Global maps of climate change impacts on the favourability for human habitation and economic activity
Bibliographic record
Abstract
This paper analyzes the statistical relationship between climatic factors and the global distribution of population and economic activity. Building on this analysis, a new method is developed for assessing geographically explicit impacts of climate change on the suitability of regions for human habitation and economic activity. This method combines information about differences in the conditional distributions of population density and economic activity across climate categories with climate change projections from an ensemble of general circulation models. In contrast to other cross-sectional analyses of the economic impacts of climate change, the method applied here does not require specific assumptions about the functional form of the relationship between climatic and non-climatic factors on the one hand, and population density and economic activity on the other. The results indicate that climate change will improve the habitability of some scarcely populated regions, in particular in Canada, Scandinavia, Russia, Mongolia, northern China, Tibet, and parts of Central Asia, but it will impair the habitability of many densely populated regions in the eastern USA, southern Europe, northern and southern Africa, eastern China, and parts of Australia. Most parts of India, South-East Asia and Oceania, Central America and northern South America, the Sahara and the Sahel are projected to experience climatic conditions during this century that have no geographical analogue in the present climate. Hence, a large majority of the world’s population is living in regions whose habitability is either projected to decrease or that are projected to experience globally unprecedented climate conditions within this century under a business-as-usual emissions scenario.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".