How to slash greenhouse gas emissions in the freight sector: Policy insights from a technology-adoption model of Canada
Why this work is in the frame
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Bibliographic record
Abstract
Freight or goods-movement transportation accounts for 6% of global greenhouse gas (GHG) emissions and 10% of emissions in our case study of Canada – mostly from heavy-duty trucks. Little research has explored the types of policies needed to achieve 2050 GHG mitigation goals in the land freight sector, i.e., 80% reductions from 2005 levels. We use a behaviourally-realistic technology-adoption model (CIMS-Freight) to simulate the GHG impacts of several climate policies, individually and in combinations, on the land freight sector (trucking and rail). Results indicate that current policies in Canada (including standards and carbon pricing) will not achieve GHG reduction targets for this sector – in fact, emissions continue to rise. Further, no individual policy has a high probability of achieving 2030 or 2050 GHG targets, including more stringent versions of the carbon tax, fuel efficiency standards, low-carbon fuel standard (LCFS), or a zero-emissions vehicle (ZEV) mandate for trucks. Finally, we identify several policy combinations that 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, Canada's present and proposed policies are not stringent enough to reach deep GHG targets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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 it