Tackling the social acceptance in deep geothermal projects: best practices and lessons learned
Bibliographic record
Abstract
Although geothermal energy is a valuable resource, the development of deep geothermal projects often encounters social resistance, which can hinder or even damage the sector nationally. Local community acceptance is crucial and influenced by factors such as low awareness of the technology, associated risks, and potential benefits. This paper draws from a review conducted within the Horizon Europe COMPASS project (www.compass-geothermal.eu), which examined case studies from 14 countries (9 in Europe) to identify strategies for improving social acceptance and avoiding the NIMBY effect.Data sources included literature, internal project partner information, and EU-funded project reports. Key findings stress the importance of understanding the local socio-economic context and conducting preliminary studies before launching communication efforts. Effective communication must be ongoing, transparent, bidirectional, and tailored to diverse audiences. Tools like newspapers, social media, websites, and site visits can support this effort.The study highlights that communication, community engagement, and clearly stated local benefits are essential to gaining acceptance. If any of these elements are poorly addressed, projects risk being opposed. While no universal solution exists, the research offers recommendations to support citizen involvement in decision-making and the need to adapt strategies throughout the project’s duration.
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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 | 0.057 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".