Mapping the Unheard: Analyzing Tradeoffs Between Fisheries and Offshore Wind Farms Using Multicriteria Decision Analysis.
Identifying offshore wind energy sites involves analyzing multiple variables, such as wind speed, proximity to the coastline, and sociocultural factors. This complex decision-making process often involves many stakeholders, resulting in conflicting data and goals. Decision analysis that promotes col...
| Publicado en: | Annals of the American Association of Geographers Vol. 114; no. 3; pp. 536 - 555 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=175644753&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 175644753 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2024 vid: 114 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 175644753 10.1080/24694452.2023.2285371 ppf: 536 ppct: 19 formats: tig: atl: Mapping the Unheard: Analyzing Tradeoffs Between Fisheries and Offshore Wind Farms Using Multicriteria Decision Analysis. aug: au: Song, Zhenlei Chapman, Piers Tao, Jian Chang, Ping Gao, Huilin Liu, Honggao Brannstrom, Christian Zhang, Zhe affil: Department of Geography, Texas A&M University, USA Department of Oceanography, Texas A&M University, USA Department of Visualization, Texas A&M University, USA Department of Civil and Environmental Engineering, Texas A&M University, USA High Performance Research Computing (HPRC), Texas A&M University, USA Department of Geography and Department of Electrical and Computer Engineering, Texas A&M University, USA su: California Sociocultural factors Offshore wind power plants Wind power & the environment Fisheries Sustainability Multiple criteria decision making Analytic hierarchy process sug: subj: Sociocultural factors California Finfish Farming and Fish Hatcheries Offshore wind power plants Wind power & the environment Fisheries Sustainability Multiple criteria decision making Analytic hierarchy process keyword: human–environment interactions multicriteria decision-making offshore wind energy site selection sustainability energía eólica marina interacción humano–ambiental selección de sitios para emplazamiento eólico sustentabilidad toma de decisiones multicriterio 人地关系 可持续性 多准则决策 海上风能 选址 human–environment interactions multicriteria decision-making offshore wind energy site selection sustainability energía eólica marina interacción humano–ambiental selección de sitios para emplazamiento eólico sustentabilidad toma de decisiones multicriterio 人地关系 可持续性 多准则决策 海上风能 选址 ab: Identifying offshore wind energy sites involves analyzing multiple variables, such as wind speed, proximity to the coastline, and sociocultural factors. This complex decision-making process often involves many stakeholders, resulting in conflicting data and goals. Decision analysis that promotes collaboration, transparency, understanding, and sustainability is key. This study presents a unique model of human–environment interaction that reconciles different perspectives and visualizes the balance between fisheries and wind power. Using three multicriteria decision models (weighted aggregated sum product assessment [WASPAS], technique for order of preference by similarity to ideal solution [TOPSIS], and analytical hierarchy process [AHP]), we analyze the decision mix for wind farm selection and assess the impacts on fisheries using historical data. Our approach was applied to an upwelling system in California, generating ten tailored decision scenarios for different stakeholder groups. The results showed that adaptation scores for specific call areas in northern California decreased when the weight of fishery factors increased, and there was a tendency for high-scoring areas to shift southward as fishery parameters increased. The results of the sensitivity analysis showed that the first-order sensitivity scores of WASPAS were better correlated with the weights compared to TOPSIS, whereas the second-order sensitivity scores were generally lower, indicating a reduced interdependence of our model. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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