A multi-level perspective on learning about climate change adaptation through international cooperation

Autor(en): Vinke-de Kruijf, Joanne 
Pahl-Wostl, Claudia 
Stichwörter: BARRIERS; Climate change adaptation; Configurational analysis; Environmental Sciences; Environmental Sciences & Ecology; FRAMEWORK; International cooperation; KNOWLEDGE TRANSFER; Knowledge utilization; Learning; NETWORKS; Social learning; SUCCESS
Erscheinungsdatum: 2016
Herausgeber: ELSEVIER SCI LTD
Journal: ENVIRONMENTAL SCIENCE & POLICY
Volumen: 66
Startseite: 242
Seitenende: 249
Zusammenfassung: 
International cooperation and learning may accelerate climate change adaptation and help countries and regions to adapt more effectively and efficiently. Recognizing the importance and opportunities for mutual learning and knowledge transfer, international and supranational organizations, such as the European Commission, have put programmes for international cooperation in place. This paper presents and tests a framework for assessing multi-level learning outcomes of such international cooperation processes and the conditions that produce these outcomes. The framework distinguishes between: (1) group learning by individual process participants; (2) organizational learning by organizations represented in the process; and (3) network and societal learning by actors external to the process. We verify the analytical potential of the framework by comparing learning by six partners in an adaptation-oriented European cooperation project. The project scores rather high on group learning with participants learning from and - to a lesser extent - also with each other. Learning by partner organizations varied and was generally less whereas learning by external actors was very limited. The case study confirms our expectation that learning outcomes are produced by combinations of partner specific, process-specific and process-external conditions. The presented framework and insights can be used to stimulate learning in and from international cooperation processes. (C) 2016 Elsevier Ltd. All rights reserved.
ISSN: 14629011
DOI: 10.1016/j.envsci.2016.07.004

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