The impact of climate change on mortality in the United States: Benefits and costs of adaptation
Bibliographic record
Abstract
Abstract This paper reviews and extends the recent empirical literature on the impact of climate change on mortality and adaptation in the United States. The analysis produces several new facts. First, the reductions in the impact of extreme heat on mortality risk previously documented up to 2004 have continued up to 2019, consistent with continued investments in health‐protecting adaptations to high temperatures. The second part of the paper examines the private and external costs of electricity generation and consumption related to high temperatures, a commonly used proxy for measuring the consumption of adaptation services. Extreme temperatures increase electricity demand in the residential sector (relative to moderate temperatures), but not in the commercial, industrial and transportation end‐use sectors. The additional electricity demand in response to high temperatures results in significant external costs due to the release of local and global pollutants caused by the combustion of fossil fuels in order to produce electricity. These external costs, documented for the first time in this paper, are one order of magnitude larger than the private cost of adaptation associated with electricity consumption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".