The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance

Fengge Yao; Zenan Qin; Xiaomei Wang; Mengyao Chen; Adeeb Noor; Shubham Sharma; Jagpreet Singh; Dražan Kozak; Anica Hunjet

doi:10.1016/j.heliyon.2023.e16160

The evolution of renewable energy environments utilizing artificial intelligence to enhance energy efficiency and finance

Heliyon. 2023 May 11;9(5):e16160. doi: 10.1016/j.heliyon.2023.e16160. eCollection 2023 May.

Authors

Fengge Yao¹, Zenan Qin¹, Xiaomei Wang², Mengyao Chen³, Adeeb Noor⁴, Shubham Sharma^{5

6}, Jagpreet Singh⁷, Dražan Kozak⁸, Anica Hunjet⁹

Affiliations

¹ Finance of School, Harbin University of Commerce, Harbin, China.
² School of Tourism and Culinary Arts, Harbin University of Commerce, Harbin, China.
³ School of Media, NingboTech University, Ningbo, 315000, China.
⁴ Department of Information Technology, King Abdulaziz University, Jeddah, 80221, Saudi Arabia.
⁵ Mechanical Engineering Department, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
⁶ School of Mechanical and Automotive Engineering, Qingdao University of Technology, 266520, Qingdao, China.
⁷ Department of Computer Science and Engineering, IK Gujral Punjab Technical University, SAS Nagar, Punjab, 160055, India.
⁸ University of Slavonski Brod, Mechanical Engineering Faculty in Slavonski Brod, Trg Ivane Brlić-Mažuranić 2, HR-35000, Slavonski Brod, Croatia.
⁹ University Center Varaždin, University North 104. Brigade 3, HR-42 000, Varaždin, Croatia.

Abstract

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.

Keywords: Artificial intelligence; Energy efficiency financing; G7 economies; Renewable energy.