Use of the Spatial Economic Benefit Analysis (SEBA) for Marine Spatial Planning: A Case Including Offshore Wind Energy and Fisheries in France
海洋空間計画のための空間経済便益分析（SEBA）の活用：フランスにおける洋上風力と漁業を含むケース (AI 翻訳)
Lokesh Pawar, Bertrand Le Gallic, Jorge Ramos
🤖 gxceed AI 要約
日本語
本研究は、フランスの大西洋北東部・西海峡（NAMO）地域を対象に、洋上風力発電と漁業の社会経済的便益を空間経済便益分析（SEBA）フレームワークを拡張して評価した。公開データと産業情報を統合し、雇用・企業参加・インフラ・財務実績の地域経済マップを作成し、対話型可視化プラットフォームを提供する。結果は、洋上風力の便益が空間的に集中しつつ国際的に分散する一方、漁業は局所的だが構造的脆弱性が増していることを示し、海洋空間計画における便益配分の非対称性を明らかにした。
English
This study applies and extends the Spatial Economic Benefit Analysis (SEBA) framework to evaluate the socioeconomic footprint of offshore wind energy and fisheries in the North Atlantic-Western Channel (NAMO) region of France. Using mixed methods and an interactive visualization platform, it maps employment, firm participation, infrastructure, and financial performance. Findings reveal spatially concentrated yet internationally distributed benefits from offshore wind, contrasted with localized but vulnerable fisheries, highlighting asymmetries in benefit distribution within marine spatial planning (MSP). The research advances SEBA as a scalable decision-support tool for a more inclusive and sustainable blue economy.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では海洋空間計画（MSP）の導入が進む中、洋上風力と漁業の共存が重要な政策課題となっている。本手法は、地域経済への影響を可視化し、利害関係者間の合意形成を支援する点で、日本の海洋政策や地域振興に示唆を与える。
In the global GX context
This study contributes to global MSP scholarship by providing a transparent, data-driven method to assess socioeconomic trade-offs in ocean planning. It aligns with international efforts toward sustainable blue economy and can inform policy frameworks such as the EU's Maritime Spatial Planning Directive and similar initiatives worldwide.
👥 読者別の含意
🔬研究者:Provides a replicable SEBA framework extension for spatial socioeconomic analysis in MSP, useful for further methodological development.
🏢実務担当者:Offers a visualization platform and regional economic mapping approach that can support stakeholder engagement and decision-making in marine spatial planning.
🏛政策担当者:Highlights the need for spatial equity considerations in MSP and provides a tool to assess distributional impacts of offshore wind and fisheries policies.
📄 Abstract（原文）
Marine Spatial Planning (MSP) is a key instrument for advancing sustainable ocean governance in Europe, yet the spatial distribution of socioeconomic benefits and trade-offs remains poorly understood. This study addresses this gap by applying and extending the Spatial Economic Benefit Analysis (SEBA) framework to evaluate the socioeconomic footprint of offshore wind energy and capture fisheries in France, with a focus on the North Atlantic–Western Channel (NAMO) region. Methodologically, a mixed-methods approach is used, integrating publicly available data, industry disclosures, and spatial analysis to produce regional-scale economic maps of employment, firm participation, infrastructure, and financial performance. The study also presents an interactive, updatable visualization platform that enhances SEBA, by enabling continuous data integration and dynamic exploration, strengthening its relevance for public policy and stakeholder engagement. Results show that offshore wind energy generates spatially concentrated yet internationally distributed economic benefits, characterized by transnational value chains and uneven regional participation. In contrast, fisheries exhibit highly localized benefits but increasing structural vulnerability, reflected in fleet decline, employment contraction, and financial dependence. These findings reveal a persistent asymmetry in benefit distribution within MSP, with implications for spatial equity and sectoral inclusion. By advancing SEBA as a scalable and decision-support tool, this research aims to contribute to a more transparent, data-driven MSP and to support the transition toward a more inclusive and sustainable blue economy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.20944/preprints202607.1818.v1first seen 2026-08-14 04:56:46
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