On the effect of international human migration on nations’ abilities to attain CO2 emission-reduction targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
I merge publicly available data on CO2 emissions, with patterns of human movement, to analyze the anticipated effects of human migration on the abilities of nations to attain 2030 UNFCCC CO2 emission targets. I do so at both global (175 countries) and national (Canada and the USA) scales. The analyses reveal that mean per capita CO2 emissions are nearly three times higher in countries with net immigration than in countries with net emigration. Those differences project a cumulative migration-induced annual increase in global emissions of approximately 1.7 billion tonnes. For Canada and the United States, the projected total emissions attributable to migration from 2021 to 2030 vary between 0.7 and 0.9 billion tonnes. Although staggering, the annual and total emissions represent a small fraction of current global emissions totalling 36 billion tonnes per annum. Even so, the projected decadal immigration of nearly 4 million humans to Canada, and 10 million to the USA, represent significant additional challenges in reducing CO2 emissions. The challenges pale in comparison with poor nations that are minor contributors to climate change. Such nations face the incomprehensible burden of improving the quality of their citizens' lives without increasing global CO2 emissions. National and international strategies aimed at lowering emissions must thus acknowledge, and cooperatively address, consumptive inequities and expected increases in human population size and migration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it