How to protect and restore biodiversity? Exploring polarized citizens' expectations through embedding-based topic modeling with large language models

Magali Trelohan (), Anne-Cécile Gay (), David Moroz () and Damien Chaney ()
Additional contact information
Magali Trelohan: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
David Moroz: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Damien Chaney: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

Post-Print from HAL

Abstract: Despite increasing efforts to promote biodiversity protection, a persistent gap remains between policy measures and public expectations. Aligning biodiversity strategies with citizens' concerns is crucial to fostering support and ensuring effective implementation. Building on the environmental governance and ecosystem services literature, this study leverages Large Language Models for embedding-based topic modeling to analyze a large-scale public consultation involving 80,000 participants, 6000 proposals, and 1.8 million votes in France, identifying eight key biodiversity-related themes: Management of natural and urban spaces, Resource management and pollution control, Wildlife protection and species management, Environmental education and awareness, Agriculture and sustainable food practices, Environmental policy and regulatory measures, Sustainable mobility, energy, and marine conservation, and Emerging and niche concerns. Findings also reveal significant polarization in areas such as hunting regulations, taxation of polluting products, and energy transition, while topics like environmental education and urban greening enjoy strong consensus. These insights highlight the need for differentiated strategies: prioritizing immediate action in consensus areas while fostering dialogue and adaptive policymaking in highly polarized domains. Methodologically, this study demonstrates how Large Language Models can efficiently process and analyze large-scale citizen consultation datasets, offering a scalable approach to capturing public preferences and societal debates in a policy context.

Keywords: Polarization; Large Language Models; Environmental governance; Public consultation; Biodiversity (search for similar items in EconPapers)
Date: 2026-06-01
New Economics Papers: this item is included in nep-agr and nep-env
Note: View the original document on HAL open archive server: https://hal.science/hal-05576392v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Technological Forecasting and Social Change, 2026, 227, pp.124655. ⟨10.1016/j.techfore.2026.124655⟩

Downloads: (external link)
https://hal.science/hal-05576392v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05576392

DOI: 10.1016/j.techfore.2026.124655

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().