Analyzing Community Initiatives in UK’s Energy Transition through the Lens of Sustainable Entrepreneurship
Bibliographic record
Abstract
The low-carbon energy transition framed as a social-technical system can enable researchers to gain insight into the complex interaction between niche actors and the dominant regime under the current energy policy landscape. This paper aims to analyze community-led energy initiatives through the lens of sustainable entrepreneurship and discern business practices that these niche actors use in the social-technical setting of the energy transition. Niche actors such as Community Energy Cooperatives (CECs) develop bottom-up solutions and overcome social-cognitive norms through citizen engagement. Especially in the UK, such community initiatives face resistance from the dominant regime due to the unfavorable policies and centralized institutional arrangements. The business practices based on sustainable entrepreneurship can enable community groups to create social, economic, and environmental values for the local communities. In our analysis, we observed that CECs exhibit traits of a sustainable entrepreneur in their efforts to support energy transition. We discerned following business practices based on sustainable entrepreneurship that CECs employ in the UK: (1) mission-driven and locally focused, (2) commercial venturing and collaboration, and (3) grassroots innovations and shared knowledge. In this paper, we observed a strong connection between the CEC business practices and sustainable entrepreneurship that provides a foundation for future academic interests. Further, we noted that intermediary organizations, as part of the business ecosystem, play a crucial role in supporting the UK’s community energy sector.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".