MétaCan
Menu
← Back to cohort
Record W3123541751

The cost of displacing fossil fuels: Some evidence from Texas

2017· preprint· en· W3123541751 on OpenAlexaboutno aff
Peter Hartley

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelElectricityExternalityElectricity generationWind powerNuclear powerNatural resource economicsMains electricityEnvironmental scienceQuarter (Canadian coin)Natural gasEconomicsEnvironmental economicsPower (physics)EngineeringWaste managementMicroeconomicsGeographyEcologyElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Although technological progress can alter the relative costs of different energy sources, fossil fuels inevitably will be displaced as depletion raises their costs and makes them uncompetitive. They may be displaced sooner if they are taxed to internalize negative externalities. Currently, wind generation or nuclear power, supplemented by bulk electricity storage, are the most feasible alternatives to fossil fuels for electricity generation. The ERCOT ISO in Texas provides a realistic model for examining the costs of replacing fossil fuels by wind generation and storage, and for comparing wind power with generation based on nuclear and storage. ERCOT is relatively isolated from neighboring grids, and wind power was almost a quarter of its total generating capacity at the end of 2016. Using the ERCOT example, we also discuss how the long-run configuration of the electricity supply system affects evolution away from a system dominated by natural gas.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.053
GPT teacher head0.352
Teacher spread0.299 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in Economics→Same topicGlobal Energy and Sustainability Research→French-language works237,207→