Marginal abatement costs for GHG emissions in Canada: a shadow cost approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study approximates the marginal abatement costs (MACs) of reducing GHG emissions in Canada using the shadow cost approach. Utilizing industry level data, we are the first to offer Canadian estimates based on a Hyperbolic Output Distance Function (HODF) and the stochastic frontier estimation. Accounting for GHG emissions caused by energy consumption, we obtain an average shadow MAC of $130/t across 30 industries. In the GHG-intensive industries such as the electric utilities and non-conventional oil extraction, MACs are lower than the CO 2 levy of $50/t imposed by the federal government. Since these low-MACs sectors account for about 98 per cent of total GHG emissions and 94 per cent of total energy use in industries studied, the envisaged $50/t carbon levy could notionally result in a significant GHG abatement in Canada. Graphical abstract
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it