The trade-off between costs and carbon emissions from economic lot-sizing decisions
Bibliographic record
Abstract
Logistics decisions can have a significant impact on carbon emissions, a driver of global warming. We consider emissions reductions from better utilization of a given fleet of vehicles. We study an Economic Lot-Sizing setting in which a decision-maker determines the amount to be shipped in each period, and in which demand can fluctuate. Our paper assesses the trade-off between costs and carbon emissions. The emission parameters are based on a survey of results from empirical studies and on real-life considerations. In order to model the trade-off, we introduce a bi-objective lot-sizing model to find the Pareto optimal solutions with respect to costs and emissions. Our experiments show that it is often costly to reduce carbon emissions from the cost optimal solution, compared to carbon prices in the market. The cases in which carbon emissions can be reduced most cost-efficiently are those in which carbon emissions are large relative to costs, typically because costs are the results of past investments and can be considered sunk.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".