The landscape of Knowledge Networks supporting climate change adaptation in Europe

Pierre Van Wolleghem, Marta Bruno Soares, Gavin Lamb and Ivan Puga-Gonzalez

No vtshj_v1, SocArXiv from Center for Open Science

Abstract: Climate change adaptation (CCA) increasingly relies on collaborative efforts to generate and circulate actionable knowledge. Among these, knowledge networks (KNs) have emerged as key infrastructures for fostering transnational cooperation, enabling mutual learning, and supporting local implementation. While their theoretical value is widely acknowledged, the empirical landscape of adaptation-related knowledge networks in Europe remains poorly understood. This article offers the first systematic mapping of KNs working on CCA in Europe, with a particular focus on Transnational Municipal Networks (TMNs)—a prominent subset composed primarily of local authorities. We identify and compare 32 knowledge networks across key dimensions including structure, governance, membership, funding models, and core activities. The analysis is grounded in two original datasets: a network-level dataset comprising 32 adaptation-related KNs and a membership-level dataset encompassing over 17,000 entries, systematically collected and geo-referenced. These resources provide an unprecedented empirical basis for understanding how climate knowledge is shared, institutionalised, and mobilised across Europe. Our findings reveal a diverse and uneven landscape, with significant variation in access thresholds, support mechanisms, and geographic representation. TMNs, in particular, differ in the type and intensity of support they provide—ranging from technical expertise and financial assistance to symbolic capital and peer learning opportunities. The article contributes to ongoing debates about multilevel climate governance and the role of networks in enhancing local adaptation capacity.

Date: 2025-11-20
New Economics Papers: this item is included in nep-eur
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:vtshj_v1

DOI: 10.31219/osf.io/vtshj_v1

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