When can decision analysis improve climate adaptation planning? Two procedures to match analysis approaches with adaptation problems
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.
KeywordsClimate change adaptation Type of decision analysis Cost-benefit analysis Climate uncertainty Chesapeake Bay
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 was provided by a grant by the NOAA Regional Integrated Sciences and Assessments Program to the RAND Corporation.
- 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
- Burgman MA (2016). Trusting judgements: how to get the best out of experts. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781316282472
- Clemen RT, & Reilly T (2013). Making hard decisions with DecisionTools. Cengage LearningGoogle Scholar
- 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
- 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
- Holling CS (1978) Adaptive environmental assessment and management. John Wiley & SonsGoogle Scholar
- 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
- Keeney RL, & Raiffa H (1993) Decisions with multiple Objectives: Preferences and Value Trade-Offs. Cambridge University Press. https://doi.org/10.1017/CBO9781139174084
- 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