How can financial incentives promote local ownership of onshore wind and solar projects? Case study evidence from Germany, Denmark, the UK and Ontario
Bibliographic record
Abstract
Mobilising citizens as investors in local solar photovoltaic and onshore wind energy projects can help meet climate objectives, generate local development opportunities, and build social support for low carbon transition. This can be achieved through the introduction of financial incentives attractive to local actors. To investigate what types of financial incentives are effective at the feasibility, development, construction, and operation stages of project development, we undertake a comparative case study of their use in Denmark; Germany; the UK; and Ontario, Canada. We find that a requirement for incentives such as grants and soft loans at the feasibility and development stages is a distinguishing feature of projects with citizen involvement, reflecting their greater risk aversion, lack of technical experience and financial capacity, and their inability to balance risk across a portfolio of projects. At later project stages, market-independent supports (feed in tariffs, grants, and tax incentives) have been effective in mobilising investment, but market-based supports (feed in premiums and quota schemes) can also be tailored to the specific needs of local community actors. These findings add a new dimension to the growing academic and policy debate about how Governments can effectively mobilise investment from local communities and citizens in distributed renewable technologies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".