Decarbonizing energy industry through multi-criteria techno-economic method for preservation of renewable forest carbon sinks
再生可能な森林炭素吸収源を保全するための多基準技術経済的手法によるエネルギー産業の脱炭素化 (AI 翻訳)
Teijo Palander
🤖 gxceed AI 要約
日本語
本研究は、炭素吸収源の保全と排出価格を考慮した動的多目的線形計画法に基づく意思決定支援システム（DMOLP-DSS）を開発し、木材調達戦略を最適化した。実データを用いた評価では、調達半径の拡大により収益増加と排出コスト削減を両立し、トウヒ由来排出を年間11,528 tCO2e削減、脱炭素価値117万ユーロを達成した。
English
This study develops a dynamic multi-objective linear programming-based decision support system (DMOLP-DSS) integrating carbon sink impacts, emission pricing, and techno-economic optimization for wood procurement. Real-world data shows that expanding procurement radius increases revenues and reduces emission costs, achieving 11,528 tCO2e/year reduction in spruce-related emissions with a decarbonization value of €1.17 million.
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
フィンランドのケースだが、日本でも森林吸収源の活用とカーボンプライシングの連携が重要視されており、特に木質バイオマス発電の持続可能性評価に示唆を与える。
In the global GX context
This paper provides a practical DSS for integrating carbon pricing and multi-objective optimization into forest-based energy supply chains, relevant for EU decarbonization policies and carbon sink accounting frameworks.
👥 読者別の含意
🔬研究者:Multi-objective optimization framework for biomass procurement under carbon pricing constraints.
🏢実務担当者:Decision support tool for wood procurement planning to balance costs, emissions, and carbon sink preservation.
🏛政策担当者:Demonstrates how carbon pricing and spatial flexibility can enhance decarbonization in bioenergy without infrastructure changes.
📄 Abstract（原文）
ABSTRACT The forest bioenergy sector is increasingly affected by decarbonization policies and carbon sink conservation requirements, creating new challenges for strategic wood procurement planning. This study developed a dynamic multi-objective linear programming-based decision support system (DMOLP-DSS) framework integrating carbon sink impacts, emission pricing, and techno-economic optimization for wood procurement. The methodology was evaluated using real-world municipal-level data on procurement, revenues, emissions, logistics, and environmental constraints. Expanding the procurement radius from 120 km to 140 km increased revenues from €106.9 million to €109.1 million while reducing emission costs from €7.8 million to €3.1 million. The optimized procurement strategy reduced spruce-related emissions by 11,528 tCO 2 e/year, corresponding to a decarbonization value of €1.17 million. The total decarbonization potential for all tree species at the municipal level was estimated at €7.37 million from 68,182 MWh/year of reduced unsustainable wood harvesting. The results demonstrate that spatially flexible biomass procurement can improve carbon sink preservation without requiring modifications to existing logistics systems. The proposed DSS provides a practical tool for integrating carbon pricing and multi-objective optimization into sustainable forest-based energy supply chains. Although broader cross-regional application requires improved harmonization of forest resource datasets, the framework provides scalable support for regional decarbonization policy implementation. The framework also supports Industry 5.0 sustainability objectives.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.renene.2026.126038first seen 2026-06-27 04:45:00
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