Sustainable community planning : the business case to address declining transport-related quality of life in the Kuwait urban area
Bibliographic record
Abstract
Current urban transport systems are based primarily on automotive transport powered by fossil fuels, and generate complex social, economic, and environmental impacts that result in lowered Quality of Life that inevitably diminishes over time and is not sustainable. The current community planning principles are a superior method to use in considering alternate models and understanding the complexity of cities and their relation to land use and transportation networks. This research utilizes proper Social Cost-Benefit Analysis in all its economic models. It found that the Light Rail Transit (LRT) alternative, powered by hydrogen fuel cell vehicle technology, offers more significant potential to deliver comprehensive benefits to Kuwait society and government than either the Bus Rapid Transit system or the Business-As-Usual alternatives. The results demonstrate a cost-benefit ratio of 83, with Net Present Value (NPV) of $ 674 billion. The sensitivity analysis shows that air pollution savings is the most sensitive parameter in the analysis, followed by GHG savings. Thus, the more improvements are made to air quality factors, the more profitable the LRT project will be. It is also emphasizes that avoiding the long term adverse economic effects when considering climate change and sustainability is a must in transportation projects. The research survey indicates that the majority of Kuwaitis (both citizens and residents) prefer not to drive if given alternatives, are open to using different transit modes, and demand more walkable and bikeable communities. Participants indicated ongoing issues with accessibility to work, shopping, and recreation destinations. This research argued that a substantial obstacle to transportation and public transportation reform in Kuwait is not the harsh weather or the challenge of encouraging behavioral changes, which are commonly cited, but are rather the limited transport sector’s structure and a weak planning process, which require immediate, substantial reform. This research outlines different policies and planning reforms that can preserve economic productivity and diversity, and improve communal equity in transportation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| 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".