MétaCan
Menu
Back to cohort
Record W4384928922 · doi:10.53055/icimod.1022

Alternative renewable energy in Bhutan: Key findings and policy recommendations

2023· book· en· W4384928922 on OpenAlexfundno aff
Avishek Malla, Pugazenthi Dhananjayan

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersEuropean CommissionInternational Development Research CentreWorld Bank Group
KeywordsRenewable energyEnergy subsidiesFeed-in tariffHydropowerNatural resource economicsRenewable energy creditEnergy policyContext (archaeology)BusinessHydroelectricityEnvironmental impact of the energy industryRevenueElectricityWind powerEconomicsEngineeringGeographyFinance

Abstract

fetched live from OpenAlex

Biomass and hydropower are the principal sources of energy in Bhutan. In the future too, the latter is expected to play a significant role in meeting the country’s electricity needs and in earning export revenue. But, for transportation and other purposes, the country still has to import fuel. It is in this context and because of the country’s high dependence on hydroelectricity, as well as due to rising energy usage and falling prices of other renewable energy commodities that it has been deemed necessary to look into other renewable energy sources to diversify the country’s energy mix and economy. Thus, in a concerted manner, the Royal Government of Bhutan (RGoB) has been exploring avenues of alternative renewable energy (ARE) sources. For this, the Department of Renewable Energy (DRE), has been entrusted with the responsibility of developing sustainable energy channels and promoting renewable energy technologies (RETs). This report analyses the achievements in the field of renewable energy in Bhutan and outlines the findings and recommendations so that the country is able to scale up its renewable energy capabilities.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score1.000

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)0.0050.001

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicEnergy and Environment ImpactsFrench-language works237,207