Exploring the Effect of Family Life and Neighbourhood on the Willingness of Household Waste Sorting
1
School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, China
2
School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(24), 13653; https://doi.org/10.3390/su132413653
Submission received: 13 November 2021
/
Revised: 9 December 2021
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Accepted: 9 December 2021
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Published: 10 December 2021
(This article belongs to the Special Issue Frontiers and Best Practices in Bio, Circular, and Green Growth and Eco-Innovation)
Abstract
:For developing countries, garbage classification has become an important measure to handle the environmental pollution crisis. This empirical study examined urban and rural families’ willingness to sort and deposit garbage at fixed recycling points. We found that urban residents demonstrated a significantly higher willingness to sort and deposit garbage at designated points compared to rural residents. The average number of family meals per month, average monthly household food expenditure, household cleanliness, and household crowding (spaciousness) are significantly related to a family’s willingness to sort garbage. In terms of neighbourhood factors, families living in neighbourhoods with property management are more likely to have a higher willingness to sort garbage. The degree of air pollution in the neighbourhood also has an impact on the family’s willingness to sort garbage. This study proposes that neighbourhood factors have a non-negligible influence on a household’s willingness to sort and put garbage in designated locations. Especially in urban neighbourhoods, the willingness of residents living in commodity housing neighbourhoods to sort and place garbage at designated locations is significantly higher than that of residents living in other neighbourhoods. To improve the implementation effect of the waste sorting policy, we suggest that the configuration of neighbourhood garbage recycling services and facilities should be improved so that people can sort garbage more conveniently.
1. Introduction
With rapid industrialisation and urbanisation, and the resultant improvements in people’s incomes and living standards, household garbage generation has also increased, and the garbage composition process has increased in complexity [1]. The increase in the volume of household garbage has brought about a series of problems related to environmental pollution, public health crises, land resource occupation, and resource wastage, which have become global challenges [2]. Since the mid to late 20th century, in response to environmental problems caused by domestic waste, many developed countries have begun to pay attention to and explore social policies for waste management. For example, in Japan, which has high population density but scarce land, incineration is the most important method of waste disposal [3]. Japanese waste management measures also include pollution prevention, recycling, and reusing. In the 1990s, Finland set a national goal of garbage source classification and recycling. They also found that higher social participation and classification effectiveness could reduce garbage collection costs [4]. Reducing, reusing, and recycling, which are commonly referred to as the 3Rs, are regarded as effective waste management measures [5]. For instance, since the 1990s, Sweden took many measures to promote waste recycling activities. All Swedish citizens are required to sort their generated waste and participate in local recycling programs [6,7]. Germany formulated “The New German Closed Cycle Management Activity” to transform waste management into resource management and trigger the innovation of waste treatment technologies to improve the recycling capacity [8]. Garbage sorting has become one of the important requirements for sustainable development. However, the popularity of garbage sorting in developing countries is not high [9,10,11]. A large amount of waste generation had a great negative impact on developing countries’ living environment. For example, in India, due to the lack of sufficient financial funds to build a garbage disposal system, a large amount of garbage accumulated at the outskirts of cities [12]. Vietnam also started a waste classification demonstration project in 2000, but it produced unsatisfactory results [13]. Furthermore, green circular economy has become the development trend of the global economy [14,15]. On the path to sustainable development, a green lifestyle is as important as green production and consumption. Among them, sorting domestic waste is one of the most important green lifestyles. Garbage classification is the basis for the establishment of a circular economy, which can also reduce environmental pollution. Waste sorting is not only a change of lifestyle, but also an exploration of establishing a circular economy system. Achieving green development requires extensive civic engagement [16]. To make waste sorting a widely accepted way of life requires the participation of all people. Therefore, research on the willingness of waste classification is helpful to better understand the micro-foundation of circular economy and sustainable society.
As the largest developing country in the world, China experienced rapid economic growth and industrialisation and, consequently, greatly increased its waste generation over the past decades [17]. Thus, in China, urban as well as rural areas now face great environmental pressures. Due to the many difficulties in implementing waste classification in China, landfilling and incineration are still the main methods of national waste disposal [18]. Landfills have the risk of infiltration of landfill leachate, which leads to pollution of the soil and groundwater around the landfill, which endangers the health of residents, and it is difficult to control pollution. Garbage incineration may also lead to the risk of heavy metal pollution in the soil and air pollution. Therefore, the implementation of garbage classification and recycling of garbage are important measures to protect the environment and reduce pollution. However, the promotion of waste sorting and waste reuse in China is still relatively low. In 2000, some pilot Chinese cities, including Beijing, Shanghai, Nanjing, Hangzhou, and Guangzhou, attempted to promote waste sorting in order to reduce waste generation and recycle waste sources [19]. In 2019, Shanghai and Beijing formulated a new series of stricter domestic waste sorting management policies, which were promoted at the neighbourhood level. In this context, many cities in China have begun to formulate policies for the classification of household waste, requiring residents to sort and put domestic waste in the designated place. However, because people’s long-formed living habits are difficult to change in a short time, many cities have suspended their garbage classification requirements after implementing garbage classification policies for a period.
The transformation of garbage classification from “consensus” to “life habits” requires that most people in the society are willing to carry out garbage classification. However, what factors affect the residents’ the willingness to sort garbage? The existing research has mainly analysed the individual’s garbage sorting behaviour from the perspective of the individual’s socioeconomic status and cultural customs [20,21]. However, since the disposal of domestic garbage is often the behaviour of the family, family lifestyle may affect garbage classification. One of the main theoretical innovations of this research is to analyse the willingness of family classification from the perspective of the family rather than the individual. Garbage sorting is not only an individual’s behaviour, but also a family’s behaviour. In addition, the behaviour of garbage classification may also be related to the external environment. For example, different types of neighbourhoods provide different garbage collection services, which may cause residents in different neighbourhoods to have different attitudes towards garbage classification. However, there is still a lack of research on the family’s willingness to sort waste. To fill this gap, we will analyse the influence of family lifestyle and neighbourhood environment on family waste sorting willingness. Furthermore, this research will help elucidate why the promotion of waste sorting is slow and will also provide guidance for improving the government’s intervention activities to raise the level of urban and rural domestic waste classification in China.
2. Methods and Materials
The data used in this study were derived from the 2016 China Labor-force Dynamics Survey (CLDS), which was conducted by the Center for Social Science Survey at Sun Yat-sen University. The collected sample used a multistage cluster and stratified probability sampling strategy. The CLDS included information at the community, family, and individual levels. This database was China’s first interdisciplinary, nationwide follow-up survey on the theme of labour. The survey covered many research topics such as family lifestyles and the neighbourhood environment. It accurately reflects the current basic conditions of social development in China. It was highly credible to use this database to analyse the household’s willingness to sort garbage. In this study, we used data from the family level. After removing samples with missing values, 12,126 valid samples were included in this study. This study mainly analysed the influence of individual social and economic conditions on a family’s willingness to classify garbage and their willingness to deposit garbage at designated places. The dependent variables included family willingness of waste sorting and family willingness to place waste at designated locations. We focused on the impact of household daily life and community living environment on household waste sorting willingness. The family’s living habits may have an impact on the willingness to sort garbage. For example, if a family has many members living together, they may produce a lot of domestic waste, which may make it more difficult to classify waste, which will negatively affect the family’s willingness to classify waste. Therefore, we selected independent variables, such as the number of family members living together, average number of family meals per month, average monthly household electricity consumption, average monthly household food expenditure, household cleanliness, and household crowding, which can reflect the daily life of the family. The degree of household cleanliness and crowding are evaluated on a 10-point scale, where 1 means very bad or very crowded, and 10 means very clean or very spacious.
In China, different types of neighbourhoods can obtain different public services. There is a huge gap between rural neighbourhoods and urban neighbourhoods in access to public services. Garbage collection is one of the public services that varies significantly by area. Urban neighbourhoods have specialised garbage disposal services, while many rural neighbourhoods do not have regular garbage disposal services. Therefore, we compare the willingness of residents living in different types of neighbourhoods to sort and place waste at designated locations. We selected neighbourhood types, neighbourhood property management, the level of air pollution, the level of water pollution, the level of noise pollution, the level of soil pollution as independent variables, which can reflect the neighbourhood living environment. The types of neighbourhoods are divided into rural neighbourhoods and urban neighbourhoods. Urban neighbourhoods can be further divided into commercial housing neighbourhoods, old neighbourhoods, unit neighbourhoods, and security housing neighbourhoods, shantytown neighbourhoods and others. Because the respondents’ willingness to sort waste is related to their perception of environmental quality, the data on the pollution degree of the living environment in this study come from the respondents’ subjective judgments, which are subjective indicators. The level of pollution (air pollution, water pollution, noise pollution, and soil pollution) is evaluated on a 4-point scale, where 1 means very serious pollution and 4 means not serious pollution. As the dependent variable was a dichotomous variable, this study used logistic regression models to analyse the influencing factors on respondents’ willingness to sort waste and willingness to place waste at designated locations.
3. Results
3.1. Descriptive Statistics
Table 1 shows the descriptive statistics of household waste sorting willingness. Among the respondents, those who were willing to classify their household garbage accounted for 83.04%, while those who are not willing to classify their household garbage accounted for only 16.96%. Most respondents were willing to deposit their household waste at the designated collection point, and only 5.41% of respondents were unwilling to deposit their household waste at the designated collection point. Regarding family lifestyle, the average number of family members living together and family meals per month were 4.14 and 19.42, respectively. The average monthly household electricity consumption and food expenditure were 137 KWH and 14,420 yuan, respectively. In terms of family living environment, the mean values of household cleanliness and household crowding were 6.26 and 6.27, respectively. Regarding the neighbourhood living environment, 21.35% of participating families lived in neighbourhoods with property management, while 78.65% lived in neighbourhoods without property management. While 59.84% of surveyed households were in rural neighbourhoods, 40.16% were in urban neighbourhoods. The average evaluations of the environmental quality of the air, water, noise, and soil of the neighbourhoods in which the interviewed households live were 3.13, 3.21, 3.20, and 3.39, respectively. Among the four types of pollution, air pollution is the most serious, while soil pollution is the least, probably because people perceive air quality most directly. Table 2 summarises the cross-tabulation results for willingness regarding household waste sorting between rural and urban residents. The proportion of rural respondents who were willing to conduct household waste sorting was significantly lower than that of urban residents (78.39% and 89.98%, respectively). The proportion of rural residents who were willing to deposit their household waste at designated collection points was also significantly lower than that of urban residents (92.05% and 98.38%, respectively). Figure 1 shows the distribution of household waste sorting willingness in seven types of urban neighbourhoods. Households living in commodity housing neighbourhoods have the highest willingness to sort garbage, reaching 93.89%, followed by living in security housing neighbourhoods (92.74%), unit neighbourhoods (90.34%), urban shantytown neighbourhoods (90.14%), residential neighbourhood changed from a rural neighbourhood (88.09%), and old residential neighbourhoods (86.13%).
3.2. Regression Results for Respondents’ Waste Sorting Willingness
Table 3 shows the regression results for respondents’ waste sorting willingness. Model 1 presents the results for all samples. Compared to residents living in rural neighbourhoods, residents living in urban neighbourhoods are more willing to sort household waste. The following variables of family lifestyle significantly and positively affected respondents’ waste sorting willingness: average number of family meals per month, average monthly household food expenditure, household cleanliness, and household crowding. The more family meals and food expenditure, the more domestic waste may be generated, but the family’s willingness to sort waste will also be higher. The cleanliness of the household can also directly reflect the willingness of household waste sorting. In terms of neighbourhood living environment, compared with families living in neighbourhoods with property management, families living in neighbourhoods without property management have relatively lower willingness to sort garbage. In addition, respondents who think that air pollution is not serious are more likely to have a lower willingness to sort garbage. Model 2 presents the regression results for rural respondents. The more the average monthly household food expenditure, the more likely the family will be willing to sort garbage. The degree of household cleanliness and household crowding can also significantly affect the household’s willingness to sort garbage. Families living in neighbourhoods without property management have relatively lower willingness to sort garbage than those living in neighbourhoods with property management. Respondents who think that air pollution is not serious are more likely to have a lower willingness to sort garbage. Model 3 presents the analysis results for urban respondents. The number of family meals per month, family food expenditure, and house spaciousness have a positive impact on the household’s willingness to sort garbage. Property management in the neighbourhoods will also increase residents’ willingness to sort garbage. Model 4 of Table 4 shows the results of the garbage sorting willingness of households living in different types of urban neighbourhoods. Compared with households living in commodity housing neighbourhoods, households living in old residential neighbourhoods, unit neighbourhoods, and a residential neighbourhood changed from a rural neighbourhood are more likely to have a lower willingness to sort waste. This shows that the family willingness to sort garbage is significantly affected by the neighbourhood environment.
3.3. Regression Results for Willingness Regarding Deposition of Household Waste to a Fixed Collection Point
Table 5 shows the regression results for respondents’ willingness to deposit household waste at a fixed collection point. Model 6 presents the results for all the samples. Compared to rural respondents, urban respondents are more willing to deposit household garbage at a designated garbage collection point. Respondents who have more family meals each month are more likely to deposit their household waste at designated collection points. Households with higher electricity consumption are also more likely to have a higher willingness to place garbage at a fixed point. The household cleanliness and spaciousness (crowding) can significantly reflect the family’s willingness to deposit waste at designated collection points. Respondents who think that air pollution is not serious are more likely to have a lower willingness to deposit waste at designated collection points, but those who think that soil pollution is not serious are more likely to have a higher willingness to deposit waste at designated collection points. This may be mainly because air pollution is more easily perceived by individuals, which can directly affect people’s behaviour. Model 7 presents the results for the rural respondents. The results of Model 7 are like those of Model 6. Model 8 presents the analysis results for urban respondents. The household cleanliness can significantly reflect the urban family’s willingness to deposit waste at designated collection points. Different from rural respondents, urban respondents who think that water pollution is not serious are more likely to have a higher willingness to deposit waste at designated collection points. To a certain extent, this shows that the better the environmental quality, the more likely people will be to protect the environment and avoid littering in urban areas. Model 5 of Table 4 shows that households living in commodity housing neighbourhoods are more likely to have a higher willingness to deposit waste at designated collection points than those living in other types of neighbourhoods (old residential neighbourhood, unit neighbourhood, residential neighbourhood changed from a rural neighbourhood, and urban shantytown).
4. Discussion
4.1. Family Lifestyle and Neighbourhood Affect the Family’s Willingness to Sort Waste
This study shows that urban residents’ willingness to classify garbage is significantly higher than that of residents living in rural neighbourhoods. The emergence of this phenomenon is related to the family lifestyle and the living environment of urban and rural neighbourhoods. This study proposes that garbage classification implementation should pay attention to the willingness of households to sort waste, because compared with the difficulty of personal waste sorting, household waste sorting is more difficult. For example, kitchen waste is generated in family life, which is difficult to handle in garbage classification. Unlike other studies that focus on the influence of individual socioeconomic status on the willingness to sort waste [22,23], we put forward that the influence of family lifestyle and neighbourhood living environment on the household waste sorting willingness cannot be underestimated. For instance, the results of all samples show that the more family meals per month, the higher the household’s willingness to sort garbage, but this result is only significant in the urban samples, and not in the rural samples. This is mainly due to the differences in the garbage collection facilities and public service provision between urban and rural areas. Compared with rural neighbourhoods, public service facilities in urban neighbourhoods are better [18,24]. For example, there will be dedicated personnel to collect and manage garbage in the neighbourhoods. Furthermore, in urban areas, systematic garbage collection, transhipment, and disposal infrastructure have advanced over decades, and the domestic garbage can be quickly recycled after it has been classified. However, the government’s investment in infrastructure has been insufficient in rural areas [25,26,27]. The facilities and management systems for rural households’ garbage remain underdeveloped, and rural residents cannot access facilities or services easily. All these conditions could result in greater difficulties regarding garbage classification implementation in rural areas.
In addition, residents’ willingness to sort waste is different between urban and rural areas. There are also significant differences in the willingness of residents in different types of urban neighbourhoods to sort waste. In this research, we further divide urban neighbourhoods into six types of neighbourhoods, including commodity housing neighbourhoods, old residential neighbourhoods, unit neighbourhoods, security housing neighbourhoods, residential neighbourhood changed from a rural neighbourhood, and urban shantytowns. Among them, the commodity housing neighbourhood is a new type of market-oriented housing that emerged in China in the 1990s [28]. Commodity housing neighbourhoods generally have better property management. However, many scholars call such neighbourhoods “gated neighbourhoods” because they are often managed in a closed manner (or in an exclusive manner) [29]. Most of the old residential neighbourhood have been standing for a long time. The built environment of this kind of neighbourhood is quite serious. Unit (Danwei) neighbourhood is a kind of residential neighbourhood originating from China’s planned economy period [30]. The housing in this neighbourhood is provided by the unit to the unit workers. However, this model of housing supply is no longer in place. There are complex housing property rights in the unit neighbourhood, and the property management of the neighbourhood is chaotic. The security housing neighbourhood is built for low-income urban residents. Although this kind of neighbourhood is provided to low-income families, most of these neighbourhoods are managed in accordance with the commodity housing neighbourhoods, but the level of property management and the quality of public service facilities are relatively low. The residential neighbourhoods that changed from rural neighbourhoods generally appear in the urban fringe areas. Due to China’s dual urban–rural land use system, the supply mode of public service facilities in urban and rural areas is different. With the spread of urban areas, rural land was converted into urban land after being expropriated by the local government. As a result, some rural neighbourhoods are restructured into urban neighbourhoods. Relatively speaking, the quality of public service facilities in these neighbourhoods is worse than those in commodity housing neighbourhoods. Through comparative analysis, we found that the willingness of residents living in commodity housing neighbourhoods to sort waste is significantly stronger than that of residents living in other neighbourhoods. The public service facilities of the commodity housing neighbourhoods are complete, and garbage collection points will be set up more scientifically and reasonably in the residential design. The residents’ willingness to sort garbage is not only affected by family living habits, but also related to the living environment of the neighbourhood in which they live. The neighbourhood differentiation and neighbourhood effect of the willingness to sort waste in China can be understood from two aspects. On the one hand, there are huge differences in property management in different types of neighbourhoods, which directly affects residents’ garbage sorting behaviour. On the other hand, this is also one of the manifestations of the social spatial differentiation of Chinese cities. People of different socioeconomic status and lifestyles live in different types of neighbourhoods, leading to distinct neighbourhood differentiation in garbage sorting behaviour.
4.2. Policy Suggestion and Research Limitations
Waste sorting is one of the important ways for developing countries to achieve sustainable development. However, the formation of personal waste sorting habits is a slow process that not only needs to change the family’s living habits, but also needs to provide more convenient public service facilities for waste sorting. First, garbage classification must become the consensus of the society, and make garbage classification a daily habit of everyone. Secondly, it is necessary to solve the problem of unequal garbage collection in urban and rural neighbourhoods. Local governments should increase investments in rural waste management systems and build the infrastructure necessary for sorting, transferring, and disposing waste in rural areas. Third, it is necessary to address the shortcomings of different types of urban neighbourhoods in garbage recycling and garbage disposal management. Some neighbourhoods have arranged staff to help residents sort garbage, but some neighbourhoods do not have such guidance. Different neighbourhood public services will cause residents in different neighbourhoods to have different degrees of willingness for garbage classification, which will lead to unsatisfactory implementation of the garbage classification policy. Developing different waste sorting assistance strategies for different types of neighbourhoods can better improve the implementation effect of waste sorting policies. There are two main research limitations in this study. First, the results of this study are limited by the database used. Because some neighbourhoods have not actually begun to implement mandatory garbage classification requirements, the residents’ willingness to sort garbage may change after the mandatory garbage classification requirements are implemented. Second, the residents’ willingness to sort garbage is affected by many factors. Although this study proposes that the external living environment has an important influence on the residents’ willingness to sort garbage, the residents’ living habits and socioeconomic status may also affect their garbage classification willingness.
5. Conclusions
Garbage classification is one of the important ways to achieve sustainable development in developing countries, but it the process of garbage sorting becoming part of people’s way of life is a gradual one. The results of the study indicate that the family lifestyle and neighbourhood environment have a significant correlation with a family’s willingness to sort garbage and to put garbage in designated locations. We conclude that in China, most people agree with the necessity of implementing garbage classification. However, the slow promotion of garbage classification may be mainly due to the relatively low quality of community garbage collection and management services, and the failure to associate community garbage collection with family life. In the process of implementing the waste sorting policy, not only must the residents’ living habits be changed, but also more neighbourhood public service facilities must be provided for waste sorting, so that people can more conveniently sort waste.
Author Contributions
Conceptualisation, H.C.; Data processing and methodology, H.C. and L.Z.; Supervision, H.C.; Writing—original draft, H.C. and L.Z.; Writing—review and editing, L.Z. and H.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Natural Science Foundation of China (grant number 51908114) and the doctoral program of first-level discipline of Architecture, School of Architecture and Urban Planning, Shenzhen University.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data used in this study were derived from the 2016 China Labor-force Dy-namics Survey (CLDS), which was conducted by the Center for Social Science Survey at Sun Yat-sen University. http://css.sysu.edu.cn (accessed on 1 December 2021).
Conflicts of Interest
The authors declare no conflict of interest.
References
- Grazhdani, D. Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in prespa park. Waste Manag. 2016, 48, 3–13. [Google Scholar] [CrossRef]
- Iyamu, H.; Anda, M.; Ho, G. A review of municipal solid waste management in the bric and high-income countries: A thematic framework for low-income countries. Habitat Int. 2020, 95, 102097. [Google Scholar] [CrossRef]
- Okuda, I.; Thomson, V.E. Regionalization of municipal solid waste management in Japan: Balancing the proximity principle with economic efficiency. Environ. Manag. 2007, 40, 12–19. [Google Scholar] [CrossRef]
- Tanskanen, J.-H.; Kaila, J. Comparison of methods used in the collection of source-separated household waste. Waste Manag. Res. 2001, 19, 486–497. [Google Scholar] [CrossRef]
- Bai, R.; Sutanto, M. The practice and challenges of solid waste management in singapore. Waste Manag. 2002, 22, 557–567. [Google Scholar] [CrossRef]
- Bernstad, A.; la Cour Jansen, J.; Aspegren, H. Life cycle assessment of a household solid waste source separation programme: A swedish case study. Waste Manag. Res. 2011, 29, 1027–1042. [Google Scholar] [CrossRef] [PubMed]
- Dahlén, L.; Vukicevic, S.; Meijer, J.-E.; Lagerkvist, A. Comparison of different collection systems for sorted household waste in sweden. Waste Manag. 2007, 27, 1298–1305. [Google Scholar] [CrossRef]
- Nelles, M.; Gruenes, J.; Morscheck, G. Waste management in germany–development to a sustainable circular economy. Procedia Environ. Sci. 2016, 35, 14. [Google Scholar] [CrossRef]
- Zen, I.S.; Noor, Z.Z.; Yusuf, R.O. The profiles of household solid waste recyclers and non-recyclers in kuala lumpur, malaysia. Habitat Int. 2014, 42, 83–89. [Google Scholar] [CrossRef]
- Charuvichaipong, C.; Sajor, E. Promoting waste separation for recycling and local governance in thailand. Habitat Int. 2006, 30, 579–594. [Google Scholar] [CrossRef]
- Marshall, R.E.; Farahbakhsh, K. Systems approaches to integrated solid waste management in developing countries. Waste Manag. 2013, 33, 988–1003. [Google Scholar] [CrossRef] [PubMed]
- Zurbrügg, C.; Drescher, S.; Patel, A.; Sharatchandra, H. Decentralised composting of urban waste–an overview of community and private initiatives in indian cities. Waste Manag. 2004, 24, 655–662. [Google Scholar] [CrossRef]
- Nguyen, T.T.P.; Zhu, D.; Le, N.P. Factors influencing waste separation intention of residential households in a developing country: Evidence from hanoi, vietnam. Habitat Int. 2015, 48, 169–176. [Google Scholar] [CrossRef]
- D’Amato, D.; Droste, N.; Allen, B.; Kettunen, M.; Lähtinen, K.; Korhonen, J.; Leskinen, P.; Matthies, B.D.; Toppinen, A. Green, circular, bio economy: A comparative analysis of sustainability avenues. J. Clean. Prod. 2017, 168, 716–734. [Google Scholar] [CrossRef]
- Sadik-Zada, E.R. Natural resources, technological progress, and economic modernization. Rev. Dev. Econ. 2021, 25, 381–404. [Google Scholar] [CrossRef]
- Radtke, J. A closer look inside collaborative action: Civic engagement and participation in community energy initiatives. People Place Policy 2014, 8, 235–248. [Google Scholar] [CrossRef] [Green Version]
- Zhuang, Y.; Wu, S.-W.; Wang, Y.-L.; Wu, W.-X.; Chen, Y.-X. Source separation of household waste: A case study in china. Waste Manag. 2008, 28, 2022–2030. [Google Scholar] [CrossRef]
- Zhang, D.Q.; Tan, S.K.; Gersberg, R.M. Municipal solid waste management in china: Status, problems and challenges. J. Environ. Manag. 2010, 91, 1623–1633. [Google Scholar] [CrossRef]
- Tai, J.; Zhang, W.; Che, Y.; Feng, D. Municipal solid waste source-separated collection in china: A comparative analysis. Waste Manag. 2011, 31, 1673–1682. [Google Scholar] [CrossRef]
- Padilla, A.J.; Trujillo, J.C. Waste disposal and households’ heterogeneity. Identifying factors shaping attitudes towards source-separated recycling in bogotá, colombia. Waste Manag. 2018, 74, 16–33. [Google Scholar] [CrossRef]
- Massarutto, A.; Marangon, F.; Troiano, S.; Favot, M. Moral duty, warm glow or self-interest? A choice experiment study on motivations for domestic garbage sorting in italy. J. Clean. Prod. 2019, 208, 916–923. [Google Scholar] [CrossRef]
- Nutbeam, D.; Aaro, L.; Wold, B. The lifestyle concept and health education with young people: Results from a who international survey. J. Inst. Health Educ. 1991, 29, 98–103. [Google Scholar] [CrossRef]
- Vereecken, C.A.; Maes, L.; De Bacquer, D. The influence of parental occupation and the pupils’ educational level on lifestyle behaviors among adolescents in belgium. J. Adolesc. Health 2004, 34, 330–338. [Google Scholar] [CrossRef]
- Chung, S.-S.; Poon, C.-S. A comparison of waste-reduction practices and new environmental paradigm of rural and urban chinese citizens. J. Environ. Manag. 2001, 62, 3–19. [Google Scholar] [CrossRef] [PubMed]
- Zeng, C.; Niu, D.; Zhao, Y. A comprehensive overview of rural solid waste management in china. Front. Environ. Sci. Eng. 2015, 9, 949–961. [Google Scholar] [CrossRef]
- Shenggen, F.; Zhang, X. Infrastructure and regional economic development in rural china. China Econ. Rev. 2004, 15, 203–214. [Google Scholar]
- Yu, X. Is environment ‘a city thing’in china? Rural–urban differences in environmental attitudes. J. Environ. Psychol. 2014, 38, 39–48. [Google Scholar] [CrossRef]
- Wu, F. Residential relocation under market-oriented redevelopment: The process and outcomes in urban china. Geoforum 2004, 35, 453–470. [Google Scholar] [CrossRef]
- Li, Z.; Wu, F. Tenure-based residential segregation in post-reform chinese cities: A case study of shanghai. Trans. Inst. Br. Geogr. 2008, 33, 404–419. [Google Scholar] [CrossRef]
- Bjorklund, E. The danwei: Socio-spatial characteristics of work units in china’s urban society. Econ. Geogr. 1986, 62, 19–29. [Google Scholar] [CrossRef]
Figure 1.
Distribution of households’ willingness to sort garbage in different types of urban neighbourhoods.
Figure 1.
Distribution of households’ willingness to sort garbage in different types of urban neighbourhoods.
Table 1.
Descriptive statistics for the variables (N = 12,126).
| Mean Value/Proportion | |
|---|---|
| Neighbourhood types (%) | |
| Rural neighbourhood | 59.84 |
| Urban neighbourhood | 40.16 |
| Household waste sorting willingness (%) | |
| Willing | 83.04 |
| Unwilling | 16.96 |
| Willingness to deposit household waste at a fixed collection point (%) | |
| Willing | 94.59 |
| Unwilling | 5.41 |
| Number of family members living together (1–18) | 4.14 (SD = 2.10) |
| Average number of family meals per month (0–90) | 19.42 (SD = 27.86) |
| Average monthly household electricity consumption (100 KWH) | 1.37 (SD = 1.29) |
| Average monthly household food expenditure (1000 yuan) | 14.42 (SD = 23.83) |
| Household cleanliness (1–10) | 6.26 (SD = 1.76) |
| Household crowding (1–10) | 6.27 (SD = 1.77) |
| Neighbourhood property management (%) | |
| Have | 21.35 |
| Do not have | 78.65 |
| The level of air pollution (1–4) | 3.13 (SD = 0.85) |
| The level of water pollution (1–4) | 3.21 (SD = 0.79) |
| The level of noise pollution (1–4) | 3.20 (SD = 0.85) |
| The level of soil pollution (1–4) | 3.39 (SD = 0.69) |
N is an abbreviation for Number. SD is an abbreviation for standard deviation.
Table 2.
Cross-tabulation for household waste sorting willingness between rural and urban residents (N = 12,126).
Table 2.
Cross-tabulation for household waste sorting willingness between rural and urban residents (N = 12,126).
| Rural Residents | Urban Residents | Chi-Square | p Value | |
|---|---|---|---|---|
| Household waste sorting willingness (%) | 277.97 | 0.000 | ||
| Willing | 78.39 (N = 1568) | 89.98 (N = 4382) | ||
| Unwilling | 21.61 (N = 5688) | 488 (N = 10.02) | ||
| Willingness to deposit household waste at a fixed collection point (%) | 228.17 | 0.000 | ||
| Willing | 92.05 (N = 6679) | 98.38 (N = 4791) | ||
| Unwilling | 7.95 (N = 577) | 1.62 (N = 79) |
N is an abbreviation for Number.
Table 3.
Regression results for respondents’ waste sorting willingness.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Urban neighbourhoods (ref: rural neighbourhoods) | 1.814 *** | [1.577, 2.086] | ||||
| Number of family members living together | 0.983 | [0.961, 1.006] | 0.988 | [0.963, 1.014] | 0.961 | [0.913, 1.010] |
| Average number of family meals per month | 1.002 *** | [1.001, 1.004] | 1.002 | [0.999, 1.004] | 1.005 *** | [1.001, 1.008] |
| Average monthly household electricity consumption | 0.996 | [0.956, 1.039] | 1.003 | [0.952, 1.057] | 0.976 | [0.911, 1.046] |
| Average monthly household food expenditure | 1.005 ** | [1.001, 1.009] | 1.005 * | [0.999, 1.010] | 1.006 * | [0.999, 1.012] |
| Household cleanliness | 1.118 *** | [1.077, 1.162] | 1.145 *** | [1.096, 1.197] | 1.041 | [0.967, 1.122] |
| Household crowding | 1.082 *** | [1.042, 1.123] | 1.060 *** | [1.014, 1.108] | 1.141 *** | [1.061, 1.227] |
| Neighbourhood property management (ref: have) | 0.779 *** | [0.652, 0.930] | 0.610 * | [0.369, 1.007] | 0.806 ** | [0.662, 0.983] |
| The level of air pollution | 0.891 *** | [0.819, 0.968] | 0.838 *** | [0.758, 0.927] | 1.013 | [0.873, 1.175] |
| The level of water pollution | 1.024 | [0.945, 1.109] | 1.007 | [0.918, 1.104] | 1.127 | [0.957, 1.327] |
| The level of noise pollution | 0.948 | [0.876, 1.026] | 0.922 | [0.834, 1.020] | 0.970 | [0.852, 1.105] |
| The level of soil pollution | 1.063 | [0.968, 1.168] | 1.134 ** | [1.015, 1.268] | 0.926 | [0.777, 1.103] |
| Number of samples | 12126 | 7256 | 4870 | |||
| Log likelihood | −5260.662 | −3699.207 | −1550.743 | |||
| χ2 | 517.623 | 175.740 | 69.214 | |||
OR, odds ratio; CI confidence interval. * p < 0.10, ** p < 0.05, *** p < 0.01. χ2 represents the chi-square test value.
Table 4.
Regression results for urban respondents’ willingness to sort waste and willingness to place waste at designated locations.
Table 4.
Regression results for urban respondents’ willingness to sort waste and willingness to place waste at designated locations.
| Model 4: Willingness to Sort Waste | Model 5: Willingness to Place Waste at Designated Locations | |||
|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | |
| Classification of urban neighbourhood (ref: commodity housing) | ||||
| Old residential neighbourhood | 0.492 *** | [0.371, 0.652] | 0.217 *** | [0.099, 0.478] |
| Unit neighbourhood | 0.688 ** | [0.511, 0.926] | 0.253 *** | [0.114, 0.560] |
| Security housing | 0.942 | [0.462, 1.920] | 0.745 | [0.093, 5.965] |
| Residential neighbourhood changed from a rural neighbourhood | 0.593 *** | [0.417, 0.842] | 0.338 ** | [0.132, 0.870] |
| Urban shanty town | 0.671 | [0.367, 1.226] | 0.232 ** | [0.060, 0.891] |
| Other | 0.402 *** | [0.271, 0.597] | 0.422 | [0.124, 1.437] |
| Number of family members living together | 0.974 | [0.925, 1.026] | 0.970 | [0.859, 1.095] |
| Average number of family meals per month | 1.004 ** | [1.001, 1.008] | 1.003 | [0.995, 1.012] |
| Average monthly household electricity consumption | 0.975 | [0.910, 1.045] | 0.925 | [0.793, 1.079] |
| Average monthly household food expenditure | 1.005 | [0.999, 1.012] | 0.999 | [0.991, 1.006] |
| Household cleanliness | 1.021 | [0.947, 1.100] | 1.242 ** | [1.029, 1.498] |
| Household crowding | 1.132 *** | [1.052, 1.218] | 1.003 | [0.834, 1.207] |
| Neighbourhood property management (ref: have) | 0.968 | [0.786, 1.191] | 1.624 ** | [1.004, 2.627] |
| The level of air pollution | 1.010 | [0.870, 1.172] | 0.803 | [0.557, 1.156] |
| The level of water pollution | 1.104 | [0.937, 1.301] | 1.610 ** | [1.120, 2.315] |
| The level of noise pollution | 0.978 | [0.858, 1.114] | 0.871 | [0.631, 1.202] |
| The level of soil pollution | 0.931 | [0.782, 1.109] | 1.184 | [0.799, 1.756] |
| Number of samples | 4870 | 4870 | ||
| Log likelihood | −1534.096 | −379.190 | ||
| χ2 | 102.509 | 49.513 | ||
OR, odds ratio; CI, confidence interval. ** p < 0.05, *** p < 0.01. χ2 represents the chi-square test value.
Table 5.
The regression results for respondents’ willingness to place waste at designated locations.
Table 5.
The regression results for respondents’ willingness to place waste at designated locations.
| Model 6 | Model 7 | Model 8 | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Urban neighbourhoods (ref: rural neighbourhoods) | 3.553 *** | [2.632, 4.795] | ||||
| Number of family members living together | 0.995 | [0.959, 1.032] | 0.996 | [0.958, 1.035] | 0.964 | [0.856, 1.085] |
| Average number of family meals per month | 1.005 *** | [1.001, 1.008] | 1.005 ** | [1.001, 1.008] | 1.004 | [0.996, 1.013] |
| Average monthly household electricity consumption | 1.086 * | [0.998, 1.182] | 1.122 ** | [1.016, 1.239] | 0.941 | [0.805, 1.099] |
| Average monthly household food expenditure | 1.004 | [0.997, 1.012] | 1.007 | [0.997, 1.017] | 1.000 | [0.991, 1.008] |
| Household cleanliness | 1.183 *** | [1.111, 1.258] | 1.166 *** | [1.091, 1.246] | 1.275 ** | [1.059, 1.536] |
| Household crowding | 1.055 * | [0.992, 1.122] | 1.058 * | [0.990, 1.131] | 1.029 | [0.858, 1.234] |
| Neighbourhood property management (ref: have) | 0.933 | [0.637, 1.368] | 0.436 | [0.160, 1.190] | 1.262 | [0.791, 2.014] |
| The level of air pollution | 0.679 *** | [0.583, 0.791] | 0.645 *** | [0.544, 0.766] | 0.841 | [0.587, 1.206] |
| The level of water pollution | 1.037 | [0.909, 1.183] | 0.994 | [0.862, 1.148] | 1.615 *** | [1.130, 2.310] |
| The level of noise pollution | 0.924 | [0.801, 1.066] | 0.930 | [0.790, 1.095] | 0.866 | [0.628, 1.192] |
| The level of soil pollution | 1.214 ** | [1.036, 1.423] | 1.221 ** | [1.026, 1.453] | 1.180 | [0.797, 1.747] |
| Number of samples | 12126 | 7256 | 4870 | |||
| Log likelihood | −2336.612 | −1933.485 | −389.600 | |||
| χ2 | 429.664 | 161.509 | 28.693 | |||
OR, odds ratio; CI, confidence interval. * p < 0.10, ** p < 0.05, *** p < 0.01. χ2 represents the chi-square test value.
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Zhao, L.; Chen, H. Exploring the Effect of Family Life and Neighbourhood on the Willingness of Household Waste Sorting. Sustainability 2021, 13, 13653. https://doi.org/10.3390/su132413653
AMA Style
Zhao L, Chen H. Exploring the Effect of Family Life and Neighbourhood on the Willingness of Household Waste Sorting. Sustainability. 2021; 13(24):13653. https://doi.org/10.3390/su132413653
Chicago/Turabian StyleZhao, Liyuan, and Hongsheng Chen. 2021. "Exploring the Effect of Family Life and Neighbourhood on the Willingness of Household Waste Sorting" Sustainability 13, no. 24: 13653. https://doi.org/10.3390/su132413653
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