Introduction

Rapid economic progress pulled billions out of poverty; at the same time, it has also wrought unprecedented environmental degradation (Ahmad & Wu, 2022; Fidelis et al., 2020; Shi et al., 2021; Wu et al., 2020). Global production and consumption have risen, and with them, waste. The accumulation of waste is one of the most significant contemporary environmental issues hindering human development and well-being (Li & Zhou, 2020; Shi et al., 2021; Vyas et al., 2022). In response, policymakers are designing waste-management interventions to reduce, recycle, and reuse waste.

That China is the largest emitter of greenhouse gases (GHG) globally is well-publicized in the media and policy circles. However, the surge in China's rural solid waste has been largely under the radar—it has grown from 46.7 billion tons in 2013 to 52.2 billion tons in 2019 (Intelligence Research Group, 2020). At the same time, a considerable proportion (around 30–60 percent) of rural garbage has been mismanaged (Wang & Hao, 2020), leading to economic losses and environmental degradation. The garbage has contaminated arable land and drinking water, having deleterious effects on rural residents' health and economic welfare (Liu et al., 2020). The rapid increase in solid waste and its adverse effects on society underscore the significance of effective waste management practices in rural China.

Recognizing the gravity of this situation, the Chinese government is implementing policies to increase the uptake of garbage classification—an integral aspect of waste management—in rural areas. In China, garbage classification entails separating the waste into different pre-defined categories, packaging it, transporting it to disposal sites, and finally processing it for further use or dumping it in landfills (Chen et al., 2020; Liu et al., 2020). These practices have shown promise and proven effective in keeping cities, towns, and villages clean and mitigating environmental degradation. Understandably, China's government is pressing forward to establish these practices. For instance, at the beginning of the "Thirteenth Five-Year Plan" (2016–2020), eight cities were selected by the central government to pilot garbage classification programs.Footnote 1 In 2019, China introduced a law on garbage disposal: "Law of the People's Republic of China on the Prevention and Control of Environmental Pollution Caused by Solid Wastes." Nevertheless, at 44 percent, the participation rate of garbage classification in rural China remains very low (Wang & Hao, 2020). Thus, there is a pressing need to accelerate the adoption of garbage classification practices among rural residents for environmental preservation and sustainable development in rural China.

Research devoted to garbage classification abounds and can be broadly classified into two strands. The first strand investigates the determinants of residents’ garbage classification. Most studies of these studies show that sociodemographic characteristics (Fan et al., 2019; Ma & Zhu, 2020; Pakpour et al., 2014; Zhang et al., 2015) and social norms (Lee et al., 2019; Park & Ha, 2014; Peng et al., 2021) significantly influence the garbage classification practices of residents. For instance, Zhang et al. (2017) and Fan et al. (2019) found that urban residents’ awareness of garbage classification contributes significantly to this practice in China and Singapore. Nepal et al. (2020) reported that adherence to social norms has also proven to be significantly and positively associated with the participation of urban residents in garbage classification. Ma and Zhu (2020) suggested that Internet use can make rural residents more willing to classify their domestic waste in China. Furthermore, other studies argue that urban garbage classification could be driven by political interventions such as publicity, subsidies, regulations, and infrastructure (Ao et al., 2022; Kirakozian, 2016; Lee et al., 2019; Li et al., 2019; Peng et al., 2021; Torres-Pereda et al., 2020). For instance, Ao et al. (2022) found that publicity influenced pariticipation in garbage classification in rural areas. Li et al. (2019) showed that sufficient financial support was a key driver for rural residents adopting garbage classification.

The second strand emphasizes the positive impacts of garbage classification (e.g., Babalola, 2015; Ghisellini et al., 2016; Giannis et al., 2017; Tong et al., 2020; Wang and You 2021; Xiong, 2019). Generally, the benefits of processing garbage classification are two-fold: economic development and improvements in environmental quality. Previous studies have shown that garbage classification reduces the volume of garbage and accelerates garbage disposal, thereby improving environmental quality (Giannis et al., 2017), reducing GHG emissions (Calabrò, 2009; Wang and You 2021), and mitigating water and soil contamination (Nie et al., 2018; Tong et al., 2020). Previous studies have also found that garbage classification promotes recycling (Nie et al., 2018; Ning & Cao, 2019; Pei, 2019; Xiong, 2019), which helps avert and attenuate resource crises and stimulate the economy. Taking Tianjin (China) as an example, Wang and You (2021) showed that a one percent increase in the participation of garbage classification could reduce the area allocated to landfills by more than 500 m2 while contributing to the province’s GDP.

Notwithstanding the considerable research on garbage classification, previous studies have not explored the association between garbage classification and people’s subjective well-being. One exception is Qi et al. (2022), who documented that environment protection behaviour (e.g., garbage classification and donating money for environmental protection) can enhance farmers’ subjective well-being in China. However, they only considered garbage classification as an element of the synthesized key explanatory variable (i.e., environmental protection behaviour), shedding no light on the association between garbage classification and subjective well-being. Accordingly, the impact of garbage classification on farmers’ subjective well-being remains undetermined. Whether and to what extent garbage classification influences people’s subjective well-being has implications for the design and effectiveness of waste management policies. For instance, should classifying garbage contribute positively to people's subjective well-being, they would be more amenable to policies designed to curb the mismanagement of garbage; how the populace perceives garbage classification can also inform the messaging of awareness programs promoting this practice. This study addresses both of these gaps, making two significant contributions to the literature on garbage classification.

First, we explore the determinants of rural residents’ garbage classification. Inadequate management of rural garbage aggravates environmental pollution, contributes to adverse health outcomes, and strains natural resources, thereby hindering rural development (Li et al., 2019; Liu et al., 2020; Sosna et al., 2019). This is especially true for rural China, home to 540 million people. Furthermore, compared with urban areas, the unique features of rural domestic garbage, i.e., wide dispersion, a steep rise in volume, and composition variability, impede garbage classification and disposal (Shi et al., 2021). Therefore, rural residents’ garbage classification participation warrants attention. We provide the first attempt to investigate the determinants of rural garbage classification participation.

Second, we examine the linkages between garbage classification and subjective well-being. The latter is vital to one’s quality of life and foundational to lasting and effective rural development (Hu et al., 2021; Zheng & Ma, 2021). Whether and how garbage classification— a practice that also mitigates environmental degradation and improves the standard of living—affects subjective well-being has direct implications for developmental and environmental policies. For instance, having lasting engagement with garbage classification practices is more likely to take hold should people feel positively about these practices and derive meaning, purpose, happiness, and satisfaction from them. It may be unnecessary for local governments to monitor adherence to garbage disposal practices of people intrinsically motivated to classify their garbage, say to preserve the environment, reduce landfills, save energy, or set a good example for their children. On the other hand, should people find garbage classification onerous, inconvenient, and futile, frequent monitoring and stringent regulations may be necessary to ensure compliance with garbage disposal mandates.

In this study, we analyze how garbage classification affects the subjective well-being of rural households in Jiangsu province in China. To this end, we analyze the 2020 China Land Economic Survey (CLES) data collected by Nanjing Agricultural University, located in Jiangsu. It bears emphasis that pro-environmental practices are not randomly assigned among rural residents; garbage classification is no exception. The residents decide on their own whether to classify their garbage. Thus, there are systematic differences between those who classify their garbage and those who do not. Moreover, these differences may stem from observed factors (e.g., age, education level, sex, and household size) and unobserved factors (i.e., motivations and preferences), rendering garbage classification endogenous. To account for this endogeneity, we employ the endogenous treatment regression (ETR) model to estimate the effects of classifying garbage on the subjective well-being of rural residents. The modeling proceeds in two stages. First, we identify the determinants of participation in garbage classification. Then, we study the effects of garbage classification on subjective well-being. We show that garbage classification participation significantly improves rural residents’ happiness and life satisfaction, two measures of subjective well-being.

The remainder of this paper is structured as follows.“Why Study the Jiangsu Province?“ section discusses why studying Jiangsu is apt for this study. "Analytical Framework: How Classifying Garbage Affects Subjective Well-Being" section outlines the theoretical framework. "Empirical Strategy" and " Data and Descriptive Statistics " sections describe the empirical strategy and data, respectively. The empirical results are presented and discussed in "Empirical Results and Discussions" section. The final section concludes this study, laying out its policy implications, drawing attention to its limitations, and offering avenues for future research.

Why Study the Jiangsu Province?

Jiangsu is an interesting case study on the effects of garbage classification on subjective well-being. The province has long been a testing ground for various pilot studies related to economic and environmental policies. For instance, programs designed to abolish the agricultural tax, reform state-owned enterprises, and build beautiful and livable cities were first piloted in Jiangsu. Apropos garbage classification, Nanjing, the capital city of Jiangsu, was chosen as one of the eight pilot cities for testing waste management practices in 2015. So far, garbage classification participation rates among urban residents have improved in 13 cities in Jiangsu (China Construction News Network 2021). However, like the rest of China, garbage classification is not taking hold in the province’s rural areas. Data show that only 40.14 percent of its administrative villages have implemented garbage classification (China Construction News Network 2021), which is close to the national rate. In this sense, Jiangsu is nationally representative of garbage classification uptake in China.

Furthermore, Jiangsu is emblematic of China's happiness paradox: even though China has made rapid economic progress since joining the World Trade Organization in 2001, the subjective well-being of the Chinese has not risen commensurately (Cheng et al., 2018). Although Jiangsu had the third-highest GDP per capita (121.23 thousand yuan) in 2020 (NSBC, 2021), suicide by pesticide self-poisoning was disconcertingly high in the province (Wang et al., 2020). Thus, it is important to devise policies that simultaneously promote economic growth and subjective well-being and demonstrate that neither has to be sacrificed to attain the other. How garbage classification affects subjective well-being in Jiangsu may provide valuable insights that may be leveraged to roll out such policies in the rest of the country.

Analytical Framework: How Classifying Garbage Affects Subjective Well-Being

Research has uncovered several pathways through which garbage classification can potentially influence the subjective well-being of rural residents. Leaning on previous studies, we illustrate these pathways in Fig. 1.

Fig. 1
figure 1

How garbage classification affects subjective well-being: potential pathways

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The top half of the figure shows four pathways through which garbage classification can positively affect subjective well-being. Studies have shown that classifying garbage improves people's physical and mental health by reducing their exposure to garbage (Li & Zhou, 2020; Orru et al., 2016; Tanaka, 2015); it also protects the environment by promoting reuse and recycling, mitigating pollution and environmental contamination (Eriksson et al., 2005; Fidelis et al., 2020; Wang and You, 2021). Being in good health and living in a clean environment enhances subjective well-being (Li & Zhou, 2020; Zheng & Ma, 2021).

Garbage classification also affects subjective well-being by reducing the cost of living. Meng et al. (2019) have shown that garbage classification helps rural residents reuse solid waste such as shopping bags and carton boxes, thereby reducing their household expenses, leaving them with more financial resources to allocate toward education, leisure, and healthcare, in turn, improving their subjective well-being.

Although individuals classify garbage mainly for personal reasons, this practice also generates significant positive externalities for their communities, earning them praise and respect from others. According to the theory of social recognition, practices such as garbage classification that yield positive externalities can enhance people's reputations and help them build harmonious interpersonal relationships, thus improving subjective well-being (Chen et al., 2021; Fidelis et al., 2020; Meng et al., 2019; Ghisellini et al., 2016).

Nevertheless, classifying garbage can also compromise subjective well-being. The bottom half of Fig. 1 points out three mechanisms through which this can happen. Rural Chinese are wont to discard their garbage carelessly, without classifying it (Liu et al., 2009)—they find it convenient. On the other hand, garbage classification guidelines call for separating the garbage into different categories (i.e., recyclable waste, hazardous waste, food waste, and residual waste) and dropping it into designated bins. Thus, classifying garbage can be time-consuming and inconvenient, leading to lower subjective well-being.

Although garbage classification can help households save money (as noted above), it can also increase their cost of living. Garbage classification guidelines require people to separate garbage into small bags (Tong et al., 2020). Because people may have to purchase bags and bins for proper garbage disposal, adherence to these guidelines may increase living expenses, requiring people to curb their expenditure on leisure, food, and healthcare, thereby reducing their subjective well-being.

The logistics and practical realities of classifying garbage bear emphasis, as they affect how people engage in this mundane practice. Often, the signage on the designated bins is illegible and not standardized, causing confusion and misleading people. And if people dispose of the garbage incorrectly, they may draw criticism from the government and their neighbors. Exposure to criticism, even just anticipating it, can induce stress and anxiety, thus lowering one’s subjective well-being. To summarize, it remains an open question whether garbage classification improves or compromises subjective well-being.

Empirical Strategy

Participation in garbage classification is dichotomous: either people participate, or they do not. We assume that rural residents decide between participating and not participating in garbage classification to maximize their expected utilities. Let \({U}_{1}\) be the utility of a rural resident derived from participating in garbage classification, while \({U}_{0}\) be the utility derived from not participating. A rational and risk-neutral rural resident will participate in garbage classification only if the resident perceives a positive net utility (\({G}_{i}^{*}\)) between participation and non-participation, that is, \({G}_{i}^{*}={U}_{1}-{U}_{0}>0\). Although \({G}_{i}^{*}\) is unobservable, rural residents’ decisions to participate in garbage classification can be expressed by a latent variable model as follows:

$${G}_{i}^{*}=\gamma {Z}_{i}+{\mu }_{i}, with {G}_{i}=\left\{\begin{array}{c}1, if {G}_{i}^{*}>0\\ 0, if {G}_{i}^{*}\le 0\end{array}\right.$$
(1)

where \({G}_{i}^{*}\) is a latent variable that indicates the probability of household head \(i\) deciding to classify household garbage. It is denoted by a dummy variable (\({G}_{i}\)), which represents the garbage classification participation status of rural residents (1 for garbage classification participants and 0 otherwise). \({Z}_{i}\) refers to a vector of exogenous variables, such as age, sex, education, and asset ownership. \(\gamma\) refers to a vector of parameters to be estimated, and \({\mu }_{i}\) refers to the error term.

Following Zheng and Ma (2021) and Yuan et al. (2021), we assume that happiness and life satisfaction, the two measures of subjective well-being, are linear functions of garbage classification participation (\({G}_{i}\)), as well as a vector of exogenous variables. The empirical specification is expressed as follows:

$${S}_{i}={\alpha }_{i}{G}_{i}+{\beta }_{i}{X}_{i}+{\varepsilon }_{i}$$
(2)

where \({S}_{i}\) measures the level of subjective well-being (happiness or life satisfaction) of household head \(i\). \({X}_{i}\) is a vector of exogenous variables. \(\alpha\) and \(\beta\) are parameters to be estimated. \({\varepsilon }_{i}\) is the error term.

If the treatment variable, i.e., garbage classification, is randomly assigned, the impact of garbage classification on subjective well-being can be estimated using ordinary least square (OLS) regressions as specified in Eq. (2). However, rural residents’ decisions to participate in garbage classification are influenced by both observed factors and unobserved factors, which may also influence their subjective well-being. That is, those rural residents who classify garbage and those who do not may differ systematically, and these differences can lead to observed and hidden selection bias—an unbiased impact assessment cannot be made without addressing the selection bias.

Several empirical strategies have been developed and utilized to address selection bias. For analyzing cross-sectional data with endogenous binary treatment variables and discrete outcomes, empirical strategies such as propensity score matching (PSM), the augmented inverse probability weighted (AIPW) estimator, the inverse probability weighted regression adjustment (IPWRA) estimator, and the endogenous treatment regression (ETR) model have been widely used to account for selection bias (Kurz, 2021; Li et al., 2020; Ma et al., 2020a, 2020b; Manda et al., 2018; Zhou & Ma, 2022). Among them, PSM, AIPW, and IPWRA help address selection bias arising from observed factors, but they fail to address selection bias originating from unobserved factors. In comparison, the ETR model addresses both observed and unobserved selection bias and estimates the treatment variable's direct impact on the outcome variable (Belissa et al., 2020; Dedehouanou et al., 2018; Hodjo et al., 2021; Yuan et al., 2021). Therefore, the ETR model is used in this study to analyze the direct effects of garbage classification on subjective well-being.

The ETR model jointly estimates Eqs. (1) and (2) using a maximum likelihood estimator (Ma et al., 2020a, b; Stata, 2019). The error terms in Eqs. (1) and (2) are assumed to have zero means and bivariate normal distributions, which can be specified as,

$$\left(\begin{array}{c}{\varepsilon }_{i}\\ {\mu }_{i}\end{array}\right)\sim N\left[\left(\begin{array}{c}0\\ 0\end{array}\right),\left(\begin{array}{cc}{\sigma }_{\varepsilon }^{2}& {\rho }_{\varepsilon \mu }{\sigma }_{\varepsilon }\\ {\rho }_{\varepsilon \mu }{\sigma }_{\varepsilon }& 1\end{array}\right)\right]$$
(3)

where \({\rho }_{\varepsilon \mu }\) is the correlation between \({\varepsilon }_{i}\) and \({\mu }_{i}\). \({\sigma }_{\varepsilon }^{2}\) and \({\sigma }_{\varepsilon }\) refer to the variance and standard deviation of \({\varepsilon }_{i}\), respectively. The variance of \({\mu }_{i}\) (i.e., \({\sigma }_{\mu }^{2}\)) is normalized to one. A significant \({\rho }_{\varepsilon \mu }\) points to the presence of selection bias stemming from unobserved factors, confirming the benefits of using the ETR model (Hodjo et al., 2021; Vatsa et al., 2022; Yuan et al., 2021).

To specify the ETR model, we include an identifying instrument in \({Z}_{i}\) but not in \({X}_{i}\). Specifically, we leverage the theory of reasoned action (TRA) proposed by Fishbein and Ajzen (1975) to select rural residents’ access and exposure to the promotion of garbage classification by the media as the instrumental variable. The theory hypothesized that an increase in people’s knowledge improves their awareness, which then helps them form behaviors (Sussman & Gifford, 2019). With respect to environmental behaviors, Gao et al. (2019) have confirmed that improving rural residents’ access to promotional activities via different media can enhance knowledge, awareness, and adoption of environmentally-friendly practices. In the present study, the instrumental variable, i.e., rural residents’ exposure to governmental initiatives promoting garbage classification, is based on the survey question: “Have you received any promotional information regarding garbage classification from the government?”. It is measured as a dichotomous variable (1 = Yes; 0 = No).

Given the TRA and the empirical findings showing that the chosen instrument drives pro-environmental behaviors (Gao et al., 2019; Zhang et al., 2019), we believe that it may also lead to the adoption of garbage classification. Furthermore, exposure to governmental promotions does not directly influence rural residents’ subjective well-being, except through its impact on people’s perception and adoption of pro-environmental practices. That is to say that rural residents’ exposure to governmental initiatives promoting garbage classification meets the theoretical criteria for an appropriate instrument. We also statistically test for the validity of the instrument. Following Adhvaryu and Nyshadham (2017) and Li et al. (2020), a falsification test is performed to this end. The falsification test, reported in Table A1 in the online Appendix, suggests that the instrument is positively and significantly correlated with garbage classification but is uncorrelated with happiness and life satisfaction. The results of the falsification test confirm the validity of the chosen instrument.

Data and Descriptive Statistics

Data

We use the 2020 China Land Economic Survey (CLES) data collected by Nanjing Agricultural University, Nanjing, China. The data were collected using a three-stage probability proportional to size (PPS) sampling procedure. In the first stage, two counties were randomly selected from each of the 13 cities in Jiangsu, resulting in 26 sampled counties. In the second stage, two villages or communities were randomly chosen within each county. In the third stage, around 50 household heads were randomly selected from each selected village for face-to-face interviews, resulting in a sample of 2,600 rural households. We analyzed 2,254 out of the 2,600 observations by removing observations with missing values and anomalous answers.Footnote 2 Specifically, first we deleted 18 observations with missing values on garbage classification participation. Then, we dropped 21 observations with missing values or anomalous answers on indicators of subjective well-being. Furthermore, we also removed 307 observations with anomalous and missing values for the control variables.

The CLES was conducted by a team of postgraduate students from Nanjing Agricultural University who can speak both Mandarin and regional dialects of the selected counties. Utilizing a detailed structured questionnaire, this survey collected information on multiple aspects of household heads, such as demographic and household characteristics (e.g., age, sex, and household size), asset ownership, agricultural management, and living conditions. Following prior studies (e.g., Nie et al., 2021; Sujarwoto et al., 2018; Zheng & Ma, 2021), we use two indicators, happiness and life satisfaction, to measure subjective well-being. The information was collected based on two survey questions: “How happy are you?” and “How satisfied are you with your life?”. Specifically, the survey assigns an ordered variable for measuring rural residents’ happiness and life satisfaction, quantified using a 10-point Likert scale, ranging from 1, denoting very unhappy or very unsatisfied, to 10, denoting very happy or very satisfied.

Responsibly disposing of waste is a household decision (Li et al., 2019; Peng et al., 2021). However, it is common for specific household members to dispose of waste on the household's behalf. Thus, waste disposal is as much an individual decision as a household decision (Kip Viscusi et al., 2011; Kuang & Lin, 2021). Following previous studies (Liu et al., 2020; Meng et al., 2019), we use a dichotomous variable to capture whether a rural resident classifies household garbage. To construct this variable, we rely on the answers to the following question: “Do you classify garbage?”. Individuals who classified garbage are assigned a value of 1, whereas those who did not are assigned a value of 0.

We lean on previous studies on people’s pro-environment actions and subjective well-being (e.g., Ma et al., 2020a, 2020b; Ma & Zhu, 2020; Sujarwoto et al., 2018; Zheng & Ma, 2021) to select the control variables for analyzing the association between garbage classification and subjective well-being. Specifically, we control for demographic factors such as age, sex, education, health status, marital status, and household size. There is a general agreement that as people age, they tend to become set in their ways and more unwilling to give up habits and behaviors to which they are accustomed (Gebrezgabher et al., 2015). Therefore, we expect age to be negatively associated with classifying garbage, as this practice is relatively new in rural China. Consistent with Zhou and Turvey (2018), who argued that females tend to shoulder responsibility for housework (e.g., cleaning), we expect a negative correlation between sex (a dummy variable assigned a value of one for males and zero for females) and garbage classification. Educated individuals tend to be more informed and aware of the importance of pro-environment behaviors and have the skills to learn new tasks relatively quickly; they tend to be more adaptable (Ma et al., 2020a, 2020b). Therefore, we control for household heads’ education measured in years and expect it to be positively associated with garbage classification. Previous studies have shown that being in good health improves subjective well-being (Li & Zhou, 2020; Nie et al., 2021; Taşkaya, 2018); thus, we have included the self-reported health status of the household heads, measured on a 5-point Likert scale. We have also controlled for the marital status of the household heads. Married couples tend to have more labor within the household at their disposal, which they can allocate to different tasks. Married couples also tend to live in larger households relative to unmarried individuals, reaping the benefits of household economies of scale (Bimber et al., 2003). Given this, we expect being married to be positively associated with classifying garbage. Household economies of scale suggest that large households are likely to have lower per capita costs of classifying garbage, making them more likely to do so. With this in mind, we also control for household size.

We also include how trusting of others the household head is and whether the household head owns assets (denoting wealth) to control for the effects of societal expectations and financial factors. Trust is foundational to building social capital; it engenders cooperation and fosters a sense of social responsibility. The desire to do what is in the best interest of society may nudge people to engage in activities—such as garbage classification—that have positive externalities. Thus, we expect the variable respresenting trust to be positively associated with garbage classification. TWealth enhances people’s purchasing power, making them early adopters of novel products, services, and ways of life to improve their living standards (Charles et al., 2019; Lim et al., 2020; Zheng & Ma, 2021). Accordingly, asset ownership is included and expected to foster garbage classification. Moreover, we use three dummies representing rural household income tertiles to serve as wealth proxies and expect subjective well-being to be positively associated with higher household income. We also control for adversity, distress, and hardship using a dichotomous variable (i.e., negative shock), as the death of loved ones, injury, and ill health can profoundly decrease subjective well-being (López-Feldman & Porro, 2021).Footnote 3 We expect to find a negative association between the variable negative shock and subjective well-being.

Studies have shown a link between people's attitudes toward risk and their propensity to adopt pro-environment behaviors. Gong et al. (2016) noted that risk-averse people are less likely to lead environmentally friendly lives. Given this, we incorporate a dummy variable representing rural residents’ risk attitudes (i.e., risk-averse) into our regression model.Footnote 4 We expect risk-aversion to be negatively associated with garbage classification. Studies have also shown that those who perceive the environmental quality to be poor report lower subjective well-being and are more likely to adopt pro-environmental practices (Li & Zhou, 2020; Sulemana et al., 2016). Therefore, we also include a binary variable representing rural residents’ perception of pollution and expect it to affect garbage classification positively.Footnote 5

Whether rural residents classify garbage also depends on village-level factors such as economic conditions and democratization. Rural residents in economically developed villages have greater access to public amenities (such as community centers and sanitary facilities), promoting pro-environmental behaviors. Thus, we expect people living in economically developed regions to be more likely to classify garbage than those living in economically disadvantaged regions. Following Wang et al. (2019), we use a dummy variable denoting the presence of various industries in a village to reflect its economic condition. Rural democratization allows people to get involved in community decision-making—research shows that the greater the involvement, the higher the subjective well-being (Radcliff & Shufeldt, 2016). We control for the degree of democratization by incorporating a dummy variable, which is assigned a value of one for villages in which important decisions are made in consultation with village members or their representatives and zero otherwise. Furthermore, the control variables also include three regional dummies to capture the unobserved disparities in institutional arrangements, resource endowment, and economic conditions.

Descriptive Statistics

Table 1 presents the definitions, means, and standard deviations of all selected variables. Our data show that the mean values of household heads’ self-reported happiness and life satisfaction are 7.81 and 7.69 out of 10, respectively, suggesting that, in general, rural Chinese in Jiangsu report relatively high levels of subjective well-being. These results are consistent with Zheng and Ma (2021) and Nie et al. (2021). As noted above, only 47 percent of rural households in the province participated in garbage classification; this is close to the national participation rate of 44 percent (Wang & Hao, 2020).

Table 1 Variable definitions and descriptive statistics
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Table 1 shows that 69 percent of the household heads were males, and 89 percent were married. The household heads' average age was about 61 years, and overall, they reported being in good health; they had, on average, 6.85 years of education, and only 36 percent of household heads perceived the environment to be polluted, suggesting that they may have become inured to environmental degradation. This points to opportunities to inculcate pro-environment behaviours in a large number of people through education and awareness initiatives and, in doing so, improve the environment. However, given their potential desensitization to environmental degradation, it may be challenging to impel rural households to adopt pro-environment behaviours. Table 1 also shows that only a small fraction of rural household heads (around 15 percent) experienced a negative shock in 2019. Interestingly, 74 percent of household heads are risk-averse—this is in line with Ma et al., (2020a, 2020b).

Table 2 presents the differences in the mean values of the variables for those who classify garbage and those who do not. As for the two variables representing subjective well-being, the significant mean differences suggest that garbage classification participants are happier and more satisfied with their lives than non-participants. Relative to non-participants, garbage-classification participants tend to live in smaller households and are more likely to be younger, healthier, more educated, and married; they also perceive the environment to be less polluted. Regarding the differences between the two groups apropos household income tertiles and asset ownership, the results indicate that garbage classification participants are more likely to be better off financially than non-participants. Specifically, those who classify garbage are more likely to have incomes in the third income tertile, while the incomes of non-participants are likely to fall in the second income tertile. Classifying garbage is also associated with a greater likelihood of owning a washing machine. It also shows that, relative to non-participants, participants are less likely to be risk-averse but more likely to trust others. Moreover, the significant differences in the variables representing the democratization (village meetings) and the level of economic development (village industry) of the villages for participants and non-participants suggest that the former reside in more economically developed and democratic villages. In addition, the significant mean difference in the instrumental variable indicates that the probability of those who classify garbage being exposed to government initiatives to promote garbage classification is much higher than that of those who do not—this lends credence to the aptness of the instrumental variable.

Table 2 Mean differences in selected variables between garbage classification participants and non-participants
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Evidently, the simple mean comparisons highlight systematic differences between those who classify their garbage and those who do not. Nevertheless, caution is necessary for interpreting these simplistic results, as they neither account for confounders nor the endogeneity of garbage classification. Therefore, a more rigorous approach is in order. Next, we present the results obtained from the ETR model explained above.

Empirical Results and Discussions

Diagnostic Tests

Following previous studies (Zheng & Ma, 2021), we conduct multiple diagnostic tests to ensure that our empirical models are correctly specified. We conduct Ramsey’s regression equation specification error test (RESET) to check for model misspecification, examine variance inflation factors (VIF) to test for multicollinearity, and use White’s test to see if the residuals are homoscedastic. The results for the RESET and VIFs presented in Table A2 in the online Appendix suggest that the model is not misspecified and multicollinearity is not a concern. However, White’s test points to heteroscedastic residuals. With this in mind, we use village-level clustered standard errors.

We also estimate the correlation coefficient (i.e., \({\rho }_{\varepsilon \mu }=\mathrm{corr}({\varepsilon }_{i}, {\mu }_{i})\)) utilizing the maximum likelihood estimator and present the results in the lower part of Table 3. All the estimates of \({\rho }_{\varepsilon \mu }\) are statistically significant, indicating the presence of selection bias arising from unobserved factors. This highlights the utility of using the ETR model.

Table 3 Impac The reference region is southern Jiangsu; The reference household income category is household income tertile 1; Standard errors in parentheses; *** < 0.01, ** < 0.05, and * < 0.10.t of garbage classification on the subjective well-being of rural residents: ETR model estimations
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Determinants of Garbage Classification

Columns 2 and 4 of Table 3 show the estimated coefficients of the explanatory variables. The signs of the estimated coefficients largely align with our expectations. Since Columns 2 and 4 report similar results, we will explain them together. The negative and statistically significant coefficients of the age variable suggest that older household heads are less likely to classify their garbage. Older individuals may be acclimated to traditional garbage disposal practices that do not require them to classify garbage. Furthermore, they may not be as well informed as younger individuals about classifying garbage and the benefits of doing so. These factors may underpin the negative association between age and classifying garbage.

The coefficient of education is positive and significant, suggesting that the likelihood of classifying garbage rises with the education level of the household head. This finding chimes with that of Ma and Zhu (2020), who argued that education could improve people’s environmental awareness and encourage them to adopt environmentally friendly practices. Marital status also has a positive and significant coefficient, suggesting that households headed by married individuals are more likely to classify garbage than those headed by unmarried household heads. This is unsurprising on two accounts. First, married household heads tend to have larger households and thus more labor within the household to perform different household chores and duties, including classifying garbage. Second, due to household economies of scale, the per capita cost of classifying garbage is likely to be lower among larger households, making them more likely to adopt this practice. Trust in others is also positively associated with the likelihood of classifying garbage. This is consistent with our expectations: trust engenders cooperation and fosters a sense of social responsibility (Irwin et al., 2015), prompting people to engage in pro-environmental practices (Harring et al., 2019), such as garbage classification.

Contrary to the finding of Ma and Zhu (2020), we find a negative association between pollution perception and garbage classification—those who deem rural pollution severe are less likely to classify their garbage than those who do not. Cleaning up excessively polluted rural areas may seem insurmountable, thus discouraging people from taking pro-environmental action. Overwhelmed by the magnitude of the problem, people may feel that only their actions would be insufficient to mitigate environmental degradation. Consequently, they may follow their peers and not adopt pro-environmental practices such as garbage classification. The positive and significant coefficients of the instrumental variable suggest that exposure to governmental initiatives promoting garbage classification improves the participation rate, corroborating the instrument's admissibility.

Impacts on Subjective Well-Being

Columns 3 and 5 of Table 3 report the determinants of household heads’ happiness and life satisfaction, respectively. Let us first consider the variable of primary interest: garbage classification. The results show that it is associated with improvements in both happiness and life satisfaction. Specifically, the results suggest that should people who do not classify garbage switch to classifying it, their happiness and life satisfaction would increase by 0.955 and 0.905 points, respectively, on a 10-point Likert scale. In "Analytical Framework: How Classifying Garbage Affects Subjective Well-Being" section, we discussed the possibility that classifying garbage can affect subjective well-being positively or negatively—our results confirm that the effects are positive.

Turning our attention to the control variables, we find that, in general, all the coefficients are consistent with economic theory and previous studies. For instance, education and health improve happiness and life satisfaction; asset owners and higher-income earners report higher levels of happiness and life satisfaction than non-owners. A one-year increase in household heads’ education is associated with increases of 0.025 and 0.024 points in happiness and life satisfaction, respectively. These findings are consistent with Ma and Zhu (2020), Zheng and Ma (2021), and Lai et al. (2021). Relative to those with relatively low incomes (at the household income tertile 1), rural residents with higher incomes (at the household income tertile 2 and 3) are more likely to report greater subjective well-being. This finding aligns well with the conclusions of Lim et al. (2020) and Pleeging et al. (2021), who found that income is positively associated with people’s subjective well-being. Understandably, experiencing adversity reduces subjective well-being. On average, the happiness and life satisfaction of household heads who experienced negative shocks are 0.333 and 0.288 points lower than those of household heads who did not experience such shocks. This result is in line with the findings of Charles et al. (2019) and López-Feldman and Porro (2021).

Heads of relatively large households report lower life satisfaction than those of smaller households. Larger households may be more prone to conflict—intra-household conflicts may arise from competing for household public goods; the more members there are, the greater the competition and thus the potential for conflict. A shortage of household public goods can undermine cooperation between household members, which stems from mutual affection and shared norms (Bjorvatn et al., 2020). Trust in others is also associated with greater happiness and life satisfaction. Trust is foundational to societal harmony and cooperation. It is critical to forming lasting meaningful relationships thereby fostering health, longevity, and well-being (Miething et al., 2020).

Unlike previous studies that reported a negative relationship between age and subjective well-being (e.g., Van den Broeck & Maertens, 2017), we find a positive association between the two. A one-year increase in age is associated with increases in happiness and life satisfaction of 0.009 and 0.010 points, respectively. Many researchers have reported a U-shaped relationship between age and well-being (Blanchflower & Oswald, 2004).Footnote 6 Others have suggested that happiness increases after the age of 60, whereas it remains relatively stable between ages 20 and 50 (Frijters & Beatton, 2012; Laaksonen, 2018). The increase in well-being has been attributed to reduced stress after age 60. We want to emphasize that the average age of the household heads in our sample is 61 years—the average sample age skews older than the national average. Moreover, if well-being either stays constant or increases with age, a positive link between age and subjective well-being, on the whole, stands to reason. Pollution perception has a negative and statistically significant impact on household heads’ life satisfaction, a finding that is evidenced by the work of Sulemana et al. (2016) and Li and Zhou (2020). Pollution leads to emotional and physical distress and thus reduces life satisfaction. The coefficient of the variable village meetings is positive and significant, indicating that democratization improves happiness and life satisfaction. Specifically, the levels of happiness and life satisfaction are 0.166 and 0.197 points higher for household heads living in villages where major decisions germane to the village are made inclusively through meetings involving the villagers or their representatives—democratizing decision-making increases transparency, cultivates perceptions of social fairness, and strengthens self-affirmation, thus, leading to greater subjective well-being.

Robustness Test

We estimate a two-stage residual inclusion (2SRI) model for confirming the robustness of our findings reported in Table 3. The 2SRI model can address selection bias arising from both observed and unobserved factors (Terza et al., 2008; Zhu et al., 2020). The first stage of the 2SRI model entails estimating Eq. (1) and predicting the residual term. Then, Eq. (2) is estimated in the second stage, with the residual term predicted from the first stage included as an additional regressor. The results are presented in Table A3 in the online Appendix, showing that garbage classification increases happiness and life satisfaction at the 1% level. The findings corroborate the evidence presented in Table 3.

Conclusions and Policy Implications

China's rural solid waste has risen from 46.7 billion tons in 2013 to 52.2 billion tons in 2019. At the same time, a considerable proportion (around 30–60 percent) of rural garbage has been mismanaged. Thus, there is an urgent need for effective waste management practices to prevent environmental degradation and enhance social well-being—garbage classification is an integral part of a multipronged pro-environmental policy framework. Against this backdrop, the principal question addressed in this study is, does classifying garbage affect subjective well-being? We also examine the drivers of garbage classification participation. Because participation in garbage classification is endogenous, as people self-select to adopt this practice, we employ the ETR model to analyze the 2020 CLES data while addressing the said endogeneity. In addition, this study also uses the 2SRI approach to confirm the robustness of the empirical results.

We find that garbage classification improves household heads’ happiness and life satisfaction by 0.955 and 0.905 points on a 10-point Likert scale, respectively. The positive environmental effects of garbage classification are well-documented. We show that this simple and somewhat mundane practice can also improve people's subjective well-being—its positive effects are not limited to the environment; what is good for the environment is also good for the people. Besides, age, gender, education, health, trusting others, household income, owning assets, being male, and living in villages where major village-level decisions are made democratically also positively affect both measures of subjective well-being.

Many of the same factors affect the likelihood of classifying garbage. Age is negatively associated with the likelihood of classifying garbage, whereas education, marriage, and trusting others are positively associated with it; those who perceive the pollution to be severe are less likely to classify garbage. The low adoption rate of garbage classification is not unique to Jiangsu—it is evident throughout the country. The results of this study may not translate directly or equally to other parts of the country; however, they can be used to motivate research on other provinces and inform policies. To be sure, the results may be broadly applicable to neighboring provinces and others that are economically and culturally similar to Jiangsu. Importantly, our results reaffirm the compound benefits of allocating more public resources to accelerate participation in garbage classification in rural China: garbage classification improves both environmental quality and subjective well-being. This study is novel in its use of exposure to government-sponsored media promoting garbage classification as an instrumental variable; the results demonstrate that this exposure indeed increases the adoption of garbage classification. Since media lend themselves well to scaling and reaching the masses relatively quickly, we suggest leveraging them to accelerate the uptake of garbage classification. Social media, radio advertising, and television broadcasting can be used synergistically throughout the country to spread awareness of the benefits of classifying garbage. These are cost-effective platforms with relatively low marginal costs of deploying advertising and reaching a wider audience.

Given significant regional differences in the uptake of garbage classification practices within Jiangsu, it is rather likely that there will be marked inter-provincial variations in the adoption of garbage classification and its effects on subjective well-being. China is a large and diverse country, and what is true for Jiangsu may not be so for other provinces. Therefore, a one-size-fits-all approach to promoting and implementing garbage classification may not be suitable for the country as a whole. Thus, although some standardization of policies and initiatives promoting garbage classification would be desirable to mitigate costs and send a cohesive message throughout the nation, regional characteristics ought to be borne in mind to optimize localized promotional activity. For instance, intensively promoting garbage classification in economically lagging regions such as western China and the northern part of Jiangsu may pay rich dividends in the short run, as there is much ground to cover in these areas apropos the uptake of pro-environmental practices. Furthermore, emphasizing the benefits of garbage classification to young adults can help entrench environmentally friendly practices among them and prove transformative for the country in the long run. The government may consider subsidy programs and rewards to incentivize people to classify garbage—redemption fees may be offered for recycling bottles, cans, and containers; the government may subsidize the installation of at-home compost systems to reduce the volume of waste disposed of at municipal sites. We have shown that people who perceive the environment as severely polluted are more likely to classify garbage. Therefore, bringing the environmental degradation caused by irresponsible waste disposal to people’s attention through carefully curated advertising and educational initiatives may encourage them to classify garbage; these initiatives may also speak to the positive effects of classifying garbage on subjective well-being to strengthen the promotional messaging.

Lastly, we want to draw attention to three important limitations of this study and offer avenues for future research. First, although we point to mechanisms underpinning the associations between garbage classification and the subjective well-being of household heads, we do not test them formally due to a lack of requisite data. We propose that future studies investigate these mechanisms using structural models when the data become available. Second, our analysis is based on cross-sectional data; thus, it does not shed light on the temporal shifts in the associations between garbage classification and subjective well-being. Understanding their dynamic interlinkages would become increasingly valuable as the adoption rate of garbage classification improves over time. Therefore, future studies may use panel data to investigate how garbage classification is associated with subjective well-being. Last, the effects of garbage classification in this study are estimated for household heads and may not hold for the general population. Although household heads may be deemed to represent the members of the household reliably, these results ought to be interpreted cautiously and not overgeneralized.