Emerging Nigerian Megacities and Sustainable Development: Case Study of Lagos and Abuja
Bibliographic record
Abstract
It is estimated that by 2020 half of Nigerians shall live in urban centres. Nigeria has scores of such centres, with many showing the tendency of becoming megacities in a no distant future. One of these cities, Lagos (already a megacity), shall by projection, have a population of 24 million persons by 2020. The other, Abuja, is one of the fastest growing cities on earth. Generally, the world is now known to parade far larger cities than history has ever recorded. Some of these cities are quite magnificent and glorious - London, Paris, Tokyo and New York. On the other hand, Lagos and Mumbai are classic examples of urban failures. What separate the two classes of cities are the approaches to the design of their urban fabrics and management of their ecology. Whereas the former cities have adopted robust sustainability principles in their architecture and urban design/regeneration as well as efficient urban management programmes, the latter appear to be partially or totally non-committal. This paper examines the evolving Nigerian mega cities and their potentials for sustainable survival, with particular reference to Lagos and Abuja, using indices of economic productivity, social equity and environmental concerns. The result shows that the two cities failed these sustainability tests. The cause is traceable to unsustainable architecture being practiced. About half of the total global energy consumption comes from buildings. Eco-design prescriptions of the architect would guarantee urban sustainability. Thus, this paper recommends a national green building code for Nigeria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".