Advertisement

When can decision analysis improve climate adaptation planning? Two procedures to match analysis approaches with adaptation problems

  • Rui ShiEmail author
  • Benjamin F. Hobbs
  • Huai Jiang
Article

Abstract

Climate adaptation decisions are difficult because the future climate is deeply uncertain. Combined with uncertainties concerning the cost, lifetime, and effectiveness of adaptation measures, this implies that the net benefits of alternative adaptation strategies are ambiguous. On one hand, a simple analysis that disregards uncertainty might lead to near-term choices that are later regretted if future circumstances differ from those assumed. On the other hand, careful uncertainty-based decision analyses can be costly in personnel and time and might not make a difference. This paper considers two questions adaptation managers might ask. First, what type of analysis is most appropriate for a particular adaptation decision? We answer this question by proposing a six-step screening procedure to compare the usefulness of predict-then-act analysis, multi-scenario analysis without adaptive options, and multi-scenario analysis incorporating adaptive options. A tutorial application is presented using decision trees. However, this procedure may be cumbersome if managers face several adaptation problems simultaneously. Hence, a second question is how can managers quickly identify problems that would benefit most from thorough decision analysis? To address this question, we propose a procedure that ranks multiple adaptation problems in terms of the necessity and value of comprehensive analysis. Analysis can then emphasize the highest-ranking problems. This procedure is illustrated by a ranking of adaptation problems in the Chesapeake Bay region. The two complementary procedures proposed here can help managers focus analytical efforts where they will be most useful.

Keywords

Climate change adaptation Type of decision analysis Cost-benefit analysis Climate uncertainty Chesapeake Bay 

Notes

Acknowledgments

We thank our MARISA colleagues and interviewees for their participation and comments, Fengwei Hung for his collaboration, and two anonymous reviewers for suggestions; however, the authors are responsible for any errors or opinions.

Funding information

Funding was provided by a grant by the NOAA Regional Integrated Sciences and Assessments Program to the RAND Corporation.

Supplementary material

10584_2019_2579_MOESM1_ESM.docx (28 kb)
ESM 1 (DOCX 27 kb)
10584_2019_2579_MOESM2_ESM.xlsx (25 kb)
ESM 2 (XLSX 24 kb)

References

  1. Adger WN, Barnett J (2009) Four reasons for concern about adaptation to climate change. Environ Plan A 41(12):2800–2805.  https://doi.org/10.1068/a42244 CrossRefGoogle Scholar
  2. Bakker AM, Louchard D, Keller K (2017) Sources and implications of deep uncertainties surrounding sea-level projections. Clim Chang 140(3–4):339–347.  https://doi.org/10.1007/s10584-016-1864-1 CrossRefGoogle Scholar
  3. Balducci P, Schienbein L, Nguyen T, et al. (2004) An examination of the costs and critical characteristics of electric utility distribution system capacity enhancement projects. In: Proceedings, IEEE PES Power Systems Conference and Exposition.  https://doi.org/10.1109/psce.2004.1397503
  4. Belton V, Stewart T (2002) Multiple criteria decision analysis: an integrated approach. Springer, Berlin.  https://doi.org/10.1007/978-1-4615-1495-4 CrossRefGoogle Scholar
  5. Berrang-Ford L, Ford JD, Paterson J (2011) Are we adapting to climate change? Glob Environ Chang 21(1):25–33.  https://doi.org/10.1016/j.gloenvcha.2010.09.012 CrossRefGoogle Scholar
  6. Burgman MA (2016). Trusting judgements: how to get the best out of experts. Cambridge University Press. DOI:  https://doi.org/10.1017/CBO9781316282472
  7. Clemen RT, & Reilly T (2013). Making hard decisions with DecisionTools. Cengage LearningGoogle Scholar
  8. Deb K, Agrawal S, Pratap A, & Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In international conference on parallel problem solving from nature (pp. 849–858). Springer, Berlin.  https://doi.org/10.1007/3-540-45356-3_83 CrossRefGoogle Scholar
  9. Dessai S, & van der Sluijs JP (2007) Uncertainty and climate change adaptation: a scoping study (Vol. 2007). Copernicus Institute for Sustainable Development and Innovation, Department of Science Technology and Society. Retrieved from http://www.nusap.net/downloads/reports/ucca_scoping_study.pdf
  10. Eijgenraam C, Brekelmans R, den Hertog D, Roos K (2016) Optimal strategies for flood prevention. Manag Sci 63(5):1644–1656.  https://doi.org/10.1287/mnsc.2015.2395 CrossRefGoogle Scholar
  11. Gersonius B, Ashley R, Pathirana A, Zevenbergen C (2013) Climate change uncertainty: building flexibility into water and flood risk infrastructure. Clim Chang 116(2):411–423.  https://doi.org/10.1007/s10584-012-0494-5 CrossRefGoogle Scholar
  12. Groves DG, Lempert RJ (2007) A new analytic method for finding policy-relevant scenarios. Glob Environ Chang 17(1):73–85.  https://doi.org/10.1016/j.gloenvcha.2006.11.006 CrossRefGoogle Scholar
  13. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob Environ Chang 23(2):485–498.  https://doi.org/10.1016/j.gloenvcha.2012.12.006 CrossRefGoogle Scholar
  14. Haer T, Kalnay E, Kearney M, Moll H (2013) Relative Sea-level rise and the conterminous United States: consequences of potential land inundation in terms of population at risk and GDP loss. Glob Environ Chang 23(6):1627–1636.  https://doi.org/10.1016/j.gloenvcha.2013.09.005 CrossRefGoogle Scholar
  15. Hallegatte S (2009) Strategies to adapt to an uncertain climate change. Glob Environ Chang 19(2):240–247.  https://doi.org/10.1016/j.gloenvcha.2008.12.003 CrossRefGoogle Scholar
  16. Hallegatte S, Shah A, Lempert R, Brown C, & Gill S (2012) Investment decision making underdeep uncertainty-application to climate change. The World Bank  https://doi.org/10.1596/1813-9450-6193 CrossRefGoogle Scholar
  17. Hobbs BF, Chao PT, Venkatesh BN (1997) Using decision analysis to include climate change in water resources decision making. Clim Chang 37(1):177–202.  https://doi.org/10.1023/A:1005376622183 CrossRefGoogle Scholar
  18. Holling CS (1978) Adaptive environmental assessment and management. John Wiley & SonsGoogle Scholar
  19. Hung F, Hobbs BF (2019) How can learning-by-doing improve decisions in stormwater management? A Bayesian-based optimization model for planning urban green infrastructure investments. Environ Model Softw 113:59–72.  https://doi.org/10.1016/j.envsoft.2018.12.005 CrossRefGoogle Scholar
  20. IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report. Cambridge University Press  https://doi.org/10.1017/CBO9781139177245.001
  21. Keeney RL, & Raiffa H (1993) Decisions with multiple Objectives: Preferences and Value Trade-Offs. Cambridge University Press.  https://doi.org/10.1017/CBO9781139174084
  22. Kontogianni A, Tourkolias CH, Damigos D, Skourtos M (2014) Assessing Sea level rise costs and adaptation benefits under uncertainty in Greece. Environ Sci Pol 37:61–78.  https://doi.org/10.1016/j.envsci.2013.08.006 CrossRefGoogle Scholar
  23. Kopp RE, Horton RM, Little CM, Mitrovica JX, Oppenheimer M, Rasmussen DJ et al (2014) Probabilistic 21st and 22nd century sea-level projections at a global network of tidegauge sites. Earth’s Future 2(8):383–406.  https://doi.org/10.1002/2014EF000239 CrossRefGoogle Scholar
  24. Lempert R, Nakicenovic N, Sarewitz D, Schlesinger M (2004) Characterizing climate-change uncertainties for decision-makers. An editorial essay. Clim Chang 65(1):1–9.  https://doi.org/10.1023/B:CLIM.0000037561.75281.b3 CrossRefGoogle Scholar
  25. Miller KG, Kopp RE, Horton BP, Browning JV, Kemp AC (2013) A geological perspective on sea-level rise and its impacts along the US mid-Atlantic coast. Earth’s Future 1(1):3–18.  https://doi.org/10.1002/2013EF000135 CrossRefGoogle Scholar
  26. Peng RD, Bobb JF, Tebaldi C, McDaniel L, Bell ML, Dominici F (2010) Toward a quantitative estimate of future heat wave mortality under global climate change. Environ Health Perspect 119(5):701–706.  https://doi.org/10.1289/ehp.1002430 CrossRefGoogle Scholar
  27. Polsky C, Allard J, Currit N, Crane R, Yarnal B (2000) The mid-Atlantic region and its climate: past, present, and future. Clim Res 14(3):161–173.  https://doi.org/10.3354/cr014161 CrossRefGoogle Scholar
  28. Savage LJ (1951) The theory of statistical decision. J Am Stat Assoc 46(253):55–67.  https://doi.org/10.1080/01621459.1951.10500768 CrossRefGoogle Scholar
  29. Sturm M, Goldstein MA, Huntington H, Douglas TA (2017) Using an option pricing approach to evaluate strategic decisions in a rapidly changing climate: black–Scholes and climate change. Clim Chang 140(3–4):437–449.  https://doi.org/10.1007/s10584-016-1860-5 CrossRefGoogle Scholar
  30. Thompson E, Frigg R, Helgeson C (2016) Expert judgment for climate change adaptation. Philos Sci 83(5):1110–1121.  https://doi.org/10.1086/687942 CrossRefGoogle Scholar
  31. Watkiss P, Hunt A, Blyth W, Dyszynski J (2015) The use of new economic decision support tools for adaptation assessment: a review of methods and applications, towards guidance on applicability. Clim Chang 132(3):401–416.  https://doi.org/10.1007/s10584-014-1250-9 CrossRefGoogle Scholar
  32. Weaver CP, Lempert RJ, Brown C, Hall JA, Revell D, Sarewitz D (2013) Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. Wiley Interdiscip Rev Clim Chang 4(1):39–60.  https://doi.org/10.1002/wcc.202 CrossRefGoogle Scholar
  33. Woodward M, Kapelan Z, Gouldby B (2014) Adaptive flood risk management under climate change uncertainty using real options and optimization. Risk Anal 34(1):75–92.  https://doi.org/10.1111/risa.12088 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Environmental Health & EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Energy and Environmental Economics, Inc.San FranciscoUSA

Personalised recommendations