Climate Change and Assessing Thermal Comfort in Social Housing of Southeastern Mexico: A Prospective Study Using Machine Learning and Global Sensitivity Analysis

Diana Romero, Karla A. Torres, Joanny Gonzalez, A. J. Cetina-Quiñones (), Cesar Acosta, M. Sadoqi and A. Bassam ()
Additional contact information
Diana Romero: Estudiante de Posgrado, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico
Karla A. Torres: Estudiante de Posgrado, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico
Joanny Gonzalez: Estudiante de Posgrado, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico
A. J. Cetina-Quiñones: Laboratorio de Modelado y Optimización de Procesos Energéticos y Ambientales, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico
Cesar Acosta: Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico
M. Sadoqi: Department of Physics, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
A. Bassam: Laboratorio de Modelado y Optimización de Procesos Energéticos y Ambientales, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Mérida 97302, Yucatán, Mexico

Sustainability, 2025, vol. 17, issue 21, 1-28

Abstract: Social housing in tropical regions faces critical thermal comfort challenges that will intensify under future climate change, yet current design practices lack systematic frameworks for evaluating long-term performance across multiple climate scenarios. This study assesses the thermal performance of social housing in southeastern Mexico using energy simulation, supervised machine learning, and global sensitivity analysis. Two housing typologies (single-story and two-story) were modeled across four cities (Mérida, Campeche, Cancún, and Tuxtla Gutiérrez) under climate change scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2100. Various machine learning models were trained to predict comfort temperature and cooling degree days. Regression Trees demonstrated superior performance, with R 2 values exceeding 0.98 for both thermal comfort indicators, achieving RMSE values of 0.0095 °C for comfort temperature and 0.2613 °C for cooling degree days. Global sensitivity analysis using the PAWN method revealed that ambient temperature was the most influential variable, accounting for 45–49% of the total sensitivity, followed by solar radiation (17–22%) and relative humidity (10–12%), while building-specific parameters had modest impacts (0.6–3.8%). Geographic variations were significant, with Mérida and Campeche showing higher cooling demands than Cancún and Tuxtla Gutiérrez. Future climate projections indicate substantial increases in cooling requirements by 2100, with CDD values expected to increase by approximately 40–50% under the RCP 8.5 scenario compared to current conditions. This research presents a computational framework for assessing thermal comfort in social housing, providing evidence-based insights for climate-adaptive building strategies in tropical regions.

Keywords: tropical climate; energy in buildings; thermal comfort; cooling degree day; predictive model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9596/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9596/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9596-:d:1781811

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().