The Role and Potential of Freight Transportation Sourcing in Achieving Transport Sustainability
Bibliographic record
Abstract
Climate change is a serious global environmental threat that is a direct consequence of elevated greenhouse gas (GHG) concentrations in the atmosphere. Transportation, and road transportation in particular, relies heavily on the consumption of fossil fuels, and contributes to rising levels of GHG emissions. Freight shippers have a great influence on the GHG emissions through their transportation sourcing and supplier management practices. When shippers utilize sustainability in their truck carrier selection decisions, and reward and educate in their supplier relationships, carriers have an incentive to be greener. Insights on transportation sourcing and supplier management practices affecting sustainability were developed from an online survey of Canadian shippers completed in early 2014. The survey identified the popularity of weak versus strong sustainable carrier sourcing practices. The authors find that carrier sourcing and carrier supplier management are some of the least utilized “best practice” of shippers but this strategy has high potential to reduce GHG emissions. An analysis of the motivations and barriers for adopting more sustainable carrier sourcing practices indicates that the motivation of firms to include sustainability in their sourcing decision is increasing. The major barriers to incorporating sustainability in carrier sourcing are profit impact and effective GHG footprint measurement but third party carbon measurement programs such as SmartWay and those offered by third party logistics suppliers are reducing these barriers. These findings are useful to shippers seeking to incorporate sustainability in their sourcing decisions, to carriers who seek to differentiate their product in the “green” dimension and the public sector, which can influence the motivations and barriers.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".