Community Energy Planning in the Alexander Skutch Biological Corridor
Bibliographic record
Abstract
The purpose of this major paper is to explore the possibility of developing a community energy project in rural Costa Rica. Two case communities were selected in the Alexander Skutch Biological Corridor: Santa Elena and Quizarra. The paper assessed the current energy policy framework in Costa Rica, and determined whether community energy planning could be a viable option for the communities. An energy assessment of the communities was performed through qualitative and quantitative research methods. Various energy actors in Costa Rica were also interviewed in determining the future of distributed energy generation in the country. The paper used RETScreen as a tool to analyze the financial viability of a solar PV project in the communities. Following the policy and financial assessment, the paper identified the following barriers to the success of community energy in the ASCBC as: the lack of a supporting Feed in Tariff (FIT) policy and incentives for renewable energy development in the country, financial barriers such as limited access to funding and high interest rates on loans, and a lack of institutional support. The paper provides recommendations for advancing community energy in Cost Rica, and alternative methods for lowering electricity consumption, such as energy efficiency and demand management strategies. The paper contributes to an understanding of the energy policy framework in Costa Rica, and the role that distributed energy generation can play. It also provides insight into energy usage and the needs of the ASCBC communities, and highlights the importance of energy education and community engagement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".