Warm winters enhance wellbeing: Insights into Global Warming’s Human Impact from Survey Data on Weather Satisfaction: Multivariate Estimates, USA 2016-2021, N=10,000+
Bibliographic record
Abstract
While the scientific analysis of the causes of climate change is based on a vast body of mostly rigorous, peer-reviewed research (NCAS 2023), the evaluation of the human consequences is not. Strangely, little systematic empirical evidence has been gathered or presented on how humans actually evaluate their climates and hence on their likely subjective experience of global warming. Instead, people’s reactions are more often assumed than measured. The literature, as well as the public debate, is rife with empirically poorly-grounded assertions, often plausible but rarely based on rigorous empirical measurement and peer-reviewed research. To help ameliorate this deficit, this paper takes the approach of “reading history sideways”: Comparing individual subjective wellbeing across a wide range of temperatures (net of many potentially confounding variables). Cross-sectional survey data using explicit questions about satisfaction with winter weather and satisfaction with summer weather across the entire US provide the key outcomes. Multivariate analysis of US national surveys combined with standard NOAA data on actual month-by-month temperatures at each location over many years reveal the climate effects from which we can project global warming's effects. The results show that the impact of temperature is real but small. The changes to be expected from the widely discussed 1.5° to 2° Celsius of global warming are both familiar and small, equivalent to moving from Massachusetts to New Jersey, or Virginia to North Carolina, or Arizona to Florida, or, more generally, 180 miles south. All else equal, this amount of warming is projected to increase Americans' satisfaction with winter weather, especially in the north, but somewhat to decrease satisfaction with summer in both north and south. On balance, most Americans would benefit from 4 to 6 degrees Fahrenheit of global warming, with gains from warming in the winter outweighing losses from warming in the summer. Provided that the connection between temperature and wellbeing that we find for the US holds in other countries, too, our results suggest that that for most people in Canada, Northern Europe, Russia, and other cold nations, global warming in the range anticipated over the next century will enhance well being. For people living in the equatorial zones, by contrast, global warming will reduce subjective wellbeing. Note that these results apply only to the temperature dimension of climate change, the subjective impacts of other aspects have yet to be investigated.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".