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Record W2553768023 · doi:10.1021/acs.est.6b02819

Updating the U.S. Life Cycle GHG Petroleum Baseline to 2014 with Projections to 2040 Using Open-Source Engineering-Based Models

2016· article· en· W2553768023 on OpenAlexaff
Gregory Cooney, Matthew Jamieson, Joe Marriott, Joule Bergerson, Adam R. Brandt, Timothy Skone

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of Calgary
FundersNational Energy Technology LaboratoryU.S. Department of Energy
KeywordsBaseline (sea)GasolineGreenhouse gasPetroleumEnvironmental scienceEnergy independenceWaste managementEnergy securityEngineeringChemistryRenewable energy

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The National Energy Technology Laboratory produced a well-to-wheels (WTW) life cycle greenhouse gas analysis of petroleum-based fuels consumed in the U.S. in 2005, known as the NETL 2005 Petroleum Baseline. This study uses a set of engineering-based, open-source models combined with publicly available data to calculate baseline results for 2014. An increase between the 2005 baseline and the 2014 results presented here (e.g., 92.4 vs 96.2 g CO 2 e/MJ gasoline, + 4.1%) are due to changes both in modeling platform and in the U.S. petroleum sector. An updated result for 2005 was calculated to minimize the effect of the change in modeling platform, and emissions for gasoline in 2014 were about 2% lower than in 2005 (98.1 vs 96.2 g CO 2 e/MJ gasoline). The same methods were utilized to forecast emissions from fuels out to 2040, indicating maximum changes from the 2014 gasoline result between +2.1% and −1.4%. The changing baseline values lead to potential compliance challenges with frameworks such as the Energy Independence and Security Act (EISA) Section 526, which states that Federal agencies should not purchase alternative fuels unless their life cycle GHG emissions are less than those of conventionally produced, petroleum-derived fuels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations45
Published2016
Admission routes1
Has abstractyes

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