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Record W4405366955 · doi:10.1016/j.crsus.2024.100262

A clean energy Korea by 2035: Transitioning to 80% carbon-free electricity generation

2024· article· en· W4405366955 on OpenAlex

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

VenueCell Reports Sustainability · 2024
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsClean energyElectricity generationElectricityCarbon fibersEnvironmental scienceEnvironmental economicsEngineeringEnvironmental protectionComputer scienceEconomicsElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

South Korea relies on imported fossil fuels for over 60% of its electricity generation, making it vulnerable to energy security risks and fuel price volatility. This study analyzes pathways for South Korea to achieve an economically optimal clean electricity generation mix by 2035, using capacity expansion and production cost modeling. We find that transitioning to 80% clean electricity—comprising 50% renewables and 30% nuclear—alongside investments in storage and transmission is both economically desirable and operationally feasible. Expanding renewables can reduce dependence on imported natural gas and coal while dramatically reducing emissions. Declined clean energy costs can reduce electricity supply costs by 23%–40% compared with 2022. Hourly dispatch simulations indicate that South Korea's grid can integrate high levels of variable renewables without coal generation or new natural gas power plants. Realizing these benefits requires ambitious policies, a robust regulatory framework, and effective business models to spur renewable energy growth.

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 score0.517
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
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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