Investigation of the usage of machine learning to explore the impacts of climate change on occupational health - a systematic review and research agenda

Aug 5, 2026

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Occupational accidents and illnesses can be intensified by workplace and environmental conditions, particularly climatic factors such as air temperature, wind speed, and humidity that drive heat stress and outcomes ranging from cramps to exhaustion, stroke, and death. Under climate change, measuring these variables supports worker well-being adaptation, yet traditional methods struggle with high-dimensional, multi-factor data. A systematic review across five databases evaluates machine learning applications to this problem, selecting 24 studies and proposing a future research agenda.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Climate change and occupational health

Heat stress as a key exposure

Expanding variables beyond OHS

Motivation for machine learning

TYPE Review PUBLISHED 16 June 2025 DOI 10.3389/fpubh.2025.1578558 OPEN ACCESS EDITED BY Jacob Owusu Sarfo, University of Cape Coast, Ghana REVIEWED BY Tahsin Çetin, Mugla University, Türkiye Weifeng Jiang, Quzhou City People’s Hospital, China *CORRESPONDENCE Guilherme Neto Ferrari [pg55306@uem.br](pg55306@uem.br) RECEIVED 17 February 2025 ACCEPTED 30 May 2025 PUBLISHED 16 June 2025 CITATION Ferrari GN, Leal GCL, Ossani PC and Galdamez EVC (2025) Investigation of the usage of machine learning to explore the impacts of climate change on occupational health: a systematic review and research agenda. Front. Public Health 13:1578558 . doi: 10.3389/fpubh.2025.1578558 COPYRIGHT © 2025 Ferrari, Leal, Ossani and Galdamez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Investigation of the usage of machine learning to explore the impacts of climate change on occupational health: a systematic review and research agenda Guilherme Neto Ferrari 1,2*, Gislaine Camila Lapasini Leal 1,2, Paulo Cesar Ossani 2 and Edwin Vladimir Cardoza Galdamez 1 1 Production Engineering Department, State University of Maringá, Paraná, Brazil, 2 Department of Informatics, Postgraduate Program in Computer Science, State University of Maringá, Paraná, Brazil Occupational accidents can be potentialized by factors related to the workplace or the environment, such as climatic conditions. Air temperature, wind speed, and humidity can be used to monitor occupational heat stress, leading to cramps, exhaustion, stroke, and even death. Under the climate change scenario, measuring these variables is fundamental to developing adaptation strategies for maintaining the workers’ well-being. However, when dealing with this high data volume from distinctive factors, traditional techniques are insufficient to extract all information effectively. Therefore, computational intelligence and data analytics tools can enhance data processing and analysis. Machine learning techniques have been successfully applied to occupational health and climate contexts. This paper explores the literature regarding applying these techniques to investigate the effects of climate change on occupational health. We conducted a systematic review through five scientific databases guided by three research questions, resulting in 24 selected papers. 75% of the papers screened used primary data collected from wearable sensors to monitor the well-being of workers, where we identified a trend of using supervised machine learning techniques, especially classification and regression algorithms, such as SVM, RF, and KNN. The remaining focus is on using secondary data from national databases to investigate the risk, with a trend of using feature selection techniques and classification tasks. Considering this topic is relatively new, we developed an agenda to guide future research, with suggestions to follow the trends found in this review and highlight the potential of expanding to multiple future research paths. KEYWORDS occupational health and safety, heat stress, climate change, machine learning, supervised learning 1 Introduction The lack of safety and health measures in the workplace can lead to increased occupational accidents and illnesses and a consequential drop in productivity and work capacity ( 1, 2) . Often, studies that focus on preventing and reducing accidents aim at understanding their root causes, which can be related to the type of activity that is being done, e.g., work at heights (3), or due to the lack of safety policies such as protectiv

FAQs

Why are climatic conditions important for occupational health?

Climatic conditions such as temperature, humidity, and wind speed influence heat stress, which can lead to a spectrum of health outcomes from fatigue to heat stroke and death.

How does this systematic review approach the topic?

The review uses five scientific databases guided by three research questions, resulting in 24 selected papers evaluating how machine learning is used in climate change–related occupational health research.

What research directions does the agenda propose?

Because the topic is relatively new, the agenda highlights trends observed in the reviewed studies and suggests expanding toward multiple future research paths.

Aug 5, 2026

Upload by Quinn