Examining Race and Class Disparities in Urban Heat: Towards Environmental Justice in Urban Planning
Bibliographic record
Abstract
The interplay of climate change and urbanization has led to uneven heat exposure patterns, disproportionately impacting marginalized communities and raising critical concerns about environmental justice. While studies primarily in the United States highlight the heightened heat vulnerability of low-income and ethnic minority groups, similar analyses are still lacking for other countries, creating a significant gap in understanding global environmental inequities. This gap limits assessment of socio-economic and ethnic disparities to identify shortcomings in urban planning strategies.This study aims to integrate social and environmental sciences to address environmental injustice by investigating the relationship between extreme heat exposure and socio-economic disparities across municipalities or counties for various countries including Australia, New Zealand, Canada, Germany, and the U.K. The research leverages satellite-derived land surface temperature data at daytime and nighttime for summer and census datasets from countries to examine key socio-economic indicators, such as education levels, age distribution, and the proportion of foreign-born populations. Additionally, the study delves into urban planning parameters including green spaces, building density and local climate zones to assess their correlation with land surface temperatures, and also air temperature and heat stress indices for the case of Germany.The findings reveal varying levels of heat exposure disparities across countries and its severe consequences for vulnerable populations, emphasizing the urgent need for equitable urban planning. This research calls on policymakers and urban planners to address environmental injustices by prioritizing inclusive interventions aimed at mitigating socio-economic and racial disparities in urban heat exposure. Furthermore, the study serves as a framework for conducting similar analysis worldwide, supporting the development of equitable and sustainable urban environments.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".