JOURNAL ARTICLE

From Aspiration to Public Policy: Imprinting UNGP-Aligned Footprints of Corporate Responsibility and Accountability into the Shifting Sands of International Arbitration Practice.

  • Published In: ICSID Review: Foreign Investment Law Journal, 2024, v. 39, n. 2. P. 408 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Ong, Mevelyn 3 of 3

Abstract

This article examines the evolving recognition and enforcement of corporate responsibility for human rights and the environment within international investment arbitration, focusing on the alignment of such responsibility with the UN Guiding Principles on Business and Human Rights (UNGPs). It highlights that while some international investment treaties and arbitral tribunals have begun to acknowledge corporate responsibilities, enforcement practices remain largely misaligned with the UNGPs, often prioritizing investor rights over human rights and environmental protection. The article explores the potential for developing an international public policy in arbitration that upholds corporate accountability, including its implications for admissibility of claims and damages assessment, but notes significant challenges such as the limited scope of investor-State dispute settlement (ISDS) mechanisms and the complexity of balancing diverse international law interests. Ultimately, it advocates for a multidisciplinary, multilateral approach involving States, corporations, and arbitration practitioners to strengthen corporate accountability in global investment governance.

Additional Information

  • Source:ICSID Review: Foreign Investment Law Journal. 2024/04, Vol. 39, Issue 2, p408
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0258-3690
  • DOI:10.1093/icsidreview/siae020
  • Accession Number:181524136
  • 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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