Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways
Bibliographic record
Abstract
The impacts of climate change (CC) are intensifying and becoming more widespread. Greenhouse gas emissions (GHGs) significantly contribute to CC and are primarily generated by transportation—a dominant segment of supply chains. City logistics is responsible for a significant portion of GHGs, as conventional vehicles are the primary mode of transportation in logistical operations. Nonetheless, city logistics is vital for urban areas’ economy and quality of life. Therefore, decarbonizing city logistics (DCL) is crucial to promote green cities and sustainable urban living and mitigate the impacts of CC. However, sustainability encompasses the environment, economy, society, and culture, collectively called the quadruple bottom line (QBL) pillars of sustainability. This research uses the QBL approach to review the extant literature on DCL. We searched for articles on SCOPUS, focusing on analytical scholarly studies published in the past two decades. By analyzing publication years, journals, countries, and keyword occurrences, we present an overview of the current state of DCL research. Additionally, we examine the methods and proposals outlined in the reviewed articles, along with the QBL aspects they address. Finally, we discuss the evolution of DCL research and provide directions for future research. The results indicate that optimization is the predominant solution approach among the analytical papers in the DCL literature. Our analysis reveals a lack of consideration for the cultural aspect of QBL, which is essential for the applicability of any proposed solution. We also note the integration of innovative solutions, such as crowdsourcing, electric and hydrogen vehicles, and drones in city logistics, indicating a promising research area that can contribute to developing sustainable cities and mitigating CC.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".