finance to improving GTIE. The results revealed the following: (1) the average value of China’s provincial GTIE from 2006 to 2018 was 0.42, which is relatively low and shows a trend of volatility and rising. (2) From the impulse response results, it could be seen that various variables of technology and finance have always had a positive impact on GTIE. However, there are differences in the influence degree, shock effect, and dynamic transmission mechanism. (3) The results of the variance decomposition showed that government financial technology investment had the highest contribution to the improvement of GTIE, followed by bank technology credit, then by enterprise independent R&D investment, and finally venture capital. This paper offered a reference to developing countries with regard to improving their GTIE and studying the role of technology and finance.
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spelling pubmed-90294502022-04-23 Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces Liu, Yang Yang, Yanlin Zheng, Shuang Xiao, Lei Gao, Hongjie Lu, Hechen Int J Environ Res Public Health Article In the new stage of global economic development, we hope to achieve both economic development and environmental improvement through green technology innovation. How to effectively obtain the support of technology and finance to green technology innovation is an issue worth studying. This paper constructed an improved super-SBM-DEA efficiency measurement model and combined it with the window analysis method to measure the green technology innovation efficiency (GTIE) of Chinese provinces from 2006 to 2018. Then, based on the PVAR model, the impulse response function and Monte Carlo simulation were used to study the dynamic impact of various variables of technology and finance on GTIE. Finally, the variance decomposition was used to explore the contribution degree of each variable of technology and finance to improving GTIE. The results revealed the following: (1) the average value of China’s provincial GTIE from 2006 to 2018 was 0.42, which is relatively low and shows a trend of volatility and rising. (2) From the impulse response results, it could be seen that various variables of technology and finance have always had a positive impact on GTIE. However, there are differences in the influence degree, shock effect, and dynamic transmission mechanism. (3) The results of the variance decomposition showed that government financial technology investment had the highest contribution to the improvement of GTIE, followed by bank technology credit, then by enterprise independent R&D investment, and finally venture capital. This paper offered a reference to developing countries with regard to improving their GTIE and studying the role of technology and finance. MDPI 2022-04-14 /pmc/articles/PMC9029450/ /pubmed/35457630 http://dx.doi.org/10.3390/ijerph19084764 Text en © 2022 by the authors. 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
Liu, Yang
Yang, Yanlin
Zheng, Shuang
Xiao, Lei
Gao, Hongjie
Lu, Hechen
Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title_full Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title_fullStr Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title_full_unstemmed Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title_short Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China’s Provinces
title_sort dynamic impact of technology and finance on green technology innovation efficiency: empirical evidence from china’s provinces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029450/
https://www.ncbi.nlm.nih.gov/pubmed/35457630
http://dx.doi.org/10.3390/ijerph19084764
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