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The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance
The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones th...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208837/ https://www.ncbi.nlm.nih.gov/pubmed/37234613 http://dx.doi.org/10.1016/j.heliyon.2023.e16160 |
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author | Yao, Fengge Qin, Zenan Wang, Xiaomei Chen, Mengyao Noor, Adeeb Sharma, Shubham Singh, Jagpreet Kozak, Dražan Hunjet, Anica |
author_facet | Yao, Fengge Qin, Zenan Wang, Xiaomei Chen, Mengyao Noor, Adeeb Sharma, Shubham Singh, Jagpreet Kozak, Dražan Hunjet, Anica |
author_sort | Yao, Fengge |
collection | PubMed |
description | The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones that are, and giving priority to improving renewable energy consumption and storage capabilities. From the experience of the G7 economies, the development of renewable energy (RE) is inevitable and urgent. The China Banking Regulatory Commission has recently issued a number of directives, such as the “Directives for Green Credit” and “Instructions for Granting Credit to Support Energy Conservation and Emission Reduction,” to help businesses that use “renewable energy expand”. This article firstly discussed the definition of the “green institutional environment” (GIE) and the construction of the index system. Then, on the basis of clarifying the relationship between the GIE, and RE investment theory, a semi-parametric regression model was constructed to empirically analyze the mode and effect of the GIE. Considering the balance between improving model accuracy and reducing computational complexity, the number of hidden nodes opted in this study is 300 so as to lower the time needed to predict the model. Finally, from the perspective of enterprise scale, the level of GIE played a significant role in promoting RE investment in small and medium-sized enterprises, with a coefficient of 1.8276, while the impact on RE investment in large enterprises had not passed the significance test. Based on the conclusions, the government should focus on building a GIE dominated by green regulatory systems, supplemented by green disclosure and supervision systems, and green accounting systems, and should make reasonable plans for releasing various policy directives. At the same time, while offering full play to the guiding role of the policy, its rationality should also be paid attention to, and the excessive implementation of the policy should be avoided, so that an orderly, and good GIE can be created. |
format | Online Article Text |
id | pubmed-10208837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-102088372023-05-25 The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance Yao, Fengge Qin, Zenan Wang, Xiaomei Chen, Mengyao Noor, Adeeb Sharma, Shubham Singh, Jagpreet Kozak, Dražan Hunjet, Anica Heliyon Research Article The development of a country is inseparable from the material guarantee mainly based on energy, but energy is limited, which may restrict the sustainable development of the country. It is very necessary to accelerate the adoption of programs aimed at switching non-renewable energy sources to ones that are, and giving priority to improving renewable energy consumption and storage capabilities. From the experience of the G7 economies, the development of renewable energy (RE) is inevitable and urgent. The China Banking Regulatory Commission has recently issued a number of directives, such as the “Directives for Green Credit” and “Instructions for Granting Credit to Support Energy Conservation and Emission Reduction,” to help businesses that use “renewable energy expand”. This article firstly discussed the definition of the “green institutional environment” (GIE) and the construction of the index system. Then, on the basis of clarifying the relationship between the GIE, and RE investment theory, a semi-parametric regression model was constructed to empirically analyze the mode and effect of the GIE. Considering the balance between improving model accuracy and reducing computational complexity, the number of hidden nodes opted in this study is 300 so as to lower the time needed to predict the model. Finally, from the perspective of enterprise scale, the level of GIE played a significant role in promoting RE investment in small and medium-sized enterprises, with a coefficient of 1.8276, while the impact on RE investment in large enterprises had not passed the significance test. Based on the conclusions, the government should focus on building a GIE dominated by green regulatory systems, supplemented by green disclosure and supervision systems, and green accounting systems, and should make reasonable plans for releasing various policy directives. At the same time, while offering full play to the guiding role of the policy, its rationality should also be paid attention to, and the excessive implementation of the policy should be avoided, so that an orderly, and good GIE can be created. Elsevier 2023-05-11 /pmc/articles/PMC10208837/ /pubmed/37234613 http://dx.doi.org/10.1016/j.heliyon.2023.e16160 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Yao, Fengge Qin, Zenan Wang, Xiaomei Chen, Mengyao Noor, Adeeb Sharma, Shubham Singh, Jagpreet Kozak, Dražan Hunjet, Anica The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title | The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title_full | The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title_fullStr | The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title_full_unstemmed | The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title_short | The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
title_sort | evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208837/ https://www.ncbi.nlm.nih.gov/pubmed/37234613 http://dx.doi.org/10.1016/j.heliyon.2023.e16160 |
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