Spatio-temporal evolution and the driving factors of municipal solid waste in Chinese different geographical regions between 2002 and 2020

被引:5
作者
Cui, Wenjing [1 ]
Wei, Yuan [1 ]
机构
[1] Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China
关键词
Municipal solid waste; Spatial-temporal distribution characteristics; Driving factors; SUDDEN WATER-POLLUTION; PER-CAPITA GDP; SPATIAL-PATTERNS; METAL POLLUTION; ECONOMIC-GROWTH; RESIDENT GROUPS; GENERATION; INCINERATION; IMPACT; INEQUALITY;
D O I
10.1016/j.envres.2023.117456
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urbanization and economic development have contributed to the rapid and massive generation of municipal solid waste (MSW) and significant changes in spatial patterns, which are becoming a serious pollution problem. Previously, macroscopic studies on the driving factors of MSW have been widely conducted at the national level, but the exploration of the driving factors in different geographical regions on a regional scale has not received much attention in the previous literature. This study is based on China, spatial patterns were analyzed using spatial autocorrelation and movement of center of gravity, and time series clustering was used to explore temporal trends. Subsequently, Geodector was adopted to quantify the relationship between MSW generation and driving factors. The results of the study are as follows: 1) By analyzing the spatial pattern of MSW, this study found that MSW showed a spatial pattern of high in the southeast and low in the northwest during 2002-2020, and its separating line was the same as the Hu-line; the average center of gravity of MSW generation in the past 20 years was always located in Henan Province and shifted southward by 339.7 km. 2) The local spatial autocorrelation analysis results showed that the Low-Low clusters moved from southeast to northwest from 2002 to 2020, increasing to 20 cities. High-High clusters mainly appeared in the East Coast and South Coast regions, increasing from 8 to 17 cities in the last 20 years. 3) The analysis of driving factors by Geodetector revealed that Urbanization is the most critical dimension factor influencing MSW generation, with the strongest impact on the East Coast region. The next dimension is Economy, which has the most significant impact on MSW generation in the North West region. Energy is the third dimension that influences MSW generation, with the greatest impact on the North Coast region. The results of this study reveal trends in the spatial and temporal distribution of MSW in different geographic regions of China over the past 20 years and the impact of their driving factors, which can help the Chinese government take action to control MSW in a site-specific manner.
引用
收藏
页数:14
相关论文
共 82 条
[31]   Estimating municipal solid waste generation by different activities and various resident groups: A case study of Beijing [J].
Li, Zhen-shan ;
Fu, Hui-zhen ;
Qu, Xiao-yan .
SCIENCE OF THE TOTAL ENVIRONMENT, 2011, 409 (20) :4406-4414
[32]   Factors influencing municipal solid waste generation in China: A multiple statistical analysis study [J].
Liu, Chen ;
Wu, Xin-Wu .
WASTE MANAGEMENT & RESEARCH, 2011, 29 (04) :371-378
[33]   Financial development and carbon emissions in China since the recent world financial crisis: Evidence from a spatial-temporal analysis and a spatial Durbin model [J].
Liu, Hongyan ;
Song, Yanrong .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 715
[34]   Spatial Differences and Influencing Factors of Urban Water Utilization Efficiency in China [J].
Liu, Kai ;
Liu, Wenrui ;
Wu, Jialing ;
Chen, Zhongfei ;
Zhang, Wen ;
Liu, Fang .
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
[35]  
Liu Q.L., 2019, J. Clean. Prod., V238, P14
[36]   Spatial patterns, driving forces, and urbanization effects of China's internal migration: County-level analysis based on the 2000 and 2010 censuses [J].
Liu Tao ;
Qi Yuanjing ;
Cao Guangzhong ;
Liu Hui .
JOURNAL OF GEOGRAPHICAL SCIENCES, 2015, 25 (02) :236-256
[37]   Spatiotemporal patterns and drivers of carbon emissions from municipal solid waste treatment in China [J].
Liu, Yi ;
Wang, Jianliang .
WASTE MANAGEMENT, 2023, 168 :1-13
[38]   Integrated Assessment Method of Emergency Plan for Sudden Water Pollution Accidents Based on Improved TOPSIS, Shannon Entropy and a Coordinated Development Degree Model [J].
Long, Yan ;
Yang, Yilin ;
Lei, Xiaohui ;
Tian, Yu ;
Li, Youming .
SUSTAINABILITY, 2019, 11 (02)
[39]   Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method [J].
Ma, Shijun ;
Zhou, Chuanbin ;
Chi, Ce ;
Liu, Yijie ;
Yang, Guang .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2020, 54 (15) :9609-9617
[40]   A SURVEY OF RECENT ADVANCES IN HIERARCHICAL-CLUSTERING ALGORITHMS [J].
MURTAGH, F .
COMPUTER JOURNAL, 1983, 26 (04) :354-359