| Title | Big data, green loans and energy efficiency |
|---|---|
| ID_Doc | 31560 |
| Authors | Wang, J; Deng, H; Zhao, X |
| Title | Big data, green loans and energy efficiency |
| Year | 2024 |
| Published | |
| Abstract | Green digital finance is an instrumental way to promote technological innovation, accelerate the lowcarbon transition, and foster sustainable development. With the emergence of green digital finance, how does it affect firms' energy use efficiency? Using big data and green loans as an entry point, the impact of green digital finance on corporate energy efficiency and the role of big data are examined. We provided a simple theoretical model to analyze the green loaning behavior of the banking sector after applying big data and its impact on corporate energy efficiency. Our research finds that: (1) The application of big data can make it easier for the banking sector to obtain loan companies' information and reduce loan delinquency rates. This will reduce the information and transaction costs of the banking sector and expand the scale of optimal green loans. (2) The optimal green loan scale has a negative relationship with the optimal green loan interest rate. (3) The application of green loans by firms can improve energy efficiency and have a range of impacts on firms' decision-making, including an increase in the emission reduction ratio, innovation probability, output, and profit, followed by a decrease in energy consumption and pollution emissions. This paper further clarifies the channels through which green digital finance affects energy efficiency and specifies the role of big data in green digital finance. This could help relevant policymakers design more effective green digital finance policies, contributing to carbon peaking and carbon neutrality goals. CO 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
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