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Record W4230349045 · doi:10.21203/rs.3.rs-380763/v3

Coal phase out and renewable electricity expansion under Paris targets

2021· preprint· en· W4230349045 on OpenAlex
Steve Pye, Isabela Butnar, Shivika Mittal, Sara Giarola, Adam Hawkes, Agnes Beltramo, Will Usher, Maarten Brinkerink, Paul Deane, Liliana Benitez, Taco Niet, Abhishek Shivakumar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsSimon Fraser University
FundersForeign, Commonwealth and Development OfficeGovernment of the United Kingdom
KeywordsCoalRenewable energyNuclear decommissioningElectricityElectricity generationClimate changeNatural resource economicsInterconnectionEnergy transitionEnvironmental economicsBusinessEnvironmental sciencePower (physics)EconomicsEngineeringTelecommunicationsWaste managementGeology

Abstract

fetched live from OpenAlex

Abstract This paper provides insights into the implications of a global coal phaseout in the power generation sector across different regions under international climate targets. Using modelled scenarios, we highlight the strong rate of decline in new build and decommissioning of existing coal assets, and the growth in renewable capacity to not only fill the gap but meet growing demand for electricity. We find that key uncertainties exist across many of these pathways towards system decarbonisation. We also explore how regionally connected grids can help in transitioning away from coal whilst ensuring supply of electricity. Focusing on potential interconnection between India and the Gulf region, we find that this can be an important strategy for helping enable the transition based on increasing renewables, with regional interconnection encouraging coal phase-out even without a specific coal phase-out target. The technical modelling in this report requires further consideration, in respect of the other multiple benefits of a transition towards low carbon generation, but also the policy mechanisms that will enable delivery of what is a hugely challenging transition.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score1.000

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 integrity0.0010.002
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 head0.040
GPT teacher head0.343
Teacher spread0.303 · 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