Exploring the Intersection of Science and Policy: The Case Study of Installing Solar Panels and Energy Storage System at the University of Ottawa
Bibliographic record
Abstract
Buildings account for up to a third of total world greenhouse gas GHG emissions, and this pattern is expected to persist. By 2050, cities will be home to 70 % of the world's population, demanding a significant number of buildings to be constructed. Efforts to reduce these emissions in the past had varied performance. However, several examples indicate that well thought and adequately executed mix of building technology coupled with environmental policies may reduce emissions. Therefore, cities worldwide are joining the race to decarbonize their buildings to become net-zero carbon and support green economies through a diversified bundle of policies. However, designing and selecting the appropriate mix of building technology and environmental policies is challenging to generate the most outlast net-zero carbon impacts. This research aims to uncover the intersection between science and policy's role in achieving a global net-zero energy building sector. First, an urban comparative analysis for ten environment-leading cities has been made to understand the latest progress in the building sector and draw on future recommendations. The findings are thematically grouped into five themes a) Building's energy efficiency (energy demand sector). (b) Electrified renewable grids (energy supply sector). (c) Green fiscal incentives (d) Education and capacity building. (e) Governance and collaboration. Second, the University of Ottawa has been utilized as a part of the campus as a living lab initiative to examine installing photovoltaic panels over the campus buildings as part of the university expansion program to achieve net-zero operations by 2040. The following parameters have been considered to address the PV systems viability, 1) the expected electricity output. 2) the initial and operational costs. 3) the GHG reductions in operational energy. 4) the PV system embodied carbons. RETScreen Expert software has been used to perform the Life Cycle Cost Analysis (LCCA) to assess PV system output and financial viability. One Click-LCA software to carry-out Life Cycle Assessment (LCA) to assess embodied carbons. The results indicate from analyzing 31 buildings that 20% - 107% of electricity can be offset depending on each building's energy use and solar collector area. Additionally, the 31 buildings analyzed for electricity generation collectively have the potential to save around 23% of the total campus electricity consumption with a production capacity of 18 million units (kWh) annually, including 21,108 solar panels. Also, the project shows financial viability only if the PV systems are installed as part of the whole campus with a Net Present Value (NPV) of $4,985,89 and an Internal Rate of Return (IRR) of 11.4%. The analysis shows 24% and 18% maximum sensitivity to increased initial cost and decreased electricity generation/rate. Finally, the GHG estimated reductions over 25 years from generated electricity are 14,445 tCO2, and the estimated increased embodied carbons from the Life Cycle Assessment are set to be 1,023 tCO2. Additionally, drawing upon urban analysis and the case study, the research highlights the dynamic nature of the building sector emissions reduction and city initiatives. Thirdly, a detailed analysis was carried out in the System Advisor Model (SAM) software to integrate the solar system with energy storage in the Advanced Research Complex (ARC) Building at the University of Ottawa. The study assesses the system viability and helps the university to reduce its monthly electricity bill and help Ontario to maintain its grid reliability by keeping the electricity demand low at peak times. The findings show that using an integrated solar system with an energy storage system by mitigating 100%, 90%, 75%, and 50% of the building electricity demand during the Ontario gird peak could lead to a Net Present Value of $2,01, $1.70, $1.30, and $0.864 million over 25 years the lifetime of the project through the Ontario Global Adjustment Program. The study also shows that with the absence of the Ontario Global Adjustment Program as a fiscal reform tool and relying only on the time of use electricity rates, the solar panels with an energy storage system could lead to a negative Net Present Value of $-550 thousand.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".