Climatic Change

, Volume 137, Issue 1–2, pp 143–156 | Cite as

Estimating global damages from sea level rise with the Coastal Impact and Adaptation Model (CIAM)

  • Delavane B. DiazEmail author


Coastal sector impacts from sea level rise (SLR) are a key component of the projected economic damages of climate change, a major input to decision-making and design of climate policy. Moreover, the ultimate global costs to coastal resources will depend strongly on adaptation, society’s response to cope with the local impacts. This paper presents a new open-source optimization model to assess global coastal impacts from SLR from the perspective of economic efficiency. The Coastal Impact and Adaptation Model (CIAM) determines the optimal strategy for adaptation at the local level, evaluating over 12,000 coastal segments, as described in the DIVA database (Vafeidis et al. 2006), based on their socioeconomic characteristics and the potential impacts of relative sea level rise and uncertain sea level extremes. A deterministic application of CIAM demonstrates the model’s ability to assess local impacts and direct costs, choose the least-cost adaptation, and estimate global net damages for several climate scenarios that account for both global and local components of SLR (Kopp et al. 2014). CIAM finds that there is large potential for coastal adaptation to reduce the expected impacts of SLR compared to the alternative of no adaptation, lowering global net present costs through 2100 by a factor of seven to less than $1.7 trillion, although this does not include initial transition costs to overcome an under-adapted current state. In addition to producing aggregate estimates, CIAM results can also be interpreted at the local level, where retreat (e.g., relocate inland) is often a more cost-effective adaptation strategy than protect (e.g., construct physical defenses).


Adaptation Strategy Flood Damage Representative Concentration Pathway Computable General Equilibrium Vertical Land Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the US DOE, Integrated Assessment Research Program, Grant No. DE-SC005171. The work has benefited from many constructive discussions with my PhD adviser John Weyant, as well as feedback from Klaus Keller, Robert Mendelsohn, Steven Rose, Thomas Rutherford and participants of the SEEPAC and PERR seminars at Stanford University. I thank Robert Kopp and collaborators for making their sea level projections and model code available and for answering questions about implementation. Geoffrey Blanford, Klaus Keller, Claude Reichard, Richard Tol, John Weyant and two anonymous reviewers provided helpful comments on the manuscript. All errors and opinions are mine.

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Electric Power Research Institute, Energy and Environmental Analysis GroupWashingtonUSA

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