A policy perspective on Nova Scotia's plans to reduce dependency on fossil fuels for electricity generation and improve air quality
Bibliographic record
Abstract
Installed capacity of renewable resources to generate electricity is increasing globally. The global share of renewables is expected to grow sharply in the next decade by replacing fossil fuel-fired power generating stations with hydropower, wind, and solar generation. This increasing trend will help reduce air pollution and greenhouse gas (GHG) emissions from fossil fuel combustion, and contribute to limit the global temperature increase by 1.5 °C. Nova Scotia, Canada, is committed to follow this trend by closing the remaining coal-fired power plants by 2030, although it still relies heavily on coal as its major fuel for electricity generation and failed to meet renewable electricity generation targets of 40% in 2020. Although Nova Scotia is still committed to meet a supply of 80% renewables in less than ten years, it is not clear how this will be achieved. This short review analyzes the provincial plan to reduce dependency on coal and provides an overview of recent developments in policies to reduce air emissions from fossil fuel combustion. Existing monitoring and reporting programs revealed that provincial air emission caps on the electricity sector resulted in a reduction of more than 50% emissions of nitrogen oxides and sulphur oxides from 2002 to 2020. These annual caps, which will be progressively reduced until 2025, have already proven to be an effective strategy to reduce harmful air emissions from power stations in the province. However, volatile organic compounds (VOCs) and particulate matter (PM), two harmful air pollutants also relevant to the electricity sector, are not yet regulated by provincial policy. This review recommends a revision in the monitoring and reporting programs and transition to renewables to reduce air pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".