Unconstrained Extraction of Fossil Fuels and Implication for Carbon Budgets under Climate Change Scenarios
Bibliographic record
Abstract
Hubbert's curve was first introduced to project future oil reserves and production in the US.In this paper, Hubbert's logistic function was used to estimate future production of fossil fuels in different regions of the world.The aim is to adequately fit historical data with minimum error, calculate the projected CO2 emissions that emerge from the unconstrained extraction of coal, oil and natural gas, and hence to determine the consumption of the available carbon budget.For some of the world regions considered, Hubbert's logistic function fits the data well, while others fail to fall under the bell-shaped curve due to factors not considered in the analysis, such as political decisions to restrict production.An overshoot of the carbon budget to limit global warming to 1.5 o C is expected by 2050 in the case of unconstrained production of all fuels, with major contributors being Asia & Pacific regions for coal, the Middle East for oil, and North America for natural gas.In the case of a 2 o C global warming scenario, the same major contributors again consume the available budget by 2040 except for natural gas production that stays below the threshold.This analysis emphasizes the importance of capturing and storing carbon dioxide emissions, and/or artificial limits on fossil fuel production to prevent dangerous climate change.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".