Qunxi Gong

The integration of the digital economy (DE) and green logistics (GL) is increasingly recognized as a critical pathway for modernizing logistics systems and achieving carbon neutrality. However, their spatiotemporal synergy remains underexplored. Utilizing panel data from 120 logistics hub cities (LHC) in China from 2013 to 2022, this study constructs a comprehensive framework to evaluate the coupling coordination degree (CCD) between DE and GL. We employ the Dagum Gini coefficient to dissect spatial disparities and integrate the CatBoost algorithm with SHapley Additive exPlanations (SHAP) to unravel the driving mechanisms of this coordination. The results reveal that: (1) The overall CCD in Chinese LHC exhibits a consistent upward trajectory, yet spatial development remains highly uneven, forming a distinct hierarchical spatial pattern. (2) Spatial disparities in the integration level have widened over time, with transvariation density constituting the primary source of overall inequality. (3) The CatBoost-SHAP analysis identifies urban economic density, urbanization level, advanced industrial structure, and human capital level as the paramount drivers. These findings enrich existing literature by providing nuanced, data-driven insights for policymakers to leverage digitalization, formulate spatially differentiated strategies, and foster sustainable logistics networks.

Reasonably assessing green transformation efficiency (GTE) and clarifying influencing factors can provide theoretical support for their sustainable development. This study utilizes the undesired superefficient Slack Based Measure model (SE-SBM), window analysis, kernel density estimation analysis, and Tobit model to assess the GTE of 39 resource-based cities (RBCs) in the Yellow River Basin (YRB), explore their dynamic evolution trends and influencing factors, and attempt to compensate for a lack of clarity in green transformation constraint factors. Following findings: (1) YRB’s GTE showed a “V-shaped” upward trend. There were differences between upper, middle, and lower cities: upper cities are higher. (2) GTE is evolving to a higher level, and the inter-regional equilibrium level has improved. The kernel density curve in the upper, middle, and lower reaches has its own regional characteristics and time period features. (3) Industrial structure upgrading, economic development level, and green technology innovation level are positive effects, while the opening-up level is negative. According to the findings, YRB’s RBCs should adjust measures to the current environment and urban conditions, promote digitization and intelligentization, and improve innovative economic growth, thus lifting the quality of green development. These findings also illustrate how the analysis framework mentioned in the study can enhance the understanding of urban green transformation and serve sustainable urban development.

In order to clarify the relationship between self-improvement values and green brand identity, we take the consumers’ self-improvement values as the antecedent variable, consider the self-monitoring as the moderating variable, regard consumers’ environmental self-accountability as the intermediary variable, and conduct an empirical analysis of the impact of consumers’ self-improvement values on green brand identity. The research found that:1) Self-improvement values positively affect consumers’ recognition of green brands;2) Self-improvement values positively affect consumers’ environmental self-accountability;3) Environmental self-accountability positively mediates the relationship between self-improvement values and green brand identity;4) Self-monitoring positively regulates the impact of consumer values on consumers’ environmental self-accountability and the mediating role of environmental self-accountability.

Exploring the dynamic relationship and coupling coordination between urban industrial co-agglomeration and intensive land use is vital to ensure high-quality urban development. Based on an industrial co-agglomeration model, a revised intensive land use model, and a coupling coordination model, this paper comprehensively measured the urban industrial co-agglomeration and intensive land use coupling coordination in eight cities in the Chengdu metropolitan area from 2004 to 2018. It was found that despite the structural adjustment of the secondary and tertiary industries during the study period, the industrial co-agglomeration in the Chengdu metropolitan area fluctuated, the intensive urban land use had spatial characteristics that were decreasing from the center to the periphery, and while the general coupling coordination level was increasing, the increase rate in each city was different. The coupling coordination between the urban industry co-agglomeration and the intensive land use was found to have “low-high-low” spatial characteristics from the southwest to the northeast of the Chengdu metropolitan area, which revealed the core position of Chengdu. Suggestions are provided to improve the industrial co-agglomeration and intensive urban land use coupling coordination.