Translating planetary boundaries into city systems: Ecosystem services as metrics for safe and just production-consumption space
Bibliographic record
Abstract
Accelerating urbanization has positioned cities as epicenters of water-energy-food (WEF) demand growth, yet their resource consumption patterns persistently exceed planetary boundaries. Resolving this dual challenge requires fundamentally rethinking cities' capacity to operate within ecological supply limits while ensuring basic human needs. We implement a city-scale adaptation of the doughnut framework, constructing a safe and just space (SJS) assessment model that quantifies ecosystem services (ESs) as linkages between WEF demands and ecological boundaries. Our analysis of 336 Chinese cities reveals spatial disparities: 91 % of cities in China can operate within their SJS for water provisioning, while only 25 % maintain current carbon emissions below ecological ceilings. Additionally, 28 % of cities simultaneously fulfill the SJS requirements for phosphorus purification. Regarding land use, 69 % can operate within the SJS, but most cities are approaching or already at verge of the biophysical boundary. In general, only 13 % of cities can adequately meet social demands within the boundaries of all four ESs. Random forest algorithm further identified social variables (e.g., population) and economic variables (e.g., industrial growth and gross regional domestic product) as dominant drivers of SJS performance. These findings underscore the imperative for adaptive governance that coordinates ES management through the SJS-ESs-WEF nexus, offering actionable pathways to align urban development with ecosystem stability and social justice imperatives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| 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".