Bibliographic record
Abstract
The Hubbert-Deffeyes “peak oil” (HDPO) model predicts that world oil production is about to enter a period of sustained decline. This paper investigates the empirical robustness of this claim. I use out-of-sample methods to test whether the HDPO model is capable of estimating ultimately recoverable reserves. HDPO model estimates of ultimately recoverable reserves, based on data available 30 years or more in the past, are found to be less than current observed cumulative production and discoveries. This result is robust to different specifications of the HDPO model, to applications to production and discoveries data, and to various levels of geographical aggregation. These problems stem from an attempt by the HDPO model to force a linear relationship onto data which are inherently nonlinear. This characteristic of the data is present in a wide variety of natural resources. I also show that the HDPO model is incapable of distinguishing between processes for which cumulative production is truly finite and processes for which cumulative production is unbounded. These findings undermine claims that the HDPO model is capable of yielding meaningful measures of ultimately recoverable reserves or of predicting when world oil production might peak.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".