Rapid rise in urban sprawl: Global hotspots and trends since 1990
Bibliographic record
Abstract
Dispersed low-density development–“urban sprawl”–has many detrimental environmental, economic, and social consequences. Sprawl leads to higher greenhouse-gas emissions and poses an increasing threat to the long-term availability of many vital ecosystem services. Therefore, urban sprawl is in stark contradiction to the principles of sustainable land use and to the need for a sustainability transformation. This study presents the degree of urban sprawl on the planet at multiple spatial scales (continents, UN regions, countries, subnational units, and a regular grid) for the period 1990–2014. Urban sprawl increased by 95% in 24 years, almost 4% per year, with built-up areas growing by almost 28 km 2 per day, or 1.16 km 2 per hour. The results demonstrate that Europe has been the most sprawled and also the most rapidly sprawling continent, by 51% since 1990. At the scale of UN regions, the highest relative increases in urban sprawl were observed in East Asia, Western Africa, and Southeast Asia. Urban sprawl per capita has been highest in Oceania and North America, exhibiting a minor decline since 1990, while it has been increasing rapidly in Europe, by almost 47% since 1990. The study revealed a strong relationship between urban sprawl and the level of human development as measured by the Human Development Index ( HDI ). The results suggest that it will be important for a more sustainable future to find a better balance between a high quality of life and using land more sparingly. There is an urgent need to stop urban sprawl, since current regulations and measures in developed countries are apparently not effective at limiting it. Monitoring urban sprawl can serve to guide policy development such as the implementation of targets and limits and to evaluate the effectiveness of urban growth management strategies at mitigating urban sprawl.
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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 | 0.001 |
| 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".