A generative computational workflow to develop actionable renovation strategies for renewable built environments: a case study of Sheffield

Xu, H. orcid.org/0000-0002-4063-6295 and Wang, T.-H. (2023) A generative computational workflow to develop actionable renovation strategies for renewable built environments: a case study of Sheffield. International Journal of Architectural Computing, 21 (3). pp. 516-535. ISSN: 1478-0771

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Item Type: Article
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© The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Keywords: Parametric design; urban sustainability; urban building energy modelling; building performance simulation; decarbonization
Dates:
  • Published (online): 29 May 2023
  • Published: September 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture and Landscape
Depositing User: Symplectic Sheffield
Date Deposited: 23 Sep 2025 08:38
Last Modified: 23 Sep 2025 08:38
Status: Published
Publisher: SAGE Publications
Refereed: Yes
Identification Number: 10.1177/14780771231180258
Related URLs:
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 7: Affordable and Clean Energy
  • Sustainable Development Goals: Goal 13: Climate Action
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