Investments for Carbon Footprint Reduction: An Instructional Case <sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This instructional case explores strategic decision‐making at CarCompany (CarCo), a large automaker that is transitioning to battery electric vehicles. CarCo aims to reduce the carbon dioxide (CO 2 ) emissions at their own sites and at suppliers' sites. The challenge is to select a group of CO 2 reduction initiatives that achieves the objectives set by CarCo's management while staying within budgetary restrictions. Another challenge is renegotiating some CO 2 reduction initiatives with suppliers so that greater overall CO 2 reductions can be achieved. Finally, decentralization of objectives and budgets for CO 2 reductions needs to be considered. CarCo's Sustainability Manager, Julia Neumann, has been asked to select from the list of proposed projects and present her recommendations to CarCo's board. In this case, students need to apply rigorous analyses on the basis of cost‐benefit analysis, justify their decisions, and think outside the box. They are asked to assess how existing evaluation techniques can be used and adapted to CO 2 emissions. The case delves into the complexities of the transition to cleaner production processes and the expanding network of suppliers, employees, and other stakeholders that need to be involved in the process.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".