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Record W1946400665 · doi:10.1596/1813-9450-4868

Wind Power Development: Economics And Policies

2009· book· en· W1946400665 on OpenAlexaff
G. Cornelis van Kooten, Govinda R. Timilsina

Bibliographic record

VenueWorld Bank eBooks · 2009
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWind powerSubsidyBusinessNatural resource economicsRenewable energyNameplate capacityElectricityEnvironmental economicsEconomicsElectricity generationPower (physics)EngineeringMarket economyElectrical engineering

Abstract

fetched live from OpenAlex

This study reviews the prospects of wind power at the global level. Existing studies indicate that the earth's wind energy supply potential significantly exceeds global energy demand. Yet, only 1 percent of the global electricity demand is currently derived from wind power despite 40 percent annual growth in wind generating capacity over the past 25 years. More than 98 percent of total current wind power capacity is installed in the developed countries plus China and India. It has been estimated that wind power could supply 7 to 34 percent of global electricity needs by 2050. However, wind power faces a large number of technical, economic, financial, institutional, market, and other barriers. To overcome these barriers, many countries have employed various policy instruments, including capital subsidies, tax incentives, tradable energy certificates, feed-in tariffs, grid access guarantees and mandatory standards. Besides these policies, climate change mitigation initiatives resulting from the Kyoto Protocol (e.g., CO2-emission reduction targets in developed countries and the Clean Development Mechanism in developing countries) have played a significant role in promoting wind power.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.053
GPT teacher head0.222
Teacher spread0.169 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2009
Admission routes1
Has abstractyes

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