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Non-Regression in EU Risk Governance: Testing the Limits of Pesticides Deregulation

Published online by Cambridge University Press:  01 October 2026

Matthias Hasler*
Affiliation:
Department of Law, European University Institute, Italy
Vania González
Affiliation:
Independent scholar, Netherlands
*
Corresponding author: Matthias Hasler; Email: matthias.hasler@eui.eu
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Abstract

Set against the sweeping deregulatory push unleashed by the Omnibus surge, this article advances a notion of non-regression attuned to the specificities of EU risk governance. It argues that non-regression so conceived must be normatively anchored in the precautionary principle and operationalised through proportionality. Through a precaution-inflected proportionality test, the normative commitments underpinning the precautionary principle can be translated into structured legal reasoning and leveraged via the principle’s inherent primacy of health and environmental protection. When applied to an extreme instance of pesticides deregulation – the abolition of mandatory re-approvals of pesticide substances – this test yields a disquieting picture of failure across the board, from suitability and necessity to proportionality stricto sensu. Most striking is the troubling imbalance between the deregulatory measure’s tenuous benefits and its substantial interference with the precautionary principle: in its current form, the measure would significantly erode the EU’s preventive regulatory epistemology based on sound science, along with potentially drastic and diffuse consequences for the Union’s high protection levels. While the notion of non-regression developed in this contribution may tame unfettered legislative backsliding of this sort, it is no failsafe against deregulation. Only the most extreme of cases may fall within its reach, leaving others to slip through the cracks of legislative discretion.

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Articles
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use.
Copyright
© The Author(s), 2026. Published by Cambridge University Press