Abstract
By their sheer size, adaptation investments are processes addressing multiple climate impacts at the same time. Nevertheless, cross-sectoral effects of adaptation measures are rarely taken into consideration in adaptation costing and cost-effectiveness analysis (CEA). We explicitly address joint adaptation processes by focusing on inter- or intra-sectoral adaptation synergies within a cost-effectiveness framework. A software tool – CrossAdapt - has been developed in order to quantify cross-sectoral adaptation impacts based on expert judgement. Our research shows that the calculation of cross-sectoral impacts and their integration into cost-effectiveness analysis can significantly affect the cost-effectiveness ranking of adaptation measures. Future European adaptation strategies can benefit from the analytical possibility of estimating net adaptation costs based on CrossAdapt.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anderegg L, Prall W, Harold J, Schneider H (2010) Expert credibility in climate change. Proceedings of the National Academy of Sciences of the United States of America 107:12107–12109
Baecher G, Christian J (2003) Reliability and statistics in geotechnical engineering. Wiley, England, p 618
Baker E, Peng Y (2012) The Value of Better Information on Technology R&D Programs in Response to Climate Change. Environmental Modeling & Assessment 17(1–2):107–121
Barker T, Bashmakov I, Alharthi A, Amann M, Cifuentes L, Drexhage J, Duan M, Edenhofer O, Flannery B, Grubb M, Hoogwijk M, Ibitoye FI, Jepma CJ, Pizer WA, Yamaji K (2007) Mitigation from a cross-sectoral perspective. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Climate Change 2007: Mitigation, Contribution of Working Group III to the Fourth ssessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York
Berry Pam M., S. Brown, M. Chen, A. Kontogianni, O. Rowlands, G. Simpson and M. Skourtos (this volume). Cross-sectoral interactions of mitigation and adaptation measures. Climatic Change
Bojadziev G, Bojadziev M (2007) Fuzzy logic for business, finance, and management. world scientific publishing, Singapore, 2nd ed, pp 232
Burgman M, Fidler F, Mcbride M, Walshe T, Wintle B (2006) Eliciting expert judgments: literature review. first project report, Australian Centre of Excellence for Risk Analysis, ACERA Project 0611, University of Melbourne
Burtraw D, Krupnick A, Sampson G (2012) The True Cost of Electric Power: An Inventory of Methodologies to Support Future Decision-making in Comparing the Cost and Competitiveness of Electricity Generation Technologies. Resources for the Future, Washington
DEFRA (2007) An introductory guide to valuing ecosystems services. London
Fazey I, Fazey JA, Salisbury JG, Lindenmayer DB, Dovers S (2006) The nature and role of experiential knowledge for environmental conservation. Environmental Conservation 33(1):1–10
Gay C, Estrada F (2010) Objective probabilities about future climate are a matter of opinion. Climatic Change 99:27–46
Hall J, Fu G, Lawry J (2007) Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties. Climatic Change 81(3–4):265–281
Harrison PA, Holman IP, Cojocaru G, Kok K, Kotnogianni A, Metzger MJ, Gramberger M (2013) Combining qualitative and quantitative understanding for exploring cross-sectoral climate change impacts, adaptation and vulnerability in Europe. Regional Environmental Change. doi:10.1007/s10113-012-0361-y
Harrison PA, I.P. Holman and P.M. Berry (submitted for this issue) Assessing cross-sectoral climate change impacts, adaptation and vulnerability: An Introduction to the CLIMSAVE project. Climatic Change
Hsu HM, Chen CT (1996) Aggregation of fuzzy opinions under group decision making. Fuzzy Sets and Systems 79:279–285
Kacprzyk J, Fedrizzi M, Nurmi H (1992) Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets and Systems 49:21–31
Krueger T, Page T, Hubacek K, Smith L, Hiscock K (2012) The role of expert opinion in environmental modeling. Environmental Modelling & Software 36:4–18
Liu Y, Liu B (2002) Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Transactions 10(4):445–450
Lu C, Lan J, Wang Z (2006) Aggregation of fuzzy opinions under group decision-making based on similarity and distance. Journal of Systems Science and Complexity 19:63–71
Meyer MA, Booker JM (2001) Eliciting and Analyzing Expert Judgment: A Practical Guide. ASA-SIAM Series on Statistics and Applied Probability, pp 459
Morgan MG, Keith DW (1995) Subjective Judgments by Climate Experts. Environmental Science & Technology 29(10):468–476
Morgan MG, Pitelka LF, Shevliakova E (2001) Elicitation of Expert Judgments of Climate Change Impacts on Forest Ecosystems. Climatic Change 49(3):279–307
Morgan M, Adams P, Keith D (2006) Elicitation of Expert Judgments of Aerosol Forcing. Climatic Change 75(1–2):195–214
Moss R, Schneider SH (2000) Uncertainties. In: Pachauri R, Taniguchi R, Tanaka K (eds) Guidance Papers on the Cross Cutting Issues of the Third Assessment Report of the IPCC. World Meteorological Organisation, Switzerland
Nordhaus WD (1994) Expert opinion on climatic change. American Scientist 82 January-February:45–51.
Saaty TL (1977) A Scaling Method for Priorities in Hierarchical Structures. Journal of Math Psychology 15:234–281
Schneider SH, Kuntz-Duriseti K (2002) Uncertainty and climate change policy. In: Schneider SH, Rosencranz A, Niles JO (eds) Chapter 2 Climate Change Policy: A Survey. Island Press, Washington, pp 53–88
Schrader S, Riggs WM, Smith RP (1993) Choice over uncertainty and ambiguity in technical problem solving. Journal of Engineering and Technology Management 10:13–99
Titus G, Narayanan V (1996) The risk of sea level rise: A delphic monte carlo analysis in which twenty researchers specify subjective probability distributions for model coefficients within their respective areas of expertise. Climatic Change 33:151–212
Varis O, Kuikka S (1997) Bene-Eia: A Bayesian Approach to Expert Judgment Elicitation with Case Studies on Climate Change Impacts on Surface Waters. Climatic Change 37(3):539–563
Vucetic D. and S. P. Simonovic (2011) Water Resources Decision Making Under Uncertainty. The University of Western Ontario. Water Resources Research Report 073. London
Webster D, Sokolov P (2000) A methodology for quantifying uncertainty in climate projections. Climatic Change 46(4):417–446
World Bank (2010) Economics of Adaptation to Climate Change - Synthesis Report
Zadeh L (1965) Fuzzy Sets. Information and Control 8:338–353
Zadeh L (1987) Fuzzy Sets and Applications: Selected Papers. Wiley, New York
Acknowledgments
This work was supported by the CLIMSAVE Project (Climate change integrated assessment methodology for cross-sectoral adaptation and vulnerability in Europe; www.climsave.eu) funded under the Seventh Framework Programme of the European Commission (Contract No. 244031). CLIMSAVE is an endorsed project of the Global Land Project of the IGBP. The authors would like to thank all CLIMSAVE partners for their contributions to many productive discussions related to the content of this paper. The authors are also grateful to all scientists who participated in the elicitation process and kindly offered their valuable input.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of a Special Issue on "Regional Integrated Assessment of Cross-sectoral Climate Change Impacts, Adaptation, and Vulnerability" with Guest Editors Paula A. Harrison and Pam M. Berry.
Rights and permissions
About this article
Cite this article
Skourtos, M., Tourkolias, C., Damigos, D. et al. Incorporating cross-sectoral effects into analysis of the cost-effectiveness of climate change adaptation measures. Climatic Change 128, 307–321 (2015). https://doi.org/10.1007/s10584-014-1168-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-014-1168-2