MétaCan
Menu
Back to cohort

Konflik sosial dan lingkungan di sektor energi terbarukan: Tinjauan pada skala global

2024· article· en· W4400334508 on OpenAlexaboutno aff
Hannysyah Roesdi, Rahmawati Lestari, Ratna Amini

Bibliographic record

VenueEnvironment Conflict. · 2024
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesEnvironmental sciencePhilosophy

Abstract

fetched live from OpenAlex

The increasing social and environmental issues and the depletion of natural resources have led many countries to shift from using non-renewable energy sources (fossil fuels) to renewable energy sources (solar energy, geothermal, etc.). This energy transition is aimed at sustainable practices. However, the process poses new social, economic, and environmental challenges that policymakers must address effectively. Economic support from the government is a key factor in the success of implementing this transition. Nevertheless, it is undeniable that the shift to renewable energy has generated social conflicts among stakeholders, such as debates over the construction of power plants that are perceived to have limited benefits for the communities around the plant area. The need for renewable energy arises as a solution to the insufficient electricity supply in developing countries, especially in rural areas. Various stakeholders offer solutions to overcome social challenges in the community during this transition. The objective of this literature review is to identify and analyze social and environmental issues arising from the transition from non-renewable to renewable energy sources. The results obtained will be presented descriptively, supplemented with matrices or tables. The articles used in this study draw examples from the implementation of renewable energy transitions in various countries such as Taiwan, Canada, Brazil, Poland, South Korea, Indonesia, and others. The challenges faced by each country in implementing renewable energy transitions exhibit similarities, and the solutions used to mitigate these challenges also share commonalities.

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)0.000
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: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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
GenreReview

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment Conflict.Same topicEnergy, Environment, and Transportation PoliciesFrench-language works237,207