City of Saskatoon: Energy Efficiency Programs and the Split-Incentive Effect
Bibliographic record
Abstract
Canada’s commitment to the Paris Agreement includes achieving net-zero greenhouse gas (GHG) emissions by 2050, guided by legislation such as the Canadian Net-Zero Emissions Accountability Act (CNZEAA). This goal requires provinces to prioritize reducing emissions that contribute to climate change, particularly from burning fossil fuels, which negatively impact individuals’ health and well-being—especially people with pre-existing health conditions (IPCC, 2023). As a result, energy efficiency programs and initiatives, such as energy-efficient technologies and home retrofits, have become critical tools to lower community emissions and energy costs. These efforts align with multiple United Nations Sustainable Development Goals (SDGs), including Affordable and Clean Energy (Goal 7), Reduced Inequalities (Goal 10), and Climate Action (Goal 13) (United Nations, 2024). In Saskatoon, Saskatchewan—a region facing extreme winters and hot summers due to climate change, and heavily reliant on natural gas and coal for electricity—achieving net-zero emissions is essential. This study investigates energy efficiency programs offered in Saskatoon, including the Energy Assistance Program (EAP), the Home Energy Loan Program (HELP), and Canada Greener Affordable Homes (CGAH), along with the barriers to participation—particularly for low-income households facing energy poverty. It also examines the split-incentive effect, where landlords bear program upgrade costs while renters receive the benefits. Landlord participation in the available programs is therefore vital for renters to access these initiatives. These barriers perpetuate energy injustices, which are critically analyzed using the energy justice framework.
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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 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".