JOURNAL ARTICLE

Mitigating Climate Policy Shocks Through Mergers and Acquisitions? An Empirical Investigation of International Oil and Gas Firms.

  • Published In: Corporate Social Responsibility & Environmental Management, 2025, v. 32, n. 3. P. 2921 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Yajie; Zhou, Chengchen; Zhang, Dayong; Xie, Jun; Managi, Shunsuke 3 of 3

Abstract

Climate change and its related policies have significant impacts on energy industries, leading to a considerable number of stranded assets and poor financial performance. Using a global sample of 1147 listed oil and gas firms from 2000 to 2021, this paper investigates whether mergers and acquisitions (M&As) mitigate climate policy shocks, focusing on the consequential financial impacts. Taking the Paris Agreement as the major climate policy shock, we first confirm the negative impacts of climate policy on the financial performance of oil and gas firms, after which we demonstrate M&As can alleviate the adverse effects. Mechanism analysis reveals that the financial benefits of M&As are stronger in upstream firms, those with better corporate governance and sufficient cash flows. Firms in countries with high‐level economic development and carbon risk can benefit from M&As. Furthermore, conglomerate M&As increased following the Paris Agreement, indicating that these energy firms responded to climate policy shocks through diversification. These findings can help us understand the global impacts of climate policies and have important implications for how the energy sector should respond to policy shocks. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Corporate Social Responsibility & Environmental Management. 2025/05, Vol. 32, Issue 3, p2921
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1535-3958
  • DOI:10.1002/csr.3108
  • Accession Number:185030923
  • Copyright Statement:Copyright of Corporate Social Responsibility & Environmental Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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