Zequn Dong , Chaodan Tan , Biao Ma , Zhaoshuo Ning
{"title":"The impact of artificial intelligence on the energy transition: The role of regulatory quality as a guardrail, not a wall","authors":"Zequn Dong , Chaodan Tan , Biao Ma , Zhaoshuo Ning","doi":"10.1016/j.eneco.2024.107988","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, the economic impact and environmental contribution of Artificial Intelligence (AI) have gradually become a new focus in academia. This study uses a panel data sample of 50 countries to explore the impact of AI on energy transition (ET), aiming to fill an important research gap. The results highlight several critical insights. First, AI has had a significant positive impact on facilitating the ET. This conclusion still holds after a series of robustness tests. Second, AI positively affects ET by promoting renewable energy technology innovation and upgrading the electricity structure, resulting in both technological and structural effects. Third, the impact of AI on ET is non-linear. Threshold effect models show that AI impacts ET differently at various levels of regulation quality (RQ), exhibiting a double threshold effect. AI hinders ET when RQ is lower than the first threshold value. When RQ is in the second range, AI significantly facilitates ET. However, when RQ exceeds the second threshold value, AI hinders ET again. These findings provide insights into the mechanisms of AI's impact on ET and emphasize that an appropriate level of regulation is crucial for AI to facilitate ET. Finally, this study analyzes heterogeneity and offers targeted policy recommendations.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107988"},"PeriodicalIF":13.6000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Economics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140988324006960","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the economic impact and environmental contribution of Artificial Intelligence (AI) have gradually become a new focus in academia. This study uses a panel data sample of 50 countries to explore the impact of AI on energy transition (ET), aiming to fill an important research gap. The results highlight several critical insights. First, AI has had a significant positive impact on facilitating the ET. This conclusion still holds after a series of robustness tests. Second, AI positively affects ET by promoting renewable energy technology innovation and upgrading the electricity structure, resulting in both technological and structural effects. Third, the impact of AI on ET is non-linear. Threshold effect models show that AI impacts ET differently at various levels of regulation quality (RQ), exhibiting a double threshold effect. AI hinders ET when RQ is lower than the first threshold value. When RQ is in the second range, AI significantly facilitates ET. However, when RQ exceeds the second threshold value, AI hinders ET again. These findings provide insights into the mechanisms of AI's impact on ET and emphasize that an appropriate level of regulation is crucial for AI to facilitate ET. Finally, this study analyzes heterogeneity and offers targeted policy recommendations.
期刊介绍:
Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.