Intelligent Optimization of Spatial Parameters for Low-Carbon Campuses: A Study Based on Generative Design and Genetic Algorithms
低炭素キャンパスの空間パラメータの知的最適化：ジェネレーティブデザインと遺伝的アルゴリズムに基づく研究 (AI 翻訳)
Yanqi Tang
🤖 gxceed AI 要約
日本語
本研究は、GISデータ処理、パラメトリック生成設計、NSGA-IIを用いて低炭素キャンパスの空間形態を多目的最適化する。成都東キャンパスを対象に、緑地率、樹冠被覆率、透水性舗装率、道路網密度などの8変数をハイブリッド符号化し、ライフサイクル炭素排出量、屋外熱快適性、歩行性を最適化した。結果、36の非劣解が得られ、バランス解では正味炭素排出量が148.6 tCO₂eから117.9 tCO₂eに削減され、熱快適面積率が43.2%から64.5%に向上し、歩行性が0.614から0.801に向上した。
English
This study applies GIS data processing, parametric generative design, and NSGA-II to multi-objectively optimize spatial form for low-carbon campuses. Using Chengdu East Campus as a case, eight variables (green space ratio, canopy cover, permeable paving ratio, road network density, etc.) were hybrid-encoded, optimizing life-cycle carbon emissions, outdoor thermal comfort, and walkability. Results yielded 36 non-dominated solutions; the balanced solution reduced net carbon emissions from 148.6 to 117.9 tCO₂e, increased thermally comfortable area from 43.2% to 64.5%, and improved walkability from 0.614 to 0.801.
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
日本のキャンパスや都市計画において、SSBJやカーボンニュートラル宣言に対応するための設計手法として参考になる。特に、緑地や透水性舗装などのグリーンインフラを数値最適化するアプローチは、自治体や大学の脱炭素計画に応用可能。
In the global GX context
This work contributes to global discourse on low-carbon urban design by demonstrating a computational optimization framework that balances carbon reduction, thermal comfort, and walkability. It offers a replicable methodology for campus and district-scale decarbonization, relevant to ISSB-aligned climate transition planning and sustainable urban development.
👥 読者別の含意
🔬研究者:Provides a multi-objective optimization framework for low-carbon campus design, useful for urban planning and carbon accounting research.
🏢実務担当者:Offers a data-driven design tool for campus planners and sustainability teams to reduce carbon footprint while improving occupant comfort and accessibility.
🏛政策担当者:Demonstrates how computational design can support municipal and institutional climate targets, informing zoning and green infrastructure policies.
📄 Abstract（原文）
In order to improve the computability and multi-objective coordination ability of the spatial form design of low-carbon campuses, in this study, GIS data processing, parametric generative design and NSGA-II are used. Eight variables such as green space ratio, canopy cover, permeable paving ratio and road network density were hybrid-encoded, while the net life-cycle carbon emissions, outdoor thermal comfort, and walkability were set as the optimization objectives by taking a typical open space at Chengdu East Campus as a study case. The results indicate that 36 non-dominated solutions were obtained from the algorithm. The comprehensive balanced solution resulted in a reduction of the net carbon emissions from 148.6 tCO₂e to 117.9 tCO₂e, an increase of the proportion of thermally comfortable area from 43.2% to 64.5% and an increase of walkability from 0.614 to 0.801. Spatial continuity, canopy supplementation and direct path connectivity allows for the synergic optimization of carbon reduction, environmental improvement and circulation efficiency within a limited site.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70917/ijcisim-2026-3329first seen 2026-07-31 05:46:29
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