Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition

能量转换 业务 信息不对称 环境经济学 企业社会责任 产业组织 高效能源利用 知识管理 经济 计算机科学 公共关系 工程类 财务 医学 替代医学 病理 灵丹妙药 政治学 电气工程
作者
Zhongzhu Chu,Zihan Zhang,Weijie Tan,Pengyu Chen
出处
期刊:Journal of Environmental Management [Elsevier]
卷期号:357: 120806-120806 被引量:41
标识
DOI:10.1016/j.jenvman.2024.120806
摘要

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.
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