Exploring spatial heterogeneity and factors influencing construction and demolition waste in China

Abstract

Natural disasters, new urbanization and urban renewal activities generated a large amount of construction and demolition waste (C&DW), and managing C&DW has become an urgent problem to be solved in the construction of "Zero-waste cities". Based on the calculation of C&DW generation in China from 2005 to 2019, this study analyzed the temporal and spatial characteristics of C&DW generation in China, and empirically explored the factors influencing factors C&DW of China using spatial autocorrelation and geographically weighted regression. The results showed that: (1) C&DW generation in China increased every year, and the overall distribution was characterized as "high in the east and low in the west", with distinct regional differences. The provinces with the highest per capita C&DW generation were Zhejiang, Jiangsu, Beijing, Shanghai, and Fujian. The generation intensity of C&DW in China and all its provinces showed a decreasing trend every year. The regions with rapid growth of C&DW generation in China were concentrated in the eastern coastal areas, with distinct differences between the east and west, and there was significant spatial heterogeneity in the growth trend.(2) There is a significant spatial autocorrelation in C&DW generation in China. Overall, the hot spots for C&DW generation were distributed in Jiangsu, Zhejiang, and Shandong provinces, and the spatial agglomeration effect of C&DW generation in provinces was evident. (3) Factors such as population size, per capita Gross Domestic Product and the scale of the construction industry played a positive role in promoting C&DW generation in each province, whereas labor efficiency played a negative role inhibiting C&DW generation; there was significant temporal and spatial heterogeneity. Finally, differentiation and cross-regional joint treatment strategies according to regional conditions were proposed to achieve precise measures of C&DW reduction management.

Spatiotemporal patterns of gross ecosystem product across China's cropland ecosystems over the past two decades

“…Our results also showed that the major reasons why GEP was increasing in most of China's provinces were the effects of variation in price, planting area, and precipitation. Similarly, the findings on population, socialeconomic level, and industry scale for spatial heterogeneity have been proved in other studies (Gordeev et al, 2021;Wang et al, 2021Wang et al, , 2022Zeng and Wang, 2022). In China, the effects of population growth and urbanization were significant.…”

Spatial and Temporal Evolution Patterns of Construction Waste Generation and Influencing Factors - China Case Study

“…Common estimation methods currently used for construction waste include material flow analysis, unit generation, per capita multiplier method, and field investigation (Wu et al, 2019;Wu et al, 2014). In the area estimation method, construction waste is divided into new construction waste, renovation waste, and demolition waste based on the stage of construction waste generation, which resolves the problem that a large amount of data is difficult to collect and the collection process is prone to data omission, with less error, and this method of estimation has been used quite frequently in previous studies (Z. S. Wang, Zhang, et al, 2022). For this reason, in this study, the area estimation method was used to calculate the amount of construction waste produced by each province in China from 2013 to 2021.…”

“…Finally, this paper identifies the production of construction waste as the explanatory variable, and identifies 12 factors as the explanatory variables from three perspectives: demographic factors, level of affluence, and technological factors, and the basic information of the influencing factors is given in Table 1, and the data is taken from the China Statistical Yearbook. In reference to Wang's treatment of the original data (Z. S. Wang, Zhang, et al, 2022), in an attempt to remove heteroscedasticity from the model and increase the smoothness of the data, with the exception of the independent variable 𝑥 7 , the rest of the variables are taken to be natural logarithms, The final extended STIRPAT theoretical model is constructed according to Eq. 2.…”

The Practical Experience of “Zero Waste City” Construction in Foshan City Condenses the Chinese Solution to the Sustainable Development Goals

“…Unreasonable disposal methods can produce heavy metal pollution to water and soil and dust pollution to the atmosphere, which has become an urgent problem to be solved in ZWC construction. By analyzing the changing trends of construction waste generation in different cities in China over the past 15 years, Wang [37] proposed strengthening research on construction waste recycling technology, regulating the scale of construction industry development, improving labor efficiency and product quality through information technology, and improving the restraint and incentive policies for supporting construction waste reduction at the source. Foshan City's construction waste mainly comes from commercial housing projects, municipal projects, demolition, and relocation of construction sites, similar to Shenzhen City, which should promote energy-efficient buildings, improve the level of resource utilization of construction waste, construct adequate construction waste disposal sites, achieve the whole process of construction waste management and improve market acceptance of recycled buildings and create access to construction waste recycling [19,36].…”