The well-being of nations: an empirical assessment of sustainable urbanization for Europe
Bibliographic record
Abstract
The current integrity of the planet is being stressed beyond its biological capacity, and understanding urban landscapes is more important now than ever. A major landmark in human–planetary evolution was reached recently with a majority of people now living in cities, and rural-to-urban migration is predicted to continue into the next century. Landscape change associated with exponential population growth poses major challenges to coupled human and natural systems. Although some progress has been made, to date there exist no ‘ideal’ instrument for achieving sustainability on neither regional nor local scales. Because there is limited applied evidence investigating landscape form (e.g. configuration) and population dynamics (e.g. population density) with measures of sustainability, this research area requires further investigation. Using Human Wellbeing Index (HWI) and Ecosystem Wellbeing Index (EWI) from Robert Prescott-Allen’s The Wellbeing of Nations: A Country-by-Country Index of Quality of Life and the Environment, a macroscale empirical study was created to further understand sustainable urban development across 33 European countries. Exploratory spatial data analysis was utilized to illustrate Wellbeing clusters across the study area, and spatially enabled regression methods were employed to create regional sustainable urbanization models for explaining Wellbeing indices. With population density, two urban class configuration metrics (e.g. COHESION, PD) were found significant at explaining both HWI and EWI. Between 2000 and 2006, changes in urban morphology and population density were also assessed for 31 of the aforementioned 33 European countries. Findings suggest that conventional urbanization processes will continue to disconnect socioeconomic welfare from life-supporting ecosystem services.
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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 | 0.002 | 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 | 0.001 | 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".