Cost Benefit Framework for Cold Climate Microgrids
Bibliographic record
Abstract
For a quarter of a century, global energy policy has shifted electric utility investments away from fossil fuels toward renewable substitutes. Reducing greenhouse gas emissions is motivating improvements in the cost and efficiency of renewable energy technologies. Historically, the social and environmental values of communities were not considered in electric utility decision making in Canada. Today, community capacity building and reducing household costs are important social objectives for renewable energy integration in Canada’s northern, remote and Indigenous communities. This intersection of policy goals is encouraging the development of new decision-making tools for communities using cold climate microgrids and the utility companies who own and operate them. The purpose of this research is to understand, quantify, value and qualify the social and economic implications of alternative energy investments in remote, northern and Indigenous communities. This research adopts a case-study approach to describe the impacts of renewable energy integration, represented by a comprehensive suite of costs and benefits using cost benefit analysis. The goal of using cost benefit analysis as an economic method is to compare alternative renewable energy investments and evaluate them based on a measure of efficiency. The framework is applied using a spread sheet type model. The application includes an analysis of two scenarios (i) the baseline scenario, based on diesel generation compared to (ii) solar photovoltaic integration. The results show that social surplus in remote, northern and Indigenous communities can improve with renewable energy integration into cold climate microgrids. The findings also emphasize the enhanced effects of incorporating demand side management investments to improve economic efficiency. Moreover, renewable energy integration into cold climate microgrids has the potential to correct market failures by reducing information asymmetry and providing numerous positive externalities.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".