Estimates of household net carbon costs in 2030-31: Ontario
Bibliographic record
Abstract
Global price pressures beset Canada’s economy just as unemployment was nearing record lows amid a strong recovery from the pandemic. Policymakers face the challenge of reining in inflation without causing a recession. Strong revenues have reduced fiscal deficits even as the federal government has extended living-cost relief and announced measures to make housing and childcare more affordable. But multi-year spending commitments will make it hard to sustain budget improvements without improved tax and spending efficiency. Moreover, for Canada to escape years of weak investment and tepid productivity growth, reforms to improve the business climate are overdue. The challenge is to lift living standards with minimal environmental impact. Canada aims to eliminate its net greenhouse gas emissions by 2050. Achieving this in a resource-intensive economy requires strong incentives to phase out fossil-fuel use and encourage energy saving. To spur decarbonisation, the federal climate strategy deploys a mix of emissions pricing, green technology support and regulations. The focus should turn now to improving mitigation tools so that they work better together while addressing remaining barriers to low-cost abatement. Canada’s federal and sub-national governments must align efforts on delivering efficient and fair measures to reduce emissions and prepare communities for climate change.SPECIAL FEATURE : CANADA’S TRANSITION TO NET-ZERO EMISSIONS
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".