Spatio-temporal pattern and driving factors of municipal solid waste generation in China: New evidence from exploratory spatial data analysis and dynamic spatial models

In recent years, the generation of municipal solid waste (MSW) in China has entered an accelerated rising stage, and its spatial pattern is changing significantly, which must be considered in policy design. Through exploratory spatial data analysis (ESDA), this study finds that from 2006 to 2017, the characteristics of spatial heterogeneity of China's MSW generation gradually change from high in the north and low in the south to high in the east and low in the west, and the gravity center of MSW generation level has moved 287.5 km to the southeast. According to the results of local indications spatial association analysis, the High-High clusters are all located in northern China in 2006, but the number has decreased significantly in later years; additionally, new High-High clusters are emerging in the Yangtze River Delta and Pearl River Delta. The Low-Low clusters are mainly located in the south but gradually shrink to middle and western China. Many central and western cities always maintain the Low-Low association, but the High-Low association is emerging in the provincial capitals or municipalities of these regions. Subsequently, through the dynamic spatial econometric analysis, it is found that the previous MSW generation can result in a spatial spillover effect in the current period, and the impact law of economic development can fit an inverted N-shaped environmental Kuznets curve. Population, technology, urbanization and green coverage rate can all inhibit MSW generation, and industrial structure, per capita number of sickbeds, and road density are driving factors of MSW generation. The aforementioned effects are consistent with the simulation results of short-term effects, but in the long-term, there are only significant indirect effects (spatial spillover) from the factors, and their directions are the opposite of the short-term effects. (C) 2020 Elsevier Ltd. All rights reserved.