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

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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.

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Notes

  1. We have used “actors” to refer to organizations and individuals in the generic sense of entities that have agency; “actors” is generally used in the theoretical section where concepts of policy learning and policy networks are described and discussed. We have used stakeholders to refer to individuals and types of organizations which include non-government, private sector organizations involved in policy learning. We use this term to emphasize the variety of organizations and individuals involved in policy learning.

  2. We refer to transnational municipal networks as ‘city networks’ in this paper; the membership of these networks comprises municipal governments and representatives of these governments. Network membership typically does not include other organizations operating in the cities, such as non-government or private sector organizations.

  3. While policy diffusion is another theoretical approach, we use policy learning as it is (1) analytically identical to policy diffusion through a network (Henry 2009), and (2) an easier concept for survey respondents to understand than policy diffusion and therefore more likely to return accurate results.

  4. Hall (1993) refers to first-order learning as changes to the policy settings to fine tune the policy instrument as a result of previous performance; second-order learning as changes to the policy instruments used while maintaining existing policy goals; and third-order learning as changes to the hierarchy of the policy goals, and subsequent changes to the policy instruments and settings.

  5. We use the terms information seeking, policy learning, and learning interchangeably in this paper given the similarity of their definitions.

  6. The C40 network comprises the following cities: Addis Ababa, Amsterdam, Athens, Austin, Bangkok, Barcelona, Basel, Beijing, Berlin, Bogotá, Buenos Aires, Cairo, Caracas, Changwon, Chicago, Copenhagen, Curitiba, Delhi, Dhaka, Hanoi, Heidelberg, Houston, Ho Chi Minh City, Hong Kong, Istanbul, Jakarta, Johannesburg, Karachi, Lagos, Lima, London, Los Angeles, Madrid, Melbourne, Mexico City, Moscow, Mumbai, New Orleans, New York, Paris, Philadelphia, Portland, Rio de Janeiro, Rome, Rotterdam, Salt Lake City, San Francisco, Santiago de Chile, Sao Paulo, Seattle, Seoul, Shanghai, Stockholm, Sydney, Tokyo, Toronto, Warsaw, Yokohama.

  7. The density of the network is calculated as (2 × 74)/2 × (33 × (33−1)).

  8. Network analysis is to estimate how attributes of actors influence network structure. Here, network structure does not necessarily mean individual tie formation (in this case, who learns from whom as a dyadic relation) but the whole observed tie formation of the actors as one possible appearance of relations. Thus, examining how network structure influences attributes or behaviors of actors is a different domain to social network analysis. One example of such study can look at how centrality (one characteristics of network structure) influence cities’ climate change policies, using regression models.

  9. This study investigates factors influencing learning within the network, and therefore, more detailed analysis of the types of climate change policies learned (i.e., the “what” was learned) is beyond the scope of this paper.

Abbreviations

AIC:

Akaike information criterion

C40:

C40 cities climate leadership group

CDP:

Carbon disclosure project

CIA:

Central intelligence agency

CCP:

Cities for climate protection

ERG:

Exponential random graph

GHG:

Greenhouse gas

ICLEI:

International council for local environmental initiative

NGO:

Non-government organization

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Acknowledgments

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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  1. Department of Asian and International Studies, City University of Hong Kong, Tat Chee Ave., Kowloon, Hong Kong

    Taedong Lee & Susan van de Meene

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Lee, T., van de Meene, S. Who teaches and who learns? Policy learning through the C40 cities climate network. Policy Sci 45, 199–220 (2012). https://doi.org/10.1007/s11077-012-9159-5

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