On the (un)successful deployment of renewable energies: territorial context matters. A conceptual framework and an empirical analysis of biogas projects
Bibliographic record
Abstract
Given the goal set by the French government to open 1000 biogas plants by 2020, we believe it is important to investigate the factors linked to the success or failure of anaerobic digestion projects, especially as the inherent challenges mean that there are barely 300 in operation today. We thus developed a conceptual framework to help us to study territorial energy transition projects which we applied to an empirical analysis of the biogas production process. We conducted a quantitative study (logit model with 91 anaerobic digestion projects) and a qualitative study (49 semi-structured interviews and 455 articles from the regional daily press) to identify and understand the processes that anaerobic digestion projects go through for a successful outcome or, conversely, to ultimately fail. Our findings indicate that projects may be abandoned or interrupted due to the presence of a group of objectors who are often wary of such projects and do not trust the project leaders. Lack of anticipation and early dialogue tend to inhibit success. Furthermore, social acceptance appears to be correlated with proximity to the biogas plants but not to the size of the digester. Finally, operating and/or investment subsidies are seen to have a positive and significant effect on a project’s success. In this study, we highlight the need to implement place-based policies rather than one-size-fits-all policies to develop renewable energy in specific regions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".