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Record W2918555642 · doi:10.2118/0319-0062-jpt

An Actionable Path for Oil and Gas in the Fight Against Climate Change

2019· article· en· W2918555642 on OpenAlexaff
Nansen G. Saleri, Christine Ehlig‐Economides, Howard J. Herzog

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsImpact
Fundersnot available
KeywordsUpstream (networking)Greenhouse gasClimate changeNatural resource economicsFossil fuelDownstream (manufacturing)PremisePetroleum industrySine qua nonBusinessEconomicsEnvironmental sciencePolitical scienceEngineeringMarketingLaw

Abstract

fetched live from OpenAlex

Special Section: The Value and Future of Petroleum Engineering Global climate concerns, amplified in the public consciousness by a steady stream of violent weather events such as hurricanes and California wildfires, are generating a new set of realities for the energy industry. The oil and gas upstream sector, accounting for approximately 60% of current world energy needs, faces existential threats to its market share—where inaction and/or insistence on marginal improvisations on past practices do not offer constructive and, ultimately, impactful solutions that the industry is most capable of delivering. Central to the issues at hand are questions that demand unambiguous answers: What should be ambitious yet achievable goals for the upstream industry over the short and long term (e.g., by the year 2050) and what specific programs in the spirit of an Apollo project for oil and gas should be envisioned? The often-cited argument that upstream companies are “extractors and not emitters,” and thus its responsibility in climate matters confined only to the extraction process, is shortsighted and dilutes steps that could be taken to maintain the industry’s leading role and capacity in providing the world’s energy supplies. Net GHG Emissions As a basic premise, it is the net emissions of all greenhouse gases (GHG), not just CO2, that drive climate change. Hence, the upstream industry’s overriding goal should be reduction and eventual elimination of net GHG emissions. Here the key operative words are “net GHG emissions,” a distinction worth highlighting. This opens up numerous GHG management options, including CO2 capture and storage (CCS), utilization, and removal (CDR) pathways such as afforestation, reforestation, and bio-energy with CCS. This diverse portfolio enhances the ability of both market forces and new technologies to produce evergreen solutions for reducing net GHG emissions. Equally flawed as the “upstream are only extractors” notion is the idea that the oil and gas industry should be accepting a carbon-free world energy model fueled 100% by renewable energy sources. While renewables are an important part of the solution in addressing climate change, they are nowhere nearly capable of replacing what oil and gas offers in support of the modern lifestyle. Substantive life-style sacrifices, however, are unlikely at a global scale and so should not constitute the underlying assumption for an ecofriendly energy future. As a further tenet for clean energy, electric vehicles, power grids (currently 85% fueled by fossil and nuclear), and battery manufacturing plants should also be judged on net emission standards. There are no silver bullets in the fight against climate change. We need every bullet in our arsenal. Eliminating certain solution pathways, such as nuclear or fossil fuels, just makes a difficult task much more difficult and expensive. By the same token, the prospect of oil and gas playing an active role will only enhance the odds of achieving the ultimate goal—to have a positive, substantive impact on climate change.

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)0.001
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 score0.832
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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