大規模学術イベントにおけるカーボンフットプリント評価と持続可能性最適化のための計算工学的フレームワーク
A COMPUTATIONAL ENGINEERING FRAMEWORK FOR CARBON FOOTPRINT ASSESSMENT AND SUSTAINABILITY OPTIMIZATION IN LARGE-SCALE ACADEMIC EVENT (原題)
Gouthami Eragamreddy, Indrajit Pal, S. Ramacharan, Prąsun Chakrabarti
🤖 gxceed AI 要約
日本語
本論文は、学術イベントの環境負荷を評価・最適化する計算工学的フレームワークを提案する。活動ベースの炭素排出モデリング、IoT-Edge-Cloudセンシング、多目的最適化（MGWO・SPEA2）を統合し、Java製ツールで試算した。1,000人規模の比較で、エネルギー34.5%減、炭素排出45.2%減、廃棄物37.1%減、循環性指数94.3%向上を達成した。
English
This paper proposes a computational engineering framework for assessing and optimizing the environmental footprint of academic events. It integrates activity-based carbon modeling, IoT-Edge-Cloud sensing, and multi-objective optimization (MGWO, SPEA2), implemented in a Java-based Green Event Planner. In a 1,000-person scenario, the optimized setup cut energy by 34.5%, carbon emissions by 45.2%, and waste by 37.1%, while raising the Circularity Index by 94.3%.
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
日本では企業の脱炭素開示が進む一方、イベントや会議のカーボンフットプリント算定はScope 3のカテゴリ6（出張）やカテゴリ5（廃棄物）に関連するが、本論文は学術イベントに特化しており、日本企業の実務への直接的な示唆は限定的である。ただし、活動ベースの算定手法や最適化アプローチは、企業のイベント・会議運営の脱炭素化に応用可能である。
In the global GX context
While global disclosure frameworks (TCFD, ISSB) increasingly cover Scope 3 categories such as business travel and waste, this paper focuses narrowly on academic events. Its activity-based modeling and multi-objective optimization approach could inform corporate event sustainability strategies, but it does not directly address mainstream disclosure standards.
👥 読者別の含意
🔬研究者:イベントの炭素フットプリント算定と多目的最適化を組み合わせた手法に関心のある研究者に有用。
🏢実務担当者:大規模イベントを運営する組織が、エネルギー・廃棄物・交通の削減策を検討する際の参考になる。
🏛政策担当者:学術イベントや公共イベントの環境基準策定に際し、算定・最適化の枠組みとして参考になり得る。
📄 Abstract（原文）
Problem: Academic events, which require considerable amounts of energy, transportation, catering, water, material usage, and waste generation, lack integrated computational methodologies for assessment and optimization of their ecological footprint. Approach: The paper suggests a framework for computational engineering analysis and sustainability optimization of academic events, which involves activity-based modeling of carbon emissions, circular economy principles, IoT-Edge-Cloud sensing and monitoring architecture, and multi-objective optimization. A constrained four-objective problem (minimization of carbon emissions, energy, and waste, maximization of Circularity Index) is solved by MGWO and SPEA2 algorithms, and a Java-based Green Event Planner tool calculates initial, minimized and residual emissions based on activity information and emission factors. 1,000 people scenario is used to compare a conventional setup (Scenario A) with a green event setup (Scenario B). Results: In terms of resource performance, the optimized configuration lowered energy use by 4,200 to 2,750 kWh (34.5%), carbon footprinting by 3,250 to 1,780 kg CO₂e (45.2%; from 3.25 to 1.78 kg CO₂e per participant), water use by 30% and solid waste by 37.1%. There was an increase in the share of renewable energy sources from 15% to 40%, the share of materials with recyclable/reusable potential from 22% to 65%, the Circularity Index from 0.35 to 0.68 (+94.3%) and the Sustainability Index Score from 0.47 to 0.82 (+74.5%). Travel interventions can be credited for 48.97% of reductions estimated by the source-wise planner and 81.6% according to the ablation study; this result is consistent with the maximum elasticity coefficient (+0.82) for the transportation category. Conclusion: The proposed framework offers a data-based approach for supporting resource-efficient, sustainable and low-carbon oriented decisions about organizing academic events.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70102/afts.2026.1836.1134first seen 2026-10-14 05:10:04
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