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Who teaches and who learns? Policy learning through the C40 cities climate network

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  • Taedong Lee

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  • Susan Meene

    ()

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    Abstract

    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. Copyright Springer Science+Business Media, LLC. 2012

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    File URL: http://hdl.handle.net/10.1007/s11077-012-9159-5
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    Bibliographic Info

    Article provided by Springer in its journal Policy Sciences.

    Volume (Year): 45 (2012)
    Issue (Month): 3 (September)
    Pages: 199-220

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    Handle: RePEc:kap:policy:v:45:y:2012:i:3:p:199-220

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    Web page: http://www.springerlink.com/link.asp?id=102982

    Related research

    Keywords: Policy learning; Climate change; Transnational network; Multi-stakeholder governing body; Social network analysis; Exponential random graph model;

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    1. Melissa Gabler, 2010. "Norms, Institutions and Social Learning: An Explanation for Weak Policy Integration in the WTO's Committee on Trade and Environment," Global Environmental Politics, MIT Press, vol. 10(2), pages 80-117, May.
    2. Kristine Kern & Harriet Bulkeley, 2009. "Cities, Europeanization and Multi-level Governance: Governing Climate Change through Transnational Municipal Networks," Journal of Common Market Studies, Wiley Blackwell, vol. 47, pages 309-332, 03.
    3. Ray Reagans & Linda Argote & Daria Brooks, 2005. "Individual Experience and Experience Working Together: Predicting Learning Rates from Knowing Who Knows What and Knowing How to Work Together," Management Science, INFORMS, vol. 51(6), pages 869-881, June.
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    7. Jacky Swan, 2001. "Knowledge Management: Concepts and Controversies," Journal of Management Studies, Wiley Blackwell, vol. 38(7), pages 913-921, November.
    8. Joanna Depledge, 2006. "The Opposite of Learning: Ossification in the Climate Change Regime," Global Environmental Politics, MIT Press, vol. 6(1), pages 1-22, 02.
    9. Marsden, G. & Frick, K.T. & May, A.D. & Deakin, E., 2011. "How do cities approach policy innovation and policy learning? A study of 30 policies in Northern Europe and North America," Transport Policy, Elsevier, vol. 18(3), pages 501-512, May.
    10. Mark S. Handcock & David R. Hunter & Carter T. Butts & Steven M. Goodreau & Martina Morris, . "statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data," Journal of Statistical Software, American Statistical Association, vol. 24(i01).
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    12. Rongxing Guo, 2007. "Linguistic and Religious Influences on Foreign Trade: Evidence from East Asia ," Asian Economic Journal, East Asian Economic Association, vol. 21(1), pages 101-121, 03.
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