Dining Liu

Abstract A resilient transport network, which is significant for urban sustainability and security, is characterized by its ability to recover from disruptions subject to natural and man-made disasters. Bike sharing could act as a viable alternative in the case of public transit disruptions given its flexibility and various social, environmental, and economic benefits. This study aims to estimate quantitatively the potential of bike sharing to promote transport resilience, by using autoregressive negative binomial time series model to investigate the effects of public transit closures on bike sharing demand in Washington, D.C. area during 2015–2017. We find that (1) bike sharing can act as a supplementary mode to enhance urban transport resilience in the case of complete transit closure; (2) the proximity of bike sharing docks to metro stations has a powerful effect on propensity to use a bike sharing program; and (3) extreme weather is one of major barriers to bicycling. Planners can enhance resilience of urban transport networks by fully considering the capacity and usage of bike sharing docks, as well as the coherence of metro stations and bike sharing docks, in distributing and rebalancing activities.

This study explores the relationship between intercity supply chain networks and urban carbon emission intensity (CEI) in China. While most research investigates the environmental implications of geographical proximity among city clusters, the environmental effects of functional intercity relationships remain insufficiently explored. This study examines how the network topology of economic relations among cities mediates CEI, analysing 29,580 firm-level supplier-buyer transactions from listed companies (2008–2017), aggregated at the prefecture-city level. Using data from the CSMAR database, we construct directed, weighted intercity networks and calculate key network indicators (in-degree, out-degree, closeness, betweenness, eigenvector centrality, and clustering coefficient). These network indicators are integrated into an XGBoost model, with SHapley Additive exPlanations (SHAP) employed to measure the non-linear effects of city positionality on CEI. Results reveal scale-free intercity supply chain networks dominated by key hubs such as Beijing, Shanghai, and Shenzhen. Cities with higher in-degree, out-degree, and closeness centrality in the networks exhibit lower CEI. Moreover, cities with initially low in-degree centrality may exhibit high CEI but improve once their network authority exceeds a certain threshold, revealing a non-linear pattern. Overall, this study advances understanding of urban CEI. via networked geographies and offers critical insights for green supply chain governance in China.