Fraser Forum Emission Scenarios & Recent Global Warming Projections
Bibliographic record
Abstract
widespread increase in real per capita income: 60 percent in the US, 74 percent in the UK, 77 percent in Canada, 112 percent in Japan, etc. (Easterly and Sewadeh, 2001). Nonetheless, average carbon dioxide emissions per capita did not rise for the world as a whole. So there is reason to believe that per capita CO 2 emissions are somewhat invariant to economic growth, at least at a globally-averaged level. We could likely rule out, for instance, the possibility that per capita emissions will exceed 2 tC in the next few decades. by Ross McKitrick In the ongoing debates about the nature of the global warming threat there has been a lot of attention paid to some core scientific issues such as natural variability, the validity of climate models, the quality of atmospheric temperature data, the connection between climate and extreme weather, and so forth. One area that is receiving increasing attention is the socioeconomic modeling that underpins the emission projections that in turn gave rise to the famous warming projections of +1.4 to +5.8 degree C that have so alarmed policymakers. This article explains why the emission scenarios are almost certainly too high and ought to be revised as quickly as possible. 0.8 tC to 1.2 tC from 1960 to the early 1970s, and fell thereafter to about 1.15 tC. Since 1970, the average has been just below 1.14. The steadiness of this average during the interval from 1970 to 1999 is quite striking since global per capita income grew during this period. The growth was not evenly felt, especially in developing regions. For instance, Brazil’s per capita income rose 80 percent while Nigeria experienced no real growth at all. But in the developed countries there was a Currently there are about 6.1 billion people in the world. The United Nations currently projects world population will reach about 9.3 billion persons by 2050 (UN, 2002). Population projections have tended to fall because fertility rates are dropping more quickly than demographers expected in the 1970s and ’80s. But taking this projection as given, if CO 2 emissions per capita are 1.14 tC for the next 50 years, that would imply total global emissions of 10.6 billion tC by 2050. If emissions per capita range from 1.09 to 1.31 tC by
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.037 |
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".