## 5 Conclusions  
In order to deal with environmental legislation and ease the pressure of resources, logistics management has evolved from a linear model to a higher level closed-loop mode including reverse logistics. Due to the uncertainty of WEEE return, the prediction of waste product return in WEEE reverse logistics has always been a difficult problem for industry and academia. From a new perspective, considering the spatial correlation of WEEE recycling sites, this paper introduces the spatial statistical Kriging method to establish the spatial model of reverse logistics return prediction, which provides a new method and theoretical basis for the return prediction of WEEE of reverse logistics.  
According to the results of Monte Carlo simulation and J company's case, the spatial model based on the Kriging method can accurately predict the WEEE return amount, and the total prediction error percentage is 1.21% and 1.43%, respectively, which indicates good accuracy of the proposed model.  
Three variograms commonly used in simulation examples are utilized to analyze the spatial correlation structure of WEEE return. The prediction error percentage of exponential variogram is 0.77%, less than those of spherical variogram (1.01%) and Gaussian variogram (1.85%). Among the three types of variograms, the exponential variogram achieves better prediction results. Therefore, the exponential variogram is recommended when considering the spatial correlation of WEEE return. The case study of J company also proves that the exponential variogram has achieved better results.  
For the WEEE return system with different number of recycling sites, the number of recycling sites in the simulation example ranges from 10 to 100. For a practical reverse logistics WEEE return system, the number of return sites is basically covered. There is no significant prediction error fluctuation when using this model, which proves the universal applicability of the proposed method.  
In the case of J company and the simulation example, there is a correlation length a for the WEEE return system. When the distance between any two sites is less than a, there is a correlation between the return amounts of different sites. With the increase of the distance, the spatial correlation weakens, and when the distance is greater than a, the spatial correlation disappears. The simulation results show that the variation distance is about 90 km by using three commonly used variograms, which provides a useful reference for the spatial correlation analysis of WEEE reverse logistics recycling system.  
For future work, there are two main aspects worthy of further study. On the one hand, due to the technical innovation and policy incentives, the return amount might increase greatly and sharply. Once the return amount does not meet the second-order randomness assumed in this paper, a new theoretical method needs to be deduced and verified. On the other hand, it is worth establishing a mathematical model which considers both temporal and spatial correlation of the return amount. It might be difficult in theoretical derivation, but it is of great significance for the actual return prediction of WEEE reverse logistics.  
[PERSON] developed the prediction model, completed Monte Carlo simulation, the case study and writing--original draft preparation. [PERSON] performed the writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Pujiang Program (Grant No. 18 PJC031).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.