Accounting for Natural Capital in Productivity of the Mining and Oil and Gas Sector
Bibliographic record
Abstract
This paper presents a growth accounting framework in which subsoil mineral and energy resources are recognized as natural capital input into the production process. It is the first study of its kind in Canada. Firstly, the income attributable to subsoil resources, or resource rent, is estimated as a surplus value after all extraction costs and normal returns on produced capital have been accounted for. The value of a resource reserve is then estimated as the present value of the future resource rents generated from the efficient extraction of the reserve. Lastly, with extraction as the observed service flows of natural capital, multifactor productivity (MFP) growth and the other sources of economic growth can be reassessed by updating the income shares of all inputs, and then, by estimating the contribution to growth coming from changes in the value of natural capital input. This framework is then applied to the Canadian oil and gas extraction sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".