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Record W4307237985 · doi:10.26443/glsars.v2i1.187

Study of Legal Adaptation in China's Wind Power Development

2022· article· en· W4307237985 on OpenAlex
Tian Li

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 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".

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

VenueMcGill GLSA Research Series · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRenewable energyWind powerChinaElectricityElectricity generationElectricity marketBusinessEnergy developmentEnvironmental economicsEnergy lawNameplate capacityEconomicsMarket economyPower (physics)EngineeringLawElectrical engineeringEnvironmental lawPolitical science

Abstract

fetched live from OpenAlex

Legal adaptation is considered a crucial part and one of the most effective tools of the global energy transition. The energy transition process promotes the rise of the renewable energy industry and brings a tremendous challenge to the law. How could or should the law adapt to the challenge of this global trend? This article will start this study from the case of Chinese wind energy development.China is one of the fastest-growing countries in the world for renewable energy. Although the large-scale development of wind energy started in 2000, China's wind power installed capacity reached 300 million kilowatts by 2021, and power generation accounted for about 7% of the total electricity consumption . This year's installed capacity of coal power is approximately 1 billion kilowatts, but its power generation accounted for 71.27% of the whole country . Two energy sources with three times the difference in installed capacity have ten times the difference in power generation. Why is China's electricity market so biased towards traditional energy? How did the large-scale wind curtailment in China occur? And what role should the law play in China's energy transition game to adapt and regulate the development of the electricity market and guide China's energy transition?This paper will use game theory to analyze China's power pricing system and the operation of China's national electricity transmission grid, so as to explore how the law has and should adapt to China's renewable energy development under its unique power market system and power administrative management system, to minimize the rent-seeking behavior generated in power transmission and support the development of wind energy. This paper will propose solutions to the problem of wind curtailment in China's energy transition from a legal adaptation perspective and provide a reference for other countries.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.285
Teacher spread0.249 · 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