Carbon Footprint Analysis of the Freight Transport Sector Using a Multi-Region Input–Output Model (MRIO) from 2000 to 2014: Evidence from Industrial Countries
Bibliographic record
Abstract
Freight transportation performs a critical role in the supply networks of the global economy and is heavily influenced by the activities of the industrial and manufacturing sectors, contributing significantly to their global carbon footprint (CFP). This research evaluates the lifecycle-based CFP emissions of freight transport activities in seven selected countries (China, Japan, the United States, Canada, Brazil, Great Britain, and Germany) over fifteen years, considering international trade linkages with the rest of the world. In the literature, most researchers have investigated the CFP of the transportation sector in general or analyzed the CFP of two or three countries, such as the USA and China. However, this research is novel in that it examines the CFP of the freight transport sectors of the seven biggest industrial countries. In addition, a positive relationship was found between the CFP and the gross domestic product (GDP), population, level of urbanization, and area of these countries. Therefore, this study investigates the relationship between global CFP, GDP, population, level of urbanization, and country area. A total of 15 stochastic model-based multi-regional input–output lifecycle assessments were built for each country, comprising 35 key industries. Statistical modeling tools were used to assess carbon emissions. The results show that China is the largest contributor to the freight-related CFP, while the U.S. is the second largest. The manufacture of coke and refined petroleum products represents the dominant sector. In contrast, warehousing and support activities have the most significant contributions in Germany and Great Britain. Land transport and transport via pipelines contribute the most to Canada’s CFP. The results of the regression analysis show that there is a positive relationship between the investigated variables.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".