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. 2018 Nov 2;15(11):2448.
doi: 10.3390/ijerph15112448.

Evaluating the Efficiency of Municipal Solid Waste Management in China

Qing Yang  1 , Lingmei Fu  2 , Xingxing Liu  3 , Mengying Cheng  4
Affiliations

Evaluating the Efficiency of Municipal Solid Waste Management in China

Qing Yang et al. Int J Environ Res Public Health. .

Abstract

Poor public health is always associated with the mismanagement of municipal solid waste (MSW). Many cities are besieged by MSW in the world. It is essential to do a good job in MSW management (MSWM). In order to improve the efficiency of MSWM, the Chinese government has intensively implemented relevant policies. There are still few studies on MSWM efficiency in China. The research aims to comprehensively analyze MSWM efficiency, find high-efficiency MSWM policy implementation routes and the breakthrough on improving MSWM efficiency. To measure Chinese MSWM efficiency accurately, this paper introduced the three-stage data envelopment analysis (DEA) model into the research. According to the results of DEA, Fuzzy c-Means algorithm was used to the cluster analysis of 33 typical cities. After eliminating the interference of the external environment and random disturbance, the mean value of MSWM efficiency declined from 0.575 to 0.544. The mean of pure technical efficiency (PTE) was declined from 0.966 to 0.611, while the mean of scale efficiency (SE) increased from 0.600 to 0.907. The PTE of central and northeastern cities was relatively low. The SE of western cities was comparatively high and the efficiency distribution of the eastern region was relatively scattered. In general, MSWM efficiency is low and expected to be improved. Regional differences in MSWM efficiency have been shown. The management effectiveness of eight pilot cities (MSW classification) is affirmative but not that significant. To improve MSWM efficiency, differential management for four types of cities should be carried out.

Keywords: management efficiency; municipal solid waste; pilot cities; sustainable construction; three-stage data envelopment analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The framework of the established three-stage data envelopment analysis (DEA) model.
Figure 2
Figure 2
Different types of cities before and after the Adjustment of MSWM efficiency means.
Figure 3
Figure 3
Clustering Diagram of MSWM.
Figure 4
Figure 4
Regional distributions of four types of MSWM efficiency. (a) Regional distribution of the first type of MSWM efficiency; (b) Regional distribution of the second type of MSWM efficiency; (c) Regional distribution of the third type of MSWM efficiency; (d) Regional distribution of the fourth type of MSWM efficiency.
See this image and copyright information in PMC

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