A Framework for Reducing Global Manufacturing Emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article establishes outsourcing as a cause of increase in global emissions, presents an original framework to account for greenhouse gas emissions from outsourcing, and recommends a carbon tax on differential emissions between importing and exporting countries. The impact of the carbon tax is shown through examples illustrating the potential financial implications for nations (i.e., the United States, Germany, and China) and an organization heavily engaged in sourcing from energy inefficient nations (i.e., Walmart). The framework provides a basis for developing government policy and assisting corporations in choosing environmentally friendly destinations. The motivation for the research is that current emissions accounting practices are based on places of generation, which provides an incomplete accounting of a nation’s emissions inventories and does not provide organizations with insight into the environmental impacts of their offshore operations. The proposed model is transparent, scalable, and relatively simple to implement. The model can provide a basis for improved national policies that will encourage corporations to choose energy-efficient destinations for offshore outsourcing and to help reduce global greenhouse gas emissions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it