Assessing the cost feasibility of solar projects in Canada using the RETScreen Expert software
Bibliographic record
Abstract
The climate emergency is melting glaciers, destroying forests and increasing sea water levels worldwide. The burning of fossil fuels, current urbanization and transportation patterns, careless industrialization all release harmful greenhouse (GHG) gases to the atmosphere leading to the biggest issue faced by humanity: climate change. Countries around the world are addressing GHG emissions by transitioning to renewable energy sources. Energy experts calculate that offsetting 50% of all future demand growth in thermal electricity generation by solar photovoltaics (PVs) would reduce annual global carbon dioxide emissions by 10% in 20 years and 32% in 50 years. Installing more renewable energy projects worldwide to reduce GHG emissions is a way forward to avert the climate emergency. Countries like China, India and Germany on the one hand are the top greenhouse gas emitting countries in the world and on the other are becoming world leaders in installing solar projects, thereby reducing global installation costs and making solar power more financially and technically viable. By investing in research, development, and deployment those three countries are making solar PV systems better and extremely cost-effective for every other country. Empirical data clearly shows that today solar PV is now the cheapest source of electricity in the world and has reached grid parity in comparison to fossil fuel sources. Canada is amongst the top 10 countries in the world in terms of generating the most greenhouse gas emissions. In 2019, Canada emitted more than 1,982 MTCO2 emissions on this earth and is ranked eighth in the world in terms of emissions. As per the Paris Agreement, 2015, Canada had committed to reduce its GHG emissions by 30 per cent from 2005 levels by 2030. To achieve that ambitious goal, Canada needs to focus on policies and processes that would help reduce GHG emissions in the long run. But is Canada making sufficient efforts to reach their target and to change the policies that favor more renewable energy installations, especially solar energy? This study reveals that Canada has a history of scraping decisions and policies that were made to promote and benefit the solar industry. No incentive systems are active at present, and the government is not rolling out plans for subsidies to solar projects that would have helped the sector achieve low-cost projects and hence acted as a motivation for more installations. By analyzing an actual quote given by a company for a 10-kW solar project in Ontario and assessing the costs using the RETScreen Expert software, this study concludes that a solar project is not feasible in Ontario if there are no incentives or subsidies provided to offset the high costs. This is further backed up by Canada’s ranking at the international level as compared to the other countries in terms of total installed solar capacities and the cost of solar installations which shows that Canada has not been focusing on increasing solar installations that would have brought down the costs and \nmade the projects feasible, and hence would fail to achieve the 2030 targets as it is not creating positive a policy environment with yearly targets and aim to reduce the GHG emissions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".