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Record W2606734843 · doi:10.3390/w9040287

Hydropower Royalties: A Comparative Analysis of Major Producing Countries (China, Brazil, Canada and the United States)

2017· article· en· W2606734843 on OpenAlexafffundabout
Pierre‐Olivier Pineau, Lucile Tranchecoste, Yenny Vega Cárdenas

Bibliographic record

VenueWater · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversité de MontréalUniversité LavalHEC Montréal
FundersHEC Montréal
KeywordsHydropowerExternalityRenewable energyNatural resource economicsSustainabilityChinaSmall hydroElectricityBusinessOrder (exchange)Distribution (mathematics)Resource (disambiguation)Consumption (sociology)Sustainable developmentEnvironmental economicsProduction (economics)EconomicsGeographyEngineeringMicroeconomicsEcologyFinance

Abstract

fetched live from OpenAlex

Hydropower is the leading renewable source of electricity generation and a low emission energy source. In order to be developed sustainably, it is important that its costs and benefits are adequately set and distributed. Different mechanisms, such as royalties, can be used for this purpose. Governments have usually kept hydropower royalty rates low, without internalizing negative externalities. This strategy is inefficient because it leads to larger electricity production and consumption, and hence exacerbates environmental impacts. This paper reviews the criteria proposed and used to set hydropower royalties. It also compares practices of the four main hydropower producers in the world: China, Brazil, Canada and the United States. Results show that royalty rates and distribution policies are determined in an arbitrary and unsystematic manner, but also that water use is usually undervalued. In order to encourage the development of this key renewable resource, in a sustainable and responsible way, national and international efforts should be made to rationalize and harmonize hydropower royalty rates.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 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".

Quick stats

Citations21
Published2017
Admission routes3
Has abstractyes

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