Last-Mile Logistics under E-commerce Growth: Efficiency and Emissions
Bibliographic record
Abstract
Background: E-commerce growth has intensified last-mile logistics, a stage often described as the final leg from a distribution hub to the customer and frequently cited as a disproportionate share of total delivery cost. This review evaluates operational strategies that improve efficiency while reducing emissions, framing the problem as a coupled cost–carbon optimization. Methods: We synthesize work on last-mile delivery, consolidation, micro-fulfillment, electrification, and cargo-bike deployment, and propose a decision framework linking service promises to routing and fleet choices. Results: Interventions that combine consolidation with low-emission modes can reduce emissions without sacrificing service, but they require urban infrastructure and governance of delivery windows and pickup options. Conclusions: Sustainable last-mile performance is achieved through network design (where to stock), operational control (how to route), and demand shaping (how customers choose speed and delivery format).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".