Digital Twins For Urban Mitigation: Modelling City-Wide Carbon Neutrality Scenarios Via High-Performance Computing (HPC)
都市緩和のためのデジタルツイン：ハイパフォーマンスコンピューティング（HPC）による都市全体のカーボンニュートラルシナリオのモデル化 (AI 翻訳)
Nilesh Patil, Balajee Maram
🤖 gxceed AI 要約
日本語
本論文は、都市デジタルツインとハイパフォーマンスコンピューティング（HPC）を統合し、都市規模でのカーボンニュートラル経路をモデル化するフレームワークを提案する。建築エネルギー、交通、再生可能エネルギー、都市微気候のモデルを統合し、不確実性下での多目的最適化を実現。合成都市のケーススタディにより、電化、深層改修、太陽光発電、蓄電、デマンドレスポンスの組み合わせが排出削減とコスト・レジリエンスのトレードオフを明らかにする。
English
This paper proposes a framework integrating urban digital twins with high-performance computing (HPC) to model city-wide carbon neutrality pathways. It combines building energy, transport, renewable generation, and microclimate models, enabling multi-objective optimization under uncertainty. A synthetic city case study explores electrification, deep retrofit, PV, storage, and demand response portfolios, revealing trade-offs between decarbonization rate, energy cost, and resilience.
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GX文脈では、自治体のカーボンニュートラル計画（ゼロカーボンシティ）やSSBJの情報開示に向けたシナリオ分析に応用可能。HPCを活用した都市規模のシミュレーションは、政策決定のエビデンスとして有望だが、実都市データへの適用が今後の課題。
In the global GX context
Globally, this work aligns with ISSB's scenario analysis requirements and the growing interest in digital twins for climate transition planning. The HPC-enabled multi-objective optimization offers a methodological advance for city-level decarbonization strategies, though the synthetic case limits immediate policy applicability.
👥 読者別の含意
🔬研究者:Provides a novel HPC-digital twin framework for urban carbon neutrality modeling with multi-objective optimization under uncertainty.
🏢実務担当者:Offers a decision-support tool concept for city planners to evaluate trade-offs in decarbonization portfolios.
🏛政策担当者:Demonstrates how HPC-based simulations can inform city-level climate action plans and scenario analysis.
📄 Abstract（原文）
This paper presents a comprehensive framework for the context of integrating urban digital twins with the high-performance computing (HPC) to model, analyse, as well as accelerate pathways to city-wide carbon neutrality. Urban abstract simulations consist of digital twins of cities integrating heterogenous streams of data, physical models, and machine learning surrogates to simulate the behaviour and conditions of cities both spatially and temporally. HPC provides computational envelope capable of executing detailed building energy modelling, transport systems modelling, renewable generation modelling and coupled atmosphere-urban microclimate modelling both at city scale and at a temporal scale suitable to do sound policy analysis. The modular digital twin system comprises a system of interaction between building energy simulation and urban mobility models and distributed energy resource (DER) models and an urban carbon scoring engine. Its structure experiences HPC in order to compute a great number of scenarios in order to measure uncertainty and apply multi-objective analysis with the aim of minimizing emissions, costs and resilience. To explain the approach, a case study of synthetic city is conducted, and sensitivity experiments of electrification, deep retrofit, distributed photovoltaics, storage, and demand response portfolios is carried. Results quantify trade-offs between decarbonization rate, cost of energy, and stressors resilience and show that HPC-based digital twin’s communities can be used to discover near-Paretooptimal trade-offs in the presence of epistemic and aleatory uncertainty. The last point is on the implementation problems, data handlings and research plans to operationalize the city digital twins as a decision support system to simplify into carbon neutral city.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.25258/ijddt.16.6s.110first seen 2026-05-05 19:12:51
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。