Navigating the safe and just operating space for urban systems: A cross-scale landscape approach

Abstract

Safe and just operating spaces (SJOS) are influenced by complex cross-scale interactions and cascading effects spanning global, regional, and local landscape scales. However, existing SJOS research has often focused on single-scale assessments, overlooking the impacts of multiscale interactions and within-region heterogeneity on urban SJOS. To address this gap, we developed a cross-scale framework for assessing urban SJOS, explicitly incorporating top-down influences from upper-level constraints and bottom-up effects from lower-level heterogeneity. This approach was applied to China’s five major metropolises to examine the states and cross-scale dynamics influencing urban SJOS between 1990 and 2020. Our findings reveal that the SJOS of China’s metropolises were primarily influenced by factors at national and local landscape scales, with weaker influences from the global and continental scales. A persistent trade-off between social justice and environmental safety was identified across spatiotemporal scales. For instance, Chongqing in southwestern China lagged behind the eastern four metropolises in social performance but exhibited stronger environmental safety due to its extensive natural landscapes, which mitigated the anthropogenic impacts of urban centers. Regional issues, such as the overshoot of PM_2.5 and ecological footprints (EF), were primarily driven by the bottom-up accumulation of localized pressures, while the overshoot of CO₂ was attributed to national policy constraints and the universal exceedance of safe thresholds across scales. Addressing urban sustainability requires avoiding adverse cascading effects from other levels by emphasizing landscape heterogeneity within metropolises and fostering coordinated collaboration across scales, particularly at the regional landscape and national levels.

Keywords

Safe and just operating space / Cross-scale interaction / Landscape approach / Metropolises

CRediT authorship contribution statement

Hongyan Bian: Writing – original draft, Visualization, Methodology, Data curation, Conceptualization. Jianguo Wu: Writing – review & editing, Supervision. Runxi Jia: Visualization, Methodology, Data curation. Linyong Wang: Data curation. Zihan Zhu: Data curation. Mengyu Wei: Data curation. Jie Gao: Formal analysis.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 42101296), the Natural Science Foundation of Chongqing (Grant No. CSTB2023NSCQ-MSX0041), and Chongqing Municipal Training Program of Innovation and Entrepreneurship Project (Grants No. S202410635155 and X202410635116).

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