Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives.

Urban taxi systems contribute to carbon emissions through inefficient cruising and prolonged idle times. This study develops a novel spatiotemporal framework integrating complex network analysis with Markov process modeling to optimize taxi operations for sustainable transportation. Using GNSS trajectory data from 7767 Shanghai taxis, we construct a city-wide taxi network and apply k-core decomposition to identify optimal operational strategies. Our analysis reveals that Shanghai’s taxi network exhibits a polycentric structure with a few high-value hubs, and demonstrates that drivers operating in high k-core (central network) regions achieve higher earnings while reducing idle time compared to peripheral areas. The framework provides data-driven, actionable insights for traditional cruising taxi operations, offering practical guidance for driver decision-making and supporting sustainable urban mobility planning with direct implications for reducing transportation-related carbon emissions in megacities worldwide.

Understanding how local interactions generate social polarization is a central challenge in the study of collective dynamics. Prior work has established that homophily and social balance can trigger a first-order phase transition to polarization in homogeneous small-world networks. However, real-world social networks are structurally heterogeneous, featuring power-law degree distributions dominated by a few highly connected hubs. In this study, we investigate how this structural heterogeneity affects polarization dynamics by introducing the co-evolutionary opinion model on scale-free networks generated via a modified Holme–Kim model. We find that, in contrast to the discontinuous transition in small-world networks, social polarization for scale-free networks grows continuously with increasing connectivity, emerging earlier but reaching a lower magnitude. Individual-level analysis reveals that polarization is a hierarchical process concentrated among large-degree hubs, while most peripheral nodes remain weakly polarized. Counterfactual experiments demonstrate that neutralizing only the top 5% of large-degree nodes is sufficient to suppress system-wide polarization to the random baseline. Our findings reveal that network topology fundamentally alters the nature of polarization transitions and suggest that targeted interventions on influential nodes may be more effective than broad-based approaches for managing social polarization in heterogeneous societies.

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments.