In recent years, 5G technology has rapidly developed, which is widely used in medical, transportation, energy, and other fields. As the core equipment of the 5G network, 5G base stations provide wireless coverage and realize wireless signal transmission between wired communication networks and wireless terminals. However, as the scale of 5G base stations gradually increases, problems such as poor user experience and insufficient coverage area frequently occur. Hence, it is necessary to evaluate the comprehensive performance of 5G base stations, so as to clarify the problems existing in the construction of base stations. First, the performance evaluation index system is constructed from the perspectives of operational performance, financial performance, environmental impact, and social influence. Then, a novel hybrid multicriteria decision-making (MCDM) model based on the Bayesian best-worst method (BBWM) and difference-quotient gray relational analysis (DQ-GRA) technique is adopted. Finally, sixteen 5G base stations are taken as examples for analysis. The result shows that the signal coverage area and per capita input cost are the most important indicators greatly affecting the overall performance of the 5G base station. Compared with the two other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for performance evaluation of 5G base stations.

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With the growth of residential electricity consumption and the development of power energy conservation, exploring the factors that affect residential electricity consumption is of great significance for promoting the sustainable development of the regional economy-power system. This paper examines the influencing factors of residential electricity consumption according to the data in 6 provinces in North China over 2008-2018, and two panels named urban panel and rural panel are constructed. Three conventional influencing factors are selected in this paper, namely, population (POP), per capita disposable income (DI) and per capita consumption expenditure (PCCE). Furthermore, considering that household characteristics have an impact on residential electricity consumption, this paper adds the number of household appliances (HA) and the per capita housing area (LS) into the factor set. Heterogeneous panel analysis techniques are applied to achieve the analysis, finding that household characteristics have significant impacts on electricity consumption of urban and rural residents, and the impact on electricity consumption of urban residents is greater than that on rural residents. Based on the empirical results, this paper puts forward several policy recommendations to effectively improve the residential electricity consumption and reduce the gap between urban and rural residential electricity consumption.