AI integration for climate risk mitigation: The role of organizational context

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change Pub Date : 2025-08-19 DOI:10.1016/j.techfore.2025.124327

Muhammad Kaleem Khan , Muhammad Jameel Hussain , Muhammad Wasim Hussan , Afifa Qadeer , Anona Armstrong , Shanshan Li

{"title":"AI integration for climate risk mitigation: The role of organizational context","authors":"Muhammad Kaleem Khan , Muhammad Jameel Hussain , Muhammad Wasim Hussan , Afifa Qadeer , Anona Armstrong , Shanshan Li","doi":"10.1016/j.techfore.2025.124327","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines the relationship between artificial intelligence (AI) adoption and Firm-Level Climate Change Risk (FLCCR) among Chinese enterprises. Using comprehensive firm-level data on AI implementation and FLCCR exposure, we analyze the contextual effectiveness of AI across diverse ownership structures, industry sectors, and corporate governance frameworks. Our empirical analysis reveals a robust association between AI adoption and reduced FLCCR, with findings consistent with established economic theories. The results remain statistically significant after addressing potential endogeneity concerns through multiple robustness checks. Our findings reveal that AI's climate risk-reduction potential is not uniform but context-dependent, varying significantly across ownership types, sectors, and governance characteristics. Notably, the risk-mitigating effects of AI appear particularly pronounced in state-owned enterprises, firms operating in pollution-intensive or high-technology sectors, and organizations with strong corporate governance mechanisms, specifically those characterized by board independence and gender diversity. These findings contribute to the growing literature on technological solutions for environmental challenges while providing actionable insights for corporate decision-makers and policymakers seeking to enhance climate resilience through strategic AI integration. The study underscores the potential role of AI as a tool for sustainable development while acknowledging the complex interplay between technological adoption and organizational factors in risk mitigation outcomes.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"220 ","pages":"Article 124327"},"PeriodicalIF":13.3000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technological Forecasting and Social Change","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040162525003580","RegionNum":1,"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

This study examines the relationship between artificial intelligence (AI) adoption and Firm-Level Climate Change Risk (FLCCR) among Chinese enterprises. Using comprehensive firm-level data on AI implementation and FLCCR exposure, we analyze the contextual effectiveness of AI across diverse ownership structures, industry sectors, and corporate governance frameworks. Our empirical analysis reveals a robust association between AI adoption and reduced FLCCR, with findings consistent with established economic theories. The results remain statistically significant after addressing potential endogeneity concerns through multiple robustness checks. Our findings reveal that AI's climate risk-reduction potential is not uniform but context-dependent, varying significantly across ownership types, sectors, and governance characteristics. Notably, the risk-mitigating effects of AI appear particularly pronounced in state-owned enterprises, firms operating in pollution-intensive or high-technology sectors, and organizations with strong corporate governance mechanisms, specifically those characterized by board independence and gender diversity. These findings contribute to the growing literature on technological solutions for environmental challenges while providing actionable insights for corporate decision-makers and policymakers seeking to enhance climate resilience through strategic AI integration. The study underscores the potential role of AI as a tool for sustainable development while acknowledging the complex interplay between technological adoption and organizational factors in risk mitigation outcomes.

查看原文本刊更多论文

缓解气候风险的人工智能集成：组织背景的作用

本研究探讨了中国企业采用人工智能（AI）与企业层面气候变化风险（FLCCR）之间的关系。利用有关人工智能实施和FLCCR暴露的全面公司层面数据，我们分析了人工智能在不同所有权结构、行业部门和公司治理框架中的上下文有效性。我们的实证分析显示，采用人工智能与降低FLCCR之间存在强大的关联，研究结果与既定的经济理论一致。通过多次稳健性检查解决潜在的内生性问题后，结果仍然具有统计学意义。我们的研究结果表明，人工智能降低气候风险的潜力不是统一的，而是取决于具体情况，在不同的所有权类型、部门和治理特征之间存在显著差异。值得注意的是，人工智能的风险缓解效果在国有企业、在污染密集型或高科技部门经营的公司以及具有强大公司治理机制的组织中尤为明显，特别是那些以董事会独立性和性别多样性为特征的组织。这些发现为越来越多的关于环境挑战的技术解决方案的文献做出了贡献，同时为企业决策者和政策制定者寻求通过战略人工智能整合提高气候适应能力提供了可行的见解。该研究强调了人工智能作为可持续发展工具的潜在作用，同时承认技术采用与风险缓解结果中的组织因素之间存在复杂的相互作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Technological Forecasting and Social Change Multiple-

CiteScore

21.30

自引率

10.80%

发文量

813

期刊介绍： Technological Forecasting and Social Change is a prominent platform for individuals engaged in the methodology and application of technological forecasting and future studies as planning tools, exploring the interconnectedness of social, environmental, and technological factors. In addition to serving as a key forum for these discussions, we offer numerous benefits for authors, including complimentary PDFs, a generous copyright policy, exclusive discounts on Elsevier publications, and more.