NEOM and the New Paradigm of Urban Development: Challenges, Innovations, and Global Implications for Sustainable Futures
Bibliographic record
Abstract
This article investigates NEOM, Saudi Arabia's flagship megaproject, as an ex nihilo smart city built entirely from scratch rather than adapted from existing urban fabrics. Drawing on five expert interviews, it examines NEOM's implementation dynamics, areas of innovation, and potential contribution to contemporary urban development. The analysis highlights two core dimensions shaping the project: the integration of frontier technologies, such as AI-enabled management, advanced desalination, and renewable-to-hydrogen systems, and the environmental challenges of designing large-scale infrastructure within desert and coastal ecosystems. At the same time, NEOM demonstrates advances in desalination efficiency, green hydrogen integration, adaptive construction, and predictive digital systems that may inform sustainable planning in arid environments. Whether fully realised or partially implemented, these developments provide insights for policymakers and planners seeking to design resource-efficient and climate-resilient cities. NEOM is best understood not only as a Saudi state-led venture within Vision 2030 but as a reference point for examining the opportunities and limits of ex nihilo smart urbanism. Its trajectory offers valuable perspectives for urban development in the Arab world, where rapid growth, environmental pressures, and innovation agendas converge. By combining empirical evidence with comparative cases, the article bridges aspirational narratives and grounded analysis, situating NEOM within broader debates on sustainable urban futures.
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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 | 0.002 |
| 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".