Prefiguring energy futures: Hybrid energy initiatives and just transitions in fossil fuel regions
Bibliographic record
Abstract
Energy transition, as both a material process and a process of reimagining energy futures, offers fertile grounds for broad societal transformation. However, the current state of power and politics in the historical fossil fuel regions of North America presents unique challenges. This paper explores initiatives that leverage former fossil fuels sites, infrastructure, and labor for renewable energy projects, and examines their position in prefiguring alternative energy futures in fossil fuel regions. These initiatives, which we introduce as hybrid energy initiatives (HEIs), can alleviate material, political, and cultural barriers to energy transitions by accounting for present contexts in regions of historical fossil fuel extraction, developing partnerships between renewable energy advocates and traditional fossil fuel stakeholders, and building legitimacy through discourses of equity and justice. However, discourses and technologies do not guarantee the operationalization of the just transition narratives HEIs often draw upon. We illustrate this in two case studies of initiatives, one in Appalachia, USA, and the other in Alberta, Canada, that position themselves as innovative endeavors in the utilization of former fossil fuel sites and infrastructures for new solar energy projects. Contributing to just transition scholarship we demonstrate an approach for considering the prefiguring potential of energy innovations and how elements of energy justice can be rendered acceptable within a political climate unfavorable to climate and just transition policies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".