Digital carbon accounting and spatial transition analysis for Litopenaeus vannamei aquaculture: toward data-driven low-carbon governance
デジタルカーボンアカウンティングとバナメイエビ養殖の空間的遷移分析:データ駆動型低炭素ガバナンスに向けて (AI 翻訳)
Mingming Wen, Quan Chen, Zhaoheng Lv
🤖 gxceed AI 要約
日本語
中国のバナメイエビ養殖における炭素排出の時空間変動と将来予測を分析。GIS-LCAモデル、Theil指数、カーネル密度推定、空間マルコフ連鎖を用い、2009~2023年の10沿海省を対象に評価。総排出量は増加し、鉄鋼と配合飼料が主要因。地域格差が拡大し、「南>北>東」の階層パターンと空間的クラスタリングを確認。多段階の規制戦略を提案。
English
This study analyzes the spatiotemporal evolution and trend prediction of carbon emissions from Litopenaeus vannamei aquaculture in China. Using a GIS-LCA model with Theil index, kernel density estimation, Moran's I, and spatial Markov chains across ten coastal provinces from 2009 to 2023, it finds significant total emission increases driven by steel and compound feed production. Regional disparities widened, forming a 'south > north > east' hierarchical pattern with High-High and Low-Low spatial clustering. A multi-scale regulation strategy is proposed for differentiated low-carbon governance.
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
中国の水産養殖を対象としたカーボンアカウンティング手法であり、日本でも養殖業の脱炭素化や地域別排出削減策の設計に応用可能。ただし日本独自の制度(SSBJ等)との直接的な関連は薄い。
In the global GX context
This paper provides a novel GIS-LCA spatial carbon footprint model for aquaculture, a sector often overlooked in carbon accounting. It demonstrates the use of spatial analysis tools for regional emission differentiation, which could inform global climate governance frameworks like ISSB if extended to other sectors. The findings highlight supply chain hotspots (feed, steel) relevant to Scope 3 disclosure.
👥 読者別の含意
🔬研究者:The GIS-LCA method integrating spatial analysis and carbon footprint is a methodological contribution for sector-specific carbon accounting.
🏢実務担当者:Aquaculture firms can identify emission hotspots (feed, steel) and adopt region-specific reduction strategies.
🏛政策担当者:The multi-scale regulation strategy offers a template for differentiated carbon reduction policies in regional aquaculture.
📄 Abstract(原文)
Background Guided by the concept of the Great Food View and the coordinated management of ecological thresholds in fisheries, this study examines the spatiotemporal evolution and trend prediction of carbon emissions from Litopenaeus vannamei aquaculture in China. Purpose This study aims to develop an analytical framework that promotes both industrial efficiency improvement and ecological value enhancement, to inform integrated food supply system optimization and national carbon peaking strategies. Method A novel GIS–LCA spatial carbon footprint model was developed, integrating the Theil index, kernel density estimation, Moran’s I , and spatial Markov chain methods to analyze carbon emissions across ten coastal provinces from 2009 to 2023. Finding The results show that total carbon emissions have increased significantly, with steel and compound feed production as dominant sources. Regional disparities widened, forming a hierarchical pattern of “southern > northern > eastern” marine economic zones. The emission distribution exhibited a rightward–then-leftward shift, elongated right tails, and narrowing variance, accompanied by significant High–High and Low–Low spatial clustering. Moreover, provincial emissions displayed strong spatial continuity and a “Matthew effect” during state transitions, with potential for leapfrogging shifts. Policy Based on these spatial patterns, a multi-scale regulation strategy is proposed to promote differentiated governance and accelerate the low-carbon transformation of China’s aquaculture industry, which include optimizing region-specific emission reduction policies, strengthening interregional coordination in carbon mitigation, improving carbon emission monitoring and assessment systems, enhancing the integration of ecological protection with industrial development, and advancing low-carbon governance capacity together with residents’ well-being.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/fsufs.2026.1746105first seen 2026-05-05 19:34:53
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