Probabilistic Forecasting of Urban Resilience Targets and Priority Setting Under Uncertainty: A Case Study of Ningbo, China

Abstract

Urban resilience is fundamental to sustainable city development, yet its governance faces two intertwined challenges: assessing whether resilience targets can be achieved under uncertainty represented by historical fluctuations and interdimensional covariance, and determining where limited resources should be prioritized. Taking Ningbo, China, as a case study, this paper constructs a four-dimensional resilience evaluation system based on 22 indicators from 2000 to 2023, covering economic and fiscal conditions, social well-being, ecological environment and resources, and spatial–infrastructural capacity. This paper develops a probabilistic forecasting framework that preserves the correlation structure among dimensions to assess the attainability of urban resilience targets (2024–2030) and combines obstacle-degree analysis with model-based diagnostic elasticity analysis to identify key weaknesses and priority directions for resilience enhancement. The results show that Ningbo’s comprehensive resilience has steadily improved from 0.291 in 2000 to 0.794 in 2023, while future uncertainty continues to expand. By 2030, the probabilities of achieving high-level resilience targets remain insufficient under existing development inertia alone. A dual “spatial–economic” bottleneck is identified. The study concludes that resilience governance should implement differentiated interventions based on the combined logic of “weakness severity–marginal driving capacity.” This framework can provide a methodology reference that has been locally calibrated for coastal cities with similar port economic dependencies and complex disaster exposures. However, the specific indicator rankings in Ningbo do not have direct replicability.