How Does Energy Transition Impact Vulnerable Populations? A Review of Challenges, Determinants, and Solutions for a Just Transition
Bibliographic record
Abstract
The concepts of energy justice, energy poverty, and energy equity have gained considerable recognition over the past decade. It is now understood that energy transition processes and activities, despite evident benefits, can lead to injustices. However, these downsides often remain unaddressed. This article aims to fix this shortcoming by reviewing how the energy transition can impact vulnerable populations. While it is important to recognize that the transition can positively impact vulnerable groups, this article focuses on the negative impacts to identify vulnerabilities. In addition to providing a historical overview, we present the state of the art on energy justice and vulnerability before describing different populations that are vulnerable to the energy transition. We then list measures to alleviate the negative impacts and help achieve a just transition. The article concludes with a discussion of the current knowledge gaps on the topic of a just transition for vulnerable populations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".