Accounting for imperfect displacement in life cycle assessment for refurbishing and remanufacturing operations
再生および再製造事業におけるライフサイクルアセスメントでの不完全置換の考慮 (AI 翻訳)
Antonio Cavallin Toscani, Vishal Agrawal, Atalay Atasu, Luk N. Van Wassenhove
🤖 gxceed AI 要約
日本語
本論文は、再生・再製造品が新製品を完全に代替できない「不完全置換」問題を解決するため、経営工学のモデルを応用して市場駆動型の置換率を内生化する手法を提案。エレクトロニクス産業の事例研究を通じて、地域によって環境ブレークイーブンと市場置換率が異なることを示し、実践的な計算枠組みを提供する。
English
This paper addresses imperfect displacement in life cycle assessment for refurbishing and remanufacturing by endogenizing the displacement rate using operations management models. A case study in electronics shows that geography significantly affects both break-even and market-driven displacement, providing a practical framework for firms to evaluate environmental benefits.
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
日本はエレクトロニクスや自動車などでリファービッシュ・リマニュファクチャリングが進んでおり、本論文の枠組みは日本企業が循環型戦略の環境効果を定量的に評価する際に有用。特に、SSBJ（サステナビリティ基準）やESG開示において製品ライフサイクル全体の環境負荷を正確に開示するための基盤となる。
In the global GX context
This paper contributes to global LCA methodology by providing a rigorous way to quantify displacement effects in circular strategies, which is crucial for accurate carbon accounting and transition finance. It offers tools for firms to assess the environmental viability of refurbishing under TCFD/ISSB-aligned disclosure requirements.
👥 読者別の含意
🔬研究者:Provides an interdisciplinary modeling framework to integrate market dynamics into LCA to assess displacement rates, advancing circular economy assessment methodology.
🏢実務担当者:Offers a practical, data-feasible method to evaluate whether refurbishing/remanufacturing strategies yield net environmental savings and which markets to target.
🏛政策担当者:Highlights the importance of geographic variation in the environmental benefits of circular strategies, informing regional policy design and extended producer responsibility (EPR) schemes.
📄 Abstract（原文）
Refurbishing and remanufacturing are often championed for their potential to reduce environmental impacts, yet their true benefits remain a subject of a debate. A primary challenge is imperfect displacement—where refurbished/remanufactured units do not fully substitute for new products—leading to rebound effects that can offset environmental gains. Recent Life Cycle Assessment (LCA) research has begun to address this through a displacement rate (DR), which compares the quantity of new products displaced to the quantity of refurbished/remanufactured products supplied to the market. However, practical approaches to estimating realistic DRs remain limited, with most studies resorting to subjective assumptions. This article shows how to endogenize DR by using an established modeling approach from the Operations Management literature. This involves modeling firm and customer decisions and estimating DR as a function of cost, market, and operational parameters. We propose that companies must verify whether this market-driven DR exceeds the 'break-even' DR—the threshold required for a circular strategy to achieve net environmental savings, as calculated through LCA. We demonstrate these notions through a case study in the electronics industry. Our results highlight that, for products with use-dominated impacts, companies must strategically select their target markets, as geography significantly influences both break-even and market-driven displacement. Overall, we provide scholars and practitioners with an interdisciplinary framework to evaluate the environmental benefits of refurbishing/remanufacturing strategies. This framework offers a practical calculation with feasible data requirements, enabling firms to simulate prospective scenarios and identify actionable levers to influence displacement with both environmental sustainability and profitability in mind.
🔗 Provenance — このレコードを発見したソース
- SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6438002first seen 2026-07-08 04:46:21
🔔 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。