Emissions from international transport in global supply chains
Bibliographic record
Abstract
Purpose This paper aims to highlight the importance and need to include carbon emissions from international transport in the sourcing decisions of corporate organizations and the calculation of national emissions inventories (NEIs). Design/methodology/approach The paper proposes a method of attributing emissions from international transportation in global supply chains and calculating their impact on the economic sustainability of corporate organizations through a carbon price. Findings An application of the original model developed in this paper showed that international transport emissions can have an important effect on NEIs. An example of the imports of manufactured items from China and Germany to the USA showed a 3 per cent increase in emissions from manufacturing activities in the USA. Research limitations/implications Introducing carbon pricing on international transport emissions is expected to motivate corporate leaders to include emissions from international transport as a factor in their sourcing decisions. Practical implications Inclusion of international transport emissions along with the imposition of a carbon tax are designed to act as disincentives to generating emissions from supply chain activities. It is argued that the implementation of the model may provide long-term benefits associated with reduced emissions and a level playing field to organizations which use efficient technologies in manufacturing. Social implications It is recognized that the implementation of a carbon tax on international transport emissions may face resistance from several stakeholders, including governments of exporting countries, corporations and customers, due to an increase in cost. Originality/value This paper provides an original method to include emissions from international transport in supply chain decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".