Designing successful agri-environmental schemes: A mechanistic analysis of a collective scheme for eco-system services in the Netherlands

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In response to the challenges posed by the fragmentation of habitats and loss of native biodiversity, climate change adaptation and mitigation, diverse agri-environmental measures have been initiated across the European Union (EU) with the aim of fostering agricultural ecosystem service delivery. Previous studies adopting a governance perspective have identified various determinants of successful agri-environmental measures. However, the explanatory value of these studies is limited as the causal processes through which context and scheme design affect implementation success remain grey -boxed. This article uses a mechanism-based approach to uncover the causal processes that underlie actions and interactions within agri-environmental governance arrangements and provides insights into the role the interplay between context and scheme design plays in the successful implementation of agri-environmental measures. The empirical focus is the governance of a successful collective agri-environmental scheme in the Netherlands. In opening the grey box of the causal mechanisms that link contextual and scheme design with their results, the paper applies theory building process tracing methods. Results show that implementation success in the case is explained by the interplay between social learning and trust-building mechanisms. We argue that EU and domestic decision-makers aiming to improve the contribution of agri-environmental measures to climate change adaptation and mitigation must consider the contextual conditions that facilitate increased cooperation between stakeholders and, ultimately, successful implementation of measures.

OriginalsprogEngelsk
TidsskriftEnvironmental Science and Policy
Vol/bind146
Sider (fra-til)123-132
Antal sider10
ISSN1462-9011
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported by the European Union's Horizon 2020 research and innovation programme [grant agreement number 817903].

Funding Information:
This work was supported by the European Union’s Horizon 2020 research and innovation programme [grant agreement number 817903 ].

Publisher Copyright:
© 2023 The Authors

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