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Record W139631435

Wind Energy and Its Impact on Future Environmental Policy Planning: Powering Renewable Energy in Canada and Abroad

2006· article· en· W139631435 on OpenAlexaboutno aff
Kamaal Zaidi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyWind powerEnergy policyEnergy (signal processing)Environmental impact of the energy industryNatural resource economicsEnvironmental impact assessmentFeed-in tariffBusinessEnvironmental economicsEconomicsEngineeringPolitical science
DOInot available

Abstract

fetched live from OpenAlex

With the rising demand for energy from finite conventional sources such as coal and natural gas, the emphasis on modern environmental policy planning for renewable energy is rapidly gaining attention. In particular, wind energy projects that include wind turbine technology is helping drive this trend towards cheaper, cleaner, and more reliable forms of energy that provide electricity to consumers. This paper provides an introductory review of wind energy, outlining its history, technology, and current legislative frameworks adopted by various nations in harnessing renewable energy. This analysis includes a thorough discussion of Canada’s approach, but continues with renewable wind programs in the United States, nations within the European Union, Australia, China, India, and Japan. The paper also updates many of the recent developments in these nations, revealing the commonalities in approaching wind energy applications. Key issues related to wind energy include legislative frameworks adopted for renewable energy, financial incentives offered by governments to companies investing and maintaining renewable sources of energy like wind, interconnection of grid systems, the development of onshore and offshore wind farms, and market-based approaches that are contributing to reducing electricity prices in the energy sector. However, the author is careful to recognize how various challenges are experienced by legislators, industry officials, and consumers towards establishing a meaningful environmental policy of renewable wind energy.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.112
Threshold uncertainty score0.815

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0120.003
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.254
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

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
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

Citations1
Published2006
Admission routes1
Has abstractyes

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