Urban flourish or peril: Navigating the safe and just space through the lens of ecosystem services in China’s urbanization
Bibliographic record
Abstract
Urbanization develops with the goal of establishing improved and more sustainable habitats for residents. Environmental and social performance must be simultaneously monitored to ascertain whether regions are progressing towards or deviating from the safe and just space (SJS) in urbanization. Despite relevant studies, the absence of indicators that bridge ecological preservation and human well-beings renders dual monitoring challenging. This study bridged the gap by exploring the interactions between urbanization, ecosystem services (ESs), and basic water, energy, and food (WEF) needs within the SJS framework across China and its provinces. By quantifying the minimum and actual demands for freshwater withdrawal, carbon emissions, phosphorus emissions, and land use, as well as the supply of ESs into unified biophysical indicators, we found that: (1) China can meet the basic WEF needs for all from 2000 to 2020, but only water and land provisioning ESs can operate within the SJS. Carbon emissions surpassed the sequestration capacity in 2010, while phosphorus purification ES has consistently been unsafe. (2) The SJS performance in terms of ecological and social fulfilment exhibited scale differences and undergone changes with urbanization. Overall, no province in China can consistently operate within all SJSs. (3) In the process of urbanization, improvements in ecological protection and production practices in most provinces expanded the size of SJS, but the continuous increase in total demand failed to steer regions toward safer spaces. Our framework emphasized the common but differentiated pathways that regions at varying stages of urbanization navigate to achieve safety and justice. It also provides an applicable solution for regions aiming to pursue urban growth while maintaining ecological conservation and social justice, ultimately achieving sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".