Evaluating the efficiency of municipal solid waste collection and disposal in the Yangtze River Delta of China: A DEA-model

ABSTRACT

As the “sixth largest city group in the world”, the Yangtze River Delta region is an important economic growth point and core economic area in China. While achieving rapid economic growth, the amount of waste generated is increasing day by day, and the investment in environmental protection is constantly increasing. Among them, MSW collection and disposal funds accounted for 57.40% of the fixed assets investment in environmental sanitation in 2017. To improve the efficiency of existing environmental protection investment, this paper is based on the existing academic research at China and abroad, with the help of the Data Envelopment Analysis (DEA) model. This paper analyzed the economic indicators of 27 cities in the Yangtze River Delta for MSW collection, transportation and disposal to measure economic efficiency, identify and improve inefficiencies. The research results show that the average efficiency of the 27 cities in the Yangtze River Delta is at a relatively high level. Six cities have the most effective comprehensive technical efficiency. These six cities constitute the effective frontier of the municipal solid waste transfer system. The overall efficiency has a comparative advantage; 9 cities have the best pure technical efficiency, accounting for 33 of all cities. %, the pure technical efficiency of the remaining 18 cities is low, indicating that these cities have not fully utilized their resource input under the current scale, and the efficiency of resource utilization needs to be improved. Finally, this paper proposes suggestions for improvement from the perspective of environmental sustainability. To improve MSW collection and disposal efficiency, differentiated management should be implemented in cities in the Yangtze River Delta.

Implications: This paper conducts an empirical analysis on the efficiency of MSW collection and disposal in the Yangtze River Delta in China in 2017. Based on the calculation of the DEA model, our conclusion is that although the collection and disposal system of MSW in the Yangtze River Delta has been significantly improved, the level of collection and disposal of some MSW has not reached the optimal level: (1) Comprehensive technical efficiency is the most effective Cities accounted for 22% of the total number of cities studied. (2) 33% of the cities in the Yangtze River Delta achieve the best pure technical efficiency; (3) Among the 27 cities, 6 cities have the best scale efficiency, and the remaining 21 cities have not reached the best scale efficiency.

Introduction

The Yangtze River Delta region, including Shanghai, Jiangsu, Zhejiang and Anhui “three provinces and one city”, has an urban agglomeration of 27 cities, with a superior geographical environment and a strong economic foundation. It is an important economic growth point and core economic area in China. Development has made important contributions. As a leader in China’s economic and social development, the Yangtze River Delta region is facing severe resource and environmental pressure while achieving rapid economic growth. The Yangtze River Delta region has continuously increased investment in environmental protection to promote the coordinated development of regional economic growth and environmental quality. However, limited government budget and worsening environmental pollution are two of the challenges faced by waste management in the Yangtze River Delta region. Increasing the efficiency of existing waste management and setting a reasonable budget can achieve a balance between maintaining a lower scale of MSW management budget and improving environmental quality within the existing economic growth framework.

Among the great variety of services offered by local authorities, that of municipal solid waste (MSW) collection and disposal is one of the most widely studied (Bel et al. 2010; Jacobsen, Buysse, and Gellynck 2013; Rogge and De Jaeger 2013). MSW collection and disposal costs demand between 75 and 80% of MSW management budgets (Bhat 1996; de Oliveira Simonetto and Borenstein 2007). However, inadequate management of MSW reduced resource allocation to collection and disposal of waste (Sharma, Ganguly, and Gupta 2019). The lack of sufficient number of collection bins, manpower, transportation vehicles for waste disposal could severely affect the efficiency of MSW collection and disposal efficiency (Sharma, Ganguly, and Gupta 2018). Inefficient MSW collection and disposal can rapidly deplete resources and energy (Alam et al. 2008). MSW collection and disposal efficiency has thus attracted increasing attention and is also a major concern among many local environmental authorities worldwide.

Measuring MSW collection and disposal efficiency has gradually become a research hotspot. The guiding principles of UN-SDGs are analogous to that of circular economy-based solid waste management. Circular economy-based sustainable solid waste management would help increase MSW collection and disposal efficiency (SDG 9), which as a tool for accelerating the progress of achieving SDGs (Sharma et al. 2021). Francesca Bartolacci (2019) proposes a new economic efficiency indicator for measuring and analyzing the income opportunities for companies operating in the collection and treatment of municipal solid waste. It is shown that the indicator can be a useful tool for generating valuable information for waste management companies and policy-makers (Bartolacci et al. 2019). These studies ignore the direct interaction between MSW collection and disposal and the extent of regional development. There are significant regional differences in China. Different MSW collection and disposal services in different regions directly affect the efficiency of MSW collection and disposal. Therefore, it is necessary to analyze and study the efficiency of MSW collection and disposal from the perspective of regional differences.

The Wasteaware ISWM benchmark indicators, widely applied as a standard methodology, allow a different city to judge its own performance regarding delivery of solid waste management services, provide information for decision-making on priorities for the limited funds available for service improvements to improve MSW collection and disposal efficiency (Wilson et al. 2015). However, the Wasteaware ISWM indicators are applicable to a broad range of cities with very different levels of income and solid waste management practices. The internal differences of urban agglomerations in China’s Yangtze River Delta are not very significant, which is not suitable for this method.

The majority of studies have used Data Envelopment Analysis (DEA) to assess efficiency of MSW collection and disposal programs across a group of municipalities or regions that are inefficient and need to be improved (Yüksel 2012). DEA has been widely applied to evaluate MSW collection and disposal efficiency and proven to be an effective tool for researchers and policy makers in Spain, Australia, Portugal, Belgium and other countries (Benito-López, Del R. Moreno-Enguix, and Solana-Ibañez 2011; Bosch, Pedraja, and Suárez‐Pandiello 2000; Marques and Simoes 2009; Pérez-López, Prior, and Zafra-Gómez 2018; Rogge and De Jaeger 2012; Worthington and Dollery 2001). Hence, DEA will be used in this manuscript to evaluate the efficiency of MSW Collection and Disposal in the Yangtze River Delta of China. Taking 27 cities in the Yangtze River Delta of China as samples, the paper measures the real MSW collection and disposal efficiency after eliminating external environment and random interference in 2017. The influence of external environment on the efficiency of MSWM is analyzed.

There are few studies on MSW collection and processing efficiency in the region. This paper attempts to supplement and contribute to the existing literature. With such an aim, the objectives of this empirical study are (I) to explore waste efficiency of areas evaluation approach; (ii) to analyze various reasons for differences within urban agglomerations; (iii) to provide government and researchers with valuable information on MSW collection and processing efficiency.

Study area

The Yangtze Delta or Yangtze River Delta (YRD) is including the whole area of Shanghai, Jiangsu Province, Zhejiang Province and Anhui Province (area of 358,000 square kilometers). Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yangzhou, Zhenjiang, Yancheng, Taizhou, Jiangsu, Hangzhou, Ningbo, Wenzhou, Huzhou, Jiaxing, Shaoxing, Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Anhui The 27 cities of Ma’anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng are the central areas (an area of 225,000 square kilometers), and radiation has led to high-quality development in the Yangtze River Delta region. The region’s traditional economic development mode has not yet fundamentally changed, and as a result, the ecological damage from this development is serious. Therefore, a new development plan for the YRZE was created, and officially published by National Development and Reform Commission of China (NDRC) in 2016, which emphasized that the priority should be given to environmental protection and outline innovative methods for ensuring the ecological health. As shown in Figure 1, the amount of clearance of MSW in the YRD reached 31,863,100 tons in 2017, accounting for over 15% of the total amount of the national clearance of MSW. Therefore, increasing efficiency of MSW collection and disposal has become one of the key issues to achieve green development in the YRD.

Figure 1. Location of Yangtze River Delta, China.

Methodology

Data envelopment analysis (DEA)

Data Envelopment Analysis (DEA, Data Envelopment Analysis) (Charnes, Cooper, and Rhodes 1978) is an efficiency evaluation method for operations research and the study of economic production boundaries. At present, it has been widely used in efficiency evaluation in the fields of economy, transportation, ecology, environment and energy, and has unique advantages in dealing with multi-index input and multi-index output. Its outstanding advantage is that it can analyze data indicators that are difficult to determine the weight and avoid the subjective error impact of artificially determining the weight.

The specific principle of the DEA model is as follows: Assume that the waste removal system has $DM{U}_n$ decision units, and $DM{U}_j$ each decision unit has $m$ types of inputs and $s$ types of outputs. $x_{ij}$ Assume the total $j$ amount of input from the $i$ first decision unit to the first type of $y_{ij}$ input; Assume the $j$ total amount of output from $i$ the first decision unit to the first type of output. For a particular $j$ first decision $DM{U}_j$ unit, its input and output indicators can $x_j={\left\{x_{1j},x_{2j},x_{3j}\cdots x_{mj}\right\}}^T$ be $y_j={\left\{y_{1j},y_{2j},y_{3j}\cdots y_{sj}\right\}}^T$ expressed $j=1,2,3\cdots n$ as and, where. For all decision-making $n$ units, assuming the total system input is expressed as, and $X=\left\{x_1,x_2,x_3\cdots x_n\right\}$ the total system output is $Y=\left\{y_1,y_2,y_3\cdots y_n\right\}$ expressed as, the model constructed is as follows:

(1) $\left\{\begin{array}{c}min\left[\theta -\epsilon \left(e^{-T}{s}^{-}+e^T{s}^{+}\right)\right]\\ s.t.\sum _{j=1}^n{X}_i{\lambda }_j+s^{+}=\theta X_0\\ \sum _{j=1}^n{Y}_j{\lambda }_j-s^{+}=Y_0\\ {\lambda }_j\ge 0,j=1,2,\cdots ,n;\\ s^{+}\ge 0;s^{-}\ge 0\end{array}\right.$(1)

where ${\lambda }_1,{\lambda }_2,\cdots {\lambda }_n$ is the weight variable of the $\theta$ decision unit, is the $s^{+}$ undetermined parameter, is $s^{-}$ the residual variable, $\epsilon$ is the relaxation variable, and is the Archimedes infinitesimal. And, $\theta \in \left(0,1\right)$ the closer to 1, the higher the comprehensive technical efficiency, and the lower the comprehensive technical efficiency. When $\theta =1$, it indicates that the removal and transportation efficiency reach the best comprehensive technical efficiency.

Introducing constraints in equation (1)(1) $\left\{\begin{array}{c}min\left[\theta -\epsilon \left(e^{-T}{s}^{-}+e^T{s}^{+}\right)\right]\\ s.t.\sum _{j=1}^n{X}_i{\lambda }_j+s^{+}=\theta X_0\\ \sum _{j=1}^n{Y}_j{\lambda }_j-s^{+}=Y_0\\ {\lambda }_j\ge 0,j=1,2,\cdots ,n;\\ s^{+}\ge 0;s^{-}\ge 0\end{array}\right.$(1) can ${\sum }_{j=1}^n{\lambda }_j=1$ transform equation (1)(1) $\left\{\begin{array}{c}min\left[\theta -\epsilon \left(e^{-T}{s}^{-}+e^T{s}^{+}\right)\right]\\ s.t.\sum _{j=1}^n{X}_i{\lambda }_j+s^{+}=\theta X_0\\ \sum _{j=1}^n{Y}_j{\lambda }_j-s^{+}=Y_0\\ {\lambda }_j\ge 0,j=1,2,\cdots ,n;\\ s^{+}\ge 0;s^{-}\ge 0\end{array}\right.$(1) into a DEA model with variable scale returns, so that the comprehensive efficiency can be decomposed into the product of pure technical efficiency and scale efficiency. Furthermore, we can use the above model to get the efficiency index as the pure technical efficiency index (denoted as), so there ${\theta }_b$ is, and $0<{\theta }_b\le 1$ at the ${\theta }_b\le \theta$ same time, the scale efficiency is defined $SE={\theta }_b/{\theta }_b$ as $0<SE\le 1$ SE. Similarly, for and, ${\theta }_b$ the $SE$ closer the value is to 1, the higher the pure technology efficiency and scale efficiency, and the closer to 0, the lower the pure technology efficiency and scale efficiency. When ${\theta }_b=1$ or $SE=1$ when, it means that pure technical efficiency or scale efficiency has reached the optimum, respectively.

Indicator selection

In the MSW collection and disposal system, the input and output indicators are not clear, but based on economics, in the optimal allocation of various resources or the efficient use of the system, the minimum input and maximum output. However, at this stage, the development level of each city in the Yangtze River Delta urban agglomeration is different, and the level of economic development is quite different. The input and output of different cities in the MSW collection and disposal system show a large difference, such as the mega-city Shanghai and the mega-city Nanjing’s investment in the MSW collection and disposal system is much higher than that in Chizhou, Zhoushan and other small cities. Therefore, it is necessary to analyze and compare the different cities’ Relative efficiency in MSW collection and disposal system and discuss related factors that affect efficiency.

In the input indicators of the MSW collection and disposal system, at least comprehensive consideration should be given to the human, material, and financial resources of each city’s investment in the MSW collection and disposal system. Based on the availability of data, it is difficult to measure the number of human resources invested in the MSW collection and disposal system at present. This paper selects the cumulative investment of urban waste treatment plants to measure the financial investment of the MSW collection and disposal system in cities. The total number of vehicles is used to measure the material resources of each city in the MSW collection and disposal system. The number of vehicle equipment configurations dedicated to the treatment of municipal solid waste has not yet formed a separate indicator, and it is temporarily replaced by number of vehicles and equipment designated for municipal environmental sanitation. Among the output indicators of the MSW collection and disposal system, the current national management of municipal solid waste is still focused on harmless treatment, and the reduction or comprehensive utilization of municipal solid waste is a secondary goal. In the selection of indicators, this paper selects the harmless treatment capacity of MSW and the removal and transportation volume of MSW as the output indicators of the MSW collection and disposal system. Table 1 provides some descriptive of the evaluation index system.

Table 1. Description of the evaluation index system.

Indicator name	Measurement methods	Index unit
Cumulative investment in municipal wastetreatment plants	2005–2017 “China Urban Construction Statistical Yearbook”Cumulative Investment in Municipal Solid Waste Treatment Plants	Ten thousand yuan
Number of vehicles and equipment designatedfor municipal environmentalsanitation	2017 China Statistical Yearbook of Urban Construction	unit
Harmless treatment capacity of MSW	2017 China Statistical Yearbook of Urban Construction	Ton/day
The amount of MSW collection	2017 China Statistical Yearbook of Urban Construction	10,000 tons

Data selection

Based on the official statistical data of China 2018 published by the National Bureau of Statistics of China and China Urban Construction Statistical Yearbook, this paper constructed a database for the DEA Model mentioned in Table 2 to calculate and evaluate the efficiency of MSW collection and disposal in the Yangtze River Delta.

Table 2. Input and output data of MSW collection and disposal system (2017).

Decision unit ($DU{M}_j$)	Cumulative investment of municipal waste treatment plant (10,000 yuan) (${\mathit{x}}_{1\mathit{j}}$)	Total number of vehicles for city appearance and sanitation (units) (${\mathit{x}}_{2\mathit{j}}$)	Harmless treatment capacity of MSW (ton/day) (${\mathit{y}}_{1\mathit{j}}$)	Clearance of MSW (10,000 tons) (${\mathit{y}}_{2\mathit{j}}$)
Shanghai	1036936	8583	24650	743.07
Nanjing	583608	2212	10765	285.06
Wuxi	49757	1083	4723	158.37
Changzhou	80637	681	4200	81.38
Suzhou	6598	3247	8939	272.65
Zhenjiang	70283	285	1450	47.42
Yancheng	592	380	1200	59.91
Taizhou	16558	1082	1000	36.55
Nantong	24310	1592	200	79.93
Yangzhou	63072	369	2820	56.32
Taizhou	15075	738	3650	109.67
Hangzhou	6125	2070	15747	351.12
Ningbo	125688	908	6600	151.83
Wenzhou	102357	609	4135	143.7
Huzhou	27448	210	1815	54.86
Jiaxing	27467	351	2250	41.84
Shaoxing	5859	518	2500	87.51
Jinhua	5530	206	1650	49.51
Zhoushan	27810	207	1140	35.03
Hefei	153153	963	4600	171.52
Wuhu	23399	312	3050	55.24
Tongling	14644	252	700	13.8
Anqing	26824	728	800	20.91
Luzhou	34271	137	700	14.36
Chizhou	3670	97	280	13.42
Xuancheng	7268	210	800	28.42
Maanshan	37273	120	1200	22.91

Results and discussion

Using the DEA model described above and using DEAP 2.1 software to analyze the input and output index data of the Yangtze River Delta urban agglomeration, select a two-stage DEA model to comprehensively examine the comprehensive technical efficiency, pure technical efficiency, scale efficiency and scale the pay trend is shown in Table 3. Technical efficiency is used to measure the ability of a decision-making unit to maximize its output under the conditions of a given element input, or to minimize its input under a given output condition. Technical efficiency is decomposed into pure technical efficiency and scale efficiency product. Pure technical efficiency reflects the management level and operation level of MSW collection and disposal facilities, and the higher the pure technical efficiency, the higher the output rate. Scale efficiency indicates whether the input–output ratio of MSW collection and disposal facilities is reasonable and whether the scale is coordinated. Scale efficiency is analyzed from the perspective of economies of scale, and scale efficiency shows an inverted U-shaped curve. That is, when the scale is small, the efficiency is lower, and as the scale expands, the efficiency increases to the maximum. When the scale continues to expand, the scale efficiency continues to decrease due to insufficient management level.

Table 3. Evaluation table of the efficiency of the MSW collection and disposal system.

Province	City	Comprehensive technical efficiency	Pure technical efficiency	Scale efficiency	Returns to scale
	Shanghai	0.334	1	0.334	decline
Jiangsu	Nanjing	0.537	0.725	0.741	decline
	Wuxi	0.599	0.757	0.791	decline
	Changzhou	0.636	0.692	0.919	decline
	Suzhou	0.569	0.702	0.810	decline
	Zhenjiang	0.637	0.663	0.960	rise
	Yancheng	1	1	1	constant
	Taizhou	0.164	0.200	0.819	rise
	Nantong	0.244	0.247	0.986	decline
	Yangzhou	0.788	0.801	0.983	decline
	Taizhou	0.671	0.768	0.874	decline
Zhejiang	Hangzhou	1	1	1	constant
	Ningbo	0.778	0.920	0.846	decline
	Wenzhou	0.903	1	0.903	decline
	Huzhou	1	1	1	constant
	Jiaxing	0.659	0.693	0.951	rise
	Shaoxing	0.869	0.872	0.997	rise
	Jinhua	1	1	1	constant
	Zhoushan	0.648	0.751	0.863	rise
Anhui	Hefei	0.682	0.836	0.816	decline
	Wuhu	1	1	1	constant
	Tongling	0.301	0.499	0.603	rise
	Anqing	0.131	0.189	0.693	rise
	Chuzhou	0.524	0.785	0.667	rise
	Chizhou	0.570	1	0.570	rise
	Xuancheng	0.559	0.676	0.827	rise
	Maanshan	1	1	1	constant
	City averageefficiency	0.659	0.769	0.850	-

Overall efficiency analysis

As can be seen from Table 3, the average comprehensive technical efficiency of all 27 cities in the Yangtze River Delta is 0.659. Six of them have the most effective comprehensive technical efficiency, that is, the comprehensive technical efficiency reaches 1, which are Yancheng in Jiangsu Province, Hangzhou, Huzhou and Jinhua in Zhejiang Province, Wuhu and Ma’anshan in Anhui Province. The cities with the most comprehensive technical efficiency accounted for 22% of the total number of cities studied. These six cities constitute the effective frontier of the MSW collection and disposal system, and the overall efficiency has a comparative advantage. Each efficiency value is 1 and the scale returns are unchanged. This indicates that the six cities have reached the input of the MSW collection and disposal system. These six cities continue to increase capital investment and improve the MSW collection and disposal infrastructure to ensure the effective advancement of MSW management. For example, the “Administrative Measures for the Environmental Improvement Special Funds for the Centralized Treatment of Domestic Waste in Hangzhou City” was issued in 2017. The environmental improvement funds will be paid in accordance with certain payment standards in the domestic waste exporting area, and the funds will be specially used for the domestic waste input to the urban infrastructure improvement and waste treatment projects. The Environmental Sanitation Department of Yancheng Urban Management Bureau upgraded the environmental sanitation infrastructure and updated and maintained equipment at the waste transfer station.

To make the best use of it and get the best output under a certain input, the other 21 cities have different levels of excess input or insufficient output, and there are problems of large scale or small scale. In non-DEA effective cities, except for cities in Anhui Province, most cities in other provinces are in a state of increasing returns to scale. In 2017, the Yangtze River Shipping Public Security Bureau found through investigation that some ships used the Yangtze River channel to load a large amount of solid waste from Jiangsu and Zhejiang provinces and illegally transferred it to Anhui Province for dumping, increasing the waste collection and disposal burden of Tongling city and other cities in Anhui Province. For provinces in the state of diminishing returns to scale, cities with a higher level of economic development such as Shanghai and Nanjing invest more funds to intelligent classification systems for MSW collection and disposal. So, there is an obvious cost here. The input–output combination should be optimized without reducing the scale to improve efficiency and reduce costs.

Analysis of pure technical efficiency

The average pure technical efficiency of 27 cities is 0.769. Shanghai, Yancheng, Hangzhou, Wenzhou, Huzhou, Jinhua, Wuhu, Ma‘anshan, and Chizhou are technically effective (PTE = 1), accounting for 33% of the total cities. The remaining 18 cities have low pure technology efficiency, indicating that these cities are currently at a low level. Under-scale resource input is not fully utilized, and resource utilization efficiency needs to be improved. From the perspective of urban agglomerations, the average pure technical efficiency of the 8 cities in Zhejiang province is as high as 0.905, higher than the average of urban agglomerations in the Yangtze River Delta. The pure technical efficiency level is high, and the pure technical efficiency of most cities reaches the efficiency frontier. The average pure technical efficiency of 10 cities in Jiangsu province is 0.656, lower than the average of urban agglomeration in Yangtze River Delta. The pure technical efficiency of some cities, such as Taizhou and Nantong, is lower than 0.3. It is necessary to learn waste management experience and waste facility operation technology from surrounding cities.

In cities with low pure technology efficiency, the gap is also relatively large, especially in Anqing, Taizhou and Nantong. The pure technical efficiency of these three cities is less than 0.3, far less than the average of the Yangtze River Delta urban clusters, and there is an urgent need to improve the level of resource utilization at the current scale. Anqing has not fully utilized the resources input at the current scale. The focus of waste treatment has been on the main urban area and disposal. Due to insufficient capital investment, the new urban area and the capacity of waste collection and transfer in various counties and cities Insufficient, the construction of transfer stations is still insufficient, and the collection and transfer of domestic waste in closed collection and transportation needs to be improved; Taizhou government has limited financial resources and failed to effectively assume the main responsibility of domestic waste treatment, resulting in slow progress in the construction of treatment facilities and low coverage of transfer stations. The completed facilities cannot operate normally; although Nantong has built 4 large-scale domestic waste incineration and disposal plants and 1 emergency landfill, compared with the annual growth rate of 10%, the disposal pressure is still relatively high.

Analysis of scale efficiency and scale returns

Among the 27 cities, the average scale efficiency was as high as 0.850. Among them, six cities of Yancheng in Jiangsu province, Hangzhou, Huzhou and Jinhua in Zhejiang province, and Wuhu, Chizhou and Ma’anshan in Anhui province have reached the optimal scale efficiency, accounting for 22% of all cities. The remaining 21 cities have not reached optimal scale efficiency. However, among the remaining 21 cities, the scale efficiency of Yangzhou, Nantong, Shaoxing, and Jiaxing has reached above 0.95, which can also be considered as scale effective. Among the non-scale and effective cities, large and medium-sized cities such as Shanghai, Nanjing and Wuxi have excess investment, scale efficiency is low, and scale returns are reduced, indicating that large and medium-sized cities such as Shanghai, Nanjing and Wuxi need to reduce their MSW collection and disposal system to increase their scale efficiency and make their scale efficiency optimal.

For small cities such as Chizhou, Tongling, and Chuzhou, there is an obvious lack of investment, and the return to scale shows an upward trend, indicating that these cities must increase investment to achieve optimal scale efficiency. Due to the limited capital investment in these small cities, there are the following problems in the construction of MSW collection and disposal systems: First, the number of MSW collection and disposal facilities is seriously insufficient, and the layout is unreasonable; second, the planning and construction of MSW collection and disposal systems are lagging and supporting facilities. Aging and insufficient renewal also make it difficult for MSW disposal to keep up with the needs of the city; third, the sanitary conditions of the MSW collection and disposal facilities currently in operation are not good, causing the surrounding residents to feel unhappy. In addition, various contradictions in the planning, construction, and formal operation of MSW collection and disposal facilities have caused MSW collection and disposal to become an issue that cannot be ignored in urban management.

Conclusion

In this paper, DEA model was used to make an empirical analysis on the efficiency of MSW collection and disposal in The Yangtze River Delta region of China in 2017. Through DEA model calculation, it can be concluded that (1) Overall, the collection and treatment of MSW in most cities in the Yangtze River delta region has good scale efficiency (mean = 0.850). (2) The average comprehensive technical efficiency value, the average pure technical efficiency value and the average scale efficiency value of the Yangtze River Delta urban agglomerations are 0. 659, 0. 769, 0. 850. Yancheng, Hangzhou, Huzhou, Jinhua, Wuhu and Ma‘anshan have reached the forefront of comprehensive technical efficiency, accounting for 22.2% of all cities in the urban agglomerations. (3) Most MSW collection and disposal systems in the Yangtze River Delta region show diminishing returns to scale, which is related to the particularity of MSW collection and disposal systems. Insufficient investment and unreasonable allocation of input elements in the collection and disposal system of MSW.

To improve the efficiency of MSW collection and disposal in the Yangtze River Delta region, improvements can be made from the following aspects: (1) Strengthen the government-led model and improve the efficiency of MSW collection and disposal. The regional governments in the Yangtze River Delta should strengthen the supervision of the collection and disposal system of MSW to prevent or minimize pollution damage to the environment. By replacing the traditional sanitary landfill treatment processes with resource utilization technology as the MSW disposal standard. To formulate policies and norms for the MSW collection and disposal that maximizes effectiveness. (2) Increase investment in science and technology and improve the technological level of MSW collection and disposal. The governments of the cities in the Yangtze River Delta should design appropriate incentives to encourage environmental protection companies to upgrade and improve the technology of the MSW collection and disposal system. By adopting waste collection and transportation technology and terminal treatment technology with higher utilization efficiency, timely update of outdated technology and equipment, improve the technological level of waste collection and disposal in the Yangtze River Delta to reduce waste discharge.

On the basis of predecessors’ study (Fan et al. 2020), we explore the efficiency differences of MSW collection and treatment within core urban agglomerations in China. By refining the scope of research objects and taking urban agglomeration as a new perspective, the results can provide reference for improving the efficiency of waste collection and treatment in other urban agglomeration. The study has some limitations. The sample focuses only on urban agglomerations in China’s Yangtze River Delta. Since the Yangtze River delta case study provides evidence of regional waste management sector, it enables the study implementation to a wider analysis. So that future research can explore and MSW collection and disposal services, geographical features to a comparative study of China’s pearl river delta and the Beijing-Tianjin-Hebei urban agglomeration, thus highlight the existing differences between the urban agglomeration. Other limiting factors of the study are linked to data availability. In fact, it would be interesting to include additional variables that affect the efficiency of MSW collection and disposal.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [ZJC], upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this paper can be accessed on the publisher’s website.

Additional information

Funding

This work was supported by the [National key research and development plan key special project] under Grant [number 2018YFC1903605]; [Fundamental Research Funds for the Central Universities] under Grant [number GK2090260158]; [the PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities] under Grant [number HEUGIP201719]; [Training Program in Response to Major National Strategic Needs—Think Tanks] under Grant [number HEUCFP201823 and HEUCFP201834]; [The Leading Research Project of Shanghai Jiao Tong University—Think Tanks] under Grant [number ZXYJ-2020017].

Notes on contributors

An Zhou

An Zhou is a Ph.D. candidate at the Harbin Engineering University, Nangang District, Harbin, China.

Wenna Wang

Wenna Wang is a Ph.D. candidate at the Harbin Engineering University, Nangang District, Harbin, China.

Zhujie Chu

Zhujie Chu is professor at the Shanghai Jiao tong University, Shanghai, China.

Shenhan Wu

Shenhan Wu is an undergraduate studying at the Harbin Engineering University, Nangang District, Harbin, China.