Distributional justice and rapid green energy transitions: citizen experiences in Kenya
Bibliographic record
Abstract
Abstract A global movement promotes and supports the concept of just energy transitions. Advocates endorse the combined goals of universal, affordable access to electricity, and the use of clean, renewable sources of energy. In the Global South and particularly Africa, international organizations are promoting these rapid green energy transitions with financing and encouraging private sector investment and innovation. What remains unclear is how rapid green energy transitions are experienced by citizens, especially the poor in the Global South. Are the transitions just or equitably shared across populations? Kenya is an important country for assessing this question. Kenya is expected to achieve SDG 7, ‘sustainable energy access for all’, by 2030, one of the few African countries to reach this milestone. Kenya’s achievements, however, mask significant tensions surrounding its rapid energy transition. This paper reveals a mismatch between national and global narratives about access to electricity compared to local-level citizen experiences. The paper argues that the current transition aims for but is not achieving distributional justice. While there are many valuable lessons to take from Kenya’s experience, there are also significant concerns as the poor are carrying a heavy burden of the transition. Our analysis is based on focus groups, qualitative interviews, and survey data. The paper concludes by reflecting on the lessons from this critical case about the relationship between the global promotion of just energy transitions and African citizen needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".