Extreme weather events and death based on temperature and CO2 emission – A global retrospective study in 77 low-, middle- and high-income countries from 1999 to 2018
Bibliographic record
Abstract
Due to rising temperatures and CO2 emissions, climate change has become one of the most important global issues. We described the relationship between extreme weather-related events and death, globally, from 1999 through 2018. We used data from the emergency events database of the Université Catholique de Louvain. We also categorized the countries’ income according to the World Bank GDP and we used the CO2 emission levels data from the Carbon Dioxide Information Analysis Center to link the GDP and CO2 emissions to years of extreme weather conditions in each country. We conducted descriptive and Poisson Regression analysis to analyze the data. A total of 77 countries reported 425 extreme weather-related events from1999 through 2018. Mortality related events were highest in middle-income countries due to severe winter conditions (N = 2,020) and cold-waves (N = 70,972). The total number of recorded deaths due to heat waves was highest in high-income countries (N = 84,344). Furthermore, the number of deaths in high-income countries, compared to low-income countries, was five-fold higher (IRR 5.18; 95%CI 4.58; 5.85, p < 0.001). The mortality rate in heat season was almost seven-fold higher than that in cold/severe winter (IRR 33.43; 95%CI 32.85; 34.02, p < 0.001). The number of deaths increased significantly with the repetition of extreme events (IRR 6.82; 95%CI 6.68; 6.96, p < 0.001). We found the number of deaths increased in high-income countries, and this was associated with an increase in the number of times extreme events occurred per year and with heat wave.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".