Optimizing Scope 3 Carbon Emission Reduction Strategies in Tier-2 Supplier Networks Using Lifecycle Assessment and Multi-Objective Genetic Algorithms
ライフサイクル評価と多目的遺伝的アルゴリズムを用いたティア2サプライヤーネットワークにおけるScope 3炭素排出削減戦略の最適化 (AI 翻訳)
Bamidele Samuel Adelusi, Abel Chukwuemeke Uzoka, Yewande Goodness Hassan, Favour Uche Ojika
🤖 gxceed AI 要約
日本語
Scope 3排出はサプライチェーン上流のティア2まで広がると削減が困難。本稿はLCAと多目的遺伝的アルゴリズム(MOGA)を組み合わせ、炭素削減・コスト・事業継続をバランスするパレート最適解を導出する枠組みを提案する。AIとデジタル可視化の重要性も示し、製造・エネルギーなど排出集約型産業への適用可能性を提示する。
English
Scope 3 emissions are hardest to manage across Tier-2 supplier networks. This study combines LCA with multi-objective genetic algorithms to find Pareto-efficient strategies balancing carbon reduction, compliance cost, and operational continuity. Case scenarios under uncertainty show the value of predictive analytics and digital supply chain visibility, offering a scalable blueprint for Net-Zero targets.
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開示やサプライチェーン排出量の把握要請が進む中、ティア2以下の間接排出をどう削減するかは実務上の課題。LCA×MOGAの枠組みはデータ制約下での優先順位付けに示唆を与え、製造業のScope 3対応や調達先との協働設計に応用できる。
In the global GX context
With ISSB/CSRD reporting pushing companies to address value-chain emissions, this paper offers a method to allocate reduction efforts across dispersed suppliers. Its Pareto-based approach suits global manufacturers facing supplier compliance costs and data gaps, complementing disclosure frameworks with operational analytics.
👥 読者別の含意
🔬研究者:Methodological blueprint for integrating LCA and evolutionary optimization in multi-tier supply chain decarbonization; useful for extending to real supplier data.
🏢実務担当者:Use Pareto solutions to prioritize supplier engagement and data collection for Scope 3 reduction without breaking operational continuity.
🏛政策担当者:Suggests that policy support for digital supply chain visibility and LCA data sharing could accelerate Scope 3 decarbonization.
📄 Abstract（原文）
Scope 3 emissions—indirect emissions from upstream and downstream activities—constitute the most challenging component of corporate carbon footprints, particularly in complex Tier-2 supplier networks where data transparency and operational alignment are limited. This study presents an integrated framework leveraging Lifecycle Assessment (LCA) and Multi-Objective Genetic Algorithms (MOGA) to optimize carbon reduction strategies across dispersed supply chains. By incorporating empirical LCA data into a MOGA-based optimization model, the framework identifies Pareto-efficient solutions that balance carbon minimization, supplier compliance costs, and operational continuity. The model is tested across case scenarios with varying emission intensities, enabling adaptive strategy formulation under uncertainty. Priority is given to sector-specific emissions profiles and data structures to ensure relevance in high-emitting industries like manufacturing and energy. Additionally, the research synthesizes insights from AI-driven decision models and sustainability-oriented supplier engagement frameworks. Results underscore the importance of integrating predictive analytics and digital supply chain visibility tools to foster accountability and accelerate Scope 3 decarbonization. This work contributes a scalable methodological blueprint for firms aiming to meet Net-Zero targets through intelligent, multi-tiered supply chain interventions.
🔗 Provenance — このレコードを発見したソース
- openalex https://ijsrst.com/IJSRST2302548first seen 2026-08-02 19:05:56 · last seen 2026-08-02 19:05:57
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