Ecological Footprints and Lifestyle Archetypes: Exploring Dimensions of Consumption and the Transformation Needed to Achieve Urban Sustainability
Bibliographic record
Abstract
The global urban transition increasingly positions cities as important influencers in determining sustainability outcomes. Urban sustainability literature tends to focus on the built environment as a solution space for reducing energy and materials demand; however, equally important is the consumption characteristics of the people who occupy the city. While size of dwelling and motor vehicle ownership are partially influenced by urban form, they are also influenced by cultural and socio-economic characteristics. Dietary choices and purchases of consumable goods are almost entirely driven by the latter. Using international field data that document urban ways of living, I develop lifestyle archetypes coupled with ecological footprint analysis to develop consumption benchmarks in the domains of: food, buildings, consumables, transportation, and water that correspond to various levels of demand on nature’s services. I also explore the dimensions of transformation that would be needed in each of these domains for the per capita consumption patterns of urban dwellers to achieve ecological sustainability. The dimensions of transformation needed commensurate with ecological carrying capacity include: a 73% reduction in household energy use, a 96% reduction in motor vehicle ownership, a 78% reduction in per capita vehicle kilometres travelled, and a 79% reduction in air kilometres travelled.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".