Neumann J.,Industrial Economics Incorporated |
Hudgens D.,Industrial Economics Incorporated |
Herter J.,Industrial Economics Incorporated |
Martinich J.,U.S. Environmental Protection Agency
Wiley Interdisciplinary Reviews: Climate Change | Year: 2011
Sea-level rise (SLR) increases the risk of permanent inundation of coastal lands and structures, while also increasing the risk of periodic damage from storms and risks to ecological resources. Prior studies have illustrated the importance of considering adaptation measures, such as armoring and beach nourishment, when estimating the economic cost of SLR, but these studies have taken the form either of careful, geographically limited case studies or national estimates based on limited samples. We present a framework for evaluating the economics of adaptation to permanent inundation from SLR that employs detailed local scale data and is spatially comprehensive, and apply the framework to estimate costs of adaptation for the full coastline of the continental US. Our results show that the economic cost of SLR is much larger than prior estimates suggest-more than $63 billion cumulative discounted cost (at 3%) for a 68 cm SLR by 2100, and $230 billion undiscounted-yet is only one-fourth the total value of low-lying property vulnerable to SLR, illustrating the importance of careful site-specific consideration of adaptation. Further, the granularity of the framework provides spatial, temporal, and response mode details useful to both national policy-makers and local adaptation planners, and can readily incorporate estimates of ecological and storm surge damages as they become available. © 2010 John Wiley & Sons, Ltd.
Martinich J.,U.S. Environmental Protection Agency |
Neumann J.,Industrial Economics Incorporated |
Ludwig L.,Industrial Economics Incorporated |
Jantarasami L.,U.S. Environmental Protection Agency
Mitigation and Adaptation Strategies for Global Change | Year: 2013
Climate change and sea level rise (SLR) pose risks to coastal communities around the world, but societal understanding of the distributional and equity implications of SLR impacts and adaptation actions remains limited. Here, we apply a new analytic tool to identify geographic areas in the contiguous United States that may be more likely to experience disproportionate impacts of SLR, and to determine if and where socially vulnerable populations would bear disproportionate costs of adaptation. We use the Social Vulnerability Index (SoVI) to identify socially vulnerable coastal communities, and combine this with output from a SLR coastal property model that evaluates threats of inundation and the economic efficiency of adaptation approaches to respond to those threats. Results show that under the mid-SLR scenario (66. 9 cm by 2100), approximately 1,630,000 people are potentially affected by SLR. Of these, 332,000 (~20%) are among the most socially vulnerable. The analysis also finds that areas of higher social vulnerability are much more likely to be abandoned than protected in response to SLR. This finding is particularly true in the Gulf region of the United States, where over 99% of the most socially vulnerable people live in areas unlikely to be protected from inundation, in stark contrast to the least socially vulnerable group, where only 8% live in areas unlikely to be protected. Our results demonstrate the importance of considering the equity and environmental justice implications of SLR in climate change policy analysis and coastal adaptation planning. © 2011 The Author(s).
Boyle K.J.,Virginia Polytechnic Institute and State University |
Parmeter C.F.,University of Miami |
Boehlert B.B.,Industrial Economics Incorporated |
Paterson R.W.,Industrial Economics Incorporated
Environmental and Resource Economics | Year: 2013
Meta-analyses are becoming a popular tool for supporting benefit transfers, but the availability of studies is a direct consequence of policy issues, research funding, and investigator interest. We investigate fragility versus robustness of the meta-equation by considering sample selection, removing one observation or study at a time with replacement, and removing/adding regressors. Several key variables are found to be robust, strengthening the argument for their use in policy prescriptions. The key insights are that these methods can be used to parse meta-data to identify the most appropriate set(s) of observations and regressors to support literature evaluations, benefit transfers and other practical applications using statical summaries of empirical data. © 2013 Springer Science+Business Media Dordrecht.