Peer-to-peer financing mechanisms to accelerate renewable energy deployment
Bibliographic record
Abstract
Despite the clear need to reduce greenhouse gas emissions, lack of access to capital and appropriate financing mechanisms has limited the deployment of renewable energy technologies (RETs). Feed-in Tariff (FIT) programs have been used successfully in many countries to make RETs more economically feasible. Unfortunately, the large capital costs of RETs can result in both the slow uptake of FIT programs and incomplete capture of deployment potential. Subsidies are concentrated in financial institutions rather than the greater population as traditional bank loans are required to fund RET projects. This paper critically analyzes and considers the political, financial and logistical risks of an innovative peer-to-peer financing mechanism. This mechanism has the goal of increasing RET deployment capacity under a FIT program in an effort to equitably distribute both the environmental and economic advantages throughout the entire population. Using the Ontario FIT program as a case study, this article illustrates how the guaranteed income stream from a solar photovoltaic system can be modeled as an investment and how peer-to-peer lending mechanisms can then be used to provide capital for the initial costs. The requirements for and limitations of these types of funding mechanisms for RETs are quantified and discussed and future work to deploy this methodology is described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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, 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".