Explainable Machine Learning-Based Assessment of Urban Climate Change Risks and Driving Mechanisms of Land-Use Characteristics in Ningbo

Coastal cities are highly sensitive and vulnerable to climate change risks. A scientifically grounded assessment of urban climate change risk and its driving mechanisms is essential for strengthening urban climate adaptation capacity and supporting sustainable development. Taking Ningbo as the study area, this paper constructs a risk assessment system comprising five categories of extreme climate indicators, namely heat, rainstorm, drought, humidity, and strong wind, based on the China Surface Climate Normals Dataset for 1981–2010 and meteorological observations from the National Centers for Environmental Information (NCEI) for 2015–2024. Using 30 m resolution land-use data for 2023, three land-use sensitivity indicators are extracted: the proportion of built-up land, the proportion of green space and forest land, and the proportion of water area. The CRITIC objective weighting method is then applied to construct an integrated climate change risk index and identify the spatial pattern of climate change risk in Ningbo. On this basis, the high-risk area identification performance of Logistic Regression, Random Forest, and XGBoost is compared. The optimal XGBoost model is selected and combined with the SHAP method to systematically reveal the direction, relative importance, and nonlinear threshold relationships through which land-use characteristics affect the formation of high-risk areas. The results show that urban climate change risk in Ningbo exhibits a pronounced spatial differentiation pattern, with higher risk in the northeastern coastal and central–eastern areas and lower risk in the western and southwestern areas. Insufficient green space and forest land buffering is the most important factor affecting the formation of high-risk areas. All three land-use variables have clear nonlinear thresholds. The critical turning points for identifying high-risk areas are 20.0% built-up land, 2.0% green space and forest land, and whether there is a water body or not. Significant interaction effects are observed among land-use variables, among which the interaction between built-up land and green space/forest land is the most prominent. These findings provide methodological support and empirical evidence for climate change risk assessment and climate-adaptive spatial planning regulation in coastal cities.