{"title":"The Impact of Artificial Intelligence on Corporate Environmental Investment","authors":"Junkai Wang;Haowen Tian;Penghao Zheng","doi":"10.1109/TEM.2024.3491940","DOIUrl":null,"url":null,"abstract":"As an important driving force for a new round of scientific and technological revolution and industrial transformation, artificial intelligence has great potential in improving corporate investment in environmental protection and promoting economic growth. However, due to data bottlenecks, there is no clear conclusion on how AI impact environmental investment at the enterprise level. Based on the annual report data of Chinese listed companies, we use machine learning methods to generate an artificial intelligence dictionary, and then constructs enterprise-level artificial intelligence indicators. Through empirical research, we find that AI can significantly improve enterprises' environmental investment. After the robustness tests such as the instrumental variable method and the propensity score matching method, the conclusion remains unchanged. Mechanism analysis shows that AI can improve firms' investment in environmental protection by alleviating financing constraints and improving information transparency. Heterogeneity analysis shows that the state-owned attributes, high tax burden and high environmental regulation of enterprises can enhance the correlation between AI and environmental protection investment. Further research finds that the increase in environmental protection investment caused by artificial intelligence can significantly reduce environmental pollution, rather than for the sake of greenwashing behavior. This article not only enriches the relevant research on the impact of corporate environmental protection investment, but also provides a theoretical basis for enterprises to further promote the development of artificial intelligence technology.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"96-114"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10769484/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
As an important driving force for a new round of scientific and technological revolution and industrial transformation, artificial intelligence has great potential in improving corporate investment in environmental protection and promoting economic growth. However, due to data bottlenecks, there is no clear conclusion on how AI impact environmental investment at the enterprise level. Based on the annual report data of Chinese listed companies, we use machine learning methods to generate an artificial intelligence dictionary, and then constructs enterprise-level artificial intelligence indicators. Through empirical research, we find that AI can significantly improve enterprises' environmental investment. After the robustness tests such as the instrumental variable method and the propensity score matching method, the conclusion remains unchanged. Mechanism analysis shows that AI can improve firms' investment in environmental protection by alleviating financing constraints and improving information transparency. Heterogeneity analysis shows that the state-owned attributes, high tax burden and high environmental regulation of enterprises can enhance the correlation between AI and environmental protection investment. Further research finds that the increase in environmental protection investment caused by artificial intelligence can significantly reduce environmental pollution, rather than for the sake of greenwashing behavior. This article not only enriches the relevant research on the impact of corporate environmental protection investment, but also provides a theoretical basis for enterprises to further promote the development of artificial intelligence technology.
期刊介绍:
Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.