1 Introduction

Municipal solid waste mainly refers to the solid waste generated in urban daily life or activities providing services for urban daily life, as well as the solid waste regarded as municipal solid waste according to laws and administrative regulations [1]. At present, the commonly used methods for domestic waste treatment are incineration, landfill and composting. However, mixed treatment exists in the collection, transportation and treatment of domestic waste in most cities in China. Moreover, the adhesion of each component reduces the separation rate, the mixing of food waste reduces the calorific value of waste and increases the moisture content of domestic waste [2]. Due to the development of economy and tourism in Tibet in recent years, a large amount of domestic waste and tourism waste have been produced [3]. Lhasa, the capital of Tibet, still mainly uses landfill for domestic waste treatment. A large amount of gas and leachate will be produced during landfill treatment, and the landfill will have an impact on the surrounding natural environment such as surface water [4], groundwater [5, 6], soil [7, 8] and landfill gas [9]. The disadvantages of landfill treatment technology are exposed and cannot meet the needs of waste treatment. Lhasa began to build the first waste incineration power plant in the Tibetan Plateau in 2014, put into trial operation in 2017, and began to accept the city’s domestic waste in 2018 [3]. Therefore, the treatment methods of domestic waste in the Tibetan Plateau include incineration and landfill. Due to the mixed treatment of waste, it is unfavorable to incineration and landfill treatment, which will increase the treatment cost of secondary pollution. The effective treatment of domestic waste can not only protect the environment and people’s health, but also provide certain resources for the society and bring economic and environmental benefits. Therefore, in order to analyze the suitable treatment methods of domestic waste in plateau area. Firstly, this paper analyzes the treatment methods of domestic waste in Tibet, the impact of domestic waste landfill treatment in Tibet, and the advantages of domestic waste incineration. Then, the combination of analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) is used to select the appropriate processing method [10,11,12]. Among them, AHP is a qualitative and quantitative decision analysis method proposed by American operations research scientist Saaty [13]. The Technique for Order Preference by Similarity to an Ideal Solution is a method proposed by Wang et al. [14]. TOPSIS is a method of ranking according to the closeness of a limited number of evaluation objects to the ideal target. It is also a method often used in multi-objective decision-making.

Therefore, in order to understand the suitability of incineration and landfill in urban domestic waste treatment in Tibet. In this paper, AHP and TOPSIS methods are proposed to analyze the appropriate domestic waste treatment methods in plateau areas. Based on the natural and social environment of Lhasa, six aspects such as social factors, technical factors, economic factors, environmental factors and waste characteristics are selected as the evaluation index system. Firstly, AHP is used to determine the weight to select the appropriate treatment method, and then TOPSIS is used to sort the advantages and disadvantages of the index system to further determine the appropriate waste treatment method. The results of the study provide a scientific reference for the selection of domestic waste treatment in plateau areas.

2 Current situation of domestic waste treatment on Tibetan plateau

2.1 1Treatment methods of municipal solid waste in Tibet plateau

With the improvement of economic development in Tibet in recent years and the corresponding improvement of urban living standards, the municipal solid waste (MSW) generation is increasing year by year, as shown in Table 1. Moreover, Tibet is rich in tourism resources, and the amount of tourism waste is also increasing year by year. As shown in Table 2, Tibet’s urban domestic waste treatment methods from 2016 to 2019, the data in the figure are from the National Bureau of statistics of China. It is obvious from the figure that with the operation of incineration power plants, the amount of municipal solid waste treated by sanitary landfill in Tibet has decreased. Among them, the harmless treatment capacity of urban domestic waste in Tibet from 2016 to 2019 was 420,000, 444,000, 518,000 and 636,000 tons. The harmless treatment is mainly sanitary landfill, which shows that when the plateau area is not put into the waste incineration power plant, the urban domestic waste treatment method is single. In addition, in remote urban areas, there are no sanitary landfills and incineration plants for MSW treatment, so most of the MSW in rural areas of Tibet adopts the methods of simple incineration, simple landfill and random disposal [15].

Table 1 Output and disposal rate of urban domestic waste in Tibet
Full size table
Table 2 Urban domestic waste removal and treatment in Tibet from 2016 to 2019
Full size table

2.2 Impact of landfill treatment in Tibet plateau

It can be seen from Table 2 that sanitary landfill is mainly used for waste treatment in Tibet. This method has the advantages of simple technology, large treatment capacity, low cost and suitable for all garbage. However, the leachate will be produced in the landfill process, which will pollute the surrounding soil, water, gas and other natural environment. In recent years, research on urban domestic waste landfills in Tibet has increased, mainly focusing on risk assessment of soil, water, landfill gas and leachate around landfills in Lhasa City [8], Xigaze City [16], Shannan City [17], Bange County [18], and other regions. In the interim, the soil around the landfill is greatly affected by heavy metals. The study also analyzed the sources of heavy metals in the soil around the landfill site. The source of As pollution may be mainly due to soil forming factors and the source of Hg pollution is mainly due to human factors [19]. Wang et al. [20] have also studied the soil in landfills and geothermal heat in Tibet, indicating that As, Hg and Cr elements have a great impact, mainly because unscientific human activities contribute 51.83% to soil pollution. It shows that the way of landfill treatment will have a certain impact on the soil, water quality, gas and other natural and human environments around the landfill site.

2.3 Current status of research on solid waste incineration on the Tibetan plateau

Waste incineration technology has the advantages of reduction, harmlessness and recycling, and waste incineration power generation can bring certain economic and social benefits to the society. Dan et al. analyzed that the moisture content of domestic waste in Lhasa is 24.39%, which is mainly due to the dry climate and less precipitation on the plateau [21]. It can be seen from Table 3 that the organic content of garbage in Lhasa is less than that in other cities in China. And the paper, rubber and plastics, textiles, wood and bamboo and other combustibles account for a relatively high proportion, which is higher than that of other cities in China, which is conducive to incineration. Han et al. analyzed the rural domestic waste in the Qinghai Tibet Plateau [15]. The research shows that the rural domestic waste in the Qinghai Tibet Plateau has the characteristics of low yield, small bulk density, low moisture content, high recyclable content and calorific value. In addition, Dan et al. also studied the characteristics of municipal solid waste incineration and flue gas emission in the Qinghai Tibet Plateau [3]. The research shows that the concentrations of SO2, CO, HCI, dust and NOx in flue gas discharged under normal working conditions under anoxic conditions are 49.79, 5.34, 48.22, 2.67 and 322 mg/m3 respectively. If the concentration of pollutants is to be low during waste incineration, the incineration conditions are that the temperature is 900℃, the oxygen content is in the range of 6 ~ 8%, and the pressure is close to 0 Pa. The above research shows that the domestic waste on the plateau has the characteristics of low moisture content and high calorific value, which shows that it provides basic feasible conditions for incineration in the plateau area.

Table 3 Physical characteristics of municipal solid waste
Full size table

3 Materials and methods

3.1 Main steps of analytical hierarchy process

AHP method mainly calculates the maximum eigenvalue and feature vector, and this method includes two calculation methods, square root method and sum product method [13]. The sum product method is mainly used in this paper, and the main steps of the method are as follows, as shown in Eqs. (1) and (8):

Normalization of each column of judgment matrix, where bij is hierarchical single sorting. i. j and k are different values in the calculation formula.:

$$\left( {\mathop {b_{ij} }\limits^{ - } = {{b_{ij} } \mathord{\left/ {\vphantom {{b_{ij} } {\sum\limits_{k = 1}^{n} {b_{kj} } }}} \right. \kern-\nulldelimiterspace} {\sum\limits_{k = 1}^{n} {b_{kj} } }}\quad \quad i = 1, \cdots ,n;j = 1, \cdots ,n} \right)$$
(1)

The vector \(\mathop W\limits^{ - }\) is obtained by summing the judgment matrix of the column by row:

$$\left( {\mathop {W_{i} }\limits^{ - } = \sum\limits_{j = 1}^{n} {\mathop {b_{ij} }\limits^{ - } } \quad \quad i = 1, \cdots ,n; \, j = 1, \cdots ,n} \right)$$
(2)

Normalize vector \(\mathop W\limits^{ - }\) to obtain vector W, \(\mathop W\limits^{ - } = [\mathop {W_{1} }\limits^{ - } ,\mathop {W_{2} }\limits^{ - } , \cdots ,\mathop W\limits^{ - }_{n} ]^{T}\):

$$W_{i} = {{\mathop {W_{i} }\limits^{ - } } \mathord{\left/ {\vphantom {{\mathop {W_{i} }\limits^{ - } } {\sum\limits_{k = 1}^{n} {\mathop {W_{k} }\limits^{ - } } }}} \right. \kern-\nulldelimiterspace} {\sum\limits_{k = 1}^{n} {\mathop {W_{k} }\limits^{ - } } }}\quad \left( {i = 1, \cdots ,n} \right)$$
(3)

W is the feature vector. \(\lambda_{{{\text{max}}}}\) is the maximum characteristic root:

$$\lambda_{\max } = \sum\limits_{i = 1}^{n} {\frac{{(AW)_{i} }}{{nW_{i} }}}$$
(4)

Calculation of consistency test. CI is the consistency indicator:

$$CI = \frac{{\lambda_{\max } - n}}{n - 1}$$
(5)
$$CR = {{CI} \mathord{\left/ {\vphantom {{CI} {RI}}} \right. \kern-\nulldelimiterspace} {RI}}$$
(6)

In Eq. (6), RI is the average random consistency index, which is shown in Table 4 [13].When CR is less than 0.1, the consistency test of calculation is acceptable. On the contrary, the matrix needs to be adjusted. If the upper layer is layer A, the indicator is aj. If the next layer is layer B, the indicator is bj. bij refers to hierarchical single sorting. The total ranking weight of all indicators in Level B is bi:

$$b_{i} = \sum\limits_{j = 1}^{m} {b_{ij} } a_{j}$$
(7)
Table 4 RI average random consistency index
Full size table

Total ranking and consistency test of analytic hierarchy process:

$$CR = {{\sum\limits_{j = 1}^{m} {a_{j} CI_{j} } } \mathord{\left/ {\vphantom {{\sum\limits_{j = 1}^{m} {a_{j} CI_{j} } } {\sum\limits_{j = 1}^{m} {a_{j} } }}} \right. \kern-\nulldelimiterspace} {\sum\limits_{j = 1}^{m} {a_{j} } }}RI_{j}$$
(8)

When \(CR\) is less than 0.1, the consistency test is passed and the final scheme can be given.

3.2 Calculation steps of technique for order preference by similarity to an ideal solution

TOPSIS method is to find out the best scheme and the worst scheme in the scheme from the normalized original data matrix. Then calculate the distance between each evaluation object and each scheme, and get the relative proximity between the evaluation object and the optimal scheme [22, 23]. The steps are as follows,as shown in Eq. (9) and (13):

Firstly, the total ranking of levels and the matrix x of the target level are normalized, and the standard synthesis matrix r is obtained:

$$r_{ij} = {{x_{ij} } \mathord{\left/ {\vphantom {{x_{ij} } {\sqrt {\sum\limits_{i = 1}^{m} {(x_{ij} )^{2} } } }}} \right. \kern-\nulldelimiterspace} {\sqrt {\sum\limits_{i = 1}^{m} {(x_{ij} )^{2} } } }}\quad \quad \left( {i = 1, \cdots ,m;j = 1, \cdots ,n} \right)$$
(9)

Then the weighted normalized synthesis matrix V is calculated. It mainly combines the weight

$${\text{vector }}\omega {\text{ and normalized matrix R}}: \, V = R\omega$$
(10)

Calculation of ideal and non ideal solutions: According to the weighted normalization matrix V, the positive ideal solution V+ with the highest index score and the negative ideal solution V− with the lowest score are selected.

$$D_{i}^{ + } = \sqrt {\sum\limits_{j = 1}^{n} {(v_{ij} - V^{ + } )^{2} } } \quad \quad \left( {i = 1, \cdots ,m} \right)$$
(11)
$$D_{i}^{ - } = \sqrt {\sum\limits_{j = 1}^{n} {(v_{ij} - V^{ - } )^{2} } } \quad \quad \left( {i = 1, \cdots ,m} \right)$$
(12)

Determine the relative proximity, and then sort by the size of the Ci value. Finally, the optimal method is the one with the largest Ci value:

$$C_{i} = {{D_{i}^{ - } } \mathord{\left/ {\vphantom {{D_{i}^{ - } } {(D_{i}^{ + } }}} \right. \kern-\nulldelimiterspace} {(D_{i}^{ + } }} + D_{i}^{ - } )\quad \quad \left( {i = 1, \cdots ,m} \right)$$
(13)

4 Results and discussion

4.1 Model analysis

The main problem of incineration treatment is the environmental pollution caused by waste incineration tail gas, while landfill mainly covers a large area and serious secondary pollution [24,25,26,27,28,29,30,31,32]. Therefore, after comprehensive consideration, 20 indicators are selected from five factors, including social factors, technical factors, economic factors, environmental factors and garbage characteristics, and then the index system is established. The order from top to bottom in the figure is the target layer (A), criterion layer (O), sub-criterion layer (C) and scheme layer (P).

Target layer (A): Selection of MSW treatment technology.

Criterion layer (O): (O1) Social, (O2) Technical, (O3) Economic, (O4) Environmental, (O5)Characteristics of waste.

Sub-criterion layer (C): (C1) Population scale, (C2) Management level, (C3) Population growth, (C4) Population density, (C5) Technology maturity, (C6) Periodic maintenance, (C7) Resource utilization, (C8) Business management, (C9) Operating costs, (C10) Economic level, (C11) Urbanization rate, (C12) Equipment investment, (C13) Transport costs, (C14) Land resources, (C15) Secondary pollution, (C16) Natural disasters, (C17) Types of waste, (C18) Calorific value, (C19) Output, (C20) Utilization value.

Scheme layer (P): (P1) Incineration treatment, (P2) Landfill treatment.

The hierarchical structure between the target layer, criterion layer, sub criterion layer and measure layer of the above established hierarchical model is shown in Fig. 1.

Fig. 1
figure 1

Hierarchical model for decision analysis of waste disposal technology

Full size image

4.2 Results and analysis of analytical hierarchy process

The main criteria were subjected to pairwise comparison with respect to the goal, and the score according to the scale table (Table 5). According to the expert score, the judgment matrix A-O of the target layer (A) to criterion layer (O) is constructed. The calculated vector is as follows: AW = [2.6742, 0.7508, 0.3840, 1.0678, 0.3169]T. Then, the value of RI is selected as 1.12 from Table 3, and the CR value is calculated from formula (6). The CR value is 0.0473 and less than 0.1, indicating that this matrix has an acceptable consistency test. The calculation results are shown in Table 6. It can be seen from the table that the social factors in the criterion layer are the first factors to be considered in the construction process, with a weight of 0.5133. This aspect mainly includes factors such as the population size, the population growth, the supervision level and population density. Secondly, the environment also needs to be considered in the construction process, with a weight of 0.2099, which is second in importance. In this regard, the land resources, secondary pollution, natural disasters and other factors during the construction process are mainly considered. Technical factors should also be considered in the construction process, including the impact of technical maturity, periodic maintenance, resource utilization and enterprise management, with a weight of 0.1436, ranking third. Economic factors also need to be considered in the construction process, the because operating costs, the economic level, the urbanization rate, the equipment investment and transportation costs will also have a certain impact, with a weight of 0.0736, ranking fourth. Finally, the characteristics of local waste should be considered in the construction process, and its weight is 0.0596.

Table 5 Scale table
Full size table
Table 6 Target layer alignment criteria into judgment matrix A–O
Full size table

The judgment matrix of the criterion layer O to the sub criterion layer C is calculated through the formula, and the results are shown in Table 7.The weights are sorted in the table. It can be seen that population scale C1, population growth C3, land resources C14 and population density C4 have a great impact. It can be seen that the influence factors of population are very large. In addition, Estay Ossandón et al. [33] also showed that the population and tourist population are the biggest factors in their research on the important factors of municipal solid waste generation.

Table 7 Hierarchical total sorting
Full size table

Finally, the judgment matrix of the sub criterion layer (C) to the scheme layer (P) is constructed, and the optimization matrix of the target layer to the scheme layer is calculated, as shown in Table 8. The matrix here is of order 2, and the value of RI is 0 from Table 3. The total weight calculated by AHP is 0.5954 (incineration technology) > 0.4046 (Landfill Technology), so incineration treatment is more suitable for MSW treatment in plateau areas.

Table 8 Optimization matrix from target layer to scheme layer
Full size table

4.3 Results of technique for order preference by similarity to an ideal solution

The comprehensive standard matrix R is obtained by formula (9). As shown in Table 9, weighted decision normalization matrix.

Table 9 Weighted decision normalization matrix
Full size table

Then, through formula (10), R is combined with the weight ω to obtain the weighted normalization matrix V. In the weighted normalization matrix V, the maximum score of the measure scheme is the positive ideal solution V+ and the minimum score is the negative ideal solution V−, as shown below:

V+ = [0.199, 0.044, 0.119, 0.094, 0.060, 0.044, 0.016, 0.019, 0.027, 0.009, 0.006, 0.017, 0.011, 0.118, 0.044, 0.031, 0.017, 0.024, 0.012, 0.005].

V− = [0.066, 0.022, 0.089, 0.047, 0.020, 0.009, 0.004, 0.006, 0.009, 0.004, 0.003, 0.003, 0.004, 0.039, 0.029, 0.010, 0.003, 0.003, 0.004, 0.002].

The values of D+, D− and Ci of the measure scheme are calculated by formulas (11–13), and the results are shown in Table 10. According to the value of relative proximity, the advantages and disadvantages of processing technology and ranking results are determined. It can be seen from Table 10 that the CI value of the relative proximity of the incineration treatment technology is greater than that of the landfill treatment technology, so the incineration treatment technology is more suitable on the plateau.

Table 10 Distance value and relative proximity value of measures in each scheme
Full size table

In conclusion, through the combination of AHP and TOPSIS, the two waste treatment technologies of incineration and landfill in plateau area are analyzed, and it is concluded that the incineration treatment technology is more suitable for plateau area. Moreover, the advantages of low moisture content, large proportion of combustible substances and high calorific value of garbage in plateau area also provide conditions for incineration treatment.

4.4 Data sensitivity analysis

In order to obtain the reliability of the data, the sensitivity analysis is carried out according to the positive ideal solution V+ and negative ideal solution V− values in TOPSIS calculation to ensure the reliability of the data. It is mainly calculated by SPSS 17.0 software. The reliability of the data is mainly through the reliability analysis module in SPSS software, and the effectiveness of the data adopts Pearson correlation coefficient and the factor analysis. Among them, the Cronbach’s Alpha value of reliability analysis is between 0.7 and 0.98, indicating that there is a high internal reliability value [34]. The effectiveness is generally greater than 0.4, which is ideal. The results are shown in Table 11.

Table 11 Reliability and validity analysis of data
Full size table

From Table 11, it can be seen that the value of Cronbach alpha in the reliability analysis is greater than 0.8, which shows that the internal consistency reliability of the research data is high. From the correlation analysis, it can be seen that the correlation coefficient is 0.888, which has a significant correlation, indicating that the data in the study is highly effective. In addition, the value of Kaiser Meyer Olkin obtained by factor analysis is 0.5, which also shows that the data has high reliability and validity.

In conclusion, the reliability analysis and the validity analysis of the data show that the data in the study has a certain stability.

5 Conclusions

In this paper, the AHP and TOPSIS methods are used to compare and analyze the current disposal methods of municipal solid waste incineration and landfill in Tibet Plateau. According to the analysis results of AHP method, it is considered that the population size, population growth, population density and other factors have a greater weight impact on the selection of urban domestic waste disposal methods. Finally, TOPSIS analysis shows that incineration is more suitable for the Tibetan Plateau than landfill.

However, the inadequacy of the study is that the indicators selected in the article are not completely and comprehensively analyzed. It is also necessary to conduct further field research and analysis through the current incineration and landfill treatment methods of urban domestic waste in Tibet. As the Tibetan Plateau is a region with fragile ecological environment, many factors should be considered in the selection of urban domestic waste treatment methods, and ecological protection should be emphasized.