Decarbonization through modal shift using a synchromodal platform: A case study in the Great Lakes
Bibliographic record
Abstract
This paper offers an empirical study to explore the relationship between transportation modalities and environmental concerns, promoting the adoption of synchromodality as a strategic pathway to achieving sustainable freight transport. The study uses a synchromodal freight transportation platform to analyze the impact of carbon tax policy on modal shift and environmental sustainability. The synchromodal platform is based on an optimization model using Mixed Integer Linear Programming (MILP), incorporating carbon tax as a surrogate measure for environmental costs. A sensitivity analysis is conducted across four distinct scenarios in a case study in the Great Lakes region, focusing on the Canada-US transborder trade. The results of this study illustrate the considerable potential for increasing the utilization of more environmentally sustainable transportation modes in this region. While the addition of carbon tax entails increased total transportation costs for each unit of cargo, the synchromodal-enabled modal shift promises to mitigate transportation’s negative externalities, including congestion, environmental impacts, and noise pollution. The results also highlight the role of synchromodality as a catalyst for sustainable freight transport decisions in the context of a carbon-conscious world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".