Evaluating CO2 Emissions Associated With International Outsourcing in Manufacturing Supply Chains
Bibliographic record
Abstract
International outsourcing is a growing phenomenon in manufacturing industries due to the limited resources and the economic benefits of this phenomenon. Although international outsourcing seems to be a cost efficient way of production, the concerns about its CO2 emissions are rising dramatically. In this research, the impacts of international outsourcing on the CO2 emissions of all industries have been quantified. Input output analysis and linear programming have been implemented by programming in the Matlab as the research methodology. Australian manufacturing industry (outsourcer) and Chinese manufacturing industry (outsourcee) and their main suppliers that emit high CO2 levels were selected as a case study. The results of this study depict that international outsourcing of Australian manufacturing industry will reduce not only the CO2 emissions of that industry but also the CO2 emissions of the other domestic industries in Australia. In contrast, this international outsourcing will increase the CO2 emissions of both China and the other countries’ industries. This will lead to the growth of global CO2 emissions. In the worst case scenario, if the Australian manufacturing industry shuts down all its production, China and other countries CO2 emissions will increase 4.88% and 0.05% respectively. On this occasion, global CO2 emissions will increase by 0.82%. This paper presents a decision support system that will be useful for policy makers to evaluate the effect of different international outsourcing scenarios on CO2 emissions before making any real outsourcing decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".