Key Drivers of Green Logistics: A Systematic Literature Review and Conceptual Framework
Bibliographic record
Abstract
The logistics sector contributes significantly to global warming, primarily through Scope 3 emissions. Green logistics practices (GLPs) can mitigate emissions and improve sustainability performance; however, their adoption remains limited due to high upfront costs and organizational barriers. This study aims to identify the driving factors of green logistics (GL) by conducting a systematic literature review following the PRISMA protocol. A total of 95 peer-reviewed articles published between 2016 and 2024 are analyzed. The review combines bibliometric and content analysis and develops a conceptual framework to guide future research. Findings reveal two main categories of drivers: (i) internal drivers, most notably top management commitment, which influences strategic direction, resource allocation, and organizational change; and (ii) external drivers, particularly institutional pressures. The study also identifies methodological patterns and theoretical gaps and proposes a theory–methodology–context agenda for future research. Practically, it shows how organizational readiness and supportive public policies can help overcome barriers, facilitate adoption, and promote more sustainable logistics systems.
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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 | 0.044 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.070 | 0.052 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".