Accurate waste electrical and electronic equipment (WEEE) recycling forecast is an essential reference for optimizing e-waste industry layout and division of labor policies, conducive to better guiding enterprises’ recycling activities and improving the efficiency of WEEE recycling in China. The nonlinear grey Bernoulli model (NGBM (1,1)) was constructed by analyzing the recycling data characteristics of WEEE from 2012 to 2020, and a particle swarm optimization (PSO) algorithm was introduced to solve the model parameters and optimize the background value coefficients. The prediction results were compared with other grey prediction models to verify the effectiveness of the improved NGBM (1,1) model for WEEE recycling prediction in China and the applicability of the PSO algorithm for improving the prediction accuracy of each grey model. Statistical data were used to forecast the WEEE recycling volume in China from 2021 to 2023, and the results show that the value of WEEE recycling will continue to grow at 9%. The value of recycling will reach 16 billion yuan by 2023, while the quantity of WEEE recycling will see a slight decline. Based on the calculation results, the WEEE recycling industry development trend is predicted to guide the promotion of the WEEE industry recycling program and the national circular economy program.

A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is proposed to solve the clustering problem of taxi carpooling on urban roads. The data field energy function is used to calculate the field energy of each data point in the passenger taxi offpoint dataset. To realize the clustering of taxis, the central point, outlier, and data points of each cluster subset are discriminated according to the threshold value determined by the product of each data point field values and point spacing. The classical algorithm and proposed algorithm are compared and analyzed by using the compactness, separation, and Dunn validity index. The clustering results of the proposed algorithm are better than those of the classical clustering algorithm. In the case of cluster numbers 25, 249, 409, and 599, the algorithm has good clustering results for the taxi trajectory dataset with certain regularity in space distribution and irregular distribution in time distribution. This algorithm is suitable for the clustering of vehicles in urban traffic roads, which can provide new ideas and methods for the cluster study of urban traffic vehicles.

As "Internet+" technology and e-commerce third-party platforms evolve, consumer options for purchasing fresh products now include both offline and online channels, giving rise to a mixed dual-channel supply chain. We examine this dual-channel supply chain, constructing a market demand function influenced by fresh produce pricing and preservation efforts.Optimal strategies for each stakeholder and the supply chain's maximum profit are analyzed through both classical and quantum game theory. The results indicate that under centralized decision-making, the equilibrium solutions are identical for classical and quantum games, with the quantum entanglement degree $ \gamma = 0 $. In scenarios where market demand exhibits low sensitivity to freshness preservation efforts, the entanglement degree between suppliers and retailers can be elevated by implementing a "freshness preservation cost-sharing + revenue-sharing contract, " thereby enhancing supply chain efficiency and profit. This study contributes to the literature by offering new theoretical insights into mitigating free-riding behavior in fresh food retail and explores the potential applications of quantum games in economics.