Striving for Rural Heat Resilience: A Systematic Literature Review
Bibliographic record
Abstract
Abstract Extreme heat, an increasing cause of weather-related deaths worldwide, poses escalating risks to both urban and rural communities. While urban heat and its impacts have received scholarly and practitioner attention for decades, rural heat has tended to be overlooked. To address this, we conducted a systematic literature review using Scopus, yielding 52 articles specifically addressing extreme heat in rural communities in the United States, Canada, and Australia. This review synthesizes the impacts of extreme heat on rural communities and current efforts and challenges in addressing rural heat risks. Among 52 articles, about three-quarters focus on the impact of extreme heat on public health across diverse groups. Key findings include the following: 1) outdoor workers (e.g., farmers) face particularly high risks due to prolonged exposure to heat; 2) the elderly, Indigenous people, and visitors in rural regions are also vulnerable to extreme heat; 3) rural heat risks are often shaped by the intersectional vulnerabilities of rural communities and governance gaps, such as inadequate or missing regulations to protect workers; and 4) compared to urban heat governance, rural heat governance remains underdeveloped in many areas, highly fragmented, and inconsistent across regions, which leaves vulnerable populations more exposed. We use a heat mitigation (e.g., home weatherization), management (e.g., occupational heat safety standards), and heat governance (i.e., policies and actions taken by governments, institutions, and communities) framework to identify gaps in current approach and discuss future research direction toward an integrated and robust approach to increase rural heat resilience. Significance Statement As climate change brings more frequent extreme heat events, rural communities in countries like the United States, Canada, and Australia face growing risks but often receive less attention than cities. This literature review explores how extreme heat impacts rural areas, which can be more vulnerable due to limited resources, aging populations and infrastructures, and outdoor-based economies like farming. By examining interdisciplinary studies, the review identifies key challenges, local responses, and opportunities for more effective policy. It emphasizes the need to understand how rural settings may differ not only from dense urban areas but also from one another and to ensure rural areas are included in heat resilience efforts, whether by building on their own initiatives or improving access to tailored, place-based governance tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".