How AI Shapes the Future Landscape of Sustainable Building Design With Climate Change Challenges?

Pengyuan Shen, Xiaoni Gao, Shuai Lu, Yi Zhang, Xing Zheng, Matthaios Santamouris

Abstract readReview

In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pengyuan ShenInstitute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.ORCID https://orcid.org/0000-0003-4325-6519

Xiaoni GaoInstitute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.

Shuai LuInstitute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.

Yi ZhangInstitute of Future Human Habitats, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.

Xing ZhengDepartment of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China.

Matthaios SantamourisSchool of Built Environment, University of New South Wales, Sydney, Australia.

Funding

Shenzhen Fundamental Research Program JCYJ20250604180231041

6 · The paper itself

Abstract

Faced with climate change challenges, artificial intelligence (AI) is redefining the way of sustainable building design. In this work, how AI technologies, including foundation models and generative systems, are reshaping architectural practice in responding to climate change is discussed. We explored how large language models, multimodal systems, and automated design generation have evolved from traditional computational methods, and the transformative potential of these novel approaches, especially when dealing with climate change challenges. While AI holds powerful tools for sustainable architecture, we argue that the successful implementation of those tools requires careful integration of technical capabilities, practice frameworks, and regulatory considerations. To advance AI-driven sustainable building design while providing effective future climate response, research priorities and policy recommendations are put forward in this study.

Indexed as

artificial intelligencebuilding simulationclimate changefoundation modelsustainable building

Identifiers

PMID41618848

DOI10.1002/advs.202523238

PMCPMC12955912

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.