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He, J. Municipal Solid Waste Management in Beijing. Encyclopedia. Available online: https://encyclopedia.pub/entry/17327 (accessed on 17 June 2024).
He J. Municipal Solid Waste Management in Beijing. Encyclopedia. Available at: https://encyclopedia.pub/entry/17327. Accessed June 17, 2024.
He, Jiahao. "Municipal Solid Waste Management in Beijing" Encyclopedia, https://encyclopedia.pub/entry/17327 (accessed June 17, 2024).
He, J. (2021, December 20). Municipal Solid Waste Management in Beijing. In Encyclopedia. https://encyclopedia.pub/entry/17327
He, Jiahao. "Municipal Solid Waste Management in Beijing." Encyclopedia. Web. 20 December, 2021.
Municipal Solid Waste Management in Beijing
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An intelligent garbage sorting system (IGSS) is an effective sorting approach for MSW management. To explore the predictors of local residents’ willingness to pay (WTP) for the IGSS, this entry applied an extended theory of planned behavior (TPB) model by adding an antecedent environmental concern (EC) prior to the main predictors of the TPB model (attitudes, subject norms, perceived behavioral control). 

theory of planned behavior (TPB) willingness to pay (WTP)

1. Background

global municipal solid waste (MSW) generation levels are estimated to increase to approximately 2.2 billion tons per year by 2025 [1]. Consequently, the traditional waste management system has encountered difficulties in disposing of large volume of MSW [1], which has led to inadequate disposals and has become a severe problem in many developing countries. China surpassed the U.S as the world’s top MSW producer in 2004. Now, China’s MSW generation is growing at an annual rate of 8–9% [1]. From 2010 to 2019, MSW production in Beijing rose from 6.35 million tons per year to 10.11 million tons, averaging 27,700 tons per day, which is 1.2 kg per person per day. With a series of policies, regulations, and measures, the composition structure of MSW management technology has changed in proportion; in 2020, Beijing reached 87% of MSW recycling (43% by incineration, 45% by landfill, 12% by composting) [2]. In addition, the main problem of MSW management in China is that the MSW composition is complicated. The average water content of MSW in Beijing reaches 50.19%, and this mixed MSW makes it difficult to sort and recycle [2]. The water contained in MSW not only pollutes recyclables in MSW, but also brings difficulties and increases the cost of MSW disposal, collection, transfer, transportation, and treatment. Therefore, the pre-sorting and treatment of MSW becomes a necessary condition for incineration [2]. In this case, effective MSW management has become necessary, not only from the human health perspective, but also from the aspect of environmental concerns [3]. Effective MSW management approaches such as smart MSW sorting systems [3] have been proposed to minimize the harmful effects of inadequate disposal in urban areas. However, in China, there is a lack of a uniform standard for MSW management charges, with local governments specifying the charges within administrative districts, one of which is levied incidentally through utility charges such as water, gas, and electricity [4]. Specifically, the payments for MSW management have normally been extracted from utilities expenses by local residents, which has become a possible cause of conflict regarding payments between residents and the local government [4]. The intelligent garbage sorting system (IGSS) is a widely used smart MSW sorting approach and is an effective way to improve urban MSW management and increase the productivity of cleaning contractors [5]. Compared with the current MSW treatment methods mainly performed in Beijing city, it can improve the efficiency of waste incineration treatment by substantially improving the efficiency of recycling and separation [5]. The MSW sorting features of IGSS are one of the most practical and economical methods and can reduce the costs of unsynchronized disposal by 80−90% [5]. In that regard, health-hazardous factors in MSW such as germs and other substrates can be substantially reduced [5]. In addition, the pre-sorting of unburnable MSW (metals, glass, etc.) can significantly reduce the costs of the second separation [5]. In this study, willingness to pay (WTP) is defined as the willingness of local residents to pay for IGSS, which is influenced by their thoughts and perceptions. Cases focusing on the WTP for IGSS have been frequently discussed in previous research and have played an important role in improving MSW management in developed countries [6][7]. In Europe, different countries have regulations on MSW separation and recycling, but there is no uniform standard for MSW management (it mainly depends on the consciousness of local residents) [8][9]. In addition, in Germany, the recycling of certain bottles can be rewarded in supermarket recycling devices (Similar to a type of IGSS), which improves the motivation of local residents for MSW separation [10]. In Japan, local residents sort MSW by different colored bags, and different levels of fees are associated with sizes and categories, which significantly increases the treatment capability of IGSS [11][12]. Compared with Europe and Japan, MSW management began late in China, with a larger amount of MSW. In this entry, investigating local residents’ WTP for MSW management will contribute to the development of government policies on MSW management.

2. Demographic Characteristics of the Respondents in Questionnaire and WTP

The social background of the respondents included gender, age, education level, household size, and monthly income per person (Shown in Table 1). Most of the respondents were in the 18 to 55 age range (94.2%). 52.1% of the respondents were male, slightly higher than the percentage of females. As for the education level, more than 75% of respondents have a bachelor’s degree or higher. More than 75% of the respondents had a monthly income of 3000–Yuan and 12% of them who earn more than 10,000 yuan a month. Regarding the family size, 73% of the families were composed of two to five family members.
Table 1. Individual demographic characteristics.
Item Response Frequency Percentage
Gender Male 162 52.1
  Female 149 47.9
Age 18–25 99 31.8
  26–35 99 31.8
  36–45 47 15.1
  46–55 48 15.4
  >55 18 5.8
Education Level Elementary school & High school 74 23.8
  Bachelor 205 65.9
  Master’s degree 32 10.3
Household Size 2 or 3 113 36.3
  4 or 5 114 36.7
  >5 84 27
Monthly income per people 1000–3000 79 25.4
  3001–5000 71 22.8
  5001–8000 60 19.3
  8001–10,000 64 20.6
  >10,001 37 11.9
The result of WTP is presented in Table 1. As the price increases, the percentage of responses agreeing to pay decreases gradually. Nearly 30% of respondents in the pre-survey and final questionnaire refused to pay. Among those who refused to pay, the top three reasons were: payments can be diverted, unable to pay currently, and the MSW management is the responsibility of the government. Respondents who chose the option such as “I don’t have the ability to pay for the fund” and “I don’t think the MSW management worth that much” are “real zero responses” (Shown in Figure 1). The survey on social background found that respondents with higher incomes were more likely to pay. (β = 0.49, p < 0.05)
Figure 1. The motivation for the zero responses.

3. Measurement and Structural Model

The structural model of the raw TPB model was estimated using the maximum likelihood method. In the first step, we tested the plausibility and reliability of the measurement model by confirmatory factor analysis (CFA), then estimated the variable structure and correlations of the current model. To ensure convergence and discriminability, and measurement reliability, we conducted confirmatory factor analysis (CFA), including mainly the components of AT, SN, and PBC (Shown in Table 2). The results showed that the model data were within the plausibility interval (Chi-Square = 247.1, GFI = 0.808, CFI = 0.867, NFI = 0.804, RMSEA = 0.096). All four variable components were included and tested. The standard regression coefficients of AT, PBC, and EC in 0.01 level. The standard regression coefficient of the subject norm in the 0.05 level. All the scales achieved internal consistency.
Table 2. Distribution of responses.
WTP 10 20 30 Total
Positive 239 226 145 610
Negative 72 32 81 185
Protest zero 53 53 53 159
Total 311 311 311 933
The validity of the questionnaire data is corroborated by the fact that the AVE values in Table 3 are all higher than 0.5, according to reference [13], if the squared correlation coefficients of the different constructs are smaller than the AVE of each construct, then the discriminant validity can be confirmed.
Table 3. Reliability and CFA for the extended TPB model.
Scales Mean (s. d.) β CR AVE
Attitude     0.83 0.77
I think paying for MSW management is very positive 4.04 (1.11) 0.485    
I think paying for MSW management is a responsibility 3.96 (1.15) 0.563    
I think paying for MSW management is a pro-environmental behavior 3.96 (1.12) 0.584    
Subject Norm     0.79 0.59
I think people who are close to me will pay for MSW management 3.83 (1.20) 0.668    
I think people who are close to me will support the action of paying for MSW management 3.72 (1.24) 0.627
I think people who are close to me will support me paying for MSW management 3.77 (1.20) 0.494
Perceived Behavioral Control     0.87 0.74
I think my payment will improve the urban environment 3.97 (1.04) 0.466    
It is not difficult for me to pay for MSW management 3.81 (1.27) 0.485    
I think I have time, money, and resources to contribute to the MSW management. 3.67 (1.26) 0.593    
Environment-concern        
I care about urban environmental issues very much 3.86(1.23) 0.475 0.82 0.71
I think I will reduce other expenses for urban environment improvement 3.84(1.19) 0.693    
Mean (s. d): Standard deviation. β: factor loading CR (composite reliability); AVE (average variance extracted).
 
 
A discriminant validity test of the scale was performed. According to reference [13], if the squared correlation coefficients of the different constructs are smaller than the AVE of each construct, the discriminant validity can be confirmed, as is shown in Table 4, the correlations between the factors of the variables in the new extended TPB model. The high correlations between AT, SN, and PBC show profound evidence of validity. Our objective was to discover whether the TPB model, in the context of an integrated framework for understanding consumers’ WTP and behavior [14] could also assess willingness towards pro-environmental behaviors that encompass “AT, SN, PBC”. These are the determinants for the WTP for IGGS for MSW management in two successive questionnaires; although a 5-point scale was used in the Likert scale data statistics, it is still reliable for the applicability of the model (Chi-Square = 262.8, GFI = 0.808, CFI = 0.834, NFI = 0.787, RMSEA = 0.094), and all structural coefficients were statistically persuasive (p < 0.01). According to the result, AT (β = 0.573, p < 0.01) and PBC (β = 0.692, p < 0.01) affecting respondents’ WTP. So, Hypothesis 1 and Hypothesis 2 can be accepted, and Hypothesis 3 was rejected.
Table 4. The scales’ discriminant validity.
Title 1 1 2 3 4
1. Attitude 0.77      
2. Subjective norms 0.48 ** 0.59    
3. Perceived behavioral control 0.45 ** 0.50 *** 0.74  
4. Environment-concern 0.42 *** 0.42 ** 0.42 *** 0.71
** p < 0.05, *** p < 0.01.
At the same time, the influence of SN on AT (β = 0.762, p < 0.01) and PBC (β = 0.800, p < 0.01) are confirmed, so Hypothesis 4 and Hypothesis 5 can be accepted. (Shown in Figure 2)
Figure 2. Raw TPB model for WTP. β represents standard regression weight.
The fit measure of the extended model can be accepted (Chi-Square = 243.8, GFI = 0.838, CFI = 0.834, NFI = 0.787, RMSEA = 0.094), and most of the structural coefficients are significant (p < 0.01).
When the facts of PBC and EC were compared, PBC had the most significant effect on WTP (β = 0.692, p < 0.01) followed by the EC (β = 0.594, p < 0.01). Hypothesis 3 and Hypothesis 9 were validated. The positive effect relationship between EC and AT (β = 0.382, p < 0.05), SN (β = 0.610, p < 0.01), and PBC (β = 0.341, p < 0.05) can be verified, thus allowing Hypothesis 6, Hypothesis 7, and Hypothesis 8 to be accepted.
Through previous speculations, we hypothesized the indirect effect of environment-concern on WTP for IGGS. Figure 3 shows that AT, SN, and PBC mediate between EC and WTP. However, the regression coefficients from the Sobel test for the Likert scale indicate that all indirect effects did not hold in this questionnaire. Thus, we can conclude that AT (β = −0.31), SN (β = −0.36), and PBC (β = −0.12) in this WTP for IGSS have no indirect effects. (Shown in Figure 3)
Figure 3. Extended TPB model for WTP. β represents standard regression weight.
Obviously, if Beijing residents think they have the extra resources to contribute to urban living garbage management, they will make a more positive response to environmental behavior Therefore, it is necessary to strengthen those who already believe they can contribute to solve the problem of positive beliefs, change those who believe that there is no corresponding resource negative beliefs It also shows that the executive branch of government plays a vital role in motivating people or residents to pay.

References

  1. Hoornweg, D.; Bhada-Tata, P. What a Waste: A Global Review of Solid Waste Management; Urban Development Series; World Bank: Washington, DC, USA, 2012; p. 15.
  2. Ding, Y.; Zhao, J.; Liu, J.-W.; Zhou, J.; Cheng, L.; Zhao, J.; Shao, Z.; Iris, Ç.; Pan, B.; Li, X.; et al. A review of China’s municipal solid waste (MSW) and comparison with international regions: Management and technologies in treatment and resource utilization. J. Clean. Prod. 2021, 293, 126144.
  3. Mian, M.M.; Zeng, X.; Nasry, A.A.N.B.; Al-Hamadani, S.M. Municipal solid waste management in China: A comparative analysis. J. Mater. Cycles Waste Manag. 2017, 19, 1127–1135.
  4. Wang, H.T.; Nie, Y.F. Municipal solid waste characteristics and management in China. J. Air Waste Manag. Assoc. 2001, 51, 250–263.
  5. Pardini, K.; Rodrigues, J.J.; Hassan, S.A.; Kumar, N.; Furtado, V. Smart Waste Bin: A New Approach for Waste Management in Large Urban Centers. In Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 27–30 August 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–8.
  6. Watson, M.; Bulkeley, H. Just waste? Municipal waste management and the politics of environmental justice. Local Environ. 2005, 10, 411–426.
  7. Kofoworola, O.F. Recovery and recycling practices in municipal solid waste management in Lagos, Nigeria. Waste Manag. 2007, 27, 1139–1143.
  8. Malinauskaite, J.; Jouhara, H.; Czajczyńska, D.; Stanchev, P.; Katsou, E.; Rostkowski, P.; Thorne, R.; Colón, J.; Ponsá, S.; Al-Mansour, F.; et al. Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe. Energy 2017, 141, 2013–2044.
  9. Bassi, S.A.; Christensen, T.H.; Damgaard, A. Environmental performance of household waste management in Europe—An example of 7 countries. Waste Manag. 2017, 69, 545–557.
  10. Folianto, F.; Low, Y.S.; Yeow, W.L. Smartbin: Smart waste management system. In Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 7–9 April 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–2.
  11. Okuda, I.; Thomson, V.E. Regionalization of municipal solid waste management in Japan: Balancing the prox-imity principle with economic efficiency. Environ. Manag. 2007, 40, 12–19.
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  13. Vicente-Molina, M.A.; Fernandez-Sainz, A.; Izagirre-Olaizola, J. Environmental knowledge and other variables affecting pro-environmental behaviour: Comparison of university students from emerging and advanced countries. J. Clean. Prod. 2013, 61, 130–138.
  14. Judith, D.G.; Linda, S. General beliefs and the theory of planned behavior: The role of environment-concerns in the TPB. Jour-Nal Appl. Soc. Psychol. 2007, 37, 1817–1836.
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