Evaluating the role of coastal habitats and sea-level rise in hurricane risk mitigation: An ecological economic assessment method and application to a business decision
Bibliographic record
Abstract
Abstract Businesses may be missing opportunities to account for ecosystem services in their decisions, because they do not have methods to quantify and value ecosystem services. We developed a method to quantify and value coastal protection and other ecosystem services in the context of a cost-benefit analysis of hurricane risk mitigation options for a business. We first analyze linked biophysical and economic models to examine the potential protection provided by marshes. We then applied this method to The Dow Chemical Company's Freeport, Texas facility to evaluate natural (marshes), built (levee), and hybrid (marshes and a levee designed for marshes) defenses against a 100-y hurricane. Model analysis shows that future sea-level rise decreases marsh area, increases flood heights, and increases the required levee height (12%) and cost (8%). In this context, marshes do not provide sufficient protection to the facility, located 12 km inland, to warrant a change in levee design for a 100-y hurricane. Marshes do provide some protection near shore and under smaller storm conditions, which may help maintain the coastline and levee performance in the face of sea-level rise. In sum, the net present value to the business of built defenses ($217 million [2010 US$]) is greater than natural defenses ($15 million [2010 US$]) and similar to the hybrid defense scenario ($229 million [2010 US$]). Examination of a sample of public benefits from the marshes shows they provide at least $117 million (2010 US$) in coastal protection, recreational value, and C sequestration to the public, while supporting 12 fisheries and more than 300 wildlife species. This study provides information on where natural defenses may be effective and a replicable approach that businesses can use to incorporate private, as well as public, ecosystem service values into hurricane risk management at other sites. Integr Environ Assess Manag 2016;12:328–344. © 2015 The Authors. Published by Wiley Periodicals, Inc. on behalf of SETAC. Key Points We developed a method to quantify and value coastal protection and other ecosystem services using linked biophysical and economic models in the context of a cost-benefit analysis of hurricane risk mitigation options for a business. Model analysis shows that marshes have the greatest potential to provide protection near shore and under smaller storm conditions, which may help maintain the coastline and levee performance in the face of sea-level rise. An application to The Dow Chemical Company's decision to protect its facility from hurricanes show that sea-level rise may increase risks from hurricanes, marshes do not provide sufficient protection to change levee design, but marshes provide additional benefits to the public and biodiversity that may help meet financial and sustainability goals. The results provide information to help screen other business sites for coastal protection ecosystem services and the methods provide a replicable approach for businesses to evaluate ecosystem services in the context of risk mitigation at other sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".