Constructing Prospective Multiregional Input--Output Tables via Scenario-Constrained Optimization for Future Environmental Footprint Assessment
将来の環境フットプリント評価のためのシナリオ制約最適化による予測的多地域産業連関表の構築 (AI 翻訳)
Peng, Sidi, Daioglou, Vassilis, Stadler, Konstantin, Roshandel, Ramin, Bruckner, Martin, Pfister, Stephan
🤖 gxceed AI 要約
日本語
EXIOBASE 2019とIMAGE SSP2シナリオを用いて、2035年の12の予測的多地域産業連関表を構築。レオンチェフ型投影と、GDP・付加価値・部門産出・エネルギー・農業消費の制約を組み合わせた最適化型投影を比較。エネルギー・農業制約は最も高いコストで満たされ、取引フローを大きく変化させた。世界のカーボンフットプリントの地域シェアは安定していたが、部門別帰属は大幅に変化し、ホットスポットの特定はシナリオ準拠度に依存した。中国の最終エネルギー需要による消費ベースカーボンフットプリントが最も速く成長し、2035年に主要な新ホットスポットとなった。
English
Using EXIOBASE 2019 and the IMAGE SSP2 scenario, 12 prospective multi-regional input-output tables for 2035 were constructed, comparing a Leontief-based projection with 11 optimization-based projections under constraints on GDP, value added, sector outputs, and energy and agriculture consumption. Constraints on energy and agriculture were satisfied at the highest cost in coefficient deviations, significantly shifting transaction flows. Regional shares of the global carbon footprint remained stable, but sectoral attribution was substantially reshuffled, and hotspot identification was sensitive to scenario compliance. China's consumption-based carbon footprint driven by final energy demand grew fastest, becoming a major new hotspot in 2035.
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
日本でもSSBJ対応や2050年カーボンニュートラルに向けたサプライチェーン分析が重要視される中、本手法は将来の産業連関構造をシナリオ制約で構築する枠組みを提供する。特に中国やインドなどアジア地域のフットプリント変化を捉える点が日本企業のリスク管理に示唆を与える。
In the global GX context
This paper advances prospective consumption-based carbon footprint assessment using scenario-constrained MRIO construction, addressing the trade-off between historical fidelity and scenario compliance. The findings on hotspot persistence and China's rising footprint are relevant for global decarbonization strategies and forward-looking disclosure under ISSB/CSRD.
👥 読者別の含意
🔬研究者:Provides methodology for constructing prospective MRIO tables with explicit trade-off quantification between historical fidelity and scenario compliance, useful for footprint modelers.
🏢実務担当者:Offers insights into future consumption-based carbon hotspots, e.g., China's energy demand and livestock in Asia, aiding supply chain risk assessment.
🏛政策担当者:Highlights the need for scenario-compliant MRIO tables to inform national and regional decarbonization targets and trade policies.
📄 Abstract（原文）
Prospective multi-regional input--output (MRIO) tables that integrate scenarios from integrated assessment models (IAMs) support forward-looking environmental footprint analysis, but the trade-off between historical structural fidelity and scenario compliance has not been quantified. We constructed 12 prospective MRIO tables from the EXIOBASE 2019 and IMAGE SSP2 scenario for 2035, including a Leontief-based projection and 11 optimization-based projections under different combinations of scenario constraints on GDP, value added, sector outputs, as well as energy and agriculture consumption. Among the constraints, those on energy and agriculture consumption were satisfied at the highest cost in coefficient deviations from the Leontief-based projection and shifted the corresponding transaction flows most significantly. Regional shares of the global carbon footprint remained stable, but sectoral attribution within each region was substantially reshuffled, and the identification of high-growth consumption hotspots was sensitive to scenario compliance level. Some hotspots persisted at all levels of compliance. The consumption-based carbon footprint driven by China's final energy demand grew the fastest and emerged as a major new hotspot in 2035. The footprint of Livestock-consumption showed persistent high growth in multiple regions and India and the Rest of Asia rose in environmental significance through their industrial sectors.
🔗 Provenance — このレコードを発見したソース
- EarthArXiv https://eartharxiv.org/repository/object/13914/download/24418/first seen 2026-07-12 04:17:46
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