Sustainable Transport Development Strategy in Developed and Developing Countries
Bibliographic record
Abstract
This study aims to explore the idea of sustainable transportation in the United States, China, Canada, and South Korea. Sustainable transportation has an essential role in developing a sustainable city that pays attention to an effectiveness-oriented transportation system that impacts the economy, the environment, and the quality of social life. The selection of case studies in four countries motivated the top four countries from the keywords sustainable transportation. This study uses a bibliometric analysis method using data sources from 306 articles (Scopus). The data search was carried out using the keyword "sustainable transportation" from 2012-to 2022. The highest number of research trends in the United States is 166 articles; China has 102 pieces, Canada has 46 papers, and South Korea has 26 articles. The data analysis stage was carried out using the Vos Viewer and Nvivo 12 Plus software. The results show that each country has a different focus measured from three aspects: planning, information, and investment. Planning factors include types of transportation, routes, costs, carbon emissions, and applications. The information aspect consists of estimation, trip, and performance. The investment aspect includes current demands and issues to shape future policies. Development strategy Sustainable Transportation in the planning stage only focuses on the use of vehicle emissions. In contrast, in the information aspect, it focuses on travel modes, then in the investment aspect, there is no attention to future policies related to issues that occur today. In the planning part of Sustainable Transportation, China has a varied focus, such as the type of transportation used, emissions, and the route used for transportation. In contrast, the Chinese state has not paid attention to this focus on the information and investment aspects. Meanwhile, Canada and South Korea have not focused on planning, information, and investment aspects. From these findings, it is hoped that it can provide input for various countries to pay more attention to these aspects to achieve sustainable transportation in smart cities. The concept of sustainable transportation is also helpful for achieving SDG's 11th goal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".