JOURNAL ARTICLE

Federalism, Decentral Governance, and Joint Decision-Making: Bad News for the Implementation of International Environmental Agreements?

  • Published In: Publius: The Journal of Federalism, 2024, v. 54, n. 2. P. 407 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gómez, Johannes Müller 3 of 3

Abstract

This article examines the impact of domestic multilevel governance structures—specifically federalism, decentral governance, and joint decision-making—on the implementation of international environmental agreements, using the Ramsar Wetlands Convention as a case study. Contrary to the prevailing "pessimistic school" in international compliance literature, which views multilevel systems as obstacles due to increased veto points and coordination challenges, the study finds that federalism, defined as subnational decision-making autonomy, positively and significantly enhances implementation in environmental conservation. Financial decentralization and bicameralism (joint decision-making) showed no significant effects in this context. The findings suggest that subnational autonomy allows local governments to act independently and respond to local environmental interests, thereby facilitating implementation, especially in policy areas like wetland protection where local benefits are tangible. The article calls for further comparative research across different environmental policy fields to clarify when multilevel governance structures hinder or help international agreement implementation.

Additional Information

  • Source:Publius: The Journal of Federalism. 2024/04, Vol. 54, Issue 2, p407
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0048-5950
  • DOI:10.1093/publius/pjae003
  • Accession Number:176590094
  • Copyright Statement:Copyright of Publius: The Journal of Federalism 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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