Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method

Huang, Jinbo; Ding, Ashley; Li, Yong; Lu, Dong

doi:10.1016/j.pacfin.2020.101373

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…In this study, Kernel density estimation was used to describe the dynamic distribution evolution of rural logistics in the urban agglomeration, with the wave height and width reflecting the magnitude of differences in rural logistics development level and the number of waves reflecting the polarization. The formula of the empirical distribution is [69]:…”

Section: Kernel Densitymentioning

confidence: 99%

Spatial Differentiation Effect of Rural Logistics in Urban Agglomerations in China Based on the Fuzzy Neural Network

Bao

2022

Sustainability

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Rural logistics is particularly important for the sustainable development of Chinese urban agglomeration, which features the coexistence of urban circles and vast countryside. However, the existing literature generally focuses on urban logistics while ignoring rural logistics. Taking the Chengdu-Chongqing agglomeration as an example, this study constructed a rural logistics index system and proposed a new dynamic assessment model for rural logistics development using a fuzzy neural network, Moran index, and Kernel density estimation. The results are as follows: the development of rural logistics has been enhanced, and gaps among cities have gradually narrowed over the past decade. In particular, the spatial distribution of rural logistics showcases a dual-core structure in the Chengdu-Chongqing agglomeration, which is different from the unipolar structure manifested in other urban agglomerations. Because of administrative barriers, the impact from the dual-core cities is very different: Chengdu has a significant spillover effect on its surrounding cities, which is not the case for Chongqing. The findings are of great significance for local governments to provide decision-making support for the sustainable development of urban agglomerations.

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Section: Kernel Densitymentioning

confidence: 99%