JOURNAL ARTICLE

Environmental Pollution Liability: A Comparative Legal Study of EU, Kazakhstan and Russia.

  • Published In: Environmental Policy & Law, 2026, v. 56, n. 1/2. P. 63 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Balobeyev, Andrey; Ilyassova, Gulzhazira; Yegemberdiyev, Yerzhan; Biyebayeva, Ardak 3 of 3

Abstract

This article focuses on a comparative legal analysis of criminal law provisions addressing environmental offences in the European Union (EU), Kazakhstan, and the Russian Federation, highlighting differences, strengths, and limitations in their approaches. The EU has recently advanced its environmental criminal law through Directive (EU) 2024/1203 and the 2025 Council of Europe Convention on the Protection of the Environment through Criminal Law, establishing comprehensive offence categories, corporate liability, and protections for whistleblowers and environmental defenders. In contrast, Kazakhstan and Russia maintain foundational but less developed frameworks, lacking criminal liability for legal entities, clear mens rea (mental element) distinctions, and institutional protections for environmental human rights defenders. The study underscores the need for Kazakhstan and Russia to modernize their legislation by incorporating corporate criminal liability, refining offence definitions, and enhancing enforcement mechanisms to better align with evolving international standards exemplified by the EU model.

Additional Information

  • Source:Environmental Policy & Law. 2026/03, Vol. 56, Issue 1/2, p63
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2026
  • ISSN:0378-777X
  • DOI:10.1177/18785395251413411
  • Accession Number:192372866
  • Copyright Statement:Copyright of Environmental Policy & Law is the property of Sage Publications Inc. 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.