Who teaches and who learns? Policy learning through the C40 cities climate network
This study examines the network structure of policy learning in the C40 Cities Climate Leadership Group, which is a network of the world’s largest cities committed to tackling climate change issues. Among forty members and nineteen affiliate members, we ask the question with whom do cities learn and why? How are policy-learning relationships associated with cities’ multi-stakeholder governing body, policy performance, and cultural similarities? While studies on learning have analyzed conditions facilitating learning, quantitative studies of local government learning in global networks are rare. To facilitate the investigation into learning, we conceptualize learning as a process comprising information seeking, adoption and policy change, and focus on information seeking as the foundation step in the learning process. This social network analysis using the exponential random graph model reveals the cities that seek information and those that are information sources are different subgroups. Furthermore, analysis of nodal attributes suggests that transmunicipal learning in the C40 network is facilitated by the presence of a multi-stakeholder governing body; homophily of culture (language and regional proximity); and higher level of climate change policy performance. Creating a multi-stakeholder governing body could ensure participatory representativeness from citizens and relevant stakeholders to enhance climate change policy engagement and decision making as well as policy learning.
KeywordsPolicy learning Climate change Transnational network Multi-stakeholder governing body Social network analysis Exponential random graph model
Akaike information criterion
C40 cities climate leadership group
Carbon disclosure project
Central intelligence agency
Cities for climate protection
Exponential random graph
International council for local environmental initiative
We would like to thank the four anonymous reviewers, the editor of Policy Sciences, Justin Robertson, and Bradley Williams for their helpful comments. Data collection for this manuscript is supported by City University Start-Up grant (7200233).
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