The Impact of a Cost Rebate on the Economics of Solar Power in Canada
Bibliographic record
Abstract
Incentives have been implemented in countries like Canada, the United States of America, and China, among others, to stimulate the deployment of solar power. Electricity from solar energy is one source of renewable energy that can reduce greenhouse gas emissions. Achieving a widespread adoption of solar projects depends on both the current subsidy for solar power and determining the ideal geographic location for solar insolation. This paper analyzes the impact of various systems cost incentives on the economics of residential solar projects in Western Canada. Eighteen cities across three provinces were analyzed for both photovoltaic and concentrated photovoltaic projects with start dates of 2016, 2018, and 2020. Additionally, this research looked at the impact of optimizing a PV system and how this impacts generated revenue. The internal rate of return (IRR) and discounted net present value (NPV) were calculated for each city under various cost incentive scenarios. The internal rate of return was then mapped for a visual estimate of the most economically viable location in which to install a solar system. This research finds that the internal rate of return for PV and CPV projects increases over time and shows the extent to which the application of additional systems cost incentives impacts this economic measurement. In order to make solar projects profitable to homeowners in Western Canada (IRR > 7%) the government needs to subsidize systems costs by 30%. The results also show that the value of the discount rate used has an impact on the net present value over time, and that the net present value generally increases over time, with the application of additional cost incentives. Moreover, the findings recommend deferring a solar project until the year 2020 where systems costs will decline and the price of electricity will rise, making solar projects an attractive investment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".