Fighting the inevitable: infrastructure investment and coastal community adaptation to sea level rise

Abstract

Coastal communities are crafting adaptation strategies to confront sea level rise (SLR). Unfortunately, cost-benefit analyses that assess SLR risks often fail to capture important political and social feedbacks. For example, adaptation measures (e.g., beach nourishment) can trigger greater development, undermining the value of adaptation infrastructure, incentivizing development, and increasing risks. We integrate diverse literature and data to develop a hypothesis and system dynamics model of coastal community responses to SLR. We apply the model to two U.S. communities, showing how political influence drives trajectories of infrastructure provision, cost, and vulnerability. We find that delayed feedbacks between perceived SLR risk and infrastructure investments mediate relationships between political capital, migration, and economic development. Community wealth and political influence may only delay the overshoot and collapse of its population and economy, as the "virtuous cycles" linking growth with infrastructure investment give way to "vicious cycles" of mounting infrastructure costs, political resistance to additional investment, and greater vulnerability.

Defining and Intervening on Cumulative Environmental Neurodevelopmental Risks: Introducing a Complex Systems Approach

“…In the environmental policy realm, cost–benefit analyses may be needed. Policy levers identified using SD can be evaluated using any of the conventional approaches for cost–benefit analysis to estimate and compare the cost of one policy over another ( Lyneis and Sterman 2016 ; Woodruff et al. 2018 ).…”

Reconstructing patterns of coastal risk in space and time along the US Atlantic Coast, 1970-2016

“…Exposure in our model only accounts for the monetary value of owner-occupied properties in coastal counties, as captured by the US Census, thus excluding other potential measures of exposure (e.g., Neumann et al, 2015;NRC, 2014;Samuels and Gouldby, 2009;Strauss et al, 2012) and requiring that we spatially aggregate our analysis to county scales. Finally, our measure of vulnerability includes no method of shoreline protection other than beach 20 nourishment, and its dynamics are underpinned by a set of broad assumptions: that beaches comprise shorelines at the county scale; that in 1970, all counties have the same initial beach width; that a beach-nourishment project always restores a beach to its full width; and that counties with intensive nourishment programmes may render themselves more vulnerable over time by masking a chronic erosion problem (Armstrong and Lazarus, 2019;Pilkey and Cooper, 2014;Woodruff et al, 2018). We do not directly address alongshore spatial interactions within or between counties (Lazarus et al, 2011;Ells and Murray, 25 2012;Lazarus et al, 2016).…”

A Systematic Review of Coastal Vulnerability Mapping

“…Some authors deemed it sufficient to use a bathtub approach solely based on DEMs to depict the exposure, e.g., [77][78][79]. Such bathtub models, based on the difference between the projected flood water height and elevation, have limited utility as they do not account for the hydrological connectivity and geophysical barriers present in the real settings [80]. Strauss et al [81] noted that this approach may fail to incorporate the reduced drainage and water movement via smaller channels and porous rock, but also acknowledged that the main goal of many assessments is to indicate areas of potential concern, and provide a broader baseline for additional analysis rather than to generate precise flood risk maps.…”

“…A few studies included the uncertainty discussion in a section on study limitations. Among studies that included a qualitative discussion on uncertainty, some papers provided a comprehensive discussion of different sources of uncertainties (e.g., secondary data, modeling, vulnerability framework, and proxy measures [106] and others), while others provided only brief and vague statements, such as "the results are strongly related to the number of variables used and the inherent element of subjectivity in the development of each index" [80] (p. 830). Given the lack of an agreed-upon methodology or set of necessary input variables, an uncertainty assessment is important for determining the credibility of an assessment.…”

“…Results show that very few studies (n = 6) provided only a map of integrated vulnerability, e.g., [62,72], while the majority of papers also displayed the physical hazard (n = 38), socioeconomic aspects (n = 31), and/or environmental variables (n = 21). Only a few studies provided maps of aggregated components of vulnerability (e.g., Mahapatra et al [80]) (Figure 3b), even though such intermediate maps may be very helpful to better understand the overall vulnerability. This seems odd given that over half of the papers indicated such subcomponents were estimated and then aggregated into a measure of vulnerability.…”