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Perceptions of urban climate hazards and their effects on adaptation agendas

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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Fig. 1

Notes

  1. 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. 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. 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. 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. 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.

References

  1. Adger WN, Arnell NW, Tompkins EL (2005) Successful adaptation to climate change across scales. Glob Environ Chang 15:77–86

    Article  Google Scholar 

  2. Anguelovski I, Chu E, Carmin J (2014) Variations in approaches to urban climate adaptation: experiences and experimentation from the global. South Global Environ Change 27:156–167

    Article  Google Scholar 

  3. Baker I, Peterson A, Brown G, McAlpine C (2012) Local government response to the impacts of climate change: an evaluation of local climate adaptation plans. Landsc Urban Plan 107:127–136

    Article  Google Scholar 

  4. Bart I (2011) Municipal emissions trading: reducing transport emissions through cap-and-trade. Clim Pol 11:813–828

    Article  Google Scholar 

  5. Birkmann Jr, Garschagen M, Kraas F, Quang N (2010) Adaptive urban governance: new challenges for the second generation of urban adaptation strategies to climate change. Sustain Sci 5:185–206

    Article  Google Scholar 

  6. Broto VC, Bulkeley H (2013) A survey of urban climate change experiment in 100 cities. Glob Environ Chang 23:92–102

    Article  Google Scholar 

  7. Bulkeley H, Betsill M (2013) Revisiting the urban politics of climate change. Environ Polit 22:136–154

    Article  Google Scholar 

  8. Carmin J, Anguelovski I, Roberts D (2012a) Urban climate adaptation in the global South: planning in an emerging policy domain. J Plan Educ Res 32:18–32

    Article  Google Scholar 

  9. Carmin J, Nadkarni N, Rhie C (2012b) Progress and challenges in urban climate adaptation planning: results of a global survey. MIT, Cambridge

    Google Scholar 

  10. CDP (2012) Measurement for management: CDP cities 2012 global report. Carbon Disclosure Project, London

    Google Scholar 

  11. Corfee-Morlot J, Cochran I, Hallegatte S, Teasdale P-J (2011) Multilevel risk governance and urban adaptation policy. Clim Chang 104:169–197

    Article  Google Scholar 

  12. Daigger GT (2009) Evolving urban water and residuals management paradigms: water reclamation and reuse, decentralization, and resource recovery. Water Environ Res 81:809–823

    Article  Google Scholar 

  13. Declet-Barreto J, Brazel AJ, Martin CA, Chow WT, Harlan SL (2013) Creating the park cool island in an inner-city neighborhood: heat mitigation strategy for phoenix. AZ Urban Ecosyst 16:617–635

    Article  Google Scholar 

  14. Fankhaeser S, Sehilleier F, Stern N (2008) Climate change, innovation and jobs. Clim Pol 8:421–429

    Article  Google Scholar 

  15. Füssel H (2007) Adaptation planning for climate change: concepts, assessment approaches, and key lessons. Sustain Sci 2:265–275

    Article  Google Scholar 

  16. Gartland L (2008) Heat islands: understanding and mitigating heat in urban areas. Earthscan, London and Sterling, VA

    Google Scholar 

  17. Hamin EM, Gurran N (2009) Urban form and climate change: balancing adaptation and mitigation in the U.S. and Australia. Habitat Int 33:238–245

    Article  Google Scholar 

  18. Heltberg R, Gitay H, Prabhu R (2012) Community-based adaptation: lessons from a grant competition. Clim Pol 12:143–163

    Article  Google Scholar 

  19. Hughes S (2013) Justice in urban climate change adaptation: criteria and application to Delhi. Ecol Soc 18:48

    Article  Google Scholar 

  20. Hunt A, Watkiss P (2011) Climate change impacts and adaptation in cities: a review of the literature. Clim Chang 104:13–49

    Article  Google Scholar 

  21. Huq S, Kovats S, Reid H, Satterthwaite D (2007) Reducing risks to cities from disaster and climate change. Environ Urban 19:3–15

    Article  Google Scholar 

  22. IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge

    Google Scholar 

  23. King G (1998) Unifying political methodology: the likelihood theory of statistical inference. University of Michigan Press, Ann Arbor

    Book  Google Scholar 

  24. Krause RM (2012) An assessment of the impact that participation in local climate networks has on cities’ implementation of climate, energy, and transportation policies. Rev Policy Res 29:585–603

    Article  Google Scholar 

  25. Lee T (2013) Global cities and transnational climate change networks. Global Environ Polit 13:108–127

    Article  Google Scholar 

  26. Lee T, Koski C (2012) Building green: local political leadership addressing climate change. Rev Policy Res 29:605–624

    Article  Google Scholar 

  27. Lee T, Koski C (2014) Mitigating global warming in global cities: participation and climate change policies of C40 cities. J Comp Policy Anal 16:475–492

    Google Scholar 

  28. Lee T, Lee T, Lee Y (2014) An experiment for urban energy autonomy in Seoul: the one less nuclear power plant policy. Energ Policy 74:311–318

    Article  Google Scholar 

  29. Lehmann P, Brenck M, Gebhardt O, Schaller S, Süßbauer E (2015) Barriers and opportunities for urban adaptation planning: analytical framework and evidence from cities in Latin America and Germany. Mitigation and adaptation strategies for global change. 1–23

  30. McGranahan G, Balk D, Anderson B (2007) The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zone. Environ Urban 19:17–37

    Article  Google Scholar 

  31. McIntosh N, Cone J (2014) Responding to the effects of coastal climate change: results of a national sea grant survey. Sea Grant Oregon, Corvallis

    Google Scholar 

  32. Measham TG, Preston BL, Smith TF, Brooke C, Gorddard R, Withycombe G, Morrison C (2011) Adapting to climate change through local municipal planning: barriers and challenges. Mitig Adapt Strateg Glob Chang 16:889–909

    Article  Google Scholar 

  33. Mees H-LP, Driessen PPJ (2011) Adaptation to climate change in urban areas: climate-greening London, Rotterdam, and Toronto. Clim Law 2:251–280

    Google Scholar 

  34. Moser SC, Ekstrom JA (2010) A framework to diagnose barriers to climate change adaptation. Proc Natl Acad Sci U S A 107:22026–22031

    Article  Google Scholar 

  35. Muller M (2007) Adapting to climate change water management for urban resilience. Environ Urban 19:99–113

    Article  Google Scholar 

  36. Mullin M (2008) The conditional effect of specialized governance on public policy. Am J Polit Sci 52:125–141

    Article  Google Scholar 

  37. National Research Council (2011) America’s climate choices. The National Academies Press, Washington

    Google Scholar 

  38. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate change on human health. Nature 438:310–317

    Article  Google Scholar 

  39. Reckien D et al (2014) Climate change response in Europe: what’s the reality? Analysis of adaptation and mitigation plans from 200 urban areas in 11 countries. Clim Chang 122:331–340

    Article  Google Scholar 

  40. Roberts D (2010) Prioritizing climate change adaptation and local level resilience in Durban, South Africa. Environ Urban 22:397–413

    Article  Google Scholar 

  41. Romero-Lankao P, Hughes S, Rosas-Huerta A, Borquez R, Gnatz D (2013) Urban institutional response capacity for climate change: an examination of construction and pathways in Mexico City and Santiago. Environ Plan C Govern Policy 31:785–805

    Article  Google Scholar 

  42. Rotterdam (2010) Rotterdam climate proof: adaptation program 2010. Rotterdam City Government, Rotterdam

    Google Scholar 

  43. Runhaar H, Mees H, Wardekker A, Sluijs JVD, Driessen PPJ (2012) Adaptation to climate change-related risks in Dutch urban areas: stimuli and barriers. Reg Environ Chang 12:777–790

    Article  Google Scholar 

  44. Saavedra C, Budd WW (2009) Climate change and environmental planning: working to building community resilience and adaptive capacity in Washington State, USA. Habitat Int 33:246–252

    Article  Google Scholar 

  45. Sassen S (1991) The global city: New York, London, Tokyo. Princeton University Press, Princeton

    Google Scholar 

  46. Sharma D, Tomar S (2010) Mainstreaming climate change adaptation in Indian Cities. Environ Urban 22:451–465

    Article  Google Scholar 

  47. Sharp EB, Daley DM, Lynch MS (2011) Understanding local adoption and implementation of climate change mitigation policy. Urban Aff Rev 47:433–457

    Article  Google Scholar 

  48. Sherbinin AD, Schiller A, Pulsipher A (2007) The vulnerability of global cities to climate hazards. Environ Urban 19:39–64

    Article  Google Scholar 

  49. Tang Z, Brody SD, Courtney Q, Liang C, Ting W (2010) Moving from agenda to action: evaluating local climate change action plans. J Environ Plan Manag 53:41–62

    Article  Google Scholar 

  50. Tanner T, Mitchell T, Polack E, Guenther B (2009) Urban governance for adaptation: assessing climate change resilience in ten Asian cities. IDS Work Paper 315:2009

    Google Scholar 

  51. Tompkins EL, Adger WN, Boyd E, Nicholson-Cole S, Weaterhead K, Arnell NW (2010) Observed adaption to climate change: uk evidence of transition to a well-adapting society global. Environ Change 20:627–635

    Article  Google Scholar 

  52. Uittenbroek CJ, Janssen-Jansen LB, Runhaar HAC (2013) Mainstreaming climate adaptation into urban planning: overcoming barriers, seizing opportunities and evaluating the results in two Dutch case studies. Reg Environ Chang 13:399–411

    Article  Google Scholar 

  53. Uittenbroek CJ, Janssen-Jansen LB, Spit TJM, Salet WGM, Runhaar HAC (2014) Political commitment in organising municipal responses to climate adaptation: the dedicated approach versus the mainstreaming approach. Environ Polit 23:1043–1063

    Article  Google Scholar 

  54. UNDP/UNEP (2011) Mainstreaming climate change adaptation into development planning: a guide for practitioners. UNDP/UNEP, Nairobi

    Google Scholar 

  55. Vignola R, Klinsky S, Tam J, McDaniels T (2013) Public perception, knowledge and policy support for mitigation and adaption to climate change in Costa Rica: comparisons with North American and European studies. Mitig Adapt Strateg Glob Chang 18:303–323

    Article  Google Scholar 

  56. Vrolijks L, Spatafore A (2011) Mittal AS comparative research on the adaptation strategies of ten urban climate plans. In: Otto-Zimmermann K (ed) Resilient cities: cities and adaptation to climate change. Proceedings of the Global Forum 2010

  57. Weber EU (2010) What shapes perceptions of climate change? Wiley Interdiscip Rev Clim Chang 1:332–342

    Article  Google Scholar 

  58. Westerhoff L, Keskitalo ECH, Juloha S (2011) Capacities across scales: local to national adaptation policy in four European countries. Clim Pol 11:1071–1985

    Article  Google Scholar 

  59. Zahran S, Grover H, Brody SD, Vedlitz A (2008) Risk, stress, and capacity: explaining metropolitan commitment to climate protection. Urban Aff Rev 43:447–474

    Article  Google Scholar 

  60. Zimmerman R, Faris C (2011) Climate change mitigation and adaptation in North American Cities. Curr Opin Environ Sustain 3:181–187

    Article  Google Scholar 

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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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Keywords

  • Climate adaptation
  • Risk perception
  • Adaptation agenda
  • Urban environment