Energy communities in social sciences: A bibliometric analysis and systematic literature review
Bibliographic record
Abstract
Research on energy communities moves from being a residual until 2014 to one of the key issues for the energy policies in 2025. To date, no systematic literature review on energy communities has comprehensively examined both the periods before and after the 2018–2019 EU Directives, while also avoiding a narrow focus on specific objectives and instead considering the entire research field. The present article is built upon a twenty-year dataset spanning the period from 2002 to 2022 collected from the Scopus database. We explore key topics and schools of thought, trend themes, international collaborations, and applied methodologies, with particular focus on the evolution of the field and the economic impacts. The study's dataset contains 813 papers from 273 journals, conference proceedings, and books. A descriptive analysis of the most influential journals and authors in the field is performed at the beginning followed by more advanced bibliometric methods. “Bibliometrix” package for R statistical software is used as a tool. Building on results of the bibliometric investigation, a systematic literature review is conducted manually focusing on titles, keywords, and abstracts. The field initially focused on UK case studies using sociological theories for replication, while Continental Europe emphasized top-down factors like policies and institutions fostering EC growth. Organizational and policy aspects dominated research, shifting toward innovative technology integration into communities with the advent of EU Directives. Outside Europe, the USA, Australia, Canada, Brazil, India, and China lead contributions. Future trends likely include further scrutinization of ‘energy commons’ at a time of increase of community models, policy comparisons and technology integration. Legal aspects and interactions with electricity markets remain underexplored.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.045 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.281 | 0.243 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".