Comparison between students and residents on determinants of willingness to separate waste and waste separation behaviour in Zhengzhou,China

Abstract

A better understanding of willingness to separate waste and waste separation behaviour can aid the design and improvement of waste management policies. Based on the intercept questionnaire survey data of undergraduate students and residents in Zhengzhou City of China, this article compared factors affecting the willingness and behaviour of students and residents to participate in waste separation using two binary logistic regression models. Improvement opportunities for waste separation were also discussed. Binary logistic regression results indicate that knowledge of and attitude to waste separation and acceptance of waste education significantly affect the willingness of undergraduate students to separate waste, and demographic factors, such as gender, age, education level, and income, significantly affect the willingness of residents to do so. Presence of waste-specific bins and attitude to waste separation are drivers of waste separation behaviour for both students and residents. Improved education about waste separation and facilities are effective to stimulate waste separation, and charging on unsorted waste may be an effective way to improve it in Zhengzhou.

Keywords

Willingness to separate waste waste separation behaviour China regression analysis residents students

Introduction

The development of China has resulted in an unprecedented increase in the amount of solid waste. The total volume of waste disposal in urban China in 2012 was approximately 170.8 million tonnes (National Bureau of Statistic of China, 2017), and the waste generation rate increased from 0.50 kg capita per day in 1980 to 0.98 kg capita^-1 per day in 2006 (Zhang et al., 2010).

As the provincial capital and the largest city of Henan Province in north-central China, Zhengzhou City is no exception to these trends. Zhengzhou produced 1.04 million tonnes of municipal solid waste (MSW) in 2006, with a per capita waste generation rate of 0.82 kg capita^-1 per day (Gao et al., 2008). With an average increase of 2% to 5% every year, municipal waste is a major environmental concern for the city (Gao et al., 2008).

At present, MSW in China is mixed and collected in bags. Although several pilot projects on waste separation are underway, source-separated collection at the household level is an uncommon practice in China (Sidique et al., 2010).

Zhengzhou has advocated waste separation since 1999. Waste specific bins for different kinds of wastes were installed in several regions of the city, but household waste separation was not formally promoted. Since the end of the 1980s, the reduction of household waste has been a basic requirement for the sustainable development envisioned by the Chinese government (Huang et al., 2014). Thus, investigating the motivation behind household participation in waste separation is important.

A large number of publications have discussed the influence of different factors on household participation in waste separation and the influence of demographic and socioeconomic factors, such as age, gender, education level, income, household size, and dwelling type, have been studied (Gamba and Oskamp, 1994; Owusu et al., 2013; Saphores et al., 2006; Sidique et al., 2010; Sterner and Bartelings, 1999; Widegren, 1998). In other cases, the influence of personal and social norms, environmental awareness, and attitude has been examined (Berglund, 2005; Domina and Koch, 2002; Meneses and Palacio, 2005; Sidique et al., 2010; Vicente and Reis, 2008).

Factors related to information, education, and awareness have also been investigated (Austin et al., 1993; Hopper and Nielsen, 1991; Hornik et al., 1995; Williams and Taylor, 2004) and a large number of studies have suggested that convenience and the level of effort required to participate in recycling are important explanatory factors for waste recycling (Bernstad, 2014; Gonzalez-Torre et al., 2003; Saphores et al., 2006; Sidique et al., 2010). Other studies indicate that economic incentives, subsidies for recycling, and the penalisation of non-compliance contribute to waste source separation (Lombrano, 2009; Owusu et al., 2013). Efficient infrastructure was also found to facilitate waste recycling (Sterner and Bartelings, 1999).

Undergraduate students are important for future waste separation and understanding the factors affecting their behaviour is necessary to promote waste separation. Although the majority of the residents received a university education before, they received additional education in the society, such as publicity of the government or enterprises. Therefore, the difference of the waste education effect between the university and wider society can be analysed by comparing the willingness to separate waste and waste separation behaviour of students and residents. This will help the government to understand the improvement opportunities of waste separation education in Higher Education and society.

Werner and Makela (1998) found that individuals who are strongly in favour of recycling are more likely to adopt and maintain their sorting practices. People who have great concern for the environment are found to be more likely to recycle (Domina and Koch, 2002). Nevertheless, Webb and Sheeran (2006) found that the intention to complete a behaviour is weakly connected with the actual completion of the behaviour. No significant differences were found between recyclers and non-recyclers in their environmental consciousness (Oskamp et al., 1991) and waste separation behaviour remains low, even in cases of high environmental awareness and a positive attitude towards waste recycling, because of inadequate accessibility to recycling facilities (Hage et al., 2008). However, relatively few studies compared the factors affecting the willingness and behaviour of undergraduate students and residents toward waste separation. Tsim et al. (2011) compared the awareness and participation of residents and students in solid waste reduction in Zhuhai, but did not fully investigate the factors affecting the awareness of and participation in waste separation.

The present study principally aims to compare the different effects of demographic and behavioural factors on the willingness to separate waste and waste separation behaviour between residents and undergraduate students. We hypothesize that acceptance of waste education, education level, gender, age, income, and knowledge about waste separation are associated with the willingness to separate waste and waste separation behaviour waste separation.

Materials and methods

Survey design and data collection

The data employed in this study come from a self-administered questionnaire survey distributed in Zhengzhou City in 2014 to assess the willingness and practices of undergraduate students and residents toward the attitudes and behaviours of undergraduate students and residents toward solid waste. In total, 285 students in the campus of three universities and 330 residents in several parks and squares located in different districts of Zhengzhou City were included in the survey. Nearly half of the respondents are female and the average age was under 35 years. Most student respondents were in lower grades and only 28% of them majored in liberal arts. The education and income levels of resident respondents were relatively high. The majority of them were workers in private enterprises and staff in government organisations, national enterprises, or public utilities.

All participants were given a questionnaire to complete themselves after gaining their informed consent. Among the sampled students, 87 came from the Zhengzhou Institute of Aeronautical Industry Management (ZZIA), 100 came from the North China University of Water Resources and Electric Power (NCWU), and the final 100 students came from the Henan College of Traditional Chinese Medicine (HACTCM). ZZIA is a liberal arts school, NCWU is an engineering university, and HACTCM is a medical college. We can compare the impact of major on waste separation by conducting a survey in these three educational establishments. The number of sampled residents in different districts was determined according to the population proportion in different districts of Zhengzhou City. Five students in NCWU were recruited and trained to conduct the questionnaire survey. The main survey was preceded by a pilot study to test the survey wording, collect respondent comments, and to evaluate the suitability of variable definitions.

Our questionnaire aims to measure the attitude, willingness, and behaviour towards waste separating, along with other demographic and behavioural variables. The questionnaire used questions such as ‘Are you willing to separate waste?’ and ‘Is waste separation necessary?’ to understand people’s willingness and attitude to the process. Another question of ‘Did you separate waste?’ was used to understand people’s waste separation behaviour. Respondents were required to evaluate their knowledge on waste separation and the frequency of accepting waste education, such as watching television public service advertisements; they were given some names of waste items, such as wasted cardboard, aluminium cans, porcelain, and construction waste, in the questionnaire and requested to identity unrecoverable waste from common wastes to test their knowledge on waste separation. Moreover, there are questions about the sanitary condition and existence of waste-specific bins in their community or campus. Demographic information was solicited from the respondents, such as age, gender, education or grade, profession or major, and income. The professions of the sampled residents were divided into staff in government organisations, national enterprises, or public utilities, workers in private enterprises, owners of individual business, and unemployed. The majors of the sampled students were divided into liberal arts and science. The survey similarly included questions about effective waste education methods, the weak points and key measures of waste separation, and attitudes toward fines for unsorted waste.

Descriptions of variables

Table 1 lists and defines the demographic and other related variables that are utilised in our analysis. INCOME is the average monthly individual income of the respondent and is divided into four levels. WILLINGNESS indicates that respondents are prepared to separate wastes if they have a chance to do it. SEBEHAVIOURS indicates that if the respondents have the experiences of separating the wastes before disposing of them. Generally, people do the things that they are willing to do, but they may not do it if the willingness is not strong enough and if the behaviour is limited by some external factors. So we separated the willingness and behaviour of respondents to evaluate the gap between them. The summary statistics of the variables presented in Table 1 indicate that most respondents are willing to separate waste and believe that waste separation is necessary. Nevertheless, respondents who separated waste are few. More than 95% of the respondents state that they are willing to separate waste, but only 10.1% and 10.3% of interviewed undergraduate students and residents actually separated their waste, respectively, which indicates a gap between willingness and behaviour. Table 1 indicates that most of the respondents have occasionally accepted waste education, with students receiving only a little more than residents, signifying the need to improve waste separation education.

Table 1.

Summary of variables included in the models of willingness and behaviours of waste separation and recycling.

Variables	Variable description	Undergraduate students		Residents
Variables	Variable description	Obs.	Mean (SD)	Obs.	Mean (SD)
WILLINGNESS	Willingness to separate waste (yes = 1, no = 0)	281	0.98 (0.16)	330	0.98 (0.13)
SEBEHAVIOURS	Has previously separated waste (yes = 1, no = 0)	287	0.10 (0.30)	331	0.10 (0.30)
MAJOR	Major (liberal arts = 1, science and engineering = 0)	286	0.28 (0.45)
GRADE	Grade (freshman = 1, sophomore = 2, junior = 3, senior = 4)	284	2.19 (1.00)
KNOWLEDGE	Knowledge on waste separation (very little = 1, little = 2, substantial = 3)	286	1.94 (0.32)	331	2.07 (0.33)
SANITARY	Sanitary condition in community or campus (dirty = 1, clean = 2, relatively clean = 3, very clean = 4)	287	2.57 (0.62)	331	2.51 (0.78)
CLASSIFYBIN	Presence of waste-specific bins in community or campus (yes = 1, no = 0)	276	0.17 (0.38)	330	0.33 (0.47)
ACCEPTANCE	Acceptance of waste education (never = 1, occasionally = 2, often = 3)	285	1.80 (0.60)	331	1.78 (0.55)
ATTITUDE	Waste separation is necessary (yes = 1, no = 0)	286	0.87 (0.34)	331	0.97 (0.16)
GENDER	Female (yes = 1, no = 0)	287	0.48 (0.50)	331	0.49 (0.50)
AGE	Age (16–25 years old = 1, 26–35 = 2, 36–45 = 3, 46–55 = 4, more than 55 years old = 5)	287	1.00 (0.08)	330	2.52 (1.08)
EDUCATION	Education level of respondent (no formal education = 1, primary school = 2, middle school = 3, high school or technical secondary school = 4, university or junior college = 5, master’s degree or higher = 6)			331	4.67 (0.78)
INCOME ^a	Average monthly individual income of respondent (less than 2000 CNY or 325 USD = 1, 2000–3000 CNY or 325–488 USD = 2, 3000–4000 CNY or 488–650 USD = 3, more than 4000 CNY or 650 USD = 4)			323	2.70 (1.00)
STAFF	Staff in government organisations, national enterprises, or public utilities (yes = 1, no = 0)			327	0.35 (0.48)
WORKERS	Worker in private enterprises (yes = 1, no = 0)			327	0.46 (0.50)
OWNERS	Owner of individual business (yes = 1, no = 0)			327	0.13 (0.33)
UNEMPLOYED	Unemployed (yes = 1, no = 0)			327	0.06 (0.25)

Source: Intercept questionnaire survey in Zhengzhou City, conducted by North China University of Water Resources and Electric Power in 2014.

Income is calculated using the average conversion rate of the US Dollar (USD) to Renminbi (CNY) in the first half of 2014 (1 USD = 6.14 CNY).

People may have less waste separation behaviour when the environment is dirty because of the broken windows theory. The survey shows that most respondents believe that their community or campus is not dirty, but only 17% of students interviewed and 33% of residents identified the presence of waste-specific bins in their campus or community, indicating that the waste separation structure of Zhengzhou requires improvement.

Estimation model

Dependent variables are if the respondents are willing to separate waste or if they have separated waste before. Variable values are set as 1 if the respondents are willing to separate waste or if they have separated waste before; otherwise the variable values are set as 0. In the instances where the dependent variable is binary (0/1), logit and probit models are most commonly used to analyse technology adoption processes (Namara et al., 2007). As the data for our dependent variables are binary variables, we use binary logistic regression to develop the estimation model because the logit regression model is derived from the logistic model.

The model is specified as follows:

P_{i} = P_{r} (Y_{i} = 1) = \frac{\exp (β_{0} + \sum_{i}^{m} β_{i} X_{i})}{1 + \exp (β_{0} + \sum_{i}^{m} β_{i} X_{i})}

(1)

where P_i denotes the dependent variable of probability that the ith respondent is willing to separate waste or has separated waste before (Y_i = 1), β₀ is the intercept, β_i represents slope parameters, and X_i denotes independent variables. Correlation analysis of independent variables was conducted before regression analysis.

Results and discussion

Factors affecting the willingness of students and residents to engage in waste separation

Table 2 presents the results of the binary logistic regression models. Results show that there is no strong correlation between independent variables. Model 1 analyses undergraduate students’ willingness toward waste separation, while Model 2 analyses that of residents. CLASSIFYBIN and professional variables, such as STAFF and WORKERS, were not included in the models because the overall correct predictions of the models that include these variables are very low. AGE, EDUCATION, INCOME, and professional variables were not included in Model 1 because undergraduate students have the same age and education level, and they have no income and profession. MAJOR and GRADE were not included in Model 2 because they are not relevant to resident participants. Cox and Snell R² (Cox and Snell, 1989) and Nagelkerke R² (Nagelkerke, 1991) were used to evaluate the goodness of fit of the models beside the overall correct prediction rates, which can be misleading under certain conditions. Models 1 and 2 have overall predictions of 97.8% and 98.8%, respectively; the Cox and Snell R² and Nagelkerke R² are relatively low with the highest R² of 0.33. The low R² may be caused by errors in data collection, such as misunderstanding of the questionnaire by the respondents or the investigators, or inappropriate variables or models.

Table 2.

Estimated binary logistic regression model for willingness to separate waste and waste separation behaviour.

	Waste separation willingness				Waste separation behaviour
	Undergraduate students(Model 1)		Residents(Model 2)		Undergraduate students(Model 3)		Residents(Model 4)
	Coeff.	Std. error	Coeff.	Std. error	Coeff.	Std. error	Coeff.	Std. error
CONSTANT	−3.35	2.49	−8.53	5.32	−1.14	1.56	−1.18	2.85
MAJOR	−1.48	0.98			0.15	0.47
GRADE	−0.01	0.47			0.18	0.21
KNOWLEDGE	2.32**	0.98	−0.04	1.35	−0.84	0.63	−0.02	0.70
SANITARY	−0.37	0.70	0.08	0.69	0.15	0.33	−0.12	0.27
CLASSIFYBIN					1.10**	0.47	0.79*	0.41
ACCEPTANCE	1.72**	0.88	1.06	1.09	0.30	0.35	0.28	0.41
ATTITUDE	1.86**	0.95	1.37	1.55	−1.23**	0.50	−1.70**	0.86
GENDER	0.94	0.95	2.23*	1.30	−0.14	0.42	−0.20	0.42
AGE			0.91*	0.58			0.02	0.22
EDUCATION			1.04*	0.64			−0.03	0.30
INCOME			1.04*	0.64			0.13	0.23
STAFF							−0.76	1.19
WORKERS							0.08	1.13
OWNERS							0.63	1.16
Observations	278		321		270		317
-2 Log likelihood	46.68		35.66		167.17		187.60
Overall correct prediction (%)	97.8		98.8		90.4		90.5
Cox and Snell R²	0.065		0.048		0.046		0.047
Nagelkerke R²	0.31		0.325		0.095		0.1

Source: Intercept questionnaire survey of the authors in 2014 in Zhengzhou City.

Statistically significant at the 10% level.

Statistically significant at the 5% level.

***

Statistically significant at the 1% level.

The KNOWLEDGE, ACCEPTANCE, and ATTITUDE coefficients are statistically significant at the 5% level for the students’ willingness toward waste separation in Model 1. Positive KNOWLEDGE and ACCEPTANCE coefficients indicate that increased knowledge about waste separation and waste education increase students’ willingness to separate waste and confirm previous findings that education plays a role in developing attitudes toward the environment (Al-Rabaani et al., 2009; Asmawati et al., 2011; Saphores et al., 2006). The positive coefficient for ATTITUDE indicates that a positive attitude is consistent with the increased willingness toward waste separation, confirming the findings by Vicente and Reis (2008).

KNOWLEDGE, ACCEPTANCE, and ATTITUDE are no longer significant in Model 2. This indicates that waste separation education has a smaller effect on residents than on students. However, GENDER, AGE, EDUCATION, and INCOME are statistically significant at the 10% level. The positive GENDER coefficient indicates that female residents are more willing to separate waste than males, conforming previous findings that females are more involved in household-level solid waste management (Owusu et al., 2013). The positive relationship between age and willingness to separate wastes is likewise consistent with previous findings that older people have a higher tendency to recycle (Meneses et al., 2005; Saphores et al., 2006; Sidique et al., 2010). The EDUCATION coefficient indicates that people with higher education may be more willing to separate waste, an anticipated result because residents with higher education tend to better understand the importance of waste separation. The coefficient for INCOME suggests that people with a higher income are more willing to separate waste, conforming previous results that low-income residents often have crowded houses with less space to separate waste (Songsore and McGranahan, 1998).

Factors affecting student and resident behaviour of waste separation

Table 2 presents the results of the binary logistic regression models that predict the behaviours of students and residents toward waste separation. Model 3 analyses undergraduate students’ waste separation behaviour, while Model 4 analyses residents’ waste separation behaviour. For the same reason stated in the previous section, AGE, EDUCATION, INCOME, and professional variables were not included in Model 3, and MAJOR and GRADE were not included in Model 4. Cox and Snell R² and Nagelkerke R² were also used to evaluate the goodness of fit of the models beside the overall correct prediction rates. The same as Models 1 and 2, Models 3 and 4 have overall correct predictions of 90.4% and 90.5%, respectively; the Cox and Snell R² and Nagelkerke R² of them are very low. There is room for improving the models or other factors impacting the goodness of fit.

CLASSIFYBIN is statistically significant at the 5% level for the waste separation behaviour of students in Model 3, and statistically significant at the 10% level for resident behaviour toward waste separation in Model 4. CLASSIFYBIN coefficient indicates that the presence of waste-specific bins in a community increases waste separation of students and residents, perhaps because people are educated by labels on the bins.

ATTITUDE is statistically significant at the 5% level for student and resident behaviour toward waste separation in Models 3 and 4, respectively. The adverse coefficient for ATTITUDE in both models indicates that a positive attitude is consistent with decreased waste separation behaviour. The result verifies the gap between willingness and behaviour. Other requirements, such as a suitable waste separation system, are needed to stimulate the waste separation behaviour.

KNOWLEDGE, GENDER, AGE, and EDUCATION are statistically significant for a willingness to separate waste, but they are not statistically significant for waste separation behaviours of respondents. This indicates that waste separation education is not good enough to stimulate the behaviours of waste separation. Furthermore, an incomplete waste separation system also limited the waste separation behaviour. Previous findings indicate that older people have a higher tendency to recycle waste, but age categories do not differ in waste separation behaviours in this case.

MAJOR, GRADE, SANITARY, and professional variables are neither statistically significant for willingness to separate waste, nor for waste separation behaviours of respondents in all the models. This indicates that current waste separation education in all majors and grades of universities is poor. This also indicates that people cannot get enough waste separation stimulation from either universities or work places, and waste separation education in both needs to be improved. Furthermore, a clean environment cannot stimulate the willingness to separate waste and waste separation behaviour.

Improvement opportunities for waste separation

Understanding the factors that influence waste separation is helpful to establish an effective waste separation system. Respondents were asked about their knowledge of waste separation, the difficulties associated with the process, and measures to improve it in Zhengzhou. The results are as follows.

Waste separation knowledge and waste education

Respondents were asked to identify unrecoverable waste from names of printed wastes in the interview. Unrecoverable waste and the proportion that was correctly classified was calculated. Results show that the average proportion of correctly classified unrecoverable waste is only 0.58 and 0.66 for undergraduate students and residents, respectively. Tsim et al. (2011) reported that undergraduate students know a little more about waste separation than residents in Zhuhai City. Our findings indicate that waste education in the universities of Zhengzhou is worse than that in Zhuhai. As knowledge of waste separation and acceptance of waste education significantly affects the willingness of undergraduate students to separate waste, improving education about the process is an effective way to stimulate it in Zhengzhou.

Figure 1 presents the rate of respondents correctly classifying different waste. It shows that aluminium cans and cardboard are most frequently correctly classified by both undergraduate students and residents, whereas construction wastes and napkins are the least correctly classified. Both groups found porcelains and batteries difficult to classify. Waste separation education should emphasize wastes that are difficult to classify.

A one-way analysis of variance (ANOVA) method was used to analyse the significance of difference between residents and students. Figure 1 shows that students have a significantly lower correct rate than residents in separating plastic tableware, leftovers, porcelains, batteries, and construction wastes at the significant level of 1%. This indicates that education of waste separation method in the university should focus on these wastes.

Respondents were asked to choose effective waste education measures from given options, such as television public service advertisement, government or school propaganda, newspapers, magazines, and so on. The results presented in Figure 2 indicate that the first and second most commonly selected waste education measures by both groups are public service advertisements on television and government or school propaganda; whereas the least commonly selected measure is an introduction through friends or classmates. This confirms earlier findings that government propaganda and television are more important than other media in waste education (Tsim et al., 2011). More residents than students think that the waste education on the Internet is effective. Nevertheless, there are no significant differences between residents and students in choosing effective waste education measures.

Difficulties and key measures for waste separation

We presented respondents with a list of difficulties in waste separation, such as lack of knowledge on waste classification, lack of awareness about environmental protection, difficulties in waste separation, and so on. The main difficulties for waste separation are shown in Figure 3, which indicates that the lack of waste classification knowledge and environmental protection awareness are the two most common difficulties, confirming the findings by Tsim et al. (2011). The least commonly selected difficulty was that waste separation is time consuming. Additionally, results of one-way ANOVA analysis indicates that there are significant differences between residents and students in choosing difficulties in waste separation. More residents than students perceive the lack of waste-specific bins as a difficulty and more students than residents think that waste separation is troublesome. Therefore, different countermeasures should be used to address these difficulties in waste separation inside and outside the campus.

Respondents were also asked to choose key measures to improve waste separation from a list of measures, such as improving waste education, constructing more waste separation facilities, and improving the supervision and management of waste separation. The results presented in Figure 4 show that the most commonly selected measures are waste classification education for undergraduate students and construction of waste separation facilities for residents, in accordance with the difficulties presented in Figure 3. The least commonly selected measure by both groups is the establishment of a waste separation system. Significantly more students than residents think that waste separation education should be improved, while significantly more residents than students think that more waste separation facilities should be constructed. As presence of waste-specific bins has a significant effect on the behaviour of students and residents toward waste separation, improving waste separation facilities may effectively promote waste separation in Zhengzhou, especially outside the campus.

Figure 5 shows the attitudes of respondents toward charges on unsorted waste. Most residents (66.8%) agree the measure, whereas only 42.8% of students did so. Tsim et al. (2011) reported that 28% of residents and 33% of students agree to charges on unsorted waste in Zhuhai (Tsim et al., 2011); suggesting that people in Zhengzhou have a more positive attitude toward waste separation than in Zhuhai. Owusu et al. (2013) found that households are willing to accept a monthly cash incentive to participate in waste separation. This indicates that charges on unsorted waste may be a suitable policy to improve waste separation. Figure 5 shows that significantly more students than residents are against charges on unsorted waste, indicating that imposing these on campus would be more difficult than in the community.

Conclusions

Despite the poor performance of waste separation in China, little research has empirically compared the willingness to separate waste and waste separation behaviour between different groups. Literature survey shows that the relationship between willingness and behaviour in waste separation is controversial (Domina and Koch, 2002; Hage et al., 2008; Oskamp et al., 1991; Webb and Sheeran, 2006; Werner and Makela, 1998). This study helps explain the underlying factors that influence the willingness and behaviour of undergraduate students and residents toward waste separation and the relationship between both. Hence, the findings are useful for establishing suitable waste separation systems for different groups of people, such as undergraduate students and residents.

The results show that knowledge of, and attitude to, waste separation and acceptance significantly affect the willingness of undergraduate students to separate waste. Nevertheless, the results show that residents and students of Zhengzhou have little waste separation knowledge, and respondents consider lack of knowledge about the classification of waste the biggest obstacle to effective separation. This indicates that improving waste separation education is an effective way to stimulate willingness to waste separation in Zhengzhou.

Residents have more knowledge of waste separation than students; this suggests that education about the process should target this sector of the population. According to the questionnaire survey results, effective waste education measures include public service advertisements on television and government or school propaganda. Moreover, people’s knowledge of classifying wastes, especially construction wastes, napkins, porcelains, and batteries, from the examples used in this study, should be further promoted. These findings can help the government to choose suitable methods in waste separation publicity.

According to the results, a positive attitude to waste separation can increase the willingness of undergraduate students to separate waste, but it is accompanied by decreased waste separation behaviour. The contradictory result highlights the big gap between willingness and behaviour of people toward the process. Other measures, such as improving supervision and management of waste separation, are needed to improve waste separation. Survey results show that 66.8% and 42.8% of interviewed residents and undergraduate students agree with charging for unsorted waste, respectively, indicating that this measure may be an optional policy to improve waste separation in Zhengzhou.

The presence of bins for specific forms of waste also has a significant effect on the behaviour of students and residents toward waste separation. People who noticed the existence of waste-specific bins are more likely to separate their waste. Constructing more waste separation facilities is recommended by the resident respondents as the most effective measure of improving waste separation. Hence, improving waste separation facilities may effectively promote the target behaviours in Zhengzhou.

While demographic factors, such as gender, age, education level, and income, indicate the willingness of residents to separate waste, these factors, with the exception of income, have no significant impact on the waste behaviour of either group. People with a higher education level may have engaged in more waste separation behaviour. Nevertheless, resident respondents who have a higher education level showed no significant increase of waste separation behaviour. This indicates that waste separation promotion should be improved and is more likely to succeed among females with higher education levels and a higher income.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge the support of the National Natural Science Foundation of China [Grant No. 51609082], the Plan for Scientific Innovation Talent of Henan Province in China [Grant No. 144100510014], the Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period [Grant No. 2012BAC19B00], the Program for the New Century Excellent Talents in University [Grant No. NCET-13-0794], the Open Research Fund of Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Henan Province, and the Program for Innovative Research Team (in Science and Technology) in University of Henan Province [Grant Nos 15IRTSTHN030, 141RTSTHN028, and 17IRTSTHN026].

References

Austin

Hathfield

Grindle

et al (1993) Increasing recycling in office environments, the effects of specific informative cues. Journal of Applied Behavior Analysis 26: 247–253.

Al-Rabaani

ABH

Al-Mekhlafi

SSM

(2009) Attitudes of Sultan Qaboos university students towards some environmental problems and their willingness to take action to reduce them. Journal of Social Sciences 5: 9–15.

Asmawati

Kadir

NBA

Yusooff

(2011) A study on the knowledge, attitudes, awareness status and behaviour concerning solid waste management. Procedia Social and Behavioral Sciences 18: 643–648.

Berglund

(2005) The assessment of households’ recycling costs; the role of personal motives. Ecological Economics 56: 560–569.

Bernstad

(2014) Household food waste separation behavior and the importance of convenience. Waste Management 34: 1317–1323.

Cox

Snell

(1989) Analysis of Binary Data. 2nd ed. London: Chapman & Hall Press.

Domina

Koch

(2002) Convenience and frequency of recycling: Implications for including textiles in curbside recycling programs. Environment and Behavior 34: 216–238.

Gamba

Oskamp

(1994) Factors influencing community residents’ participation in commingled curbside recycling programs. Environment and Behavior 26: 587–612.

Gonzalez-Torre

Adenso-Dıaz

Ruiz-Torres

(2003) Some comparative factors regarding recycling collection systems in regions of the USA and Europe. Journal of Environmental Management 69: 129–138.

10.

Gao

(2008) Present status and treatment countermeasures of municipal domestic waste in Zhengzhou City. Environmental Sanitation Engineering 16: 25–27 [in Chinese].

11.

Hage

Sandberg

Söderholm

et al (2008) Household Plastic Waste Collection in Swedish Municipalities: A Spatial-Econometric Approach. Report within the SHARP (Sustainable Households: Attitudes, Resources and Policy) project. Available at: http://www.webmeets.com/files/papers/eaere/2008/83/paper3-spatial-8.pdf (accessed 1 March 2017).

12.

Hopper

Nielsen

(1991) Recycling as altruistic behavior: Normative and behavioral strategies to expand participation in community recycling program. Environment and Behavior 23: 195–220.

13.

Hornik

Cherian

Madansky

et al (1995) Determinants of recycling behavior: A synthesis of research results. Journal of Socio-Economics 24: 105–127.

14.

Huang

Wang

Dai

et al (2014) More than financial investment is needed: Food waste recycling pilots in Shanghai, China. Journal of Cleaner Production 67: 107–116.

15.

Lombrano

(2009) Cost efficiency in the management of solid urban waste. Resources, Conservation and Recycling 53: 601–611.

16.

Meneses

Palacio

(2005) Recycling behavior: A multidimensional approach. Environment and Behavior 37: 837–860.

17.

Nagelkerke

NJD

(1991) A note on a general definition of the coefficient of determination. Biometrika 78: 691–692.

18.

Namara

Nagar

Upadhyay

(2007) Economics, adoption determinants, and impacts of micro-irrigation technologies: Empirical results from India. Irrigation Science 25: 283–297.

19.

National Bureau of Statistic of China (2017) China statistical database. Available at: http://data.stats.gov.cn/easyquery.htm?cn=C01 (accessed 1 March 2017).

20.

Oskamp

Harrington

Edwards

et al (1991) Factors influencing household recycling behavior. Environment and Behavior 23: 494–519.

21.

Owusu

Adjei-Addo

Sundberg

(2013) Do economic incentives affect attitudes to solid waste source separation? Evidence from Ghana. Resources, Conservation and Recycling 78: 115–123.

22.

Saphores

Nixon

Ogunseitan

et al (2006) Household willingness to recycle electronic waste: An application to California. Environment and Behavior 38: 183–208.

23.

Sidique

Lupi

Joshi

(2010) The effects of behavior and attitudes on drop-off recycling activities. Resources, Conservation and Recycling 54: 163–170.

24.

Songsore

McGranahan

(1998) The political economy of household environ-mental management: Gender, environment and epidemiology in the Greater Accra Metropolitan Area. World Development 26: 395–412.

25.

Sterner

Bartelings

(1999) Household waste management in a Swedish municipality: Determinants of waste disposal, recycling and composting. Environmental and Resource Economics 13: 473–491.

26.

Tsim

Wang

Bao

(2011) Comparison between resident and student on awareness and participation in solid waste reduction in Zhuhai. Guangzhou Environmental Science 26: 49–52 [in Chinese].

27.

Vicente

Reis

(2008) Factors influencing households’ participation in recycling. Waste Management & Research 26: 140–146.

28.

Webb

Sheeran

(2006) Does changing behavioral intentions engender behaviour change? A meta-analysis of the experimental evidence. Psychological Bulletin 132: 249–268.

29.

Werner

Makela

(1998) Motivations and behaviors that support recycling. Journal of Environmental Psychology 18: 373–386.

30.

Widegren

(1998) The new environmental paradigm and personal norms. Environment and Behavior 31: 75–100.

31.

Williams

Taylor

(2004) Maximising household waste recycling at civic amenity sites in Lancashire, England. Waste Management 24: 861–874.

32.

Zhang

Tan

Gersberg

(2010) Municipal solid waste management in China: Status, problems and challenges. Journal of Environmental Management 91: 1623–1633.