Local Sectoral Specialization in a Warming World
Bibliographic record
Abstract
This paper quantitatively assesses the world's changing economic geography and sectoral specialization due to global warming. It proposes a two-sector dynamic spatial growth model that incorporates the relation between economic activity, carbon emissions, and temperature. The model is taken to the data at the 1 by 1 resolution for the entire world. Over a 200-year horizon, rising temperatures consistent with emissions under Representative Concentration Pathway 8.5 push people and economic activity northwards to Siberia, Canada, and Scandinavia. Compared to a world without climate change, clusters of agricultural specialization shift from Central Africa, Brazil, and India's Ganges Valley, to Central Asia, parts of China and northern Canada. Equatorial latitudes that lose agriculture specialize more in non-agriculture but, due to their persistently low productivity, lose population. By the year 2200, predicted losses in real GDP and utility are 6% and 15%, respectively. Higher trade costs make adaptation through changes in sectoral specialization more costly, leading to less geographic concentration in agriculture and larger climate-induced migration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".