JOURNAL ARTICLE
Investment Treaties and Climate Change Policy: A Possible Legal Pathway.
Published In: ICSID Review: Foreign Investment Law Journal, 2025, v. 40, n. 2. P. 216 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Viñuales, Jorge E 3 of 3
Abstract
This article analyzes how international investment treaties can be designed to promote foreign investment in low-carbon and climate-resilient activities while preserving States' regulatory capacity to drive economic decarbonization and resilience. It highlights the growing importance of aligning investment agreements, including bilateral investment treaties (BITs) and investment chapters in free trade agreements (FTAs), with climate objectives such as those in the Paris Agreement, noting current gaps between climate finance needs and actual flows, especially in emerging and developing economies. The article reviews definitional challenges around sustainable investments, the use of environmental taxonomies (such as the EU Taxonomy Regulation), and treaty clauses that promote and facilitate climate-supportive investments. It further examines legal mechanisms in treaties that reserve policy space for States to regulate post-establishment, including carve-outs and exceptions, and discusses the limited but evolving arbitral practice interpreting these provisions. The conclusion emphasizes the need for investment agreements to balance liberalized market access for sustainable investments with sufficient regulatory space for States to implement climate policies, while acknowledging the current legal uncertainties that complicate both investment promotion and climate-related industrial policy.
Additional Information
- Source:ICSID Review: Foreign Investment Law Journal. 2025/04, Vol. 40, Issue 2, p216
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2025
- ISSN:0258-3690
- DOI:10.1093/icsidreview/siaf010
- Accession Number:191590479
- Copyright Statement:Copyright of ICSID Review: Foreign Investment Law Journal is the property of Oxford University Press / USA 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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