The Study on Climate-Driven Summer Extreme Heat and Impact on Delivery and Take Out Industry GDP
Bibliographic record
Abstract
This study investigates the impact of climate-induced summer extreme heat on the GDP growth of the delivery and takeout industries in three major Chinese cities: Shanghai, Beijing, and Xi'an. By examining the relationship between the number of highly high-temperature days and the GDP growth rates of these industries, the research reveals varying effects of high temperatures across cities and industries. The findings reveal significant variations across the cities. In Shanghai, extreme heat has a positive impact on both industries; in Beijing, it is positive for the logistics industry but negative for the takeout industry; and in Xi'an, it has little impact on the logistics industry and a negative impact on the takeout industry. The study shows that the economic impact of extreme heat differs across urban environments and sectors. This helps understand the mechanism of urban economies affected by extreme heat and provides a basis for policy formulation, emphasizing the importance of context-specific climate resilience strategies. Future research could explore how other extreme weather patterns interact with economic activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".