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Perceptions of urban climate hazards and their effects on adaptation agendas

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Abstract

Decision-makers in cities around the world are beginning to take steps to adapt to the current and future risks presented by climate change, the sum of which we refer to as a city’s adaptation agenda. However, there is significant variation in such agendas: some may focus on responding to one or two climate hazards, while others develop agendas to respond to a wide range of hazards. What causes this varying range of urban adaptation agendas? The purpose of this study is to assess how geographic, socioeconomic, and institutional features of cities as well as the perception of climate change hazards affect the scope of adaptation agendas. Utilizing regression analyses of a newly constructed database for 58 cities around the world, our findings suggest that the perception of climate change hazards held by decision-makers is a primary determinant of the scope of urban adaptation agendas. Given that each global city faces place-specific hazards from varying extreme climate events, this research provides global-scale adaptation strategies for local, national, and international institutions, suggesting that enhancing awareness as well as mapping urban climate hazards is an initial step for broadening and mainstreaming adaptation agendas.

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Notes

  1. We use the term “agenda” as a list of things to be considered or done since our focus is the scope and approach of what cities are doing to address climate adaptation. The term “climate adaptation agenda” can be interchangeably used with “adaptation strategy” or “policy,” which may focus more on systematic planning and implementation.

  2. Abidjan (Côte d’Ivoire), Addis Ababa (Ethiopia), Amsterdam (Netherlands), Atlanta (USA), Austin (USA), Bangkok (Thailand), Basel (Switzerland), Berlin (Germany), Bogotá (Colombia), Buenos Aires (Argentina), Caracas (Venezuela), Changwon (South Korea), Chicago (USA), Copenhagen (Denmark), Denver (USA), District of Columbia (USA), Dublin (Ireland), Durban (South Africa), Hamburg (Germany), Helsinki (Finland), Hong Kong (Greater China), Houston (USA), Jakarta (Indonesia), Kadiovacik (Turkey), Karachi (Pakistan), Lagos (Nigeria), Las Vegas (USA), London (UK), Los Angeles (USA), Madrid (Spain), Melbourne (Australia), Miami (USA), Moscow (Russia), New York (USA), Orista (Italy), Paris (France), Philadelphia (USA), Phoenix (USA), Pietermaritzburg (South Africa), Portland (USA), Riga (Latvia), Rio de Janeiro (Brazil), Rome (Italy), Rotterdam (Netherlands), San Diego (USA), San Francisco (USA), Santiago (Chile), Sao Paulo (Brazil), Seattle (USA), Seoul (Korea), St. Louis (USA), Stockholm (Sweden), Sydney (Australia), Tokyo (Japan), Toronto (Canada), Vancouver (Canada), Warsaw (Poland). Cities in the sample are C40 Cities Climate Leadership group member and affiliated cities of the Global South and North. Cities were not randomly sampled, thus the findings speak to the data rather than a generalizable tendency.

  3. A z-score (or a standard value) is the number of standard deviation units that a score differs from the mean, which is calculated as z = \( \left(\mathrm{X}-\overset{-}{\mathrm{X}}\right)/\mathrm{s} \), where X is a single score, \( \overset{-}{\mathrm{X}} \) is the mean of all the scores, and s is the standard deviation of the score. The climate adaptation index used here averages z-scores for all adaptation policy factors such as air quality initiative and shading public space; the climate risk index averages z-scores for all risk factors such as more intense drought and more hot days.

  4. We tested the reliability of this index using Cronbach’s alpha, which provides a measure of inter-item correlation (covariance). We found relatively high inter-item correlation (alpha = 0.80), suggesting that on average, items in the index positively co-vary.

  5. As another robustness check, we also fit negative binomial analysis and the same independent and dependent variables. Given that the dependent variable in this case is a count, using negative binomial analysis is useful because it does not assume that events are independent and have a constant rate of occurrence (unlike Poisson distribution techniques) (King 1998). The coefficients and standard errors of negative binomial analysis present a similar pattern to our two models. The analysis outcomes can be presented upon request.

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Authors and Affiliations

  1. Department of Political Science, Yonsei University, 309-2 Yonhee Bldg. Yonsei Ro 50, Seodaemun Gu, Seoul, South Korea

    Taedong Lee

  2. Department of Political Science, University of Toronto Mississauga, Mississauga, Ontario, Canada

    Sara Hughes

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  1. Taedong Lee
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Correspondence to Taedong Lee.

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Lee, T., Hughes, S. Perceptions of urban climate hazards and their effects on adaptation agendas. Mitig Adapt Strateg Glob Change 22, 761–776 (2017). https://doi.org/10.1007/s11027-015-9697-1

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