A framework for reducing greenhouse gas (GHG) emissions through carbon pricing for offshore outsourcing
Bibliographic record
Abstract
Reducing supply chain costs is a primary concern of every organization. Organizations have implemented offshore outsourcing as an effective strategy towards reducing supply chain costs. However, neither government nor corporate organizations have sufficiently taken into account the effects of this strategy on global greenhouse gas (GHG) emissions. The purpose of this research is to analyze the impact of offshore outsourcing on global GHG emissions, and the effect of changes in fuel prices in addition to a carbon price on additional emissions on supply chain costs. The purpose is supported by five key objectives. The objectives are addressed through a systematic methodology. The analysis is supported by a literature review, and the development and testing of mathematical models. Finally, a framework to reduce global GHG emissions through a carbon price on differential emissions from manufacturing and additional emissions from international transportation is proposed. The findings suggest that offshore outsourcing has increased global emissions. The fuel prices are increasing at a rate higher than the overall rate. A carbon price on excess emissions due to outsourcing coupled with increasing fuel prices impacts supply chain costs adversely and leads to bigger lot-sizes. As an illustration at the national level, the framework showed that emissions for the USA increased by about 20% for every year between 2007 and 2010. As another example from a corporate organization, the net profit (profit after tax) for Wal-Mart was reduced by about 19% for 2006 due to a carbon price on manufacturing emissions alone. The suggested framework is a major contribution for quantifying the extent of changes in the emissions due to offshore outsourcing and the value of these emissions at a prevailing rate of carbon tax in North America. It is intended to provide a basis for reducing emissions and costs from global supply chains. The proposed framework provides a level playing field to manufacturers in different countries using different technologies, provides incentives to organizations for manufacturing in locations where net emissions are low, helps national governments determine how they can generate revenue for dealing with emissions, and, most importantly, aids in reducing overall global GHG emissions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".