Community-developer collaboration and voluntary community benefits in Scotland: Are community benefits a gift or compensation?
Bibliographic record
Abstract
Community benefits are a key strategy for promoting social acceptance of renewable energy and ensuring distributive fairness of the energy transition. However, they can sometimes damage rather than strengthen the relationship between communities and developers. This paper examines stakeholder submissions to The Scottish Government's Good Practice Principles on Community Benefits from Onshore Renewable Energy Developments (2019) consultation. Communities perceive community benefits as compensation and express concerns about their voluntary nature, while developers see them as benevolent gifts, worrying about their impact on project viability. As the guidelines are voluntary, community benefit arrangements rely on collaboration between developers and communities, but power imbalance and a lack of shared understanding can impede this collaboration. Inspired by governmentality literature, this study analyses stakeholder discourses to understand how issues are framed, solutions proposed, and rationalities guiding these discussions. Qualitative system dynamics modelling is used to provide a comprehensive view of challenges in the design of community benefit arrangements in Scotland, highlighting risks to the developer-community relationship and trust in the energy transition. The analysis suggests that making community benefit arrangements mandatory or rethinking them is essential to preserve this relationship and trust.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| 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".