Analysis of Sustainable Efficiency of Freight Transport in Major European Economies: An Integrated Multi-Region Input-Output and DEA Approach
Bibliographic record
Abstract
This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were combined into a sustainable efficiency rating of the freight transport sector of Germany, France, Italy, Spain, and the Netherlands. First, the freight transport sector's carbon footprint (CFP) was quantified using the MRIO model. The lifecycle-based CFP emissions of freight transport activities were assessed using a dataset from 2000 to 2018. Nineteen stochastic model-based MRIO lifecycle assessments were built for each country. Secondly, sixty instances of DEA models were created using a linear program for each mode in the selected countries. Thirdly, the sustainable efficiency scores were determined for each freight transport mode in each country over four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The results illustrate that the sustainable efficiency score of inland, water, and air transport modes ranged from 0.38 to 1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".