Green Hydrogen for Hard-to-Abate Supply Chains: A Scenario-Based Decision Framework
ハード・トゥ・アベートサプライチェーンのためのグリーン水素：シナリオベースの意思決定フレームワーク (AI 翻訳)
S. Bruzzi, Elena Tànfani
🤖 gxceed AI 要約
日本語
本論文は、グリーン水素への転換を評価するための意思決定モデリングフレームワークを提案。6つの次元に基づく構造化と専門家による精緻化、シナリオ計画ワークフローを開発。鉄鋼製造の事例で、6561の初期シナリオから4つの代表シナリオへの絞り込みを実証。
English
This paper proposes an enterprise-oriented decision framework for evaluating green hydrogen conversion in hard-to-abate supply chains. It structures drivers into six dimensions, uses expert elicitation and scenario planning with consistency-based reduction to identify coherent scenarios. An illustrative steelmaking case shows reduction from 6561 to 4 representative scenarios.
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
日本の鉄鋼業界などハード・トゥ・アベート産業の脱炭素化において、グリーン水素の導入判断は重要。本フレームワークは不確実性下での意思決定を支援し、日本の水素基本戦略や業界の取り組みに資する。
In the global GX context
This framework provides a structured methodology for green hydrogen adoption in heavy industries, relevant to global hydrogen strategies and hard-to-abate sector decarbonization. It addresses scenario uncertainty, applicable to ISSB/TCFD-aligned transition planning.
👥 読者別の含意
🔬研究者:Offers a replicable scenario-based decision framework for hydrogen transition research.
🏢実務担当者:Provides a structured tool for evaluating green hydrogen conversion under uncertainty.
🏛政策担当者:Insights on scenario development for hydrogen policy and industrial decarbonization.
📄 Abstract（原文）
Background: Interest in green hydrogen (GH) is increasing, as it can act both as an energy carrier and as an industrial feedstock to decarbonise applications that currently rely on fossil-based (grey) hydrogen. Hard-to-abate industries, such as steelmaking, face complex and multi-dimensional uncertainties when assessing conversion to GH and the associated supply chain redesign. Materials and Methods: We propose an enterprise-oriented decision-modelling framework that structures conversion drivers into six decision-relevant dimensions (socio-economic, infrastructure, technology, market, supply chain, and enterprise). The framework is refined through a two-round expert elicitation process and operationalised through a scenario planning workflow based on discrete key-factor projections and an elicited interdependency network. Building on this dependency structure, we propose a transparent consistency-based reduction approach that integrates pairwise projection compatibility and graph-guided screening to identify internally coherent and decision-relevant scenarios. The procedure is further demonstrated through an illustrative steelmaking conversion case. Results: The expert-supported workflow identifies 14 external key factors and their decision-relevant projections, together with an elicited interdependency structure among them. The illustrative application shows how an initial scenario space of 6561 configurations, based on eight selected key factors, can be screened to 1335 internally admissible configurations and consolidated into four representative scenarios. These scenarios capture distinct decision contexts, including coordinated acceleration, demand-led but infrastructure-constrained transition, technology and policy push with limited market pull, and fragmented, delayed transition. Conclusions: The approach enhances methodological transparency in scenario-based decision support and offers hard-to-abate industries a structured basis for evaluating green hydrogen conversion under systemic interdependencies and deep uncertainty. The illustrative application further demonstrates how the framework can transform combinatorial uncertainty into a compact and interpretable set of scenarios supporting stakeholder discussion and strategic decision-making.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18115740first seen 2026-06-09 04:52:11
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