A study on the spatial network characteristics and effects of CDW generation in China

In this paper, the province construction and demolition waste (CDW) generation in China from 2005 to 2018 is calculated by using the area estimation method. Together with the use of the gravity model and the social network analysis method (SNA), the characteristics and effects of the spatial correlation structure network of China's province construction and demolition waste generation are studied. It has been found that from 2005 to 2018, the construction and demolition waste in China had seen a significant rise, and the spatial difference in province generation is significant. During that time, China's inter-provincial construction and demolition waste generation demonstrated significant characteristics of a spatial correlation network with a complex network hierarchy and showed a trend of year-by-year increase in its overall network density and number of correlation. In this spatial connection network, nine provinces, including Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Hubei and Guangdong, played a core role as bridges, Beijing, Shanghai, Jiangsu, Guangdong, Shandong, Fujian and Zhejiang provinces are beneficiaries, Chongqing, Tianjin, Hubei, Anhui, Sichuan, Henan, Jiangxi, Hunan and Hebei are brokerswhile other provinces are the damaged. Indexes such as network density, network hierarchy and network efficiency all have a significant impact on the national construction and demolition waste generation intensity. The reduction of network density, the improvement of network hierarchy and network efficiency can significantly reduce the difference in construction and demolition waste generation intensity. The structure of spatial correlation network has significant influence on the difference of construction and demolition waste generation intensity. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper calculates the generation of construction and demolition waste (CDW) in Chinese provinces from 2005 to 2018 using the area estimation method, and studies the characteristics and effects of the spatial correlation structure network. It is found that there is a significant increase in CDW in China during this period, with significant spatial differences among provinces and a complex spatial correlation network structure.