Toward a Sustainable Future: An Exploratory Approach to the Dynamics of Europe’s Urban Morphology and Sustainability
Bibliographic record
Abstract
As cities continue to expand, and more of humanity moved from rural setting into urbanized areas, there is an important need to understand the impacts of how we urbanize on the environment. Knowledge on how to urbanize sustainably can ensure that we ae managing our global footprint on the environment while still progressing as a society. The research takes a multi-step approach to understand the dynamics of urbanization across Europe and its impact on environmental sustainability. 36 metrics that comprised the analysis of the urban landscape configuration and 8 metrics used to analyse the urban land composition of countries across Europe were tested against the human development index (HDI) and ecological footprint (EF) to understand the relationships between urban morphology and sustainability. Data was retrieved across four sets of years, 2000, 2006, 2012 and 2018 to ensure correlations were consistent and to understand if there were changes over time. The study first explored the bivariate relationships between the landscape metrics and sustainability indicators. After this, spatial relationships were explored through a Global Moran’s I test at both global and local levels to reveal spatial autocorrelation among any variables. Finally, key metrics identified were used in two multivariate analysis. Ordinary Least Squares (OLS) was first computed to create global models and to find an optimized model for each year and were computed separately for each indicator. Conditional Autoregressive (CAR) models were then computed on the most optimized models to reduce bias of spatial autocorrelation among the variables and to validate against the OLS models. Finally, a temporal exploration of key variables was conducted to see the trends of variables over time. The results showed that there are important relationships among urban morphology and its impact on both socio-economic and environmental sustainability.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".