Contribution to the assessment of the sustainability of urban freight transport in Morocco: A PLS-SEM Approach
Bibliographic record
Abstract
With globalization and the expansion of cities, the movement of goods and people has significantly increased, both economically and socially. This sector serves as a vital component of any economy, fostering economic and social development. However, its detrimental effects pose significant challenges for countries seeking to pursue sustainable development policies. Being one of the most energy-intensive sectors, it emits greenhouse gases and pollutants, contributing to environmental degradation and noise pollution. While increased traffic and mobility offer benefits, they also strain resources and lead to higher energy consumption. The primary objective of this article is to assess the sustainability of freight transport in the Moroccan city of Fez and propose supportive solutions for various stakeholders in urban logistics. This involves examining the complex relationship between different factors and the sustainability of Urban Freight Transport (UFT), including accessibility, congestion, road occupancy, environmental impacts, health impacts, and road safety. The research data were collected from 100 managers and employees of logistics and transportation companies in Morocco. Structural equation modeling was utilized to test and confirm the hypotheses and the research model. The results of these analyses demonstrated a positive impact relationship between the various factors and the sustainability variable. Subsequently, we suggest the establishment of delivery areas and an urban distribution center as two sustainable logistics solutions. The analysis and its findings can be applied to any other city.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".