Introduction: In the era of the booming digital economy and global efforts to reduce carbon emissions, this study investigates how digital economy growth impacts indirect household carbon emissions (IHCEs). Understanding this relationship is critical, as the digital economy has the potential to both drive and mitigate carbon emissions, depending on its stage of development and regional context.Methods: Using panel data from the 2014–2018 China Family Panel Studies (CFPS), a fixed effects model is applied to analyze the relationship between digital economy growth and IHCEs. The study examines regional variations, levels of digital economy development, and consumption categories to identify heterogeneous effects.Results: The findings reveal an inverted U-shaped relationship between the digital economy and IHCEs. Initially, IHCEs rise due to increased consumption of energy-intensive products, but as digital technologies mature, emissions decline due to improved efficiency and sustainable consumption. Heterogeneity analysis shows that the inverted U-shaped relationship is more pronounced in less developed digital economy regions, while in advanced regions, the relationship is less significant. Regionally, the eastern region, with its advanced infrastructure and green technologies, effectively curbs IHCEs, whereas the central region experiences increased emissions, and the western region exhibits the inverted U-shaped pattern. In terms of consumption, the digital economy significantly impacts housing and food-related emissions, while other categories show mixed or minor effects.Discussion: These findings highlight the dual role of the digital economy in both driving and mitigating carbon emissions. Policymakers should adopt region-specific strategies, invest in digital infrastructure, and promote sustainable consumption practices to leverage the digital economy for carbon reduction. The study underscores the importance of managing expectations and addressing discrepancies between digital economy growth and its environmental impacts, offering valuable insights for achieving sustainable development goals.

Optimizing the layout of data centers is important for the rapid development of digital infrastructure, while also addressing the issues of energy consumption, environmental sustainability, and geographic resource distribution. Traditional strategies usually focus only on the distance to demand centers and ignore the energy and environmental costs of data centers in densely populated areas. In this paper, we propose a layout optimization model based on energy consumption constraints that combines integer linear programming with binary decision variables. The model combines energy efficiency, renewable resource availability, and regional characteristics to balance economic benefits and environmental impacts, consistent with the “East data, West computing” project. The experimental results showed that the energy efficient scenario consistently reduced costs, from ¥3.68 × 10⁸ to ¥3.08 × 10⁸ without energy constraints, and from ¥4.08 × 10⁸ to ¥3.47 × 10⁸ under energy consumption constraints. Additionally, energy constraints increased the number of required data centers from two to three. The results of the study emphasized the importance of strategic siting, especially in low electricity price areas, in order to optimize the layout and improve sustainability.

Cloud computing cost-effective, and scalable characteristics can be an effective solution to teaching college laboratory teaching uneven distribution of resources, updates slow, the shared low degree. Analysis of cloud computing at home and abroad in the application on the basis of education, the paper put forward and design college laboratory teaching management system based on hybrid cloud management function, and provide a good guide for colleges and universities to achieve cloud computing experimental teaching management.

Optimizing the layout of data centers is important for the rapid development of digital infrastructure, while also addressing the issues of energy consumption, environmental sustainability, and geographic resource distribution. Traditional strategies usually focus only on the distance to demand centers and ignore the energy and environmental costs of data centers in densely populated areas. In this paper, we propose a layout optimization model based on energy consumption constraints that combines integer linear programming with binary decision variables. The model combines energy efficiency, renewable resource availability, and regional characteristics to balance economic benefits and environmental impacts, consistent with the "East data, West computing" project. The experimental results showed that the energy efficient scenario consistently reduced costs, from ¥3.68×10^8 to ¥3.08×10^8 without energy constraints, and from ¥4.08×10^8 to ¥3.47×10^8 under energy consumption constraints. Additionally, energy constraints increased the number of required data centers from two to three. The results of the study emphasized the importance of strategic siting, especially in low electricity price areas, in order to optimize the layout and improve sustainability.

The rapid advancement of artificial intelligence has driven a surge in demand for computing power. As the core computing infrastructure, data centers have expanded in scale, escalating electricity consumption and magnifying a regional mismatch between computing capacity and energy resources: facilities are concentrated in the energy-constrained East, while the renewable-rich West possesses vast, untapped hosting capacity. Focusing on cross-regional data-center migration under the “Eastern Data, Western Computing” initiative, this study constructs a tripartite evolutionary game model comprising the Eastern Local Government, the Western Local Government, and data-center enterprises. The central government is modeled as an external regulator that indirectly shapes players’ strategies through policies such as energy-efficiency constraints and carbon-quota mechanisms. First, we introduce key parameters—including energy efficiency, carbon costs, green revenues, coordination subsidies, and migration losses—and analyze the system’s evolutionary stability using replicator-dynamics equations. Second, we conduct numerical simulations in MATLAB 2024a and perform sensitivity analyses with respect to energy and green constraints, central rewards and penalties, regional coordination incentives, and migration losses. The results show the following: (1) Multiple equilibria can arise, including coordinated optima, policy-failure states, and coordination-impeded outcomes. These coordinated optima do not emerge spontaneously but rather depend on a precise alignment of payoff structures across central government, local governments, and enterprises. (2) The eastern regulatory push—centered on energy efficiency and carbon emissions—is generally more effective than western fiscal subsidies or stand-alone energy advantages at reshaping firm payoffs and inducing relocation. Central penalties and coordination subsidies serve complementary and constraining roles. (3) Commercial risks associated with full migration, such as service interruption and customer attrition, remain among the key barriers to shifting from partial to full migration. These risks are closely linked to practical relocation and connectivity constraints—such as logistics and commissioning effort, and cross-regional network latency/bandwidth—thereby potentially trapping firms in a suboptimal partial-migration equilibrium. This study provides theoretical support for refining the “Eastern Data, Western Computing” policy mix and offers generalized insights for other economies facing similar spatial energy–demand asymmetries.