MétaCan
Menu
Back to cohort
Record W2080739960 · doi:10.3390/en6115940

Time Series EROI for Canadian Oil and Gas

2013· article· en· W2080739960 on OpenAlexaboutno aff
Alexandre Poisson, Charles Cuthbert Hall

Bibliographic record

VenueEnergies · 2013
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
FundersDepartment for International DevelopmentEuropean Social FundState University of New YorkEdna Bailey Sussman Foundation
KeywordsOil sandsFossil fueltar (computing)Environmental scienceEnergy (signal processing)Unconventional oilFraction (chemistry)Energy resourcesEnergy flowReturn on investmentNatural resource economicsProduction (economics)Petroleum engineeringWaste managementEnvironmental protectionEconomicsChemistryEngineeringStatisticsGeographyMathematicsComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Modern economies are dependent on fossil energy, yet as conventional resources are depleted, an increasing fraction of that energy is coming from unconventional resources such as tar sands. These resources usually require more energy for extraction and upgrading, leaving a smaller fraction available to society, and at a higher cost. Here we present a calculation of the energy return on investment (EROI) for all Canadian oil and gas (including tar sands) over the period 1990–2008, and also for tar sands alone (1994–2008). We used energy production and energy use data from Statistics Canada’s Material and Energy Flow Accounts (MEFA). We were able to quantify both direct and indirect energy use, the latter from Statistics Canada’s energy input-output model. We found that since the mid-1990s, total energy used (invested) in the Canadian oil and gas sector increased about 63%, while the energy production (return) increased only 18%, resulting in a decrease in total EROI from roughly 16:1 to 11:1. We also found (although with less certainty) that the EROI for tar sands alone has fluctuated around 4:1 since 1994, with only a slight increasing trend. Finally, we analyzed underlying factors possibly influencing these trends.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations50
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnergiesSame topicGlobal Energy and Sustainability ResearchFrench-language works237,207