Land urbanization enables a thorough perspective to explore the decoupling of land use environmental efficiency (LUEE) and energy use, thereby supporting the shift into low-carbon land use by emphasizing energy conservation and reducing carbon emissions. This paper first calculates LUEE from 2011 to 2021 by using the EBM-DEA model in China. The geographical detector model is used to examine the driving factors of land use environmental efficiency. The results show the following: (1) China’s LUEE is high in general but shows a clear pattern of spatial differentiation internally, with the highest values in the eastern region represented by Beijing, Jiangsu, and Zhejiang, while the central and western regions show lower LUEE because of their irrational industrial structure and lagging green development. (2) Energy consumption, economic development, industrial upgrading, population size, and urban expansion are the driving factors. Their explanatory power for the spatial stratification heterogeneity of land use environmental impacts varies. (3) Urban expansion has the greatest impact on the spatial differentiation of land use environmental effects, while energy consumption also shows significant explanatory strength. In contrast, economic development and population size exhibit relatively weaker explanatory effects. (4) The interaction of the two driving factors has a greater impact on LUEE than their individual effects, and the interaction is a two-factor enhancement. Finally, we make targeted recommendations to help improve land use environmental efficiency.

Current 5G communication services have limitations, prompting the development of the Beyond 5G (B5G) network. B5G aims to extend the scope of communication to encompass land, sea, air, and space while enhancing communication intelligence and evolving into an omnipresent converged information network. This expansion demands higher standards for communication rates and intelligent processing across multiple devices. Furthermore, traffic prediction is crucial for the intelligent and efficient planning and management of communication networks, optimizing resource allocation, and enhancing network performance and communication speeds and is an important part of B5G’s performance. Federated learning addresses privacy and transmission cost issues in model training, making it widely applicable in traffic prediction. However, traditional federated learning models are susceptible to adversarial attacks that can compromise model outcomes. To safeguard traffic prediction from such attacks and ensure the reliability of the prediction system, this paper introduces the Adaptive Threshold Modified Federated Forest (ATMFF). ATMFF employs adaptive threshold modification, utilizing a confusion matrix rate-based screening-weighted aggregation of weak classifiers to adjust the decision threshold. This approach enhances the accuracy of recognizing adversarial samples, thereby ensuring the reliability of the traffic prediction model. Our experiments, based on real 5G traffic data, demonstrate that ATMFF’s adversarial sample recognition accuracy surpasses that of traditional multiboost models and models without adaptive threshold modified. This improvement bolsters the security and reliability of intelligent traffic classification services.

As new-energy vehicles (NEVs) gradually gain public attention, their carbon-reduction issues have become a focal point in academia. This study evaluates the carbon-reduction efficiency of NEVs in 21 Chinese provinces using an improved three-stage DEA model, analyzes spatial disparities with the Dagum Gini coefficient, and decomposes carbon-emission factors using the LMDI method. Results show that the overall carbon-reduction efficiency is low, with an average value of only 0.266. Significant differences exist in production- and consumption-stage efficiencies across regions. Shanxi Province performed the best, with efficiency scores of 1 in both stages, while the carbon-reduction stage showed the lowest efficiency, ranging between 0.2 and 0.3 in most regions. The central region exhibited the highest carbon-reduction efficiency, followed by the western and eastern regions, primarily influenced by intra-regional disparities. Energy intensity significantly suppresses carbon emissions, followed by energy structure, while economic development and population size positively contribute to carbon emissions. This study provides theoretical support for regional governments to formulate policies related to the NEV industry and offers practical guidance for its further development.

The development of the digital economy provides important opportunities and conditions for China to achieve the goal of carbon peak and carbon neutrality. Based on panel data from 30 provinces in mainland China from 2016 to 2022, this research investigates the spatial spillover effect and nonlinear impact of the digital economy on the energy optimization of the manufacturing industry using the spatial econometric and panel threshold model. It is found that both the digital economy and energy optimization of the manufacturing industry have a significant positive spatial correlation. The spatial econometric models under different weights all illustrate that the regional digital economy has not significantly promoted energy optimization of the manufacturing industry in a local region but produced a significant positive influence on the energy optimization of the manufacturing industry in neighboring regions. In addition, the impact of the digital economy on the energy optimization of the manufacturing industry presents a significant single threshold effect. With the improvement of digital economy, energy optimization of manufacturing industry has a U-shaped change trend. This study integrates the digital economy and manufacturing energy optimization into a cohesive analytical framework, elucidating the mechanisms through which the digital economy influences the restructuring of manufacturing energy and enhances energy efficiency while providing innovative pathways and theoretical support for advancing energy efficiency under carbon emission reduction constraints.