Prospective operational life cycle assessment of energy and waste systems in a university building: an auditable workflow using openLCA and Brightway2
大学建物におけるエネルギー・廃棄物システムの将来型運用ライフサイクルアセスメント：openLCAとBrightway2を用いた監査可能なワークフロー (AI 翻訳)
Siyou Wang, Anthony Halog
🤖 gxceed AI 要約
日本語
大学建物の運用段階のLCAを、将来の電力背景と廃棄物分別率のシナリオを組み合わせて評価するワークフローを提案。オーストラリアの事例で、電力背景の変更によりGWPが約47%削減されることを示し、感度分析でエネルギー使用強度が主要因であることを特定。監査可能で再現性のある報告を可能にする。
English
This study presents a prospective, attributional LCA workflow for building operations, using a 2x2 scenario design (electricity background and waste diversion) for a university building. Replacing the current electricity mix with a 2050 scenario reduces operational GWP by about 47%, while waste diversion has minor impact. The workflow is auditable and reusable for building-level decarbonization reporting.
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
日本の大学や企業の建物運用における脱炭素計画に応用可能。SSBJや有報でのScope 1・2排出量算定の精緻化に寄与し、将来シナリオを考慮した投資判断を支援する。
In the global GX context
This workflow aligns with global trends in building-level decarbonization and supports TCFD/ISSB-aligned reporting by providing a prospective, auditable LCA method. It demonstrates how scenario analysis can inform operational decisions, relevant for institutions aiming for net-zero targets.
👥 読者別の含意
🔬研究者:LCA方法論の進展、特に将来シナリオと不確実性処理の統合に興味のある研究者向け。
🏢実務担当者:キャンパスや建物の持続可能性マネージャーが、脱炭素施策の優先順位付けに活用できる。
🏛政策担当者:建物部門の排出削減政策の評価に、将来シナリオを考慮したLCAの活用を示唆。
📄 Abstract（原文）
Abstract Operational life cycle assessments (LCAs) of campus buildings often use static electricity backgrounds, under-specified scenarios, and limited uncertainty treatment, reducing their usefulness for routine decarbonisation decisions. This study presents a prospective, attributional, location-based workflow for annual building operations. The case study is the Advanced Engineering Building at the University of Queensland, and the functional unit is one building-year. A 2 × 2 scenario design compares two factors: the electricity background (current Queensland mix versus Queensland-2050) and municipal solid-waste diversion (approximately 64% versus 77%). Operational greenhouse gas impacts are quantified in openLCA as 100-year global warming potential (GWP100; hereafter GWP) using the Australian Life Cycle Inventory database (AusLCI) and the Intergovernmental Panel on Climate Change (IPCC) 2013 method. A lightweight Brightway2 metamodel supports paired contrasts using common random numbers (CRNs) and sensitivity screening. The workflow can be understood as an auditable lifecycle-scenario layer linking a fixed foreground to versioned backgrounds rather than as a cyber-physical twin. Electricity dominates annual operational GWP, and replacing the current electricity background with Queensland-2050 reduces it by about 47%. By contrast, increasing diversion reduces annual operational GWP by only about 5.3 t CO 2 -eq yr⁻ 1 . Sensitivity analysis identifies energy use intensity (EUI) as the dominant driver of variance, whereas waste-related inputs remain minor within the stated scope. The workflow supports repeatable building-level reporting for operators, campus sustainability managers, energy procurement teams, and policy stakeholders. It is reusable across comparable institutional buildings and reporting years.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44498-026-00147-4first seen 2026-08-08 04:49:50
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