Evaluating the efficiency of renewable energy policy tools to incentivize deployment of renewable energy in Alberta
Bibliographic record
Abstract
This report analyzes the efficiency of renewable energy policy as a tool for Albertan policymakers to accelerate the decarbonization of the province’s electricity sector. Alberta’s electricity system is the most emissions intensive in Canada. Albertans consume only 11% of total electricity generated in Canada but produce 52% of electricity associated emissions. Increasing renewable energy deployment in Alberta could significantly reduce CO2 emissions in the sector and assist in the global fight against climate change. Policy tools have been used in provinces outside Alberta and in international peer countries to drastically increase the deployment of renewable energy. Proponents of renewable energy policy argue that similar outcomes could be achieved in Alberta. Albertan policymakers have implemented some programs to incentivize renewables development in the past, but more action will be needed to achieve deep decarbonization of the electricity system. Four policy tools are commonly used in attempts to increase the deployment of renewable energy technologies: carbon pricing, investments in intertie capacity, loan guarantees, and feed-in tariffs (FITs)1. These policies address the main barriers to renewables deployment, which are intermittency of variable renewable power generation, lack of financing opportunities, and competition with conventional fossil fuels. This paper evaluates case studies in each of the four policy tools from the Canadian provinces and international comparators to evaluate their ability to increase renewables deployment. The policy tools are then evaluated in the Alberta context to consider regional implications in their implementation. If the stated policy options are capable of efficiently incenting renewables deployment abroad, policymakers may wish to consider implementing or expanding those policy options here in Alberta.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".