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
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».