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Record W3124818481

Accounting for Natural Capital in Productivity of the Mining and Oil and Gas Sector

2015· article· en· W3124818481 on OpenAlexaboutno aff
Weimin Wang, Patrick Adams

Bibliographic record

VenueAnalytical Studies Branch Research Paper Series · 2015
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resourceNatural capitalEconomic rentEconomicsNatural resource economicsProduction (economics)Capital (architecture)ProductivityGrowth accountingAccounting methodResource (disambiguation)Natural gasMicroeconomicsTotal factor productivityMacroeconomicsAccountingGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.092
GPT teacher head0.365
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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