Examining Race and Class Disparities in Urban Heat: Towards Environmental Justice in Urban Planning
Bibliographic record
Abstract
Climate change and urbanization contribute to uneven distributions of heat exposure that disproportionately affect disadvantaged communities resulting in environmental (in)justice. While studies in the USA highlight the elevated heat exposure faced by low-income and ethnic minority groups, similar insights are lacking for other countries. This knowledge gap impedes a comprehensive understanding of environmental (in)justice experienced by various socio-economic and ethnic groups and hampers the identification of inadequacy in urban planning policies.This research seeks to bridge the gap between social and environmental sciences to address environmental (in)justice by establishing a link between extreme heat (at both regional and country level) and socio-economic disparities within individual municipalities or counties. So far our analysis covers Australia, New Zealand, Canada, Germany and the U.K. Using remotely sensed satellite data for Land Surface temperature mapping for summer and Census data of countries, the analysis explores various socio-economic indicators—such as education levels, age demographics, and the proportion of foreign populations.By recognizing the unequal distribution of urban heat and its disproportionate impact on vulnerable communities, there emerges a critical mandate to prioritize equitable urban planning policies. This research underscores the urgency for policymakers and urban planners to prioritize environmental justice interventions and integrate strategies that aim to reduce race and class disparities concerning urban heat. The research also serves as a model for similar analyses globally fostering inclusive, equitable and resilient urban landscapes.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".