Assessing seasonal building thermal adaptation through the acclimatization distance: a GIS-based machine learning framework
- DOI
- 10.3389/frsc.2026.1771368
- Published
- 2026-04-15
- Container
- Frontiers in Sustainable Cities
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/frsc.2026.1771368,
title = {Assessing seasonal building thermal adaptation through the acclimatization distance: a GIS-based machine learning framework},
author = {Daniel Jato-Espino and Francisco Requena-Crespo and Fabio Capra-Ribeiro and Vanessa Moscardó and Maysel Castillo-García},
year = {2026},
journal = {Frontiers in Sustainable Cities},
doi = {10.3389/frsc.2026.1771368},
url = {https://doi.org/10.3389/frsc.2026.1771368}
}RIS
TY - JOUR TI - Assessing seasonal building thermal adaptation through the acclimatization distance: a GIS-based machine learning framework AU - Daniel Jato-Espino AU - Francisco Requena-Crespo AU - Fabio Capra-Ribeiro AU - Vanessa Moscardó AU - Maysel Castillo-García PY - 2026 JO - Frontiers in Sustainable Cities DO - 10.3389/frsc.2026.1771368 UR - https://doi.org/10.3389/frsc.2026.1771368 ER -
APA
Jato-Espino, D., Requena-Crespo, F., Capra-Ribeiro, F., Moscardó, V., & Castillo-García, M. (2026). Assessing seasonal building thermal adaptation through the acclimatization distance: a GIS-based machine learning framework. Frontiers in Sustainable Cities. https://doi.org/10.3389/frsc.2026.1771368
Source records
- crossref · retrieved 2026-09-27T12:23:26.512Z