Impact of Cap-and-Trade vs. Carbon Tax Policy on Vehicle Routing
Bibliographic record
Abstract
Transportation is the largest contributor to Ontario’s total GHG emissions, responsible for approximately one third of GHG emissions. GHG emissions in the transportation sector can be reduced through improvements in 1) vehicle engine and fuel efficiency 2) carbon content of the fuel; 3) using alternative modes of transportation; 4) system operations (e.g. assigning traffic in a way to ensure smoother traffic flow and educating drivers to drive more efficiently). This paper focuses on the last component for the case of fleet vehicle routing. Until recently, most vehicle routing problems (VRP) were solved to find routes that would generally minimize distance, or time. However, increasing concerns about the external costs of transportation (such as emissions) from governments and customers are forcing companies to consider “greening” their operations by considering their fleet GHG emissions and thus attempting to solve the routing problem with more complex objectives. In this paper, the green vehicle routing problem (GVRP), a relatively new extension of the VRP, will be utilized for this purpose. The problem will be formulated for a firm that is assumed to have rich information about the emissions costs through the use of simulated driving cycles obtained from previous research. The objective of this paper is to investigate the differences and assert their statistical meaningfulness when routing vehicles to optimize total distance, time and emissions. The paper compares the effects of carbon taxes and cap-and-trade, the most widely used policies in carbon pricing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".