How to slash GHG emissions in the freight sector? Policy insights from a technology adoption model of Canada
Bibliographic record
Abstract
The movement of goods through freight transportation accounts for approximately 6% of total Greenhouse Gas (GHG) emissions worldwide and 10% of Canada's emissions, yet the freight sector is rarely targeted by GHG abatement research and policy.To address this gap, I use a technology adoption model (CIMS-Freight) to explore the effectiveness of policies in achieving GHG reductions in land freight (trucking and rail), and to determine scenarios that achieve Canada's ambitious GHG reduction targets (i.e.80% by 2050 relative to 2005 levels).To account for uncertainty in model parameters, I incorporate a Monte Carlo Analysis in which I run 1000 iterations of each simulation.My modeling results indicate that current policies (i.e.fuel efficiency standards as well as the federally proposed carbon price and low-carbon fuel standard) will not achieve 2030 and 2050 GHG reduction targets -where freight emissions will continue to rise, albeit at a lower rate than a "no policy" scenario.I also simulate the effectiveness of several individual policies: fuel efficiency standards, a carbon tax, low-carbon fuel standard (LCFS), a zero-emissions vehicle (ZEV) mandate for truck and purchase subsidy.Even at their most stringent levels, no individual policy has a high probability (at least 67% of Monte Carlo iterations) of achieving 2030 or 2050 GHG reduction targets.Finally, I find that several policy combinations can have a high probability of achieving 2050 goals, in particular a stringent ZEV mandate for trucks complemented by a stringent LCFS.While other effective policies and policy combinations are possible, it is clear that Canada's present and proposed policies are not nearly stringent enough to reach its ambitious emissions reductions targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".