Construction of Green Supply Chain Network Optimization Models for Textiles with Carbon Neutrality Goals
カーボンニュートラル目標に向けた繊維産業のグリーンサプライチェーンネットワーク最適化モデルの構築 (AI 翻訳)
Renyi Qiu
🤖 gxceed AI 要約
日本語
本論文は、繊維産業の高炭素排出サプライチェーンを対象に、カーボンニュートラル目標下でのコストと排出削減のトレードオフを最適化する多目的混合整数線形計画モデルを提案。炭素価格変動とグリーンインセンティブを組み込み、改善型NSGA-IIとTOPSISで解を導出。実験ではコスト18.6-25.8%、排出22.3-33.2%削減可能と示された。
English
This paper proposes a multi-objective optimization model for textile supply chains to achieve carbon neutrality. It integrates carbon trading, green subsidies, and dynamic demand response. The model reduces costs by 18.6-25.8% and emissions by 22.3-33.2% across scenarios, offering a quantitative tool for enterprises and policymakers.
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
日本の繊維産業は高排出であり、本モデルはSBTiやカーボンニュートラル目標達成のためのサプライチェーン最適化に応用可能。日本のGX戦略におけるグリーンサプライチェーン管理の実践に寄与する。
In the global GX context
This paper addresses the global challenge of supply chain decarbonization in energy-intensive industries. Its integration of carbon pricing and green incentives provides evidence for policy design under frameworks like TCFD/ISSB and supports corporate transition planning.
👥 読者別の含意
🔬研究者:The optimization framework combining carbon trading and multi-objective techniques is a valuable reference for supply chain decarbonization studies.
🏢実務担当者:Textile companies can apply the model to balance cost and emission reduction, supporting carbon neutrality roadmaps.
🏛政策担当者:The finding that a carbon tax plus green subsidy is most effective offers direct guidance for designing incentive mechanisms.
📄 Abstract（原文）
Given the high carbon intensity and complex supply chains in the textile industry, this paper investigates emission reduction and the trade-off between cost and carbon mitigation under carbon neutrality goals. A multi-objective mixed-integer linear programming (MOMILP) model is developed, integrating carbon trading mechanisms and dynamic demand response. High-carbon activities, including printing and dyeing and chemical fiber production, are considered, while decisions on raw material procurement, green process selection, facility location, production capacity allocation, and logistics routing are coordinated to achieve chain-wide emission reduction. Carbon price fluctuations are incorporated to dynamically model carbon trading, and green incentive constraints encourage the internalization of carbon externalities. The model is solved using an improved Non-dominated Sorting Genetic Al-gorithm II (NSGA-II), with the entropy weight-TOPSIS method applied for multi-attribute decision analysis on the Pareto frontier. Experiments show that the model can reduce costs by approximately 18.6–25.8% and carbon emissions by 22.3–33.2% across various carbon price scenarios. A combined carbon tax + green subsidy policy effectively reduces costs, and the improved algorithm outperforms others in convergence speed and solution di-versity. This study provides a quantitative tool for textile enterprises to balance cost and emission reduction and offers theoretical support for policymakers to design green incentive mechanisms, providing practical guidance for advancing the industry’s carbon-neutral transformation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.31881/tlr.2026.366first seen 2026-05-15 17:19:01
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