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Record W3013069196

Future CO2 Emissions and Climate Change from Existing Energy Infrastructure

2010· article· en· W3013069196 on OpenAlexaff
Steven J. Davis, Ken Caldeira, H. Damon Matthews

Bibliographic record

VenueeScholarship (California Digital Library) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsClimate changeGlobal warmingEnvironmental scienceGreenhouse gasFossil fuelRadiative forcingNatural resource economicsMeteorologyEngineeringGeographyGeologyOceanographyWaste managementEconomics
DOInot available

Abstract

fetched live from OpenAlex

Slowing climate change requires overcoming inertia in political, technological, and geophysical systems. Of these, only geophysical warming commitment has been quantified. We estimated the commitment to future emissions and warming represented by existing carbon dioxide-emitting devices. We calculated cumulative future emissions of 496 (282 to 701 in lower- and upper-bounding scenarios) gigatonnes of CO2 from combustion of fossil fuels by existing infrastructure between 2010 and 2060, forcing mean warming of 1.3 degrees C (1.1 degrees to 1.4 degrees C) above the pre-industrial era and atmospheric concentrations of CO2 less than 430 parts per million. Because these conditions would likely avoid many key impacts of climate change, we conclude that sources of the most threatening emissions have yet to be built. However, CO2-emitting infrastructure will expand unless extraordinary efforts are undertaken to develop alternatives.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 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

Citations60
Published2010
Admission routes1
Has abstractyes

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