The cost of climate change: A generalized cost function approach for incorporating extreme weather exposure into public transit accessibility
Bibliographic record
Abstract
Public transit offers urban populations physical accessibility to resources and opportunities. However, at the same time, transit trips often expose users to extreme environmental conditions, such as extreme heat and cold since transit journeys usually include out-of-vehicle trip segments including walking and waiting. Such exposure can be considered as environmental health costs because exposure to weather extremes can lead to adverse health outcomes. Even worse, climate change is increasing the intensity and frequency of extreme weather events. In this context, how can we make public transit accessibility measures ready for climate change? This paper attempts to answer this question by developing a generalized cost function approach combining travel time and environmental health costs into an integrated measure of dual accessibility: a measure of the travel costs of accessing a fixed number of destinations. We synthesize transport science, environmental health, remote sensing, and urban climatology to empower the proposed framework. To demonstrate the utility of the proposed method, we carry out an example study that incorporates transit passengers' extreme cold exposure into accessibility measures in the city of Winnipeg, Manitoba, Canada. Further, we perform a social equity analysis to investigate whether the increase in total integrated costs (i.e., decrease in accessibility) due to the inclusion of environmental health costs disproportionately affects socially disadvantaged population groups. The proposed method enables a more realistic and practical measurement of public transit accessibility under climate change; thereby, improving the readiness and resilience of our society and transport systems for future challenges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".