Accelerating the Transition to Green Building Neighbourhoods: A New Decision Support Platform
グリーンビルディング地区への移行加速：新たな意思決定支援プラットフォーム (AI 翻訳)
H. Sohier, C. Schultz, Y. Zayed, A. Kamari, Luc Jonveaux, Kostas Zavitsas, Georgia Pantelide, J. Trinanes, Asier Alejandre, Jessica Mberi, Cristian Maxim
🤖 gxceed AI 要約
日本語
EU H2020 PROBONOプロジェクトで開発された意思決定支援プラットフォーム（DSP）が、グリーンビルディング地区（GBN）への移行を加速する。6つのリビングラボでテストされ、エネルギー、排出、モビリティ、健康などの課題に対応。BIMやデジタルツイン、AIを統合し、データ駆動型の意思決定を支援する。
English
This paper presents a Decision Support Platform (DSP) developed under the EU H2020 PROBONO project to accelerate the transition to Green Building Neighbourhoods (GBNs). The DSP integrates BIM, Digital Twins, AI, and analytics, and is tested in six Living Labs across Europe, addressing energy, emissions, mobility, and health. It enhances stakeholder coordination and data-driven decision-making for sustainable urban development.
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
日本の都市開発や建築基準（CASBEEなど）の高度化に示唆を与える。統合型デジタルプラットフォームの実例として、日本のスマートシティ政策や省エネ街区形成に応用可能。
In the global GX context
This paper exemplifies how digital decision support tools can operationalize the EU Green Deal and urban decarbonization goals. It offers a replicable model for integrated neighbourhood-level planning, relevant to global climate action frameworks like the Paris Agreement and SDG 11.
👥 読者別の含意
🔬研究者:Provides a methodological framework for developing and testing decision support platforms in real-world urban settings.
🏢実務担当者:Offers a concrete tool and insights for urban planners and developers aiming to create low-carbon neighbourhoods.
🏛政策担当者:Demonstrates how digital tools can support policy implementation for building decarbonization and community engagement.
📄 Abstract（原文）
Green Building Neighbourhoods (GBNs) foster sustainable, accessible, low‐carbon living through shared energy, green mobility, and social cohesion. Steering a neighbourhood towards GBN status faces hurdles in governance, financing, citizen engagement, technological innovation, and accessible tools. Current rapidly evolving digital technologies, such as BIM and Digital Twins, accelerate this transition by offering real-time tracking and visibility, fostering better coordination among stakeholders and facilitating early issue detection. The emerging advanced decision intelligence and digital decisioning platforms incorporate artificial intelligence (AI), analytics, and large datasets. This integration enables automation, optimisation, and the consistent delivery of data-driven decisions across various ecosystems. We report on the development of a novel Decision Support Platform (DSP) within the EU H2020 PROBONO project. The PROBONO DSP is iteratively tested in six “Living Labs” in Madrid, Dublin, Brussels, Aarhus, Porto, and Prague, each at different life‐cycle stages and seeking to address themes such as energy, emissions, mobility, and health. Key functionality includes digitalizing social values in BIM, large‐scale scenario sampling, thermal‐comfort simulation and monitoring, mobility benchmarking, anomaly detection, and energy‐investment comparison. Deployment in the Living Labs has enhanced information sharing, ideation, and stakeholder discussion through concise overviews and tailored recommendations. Modular integration has enabled emergent features.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.66408/abc2.2026.23first seen 2026-05-15 21:35:51
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