Fiscal Livelihood Spending’s Effects on Multidimensional Poverty Reduction in China

This paper investigates how fiscal spending on livelihood improves multidimensional household poverty in China.Based on the panel data of the“China Health and Nutrition Survey”(CHNS)for 2004-2015,we measured the chronic multidimensional poverty index for Chinese households.We have created a multitiered model for empirical analysis.Our findings suggest that multidimensional poverty in China is predominantly capacity poverty.Fiscal spending on livelihoods significantly reduces multidimensional poverty for Chinese households,especially rural households.Investments on livelihoods are more poverty-reducing than transfer spending on livelihoods.As an innovation,this paper offers a dynamic analysis of the effects of livelihood spending on multidimensional household poverty controlling for heterogeneity between individual households and across regions.Our conclusion suggests that the government should improve policy arrangements to increase social opportunities and support sustainable development capacities for the poor,while enhancing protective social security systems.

Digital Twin-Empowered Network Planning for Multi-Tier Computing

In this paper,we design a resource management scheme to support stateful applications,which will be prevalent in sixth generation(6G)networks.Different from stateless applications,stateful applications require context data while executing computing tasks from user terminals(UTs).Using a multi-tier computing paradigm with servers deployed at the core network,gateways,and base stations to support stateful applications,we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource reservation.The coupling among different resources and the impact of UT mobility create challenges in resource management.To address the challenges,we develop digital twin(DT)empowered network planning with two elements,i.e.,multi-resource reservation and resource reservation reconfiguration.First,DTs are designed for collecting UT status data,based on which UTs are grouped according to their mobility patterns.Second,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands.Last,a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost.Simulation results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.