Synergizing urban and mobility governance: Insights from Dubai and Lahore
Bibliographic record
Abstract
Urban centers in the Middle East and North Africa (MENA) and South Asian (SA) regions face challenges like urban sprawl, low-density development, and heavy investment in road infrastructure, worsened by fragmented governance and political instability. This study adopts a qualitative approach, utilizing semi-structured interviews with decision-makers in Dubai and Lahore, to identify and analyze key factors influencing urban management and mobility governance. Grounded theory is applied to compare the distinct governance challenges, policy priorities, and strategic planning approaches of the two cities. Key findings show that that Dubai immensely benefits from effective decision-making, a clear strategic vision, long-term transport planning, and structured program implementation. In contrast, Lahore grapples with overlapping roles among multiple planning agencies, delays in Master Plan approvals, and weak implementation frameworks, all of which contribute to uncontrolled horizontal expansion and a road-centric development model. This paper advances the urban governance literature by proposing a conceptual governance framework and a maturity model offering actionable insights for developing cities striving for more sustainable and equitable urban mobility. Lessons from Dubai's Urban Governance: • Dubai's exemplary decision-making processes, strategic transport planning, and successful program implementation can be a model for urban governance excellence. • Actionable insights from Dubai's experience offer valuable lessons for decision-makers, planners and policymakers globally to enhance urban governance frameworks. Governance Challenges in Lahore: • The governance challenges in Lahore focus on issues such as political instability, overlapping institutional roles, delayed decision-making, and unclear policy priorities. • Potential obstacles in urban and mobility governance, as highlighted by Lahore's experiences, provide a cautionary tale for other emerging cities aiming to improve their governance models. Framework for Sustainable Urban Ecosystems: • A conceptual governance framework and maturity model are suggested based on insights from Dubai and Lahore, aimed at fostering efficient, sustainable, and inclusive urban and mobility ecosystems. • A practical roadmap is introduced for cities to implement integrated governance approaches, highlighting the importance of sustainability, inclusivity, and strategic planning in urban development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".