Exploring the Impact of Climate Change on Road Fatalities: A Macroscopic Panel Data Analysis
Bibliographic record
Abstract
The present road traffic safety situation faces significant challenges. Examining the influences of diverse contributing factors on road traffic crashes is of crucial importance. However, different factors may have different influences as the change of location and time and the neglect of potential heterogeneity while modelling the frequency of traffic crashes may lead to biases in parameter estimation and incorrect inference. To address the unobserved spatiotemporal heterogeneity and accurately explore the correlations between contributing factors and fatal traffic crashes, a fixed effects panel model with structural breaks is applied to identify the influences of crucial factors on fatal traffic crashes from a macroscopic level. A multisource dataset, including fatal crash numbers, socioeconomic factors, laws and regulations and climate factors is collected from the United States spanning 45 years (from 1977 to 2021). The climate change events (i.e., El Niño–Southern Oscillation phenomena) are examined for their influences on fatal traffic crashes. The experimental results illustrate that high temperatures and frequent meteorological disasters have increasing impacts on fatal crash numbers. High precipitation shows a decreasing one from a macroscope level because of the lagged effect of precipitation on crashes across days. Particularly, the climate change events (including EP E1 Niño, CP E1 Niño and La Niña) represent an adverse impact on road traffic safety. Additionally, the states with similar meteorological characteristics are categorized as high temperature, high precipitation and frequent meteorological disaster subsets for separate analysis. Under these subsets, rural trip proportion becomes a more pronounced factor that affects fatal road traffic crashes, and helmet laws are more efficient in reducing fatal crash frequency. The research findings reveal an increasingly complex road traffic safety environment in the context of global warming, offering valuable perspectives for enhancing road traffic safety.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".