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).
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This same inertia also means that past warming has already locked-in future SLR (that cannot be avoided even with aggressive greenhouse gas mitigation); this commitment to near-term rise means that coastal adaptation will be an essential part of society’s response to SLR. Adaptation and mitigation are complementary policies for climate change in general, and for coastal impacts in particular.
It is not fully understood whether the tail of the storm surge distribution will be further influenced by climate change (Grinsted et al. 2013) or other factors such as SLR, bathymetry, water depth, or wetland effects.
Prior to DIVA, the original global coastal dataset was the Global Vulnerability Analysis (GVA), consisting of 192 coastal segments (Hoozemans et al. 1993). Despite this pioneering effort, country-level resolution is not sufficient to inform adaptation decisions that are inherently local. Advances in computing technology and remote sensing have enabled more detailed and accurate coastal datasets.
CIAM models public adaptation and assumes the entire coastal segment acts in unison (as if it was enforced by policy), rather than account for heterogeneity in adaptation strategy (e.g., sorting behavior). Furthermore, the decision of one segment is assumed to have no bearing on neighboring segments.
Although perfect foresight is unrealistic, this simplified construction of SLR ‘learning’ follows from the relatively smooth near-term rise driven by thermal inertia, whereas other climate changes are likely to be more abrupt or difficult to detect (e.g., thermohaline circulation; Keller et al. 2008).
The adaptation planning period (Δt) is assumed to be 40 years; a 100-year period is considered as a sensitivity analysis, given major coastal defense structures may be planned for a longer duration.
Although capital formation and production functions may change in a warming world, and these shifts would have significant implications for impact assessments, this is beyond the scope of this analysis.
Coastal protection is generalized in CIAM as a sea wall, regardless of the specific defense installed (e.g., dike, revetment, floodgate, etc.) or whether soft measures (e.g., beach nourishment) would better suit the location.
In addition to omitting potential damages resulting from this residual vulnerability, protection may have negative externalities such as inhibiting public shore access and increased erosion that are not accounted for in the current model.
In contrast to this simplification, Grinsted et al. (2013) present a nonstationary distribution and estimate how the frequency of extreme surges could change with warming. This topic remains for future CIAM studies.
It is worth noting that although RCPs may imply different (though unspecified) socioeconomic pathways, this study does not consider alternative socioeconomic projections, although Hinkel et al. (2014) have shown such drivers may affect coastal impacts over time.
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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.
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Diaz, D.B. Estimating global damages from sea level rise with the Coastal Impact and Adaptation Model (CIAM). Climatic Change 137, 143–156 (2016). https://doi.org/10.1007/s10584-016-1675-4
- Adaptation Strategy
- Flood Damage
- Representative Concentration Pathway
- Computable General Equilibrium
- Vertical Land Movement