Integrated Management of Scope 3 Emissions in the Steel Supply Chain
鉄鋼サプライチェーンにおけるスコープ3排出の統合管理 (AI 翻訳)
Raktim Dasgupta, Sadhan Kumar Ghosh, Arup Ranjan Mukhopadhyay, Biswanath Dolui
🤖 gxceed AI 要約
日本語
本研究は、インドの鉄鋼サプライチェーンを対象に、実データに基づくScope 3排出量の統合的定量化・削減フレームワークを提案。電気アーク炉が排出の90%以上を占めるホットスポットであることを特定し、協力ゲーム理論によりサプライヤー・製造業者・流通業者の連携が最大の削減効果（189万ルピー）をもたらすことを示した。
English
This study presents an integrated framework using real operational data to quantify and reduce Scope 3 emissions in a medium-scale steel supply chain in India. The Electric Arc Furnace is identified as the dominant hotspot (>90% of emissions), and a cooperative game-theoretic model shows that full coalition among suppliers, manufacturer, and distributors yields the highest net payoff (₹1.89 million). Circularity metrics and solar energy integration further enhance decarbonization.
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
日本の鉄鋼業界もScope 3開示が進む中、本フレームワークはサプライチェーン全体での排出可視化と協調的削減の方法論を提供。特にSSBJ基準に基づくスコープ3算定や、サプライヤーとの協力スキーム設計に示唆を与える。
In the global GX context
This paper addresses the critical challenge of Scope 3 emissions in hard-to-abate sectors, offering a replicable multi-method framework combining EVSM, circularity metrics, and cooperative game theory. It provides a data-driven, equitable approach to supply chain decarbonization, relevant for global disclosure standards like ISSB and CSRD.
👥 読者別の含意
🔬研究者:The integrated methodology combining EVSM, circularity indices, and cooperative game theory offers a novel approach for Scope 3 analysis in supply chains.
🏢実務担当者:Corporate sustainability teams can adopt the EVSM and circularity metrics to identify emission hotspots and design collaborative reduction initiatives with suppliers and distributors.
🏛政策担当者:Policymakers can use the findings to promote mandatory Scope 3 reporting and incentivize cooperative mechanisms for supply chain decarbonization.
📄 Abstract（原文）
Scope 3 emissions constitute the largest and most difficult-to-manage component of the carbon footprint of the steel industry; however, they remain underexplored owing to fragmented data systems and the absence of holistic analytical approaches. This study presents an integrated, real-data-driven framework for quantifying and reducing Scope 3 emissions in a medium-scale steel supply chain in West Bengal, India. Primary operational data were collected from upstream suppliers, midstream manufacturing operations, and downstream distributors using transport logs, meter-based energy records, scrap inspection sheets, on-site walk-throughs, and structured stakeholder interviews. Environmental Value Stream Mapping (EVSM) coupled with life-cycle emission accounting was applied to six process stages (UP1, UP2, MS1, MS2, DS1, and DS2), revealing the Electric Arc Furnace (MS1) as the dominant hotspot, contributing more than 90% of the total Scope 3 emissions. Circularity metrics, namely the Scrap Quality Index (SQI) and Material Circularity Index (MCI), demonstrated that higher scrap quality and increased recycled content can significantly decrease upstream embodied emissions. A cooperative game-theoretic model quantified abatement opportunities for suppliers, the manufacturer, and distributors, showing that full coalition formation {U, M, D} generated the highest net payoff (₹1.89 million). Shapley value allocation confirmed the manufacturer as the major beneficiary (97.6%), with proportionate gains assigned to suppliers and distributors. The results highlight that collaborative governance, enhanced circularity, optimized logistics, and renewable energy integration, particularly solar-based electricity substitution, collectively offer a high-impact pathway for Scope 3 decarbonization. The proposed multi-method framework provides a transparent, equitable, and industry-ready decision support system for accelerating low-carbon transitions in the Indian steel sector.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.14710/presipitasi.v22i3.1039-1050first seen 2026-05-14 21:10:57
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。