MétaCan
Menu
Back to cohort
Record W4242655642 · doi:10.2523/98480-ms

Corporate Preparation for Carbon Markets

2006· article· en· W4242655642 on OpenAlexaboutno aff
Andrew Mingst, Arthur Lee, John W. Cain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasChevron (anatomy)Upstream (networking)Downstream (manufacturing)Variety (cybernetics)CorporationBusinessEuropean unionEmissions tradingClimate changePetroleum industryIndustrial organizationNatural resource economicsInternational tradeEnvironmental scienceFinanceEngineeringEconomicsMarketingComputer scienceEnvironmental engineeringTelecommunications

Abstract

fetched live from OpenAlex

In response to concerns about global climate change, the European Union (EU) has imposed mandatory constraints on carbon dioxide emissions from thousands of industrial facilities across Europe, including several of Chevron's upstream and downstream oil and gas operations in the United Kingdom (UK) and the Netherlands. Driven by these regulations – and the possibility of additional requirements in countries such as Canada and Japan by 2008 – carbon emissions and emissions reductions now have significant economic value in emerging global markets.Companies are responding to these markets in a wide variety of ways depending, in part, on their perceived exposure. This paper broadly reviews the key business functions of Chevron's Carbon Markets team and describes the application of tools used to support these functions. Taken together, the functions help to support the broader range of actions that the corporation is undertaking to cost-effectively manage greenhouse gas emissions.

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)0.004
metaresearch head score (Gemma)0.018
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0350.011

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 head0.111
GPT teacher head0.252
Teacher spread0.141 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicClimate Change Policy and EconomicsFrench-language works237,207