This study focuses on 55 shrinking cities selected by the urban shrinkage index using data about the urban population of 250 prefecture-level Chinese cities from 2012 to 2017. It analyzes the theoretical impacts of urban shrinkage on haze pollution and the spatial distribution and autocorrelation of urban shrinkage. The spatial error model (SEM) and the fully modified least squares (FMOLSs) regression are used to empirically examine the impacts of urban shrinkage on haze pollution at national and regional levels. The results indicate that shrinking cities showed spatial agglomeration and that northeast China had the largest number of shrinking cities. Nationwide, urban shrinkage reduced haze pollution. An increase in the proportion of secondary industries, economic development, and built-up areas intensified haze pollution, while an increase in the green area in parks alleviated such pollution. Regionally, except for west China, the impacts of urban shrinkage on haze pollution were significantly negative. Urban shrinkage in central China had the greatest impacts on haze, followed by northeast China and east China. Haze pollution was intensified by the increase in the proportion of secondary industries in east, central and west China, alleviated by economic development in east and west China, slowed down by the increase in green area in parks in northeast, east and west China, and aggravated by the rise in built-up areas in northeast, central, and west China. Targeted suggestions are proposed herein to reduce haze pollution, adapt to urban shrinkage and build quality small cities based on local conditions.

In this paper, the moment exponential synchronization problems of drive-response stochastic memristor neural networks are studied via a state feedback controller. The dynamics of the memristor neural network are nonidentical, consisting of both asymmetrically nondelayed and delayed coupled, state-dependent, and subject to exogenous stochastic perturbations. The pth moment exponential synchronization of these drive-response stochastic memristor neural networks is guaranteed under some testable and computable sufficient conditions utilizing differential inclusion theory and Filippov regularization. Finally, the correctness and effectiveness of our theoretical results are demonstrated through a numerical example.

This paper mainly explores the phenomenon of "big data ripening", that is, Internet manufacturers display the alienated price of the same commodity or service on the equipment of different users through technical means such as big data. The price displayed on the equipment of old users is slightly higher than that displayed on the equipment of new users, so as to maximize their profits, but it will infringe on the due rights and interests of consumers, Cause the loss of users, thus reducing the efficiency of market transactions. Studying the phenomenon of "big data killing" can protect the legitimate rights and interests of consumers, maintain social order and stability, and establish a good market competition mechanism. Therefore, this paper mainly uses empirical analysis methods such as questionnaire, and comprehensively uses data analysis methods such as analysis of variance and regression analysis to analyze the impact of "big data killing" phenomenon on online consumers' purchase intention, and give consumers some conclusions and enlightenment.

The collection and reuse of the e-wastes have attracted increasing attention all over the world. In order to incentivize the producer to collect and recover the e-waste, more and more countries have formulated the take-back legislation with the economic or administrative instruments based on the spirit of extended producer responsibility (EPR). Facing two different types of policy instruments (economic or administrative instrument), how the take-back legislation affects the collection and reuse of the e-waste and how to ensure the efficiencies of the two types of take-back legislation have attracted the public’s attention. Therefore, this paper establishes a stylized model consisting of a monopoly manufacturer who is responsible for collection and remanufacturing under two different types of the take-back legislation. The manufacturer’s optimal production, collection, remanufacturing decisions, and optimal profit are derived using Karush–Kuhn–Tucker (KKT) conditions. Sensitivity analysis shows that stricter mandated collection rate hurts the manufacturer’s profit, but the effects of the government’s subsidy and tax on the manufacturer’s optimal decisions rely on the cost of the new product. The firm needs to adjust the cost of the new products corresponding to the specific subsidy and tax. Finally, a neutral fiscal policy is proposed to determine the government’s optimal subsidy and tax in order to ensure the consistency of the efficiencies of the two types of take-back legislation. The government should set that the ratio of the optimal tax and subsidy under the neutral fiscal policy is exactly consistent with the mandatory collection target. This policy aims to guide the manufacturer’s voluntary collection rate to meet the government’s mandated collection target and thus change the mandatory collection mode to the voluntary collection mode.