Road network, CO<sub>2</sub> emissions, linked to sustainable development: A European analysis
Bibliographic record
Abstract
The issue of road transport and the environment is paradoxical. Road mobility provides substantial socioeconomic benefits, supporting the mobility demands of passengers and freight. On the other side, road transport activities are associated with negative environmental impacts. The transport sector is responsible for approximately one quarter of greenhouse gas emissions. Moreover, the transport sector accounted for 57% of global oil demand and 28% of total energy consumption. The main aim of this study is to investigate the relationship between investment in road infrastructure and CO 2 emissions, in the European countries, depending on the level of economic development of a country. The analysis was conducted in three separate groups: low-income, middle-income and high-income countries, according to their Gross Domestic Product (GDP). The latest IRF World Road Statistics (WRS) edition of 2022, covering data for the years 2015 to 2020, is the database which provide data for the analysis of the connection between the road networks and environmental consequences, expressed in specific indicators, in different countries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".