INTRODUCTION
Problem statement
In Vietnam, the household waste is a pressing environment issue in recent years
According to the Ministry of Natural Resources and Environment (2014), household solid waste in Vietnam totaled 12.8 million tons annually, projected to rise to 22 million tons by 2020 This escalating waste generation poses significant risks of environmental pollution and adversely affects public health Ho Chi Minh City, in particular, is grappling with numerous challenges in effective waste management.
Every day, the city has more than 7,000 tons of garbage and costs each year up to
To effectively minimize waste and recommend government policies, it is essential to understand household waste recycling behavior This study focuses on analyzing the factors that influence recycling habits among households in Ho Chi Minh City, with a financial context of 235 billion VND allocated for waste management.
Research on household behavior in Vietnam, particularly regarding green consumption practices such as water and energy use, recycling, and transportation choices, is limited Notably, a study by Luu Bao Doan and Nguyen Trong Hoai (2015) focused on household waste recycling behavior Utilizing structural equation modeling, their findings revealed that a household's attitude towards recycling significantly influences recycling behavior However, general environmental concern and knowledge do not directly impact the intention to recycle.
Therefore, the study with logit models may contribute to academic knowledge on recycling behavior in the context of a developing country like Vietnam
This study offers policymakers valuable insights into recycling behavior and its connection to psychological factors related to the environment and waste recycling By understanding these relationships, authorities can develop effective strategies to encourage citizens to adopt more sustainable recycling practices.
Research objectives
This study aims to identify the factors influencing household waste recycling behavior in Ho Chi Minh City, focusing on six specific materials: metal, carton, paper, plastic, glass, and cloth.
Research questions
This article explores the influence of socio-economic and demographic characteristics on household waste recycling behavior in Ho Chi Minh City It examines how the characteristics of primary residences impact recycling practices and investigates the role of psychological factors related to the environment and waste management in shaping these behaviors.
Research scope and data
This study aims to explore the factors influencing household recycling behaviors for six materials: paper, carton, plastic, metal, glass, and cloth It utilizes data from the survey titled “Consumption Behavior Towards Green Growth in Urban Areas of Vietnam,” which was funded by the National Foundation for Science and Technology Development (NAFOSTED) The survey was carried out in Ho Chi Minh City during April and May.
In 2014, data was collected from 200 households across Districts 1, 3, 4, 9, Binh Thanh, Go Vap, Phu Nhuan, and Thu Duc Investigators conducted direct interviews with the heads of these households, providing clear explanations of the questions and options before recording their responses.
The structure of this study
Five chapters will be constructed in this study as follows:
Chapter 1: Introduction This is the beginning section of thesis, which consists of the research topic and problem statement The research objectives, research questions and the research scope and data are also presented in this chapter The final section will provide the structure of the research tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Chapter 2: Literature review This chapter provides with theoretical and empirical reviews related to household waste recycling behavior The first section will review a related theory explaining recycling behavior, which is Random Utility Theory
Empirical research identifies three primary factors influencing individual recycling behavior: socio-economic and demographic characteristics, housing conditions, and psychological attitudes towards recycling These factors can either promote or hinder recycling practices, depending on specific circumstances and the materials involved.
Chapter 3: Methodology This chapter presents research methods and conceptual framework
Chapter 4: Empirical results This chapter starts with descriptive statistic and then provides a bivariate relationship between household recycling behavior and some important determinants Finally, the regression results and interpretation are presented
Chapter 5: Conclusions This chapter summarizes the findings and concludes with some policy implication and research limitations tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
LITERATURE REVIEW
Theoretical Review
Random Utility Theory, proposed by Marschak in 1960, offers insights into recycling behavior by suggesting that an individual's utility consists of both deterministic and random components Consequently, the total utility derived from a household's recycling efforts can be expressed as the sum of these two components.
The value function \( V_{1i} \) can be approximated as a linear function of recycling, represented by the vector \( X_i \), along with the population utility weights for each attribute denoted by \( \beta_i \) This relationship is expressed as \( V_{1i} = \beta_{1i} X_{1i} \), where \( e_{1i} \) represents a random utility component.
Similarly, the total utility of a household i with non – recycling:
The initial value, V1i, can be estimated as a linear function of recycling represented by the vector Xi, along with the population utility weights for each attribute denoted by the vector βi Thus, the equation can be expressed as V0i = β0iX0i, where e0i represents a random utility component.
The probability that a household recycles can be expressed as the probability that the utility associated with recycling is higher than the utility of non – recycling:
Or Pr(recycling) = Pr (V1i + e1i > V0i + e0i) = Pr (e1i - e0i > V0i - V1i)
Recycling probability can be expressed as Pr(recycling) = Pr(e1i - e0i > β0iX0i - β1iX1i) For the latest updates and resources, please refer to the provided email for further assistance.
We assume that the error terms of the alternatives are uncorrelated, possess equal variance, and adhere to a logistic distribution Consequently, the likelihood of a household opting to recycle is represented by a logit probability.
Empirical Review
Environmental degradation poses a significant threat to our natural habitat, making recycling an essential solution for creating a cleaner environment, conserving resources, saving energy, and reducing landfill waste (Fiorillo, 2013) Extensive research has highlighted the critical role of recycling in our lives, focusing on key aspects such as the volume of materials recycled by households (Dalen et al., 2011; Halvorsen, 2012), the types of materials most commonly recycled, and the factors that influence household recycling behaviors (Halvorsen, 2008).
Recent studies have explored various types of household waste, including e-waste and solid waste The most commonly examined materials in this research are glass, paper, food, plastic, and aluminum.
Three primary factors influence household waste recycling behavior: (i) socio-economic and demographic characteristics, (ii) housing characteristics, and (iii) psychological factors related to recycling.
2009; Afroz et al., 2011; Dalen et al., 2011)
2.2.1 Socio-Economic and Demographic characteristics Household Income
Research indicates that income significantly influences household recycling behavior Numerous studies have established a positive correlation between higher household income and increased recycling activities.
Ferrara and Missios, 2012; Fiorillo, 2013; Halvorsen, 2012; Hui Zhao et al., 2013)
Halvorsen (2008, 2012) conducted surveys involving 1,162 households in Norway and 10,251 households across 10 OECD countries, revealing a positive correlation between income levels and the likelihood of recycling household waste Higher income respondents demonstrated a greater propensity to engage in recycling practices.
In 2011, Lee et al demonstrated that higher income levels incentivize households in Seoul, Korea, to engage more in food separation and recycling, based on a survey of 196 responses Ferrara and Missios (2012) analyzed data from 10,251 respondents across 10 OECD countries, revealing that wealthier households are more likely to participate in glass recycling and do so at a higher rate for glass, plastic, and aluminum A year later, Fiorillo (2013) conducted a study involving 47,643 households, finding that individuals with higher incomes tend to recycle various materials, including paper, glass, plastic, and aluminum, although they often exclude food waste.
A study conducted in 2013 analyzed 500 questionnaires from Qingdao, revealing a positive correlation between higher income and recycling behavior Additionally, an examination of 402 households in Dhaka City indicated that solid waste recycling frequency is positively influenced by the middle-income group These findings suggest that low and middle-income households are more likely to utilize available materials to reduce the costs associated with purchasing new items.
Several other researchers have suggested a negative or insignificant correlation between income and household waste recycling behavior (Hage et al., 2008;
Research by Hage et al (2009) involving 2,800 households across four Swedish municipalities found that income does not significantly influence household recycling behavior regarding packaging waste This aligns with Nixon et al (2009), who also explored household attitudes towards recycling Conversely, Rafia et al (2011) noted no direct correlation between income and recycling behavior Interestingly, Shaufique et al (2010) revealed a negative relationship between income and residential recycling rates, indicating that for every $1,000 increase in annual per capita income, recycling rates tend to decline.
0.2 percentage point decline in the rate of recycling It was also an average income group that raises the likelihood of reaching the aim of 150kg/capita residual household waste of each municipality (Gellynck et al., 2011)
There are a great number of studies citing gender as a determinant of household waste recycling behavior (Sterner and Bartelings, 1999; Saphores et al., 2012;
Research indicates that gender significantly influences recycling behavior Sterner and Bartelings (1999) found that women are more willing to pay for waste and recycling initiatives, while Saphores et al (2011) noted that women are more likely to engage in e-waste recycling compared to men Additionally, Ferrara and Missios (2012) and Fiorillo (2013) demonstrated a positive correlation between gender and household recycling behaviors for various materials, including glass, plastic, paper, food, and aluminum However, while Ferrara and Missios suggested that men are more inclined to recycle aluminum, Fiorillo's findings indicated that women are generally more willing to recycle all types of materials These results suggest that the impact of gender on recycling trends may vary depending on the type of waste and the survey location.
A survey of 10,000 households across 10 OECD countries by Dalen and Halvorsen (2011) revealed that women are generally not inclined to participate in recycling activities Additionally, numerous studies have explored the impact of gender on recycling behavior, but the findings indicate that the correlation is either insignificant or minimal (Hage et al., 2009; Lee et al., 2011; Pakpour et al., 2013; Ayalon et al., 2013; Huffman et al., 2013).
Numerous studies indicate that education significantly influences recycling behavior, with some research highlighting a positive correlation between higher education levels and increased recycling practices (Amy W and Gosselin, 2005; Shaufique).
V.Joshi, Lupi, 2010; Dwivedy and Mittal, 2013; Pakpour et al., 2013; Lange et al.,
In a study conducted by Ando (2005), the probit and double-censored tobit models were utilized to analyze the recycling rates of 214 multifamily dwellings in Urbana, Illinois, revealing a positive correlation between the number of years of education and container recycling rates Furthermore, Anderson et al (2013) employed a logistic regression model to examine data from 2003 to 2006, highlighting the significant influence of the household head's education level on recycling decisions.
Education also showed a negative impact on recycling behavior for monetary reasons
Research by Sterner and Bartelings (1999) indicates a negative correlation between age and households' willingness to pay for recycling, suggesting that individuals with lower education levels are more inclined to invest in recycling efforts Similarly, Hage et al (2009) found that education negatively affects paper recycling rates However, several studies, including those by Nixon et al (2009), Lee et al (2011), Ferrara and Missios (2012), Byrne and O’Regan (2014), and Hui Zhao et al (2013), have reported no significant impact of education on recycling behavior.
Almost recent empirical studies have presented a positive correlation between age and household waste recycling behavior (Ando et al., 2005; Shaufique et al., 2010;
Saphores et al., 2012; Pakpour et al., 2013; Hui Zhao et al., 2013; Lange et al.,
According to Hage et al (2009), age positively influences the recycling of packaging materials by individuals and households Similarly, Ayalon, Sharon, and Shechter (2013) conducted a survey involving 12,000 households, reinforcing the significance of age in recycling behaviors.
RESEARCH METHODOLOGY
Conceptual framework and the econometric model
A conceptual framework has been developed, as illustrated in Figure 1, based on theoretical and empirical reviews Research indicates that three primary groups of factors influence individual recycling behavior: socio-economic and demographic characteristics, housing characteristics, and psychological factors related to recycling.
In this study, six logistic regression models were estimated using Stata to analyze household recycling behavior for various materials, including metal, carton, paper, plastic, glass, and cloth The investigation focused on understanding how households engage in recycling practices based on these specific materials.
“Which of the following materials does your family usually recycle or collect for vendors?” The possible answer to each material is “yes” or “no” The response for
Socio-Economic and Demographic Characteristics
Household’s awareness of impacts on environment
Willingness to protect the environment
Household’s satisfaction of waste condition at residency
Belief of economic benefits of recycling
Household waste recycling behavior tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
16 that material is coded into a binary variable which means 1 in case of “yes” and 0 otherwise
Table 1 Description of the variables
Dummy variable, = 1 if household recycles plastic bottles and utensils, = 0 otherwise
Dummy variable, = 1 if household recycles containers and utensils made of iron, steel, stainless steel, aluminum, = 0 otherwise
3 glass Dummy variable, = 1 if household recycles glass objects, = 0 otherwise
Dummy variable, = 1 if household recycles paper and newspaper, = 0 otherwise
5 carton Dummy variable, = 1 if household recycles carton, = 0 otherwise
6 cloth Dummy variable, = 1 if Household recycles old clothes, = 0 otherwise
Independent variables Socio-Economic and Demographic Characteristics
1 age Age of the respondent
2 gender Gender of the respondent (1= male 0 female)
The educational background of the respondent includes various levels, such as vocational school, college, and university A score of 1 indicates completion of higher education, while a score of 0 represents no education or only completion of elementary or secondary school.
4 ln_income Logarithm of total average income per month (in million dongs)
5 house_size Size of house (in squared meter – m 2 )
6 house_type Type of house (1 = detached or semi- detached house; 0 = apartment)
Dummy variable, = 0 if the respondent’s degree of concern over waste management
“No idea” or “Not concerned” or “Fairly concerned” ; = 1 if the respondent’s degree of concern over waste management
Dummy variable, = 0 if the respondent’s awareness degree of environmental impacts on human life “No idea” or
“Disagree” or “Fairly Agree”; = 1 if the respondent’s awareness degree of environmental impacts on human life
Dummy variable, = 0 if the respondent’s awareness degree of environmental impacts on future generation “No idea” or
“Disagree” or “Fairly Agree”; = 1 if the respondent’s awareness degree of environmental impacts on future generation “Agree” or “Strongly Agree”
The article discusses the concept of longevity and its relationship with environmental awareness It highlights the importance of understanding how individuals' awareness levels can impact their longevity The content emphasizes the need for further research and data collection to explore these connections effectively.
18 impacts on longevity “No idea” or
“Disagree” or “Fairly Agree”; = 1 if the respondent’s awareness degree of environmental impacts on longevity
A dummy variable is defined as 0 when a respondent's willingness to trade-off for environmental protection is categorized as "No idea," "Disagree," or "Fairly Agree." Conversely, it is set to 1 if the respondent indicates an "Agree" level of willingness to make such trade-offs for environmental protection.
Dummy variable, = 0 if the respondent’s willingness degree to save energy to protect the environment “No idea” or
“Disagree” or “Fairly Agree”; = 1 if the respondent’s willingness degree to save energy to protect the environment “Agree” or “Strongly Agree”
A dummy variable is used to represent respondents' willingness to save water for environmental protection, where a value of 0 indicates responses of "No idea," "Disagree," or "Fairly Agree," and a value of 1 indicates responses of "Agree" or "Strongly Agree."
A dummy variable is defined as 0 when a respondent expresses a willingness degree to treat waste for environmental protection as "No idea," "Disagree," or "Fairly Agree." Conversely, it is set to 1 if the respondent shows a higher level of agreement.
19 willingness degree to treat waste to protect the environment “Agree” or “Strongly Agree”
Dummy variable, = 0 if the respondent’s willingness degree to limited use of personal vehicles to protect the environment “No idea” or “Disagree” or
“Fairly Agree”; = 1 if the respondent’s willingness degree to limited use of personal vehicles to protect the environment “Agree” or “Strongly Agree”
Dummy variable, = 0 if the respondent’s satisfaction degree of waste condition at residency “No idea” or “Dissatisfied” or
“Fairly Satisfied”; = 1 if the respondent’s the respondent’s satisfaction degree of waste condition at residency “Satisfied” or
Dummy variable, = 0 if the respondent’s awareness degree of economic benefits from recycling “No idea” or “Disagree” or
“Fairly Agree”; = 1 if the respondent’s awareness degree of economic benefits from recycling “Agree” or “Strongly Agree”
With the variables and measurements above, the proposed functional form is: ln( )
P = α + iSEDCi + iHCi+ PFTRi + ui tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The probability of recycling, denoted as Pi, is influenced by various factors The Socio-Economic and Demographic Characteristics (SEDC) vector includes variables such as age, gender, education, and household income Additionally, housing characteristics (HC) encompass house size and type Psychological factors towards recycling (PFTR) involve households' concerns about waste, awareness of environmental impacts, willingness to protect the environment, satisfaction with waste conditions at home, and the belief in financial benefits from recycling.
Data source
The data for this study was sourced from the survey titled "Consumption Behavior Towards Green Growth in Urban Areas of Vietnam," which was funded by the National Foundation for Science and Technology Development (NAFOSTED).
Ho Chi Minh City on April and May 2014, including 200 households from District
Investigators conducted interviews with households in Binh Thanh, Go Vap, Phu Nhuan, and Thu Duc to collect data They clearly explained the questions and options to the respondents before recording their feedback.
Methodology
This article examines the factors influencing household recycling behavior for six different materials, focusing on a binary outcome as the dependent variable The analysis utilizes the logit model, which is preferred in many studies due to its mathematical simplicity (Gujarati, 2003).
Where, yi * is unobservable variable, but we have yi = 0 if yi *< 0 and yi = 1 if yi *0
The cumulative distribution function \( F \) of the variable \( u_i \) is considered, with the assumption that its probability density function is symmetric.
In the logit model, ui has logistic distribution The probability density function of ui is given: 2
Maximum Likelihood Estimation (MLE) is using to estimate the model It can be verified the cumulative distribution function (CDF) of ui is
The probability of recycling P(yi = 1):
The probability of non - recycling P(yi = 0):
is the Odds Ratio (probability of recycling over probability of non-recycling),
The log of the odds ratio for \( y_i = 1 \) is expressed as a linear function of the explanatory variables.
EMPIRICAL RESULTS
Descriptive Statistics
Table 2 presents the recycling rates of six materials—metal, carton, paper, plastic, glass, and cloth—based on a survey of 200 households in Ho Chi Minh City The highest recycling rates are observed for paper and plastic, at 73.5% and 70.5%, respectively Metal recycling follows with a rate of 56% Additionally, 46.5% of households engage in carton recycling, while glass and cloth have the lowest recycling rates at 23.5% and 32.5%, respectively.
Table 2 Prevalence of recycling toward six materials Dependent variables Observations Frequencies
Source: Author’s calculation using Stata from NAFOSTED’s Data
The study identifies three key vectors of independent variables that influence recycling behavior: socio-economic and demographic characteristics, housing characteristics, and psychological factors Specifically, the socio-economic and demographic factors include age (in years), gender (coded as male = 1 and female = 0), education level (with high school or lower coded as 0 and higher education as 1), and the natural logarithm of income, which reflects the age, gender, education, and income of households measured in million dongs.
The study examines 23 characteristics related to housing, distinguishing between detached or semi-detached houses (coded as 1) and apartments (coded as 0), along with the size of the house measured in square meters Additionally, it explores 11 psychological factors represented by dummy variables, which include household concerns about waste, awareness of environmental impacts related to life, inheritance, and longevity, as well as the willingness to protect the environment through trade-offs in energy, water, waste, and transportation Other factors include household satisfaction with waste conditions and beliefs about financial benefits from waste recycling In this context, artificial variables are used, where a value of 1 indicates the presence of an attribute and 0 indicates its absence.
Table 3 Descriptive statistics of numerical variables Variables Mean Std Dev Min Max Observations age 43.54 14.046 20 89 200 income 15.897 13.414 1.5 120 198 house_size 55.037 31.372 8 168 200
Source: Author’s calculation using Stata from NAFOSTED’s Data
Tables 3 and 4 present the survey results, revealing minimal missing values Notably, over 60% of households expressed concerns about waste, awareness of its environmental impacts, willingness to protect the environment, and belief in the financial benefits of waste recycling.
The survey reveals that the average age of respondents is approximately 44 years, with an average income of nearly 16 million dongs Notably, 41.5% of household heads are male, and 41.71% have completed high school Additionally, around 77% of households reside in detached or semi-detached homes, with an average housing size of about 55 square meters.
Table 4 Descriptive statistics of binary variables Variables Frequencies
Percentage Observations gender 83 41.50 200 education 83 41.71 199 house_type 152 76.77 198 concern 150 75 200 life 190 95 200 inheritance 190 95 200 longevity 189 94.5 200 tradeoff 141 70.5 200 energy 184 92 200 water 189 94.5 200 waste 174 87 200 transportation 130 65 200 waste_condition 126 63 200 money 152 76.38 199
Source: Author’s calculation using Stata from NAFOSTED’s Data
The next section provides the bivariate relationship between recycling behavior of household toward the six kinds of waste above and its important determinants.
Bivariate analysis
A comparison of metal recycling and non-recycling reveals significant differences in various factors The average income for recycling participants is \$16.93, while non-recyclers earn \$14.61, with a p-value of 0.227 indicating no significant difference In terms of age, recyclers average 42.19 years compared to 45.27 years for non-recyclers, with a p-value of 0.096, also showing no significant difference However, the size of housing is notably different, with recyclers having an average house size of 63.11 square meters, while non-recyclers average 44.77 square meters, resulting in a significant p-value of 0.000.
The t-test results presented in Table 5 indicate a statistically significant difference in house size and age between the recycling and non-recycling groups.
The relationship is statistically significant at both the 1% and 10% levels, with P-values of 0.000 and 0.096, respectively However, the difference in mean income between individuals who recycle and those who do not is not substantial, as the bivariate relationship is not statistically significant at the 10% level, indicated by a P-value of 0.227.
Table 6 A comparison between metal recycling and non-recycling in term of gender, type of housing and education
P-value of Chi- square test
House type detached and semi-detached
Education higher education 41.96 41.38 0.934 high school or lower 58.04 58.62
The findings in Table 6 indicate that there is no correlation between household gender and metal recycling habits Of the 112 individuals engaged in metal recycling, 41.07% are male while 58.93% are female In the non-recycling group, the gender distribution is similar, with 42.05% male and 57.95% female.
The analysis reveals that the bivariate relationship between housing characteristics and recycling behavior is not statistically significant at the 10% level, with a Pearson chi-square value of 0.019 and a p-value of 0.89 Among recyclers, 77.27% reside in detached or semi-detached homes, while 22.73% live in apartments Similarly, in the non-recycling group, 76.14% live in detached or semi-detached homes, and 23.86% reside in apartments Overall, the findings indicate no significant correlation between housing type and recycling behavior.
The Pearson chi-square value is 0.035 with a p-value of 0.851, indicating that there is no significant association between academic level and metal recycling Among households, those with higher education represent approximately 41% of the recycling group compared to the non-recycling group.
The bivariate relationship analyzed shows no statistical significance at the 10% level, with a Pearson chi-squared value of 0.006 and a p-value of 0.934.
Figure 2 A comparison between metal recycling and non-recycling in term of some selected psychological factors
Figure 2 illustrates a comparison of metal recycling and non-recycling regarding selected psychological factors, revealing minimal differences between the two groups in terms of waste concern and awareness of environmental impacts on life.
Households are more likely to recycle metal when they are made aware of the financial benefits or the inheritance associated with recycling.
A comparison of carton recycling and non-recycling reveals notable differences in housing size, while income and age show minimal variation The average income for recycling participants is \$15.85, compared to \$15.94 for non-recyclers, with a P-value of 0.963 indicating no significant difference The average age of recyclers is 42.18 years, slightly younger than the 45.27 years of non-recyclers, with a P-value of 0.074 suggesting a marginal difference However, the average house size for those who recycle is significantly larger at 63.11 square meters, compared to 44.77 square meters for non-recyclers, with a P-value of 0.000, indicating a strong statistical significance in this aspect.
The t-test results indicate a statistically significant difference in house size and age between recycling and non-recycling groups, with p-values of 0.000 and 0.074, respectively However, there is no statistically significant difference in mean income between the two groups, as evidenced by a p-value of 0.963.
Table 8 A comparison between carton recycling and non-recycling in term of gender, type of housing and education
P-value of Chi- square test
House type detached and semi-detached
Education higher education 41.94 41.51 0.952 high school or lower 58.06 58.49
The Table 8 results show that household gender is associated with carton recycling
Among 93 people recycling carton, 43.41% are male and 65.59% are female
In the remaining group, the gender distribution is 47.66% male and 52.34% female.
The analysis reveals a statistically significant bivariate relationship at the 10% level (Pearson chi 2 = 3.6, p-value = 0.058), although housing characteristics do not correlate with recycling behavior Among recyclers, 78.02% reside in detached or semi-detached homes, compared to 75.7% in the non-recycling group This relationship is not statistically significant (Pearson chi 2 = 0.148, p-value = 0.7) Additionally, the chi-square test indicates no association between education and metal recycling, with approximately 58% of both recycling and non-recycling households having higher education This relationship also lacks statistical significance at the 10% level (Pearson chi 2 = 0.003, p-value = 0.952).
A comparison of carton recycling and non-recycling reveals significant differences in selected psychological factors This analysis highlights the impact of recycling behaviors on environmental awareness and individual attitudes towards sustainability Understanding these psychological factors is crucial for promoting effective recycling practices and enhancing community engagement in environmental initiatives.
Figure 3 illustrates a comparison of carton recycling versus non-recycling based on selected psychological factors, revealing minimal differences between the two groups regarding their concern for waste and awareness of environmental impacts.
However, household have the tendency to recycle metal if they are not satisfied with waste condition at residency
Regression results
In Ho Chi Minh City, six logistic regression models were estimated using Stata to analyze household recycling behavior for various materials, including paper, carton, metal, plastic, glass, and cloth The dependent variable representing recycling behavior for each material is coded as a binary variable, where 1 indicates that a household recycles and 0 indicates that it does not.
The regression analysis results, detailed in Table 17, reveal that only four models—paper, carton, paper and plastic—show parameters that are not statistically equal to zero simultaneously In contrast, the glass and cloth models fail to reject the null hypothesis at the 10% significance level, suggesting that the variables in these models do not effectively explain recycling behavior.
Table 17 Parameter estimates for the logit models
Variable Metal Carton Paper Plastic Glass Cloth
Variables of socio-economic and demographic characteristics ln_income -0.267 -0.320 -0.256 -0.763** -0.579 -0.260 age -0.388** -0.019 -0.032** -0.013 0.000 -0.006 gender -0.067 -0.456 -0.137 -0.111 -0.065 -0.378 education -0.492 -0.392 0.442 -0.010 -0.016 0.006
Variables of housing characteristics house_ type -0.120 0.419 -0.501 1.150* 0.379 -0.610 house_size 0.270*** 0.013** 0.013* 0.007 0.012 0.001
Variables of psychological factors towards recycling concern -0.483 0.144 1.063*** -0.653 0.132 -0.304 life -0.305 -0.558 0.305 0.498 0.152 -1.978 inheritance 5.510** 1.721 1.298 -1.901 0 0 longevity -1.365 -0.172 -0.268 1.699 -1.475 0.588 tradeoff -0.400 0.610 -0.563 0.721 0.638 -0.042 energy -0.958 -0.442 -1.222 -1.048 -1.708 -1.207 water -1.903 -0.128 1.762 -1.062 -0.496 1.542 waste 0.882 -0.519 0.199 0.063 0.110 -0.558 transportation -0.001 1.205*** -0.126 -0.266 0.707 0.572 waste_condition -0.862** 0.060 0.663 0.487 0.224 0.586 money 0.758* 1.182*** 0.113 0.899** 0.725 0.407
Note: *, ** and *** indicate that the variables are significant at the p