Energy justice in Indonesia energy transition: case study of Batang Toru hydropower
Bibliographic record
Abstract
Abstract With an emphasis on the underlying power dynamics in the sustainable energy transition, this study critically evaluates how SDG 7 is being implemented in lower-income countries. Twenty online news stories (2020–2024) from Mongabay and Tempo were examined using discourse analysis with NVivo 12 Plus, combined with metadata from literature that was indexed by Scopus. The aim was to assess whether this transition upholds the principles of inclusivity and environmental justice, especially for local communities. Findings from the Batang Toru hydropower project in Indonesia show significant injustices in terms of distribution, procedures, and recognition, as well as ecological degradation and social tensions. While SDG 7 promotes clean energy, its implementation can reproduce inequality and environmental damage. Future research should investigate the interests of the actors shaping these narratives and further explore the socio-environmental harms experienced by affected communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".