Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

Understanding the spatial association network structure and carbon balance zoning of land-use carbon emissions (LUCEs) is essential for guiding regional environmental management. This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. Carbon balance zones are delineated by integrating LUCE network characteristics with economic and ecological indicators. To further examine the network dynamics, link prediction algorithms are employed to anticipate potential emission connections, while quadratic assignment procedure (QAP) regression analyzes how intercity differences in socioeconomic, ecological, and land-use attributes influence LUCE connectivity. The results reveal a pronounced core–periphery structure, with potential carbon spillover pathways extending toward both eastern and western cities. Based on the carbon balance analysis, six functional zones are identified, each aligned with targeted collaborative mitigation strategies. The QAP results indicate that intercity differences in innovation capacity, industrial structure, and economic development are positively associated with the formation of LUCE spatial networks, whereas disparities in urbanization level, government expenditure, and construction land use are negatively associated with LUCE connectivity. This study provides a differentiated governance framework to address the dual challenges of carbon emissions and land-use transformation in agro-urban regions.

Understanding how the built environment relates to urban ecological resilience is essential for resilience-oriented planning in high-density cities. Using Wuhan, China, as a case study, we constructed a 1 km grid-based Ecological Resilience Index (ERI) by integrating ecosystem resistance, adaptability, and recovery, and we confirmed significant spatial autocorrelation in ERI. We then applied a Bayesian-optimized XGBoost model (v2.0.3) with block-based spatial cross-validation to improve robustness under spatial dependence, and used SHAP to interpret nonlinear, threshold-like patterns and interactions among predictors. The results indicate that building coverage ratio (BCR), nighttime light intensity (NTL), elevation (ELE), mean building height (MBH), and precipitation (PRE) were the most influential predictors of ERI. SHAP main effects indicate clear non-monotonic and threshold-like response patterns across key predictors. SHAP interaction analysis further suggests that, under high BCR, the SHAP interaction term tends to be positive when MBH is below approximately 10 m, whereas the interaction between high NTL and low MBV is predominantly negative. This study provides fine-scale empirical evidence to inform the optimization of three-dimensional urban morphology to support urban ecological resilience.

This study examines how knowledge heterogeneity affects team creativity in the context of China’s digital intelligence transformation and analyzes the mediating effects of digital intelligence enablement and technology management capability. Grounded in the knowledge-based view and dynamic capability theory, a sequential mediation model of the effect of knowledge heterogeneity on team creativity is developed and tested using both hierarchical regression and structural equation modeling. We conducted a two-wave anonymous questionnaire survey in a knowledge-intensive enterprise in Shanghai and obtained 203 valid responses. The results indicate that we draw three main conclusions. First, knowledge heterogeneity has a significant positive effect on team creativity. Second, technology management capability and digital intelligence enablement act as mediators in this relationship. Technology management capability improves the efficiency of transforming heterogeneous knowledge, while digital intelligence enablement facilitates the integration and application of such knowledge. Finally, technology management capability and digital intelligence enablement together form a sequential mediation pathway, where heterogeneous knowledge first enhances technology management capability and then promotes digital intelligence enablement, ultimately fostering team creativity. This study deepens the understanding of how knowledge heterogeneity promotes team creativity and provides implications for advancing digital intelligence transformation and industry competitiveness.