To promote waste sorting among urban residents, researchers have long studied contributing factors and their relationships to identify appropriate interventions. Research on determinants of residents' waste-sorting behavior is mostly based on three perspectives: demographic, psychological, and contextual.

The most typical demographic factors used in studies on waste-sorting behavior are age, gender, education, and income. However, owing to the unpredictability of the statistical samples, the association between demographic factors and resident waste sorting behavior could not be reliably determined. For example, Vining and Ebreo (1990) suggested that older people are more likely to participate in categorical recycling, although Gamba and Oskamp (1994) found a minor negative correlation, and Oskamp et al. (1991) observed no relationship. Domina and Koch (2002) explained this discrepancy in generational differences.

Over the last few decades, social psychologists have developed several theoretical frameworks to analyze waste sorting behaviors and their antecedents. Previous studies highlighted the importance of attitudes, moral norms, and knowledge as psychological factors. According to the Theory of Planned Behavior (TPB), attitude is defined as a person's level of approval or disapproval of a specific behavior (Ajzen, 1991). Ari and Yilmaz (2016) found that attitude explains 60% of sorting behavior, showing that the residents' individual views about waste sorting had effect on their actual conduct, the conclusion shared by previous studies (Miafodzyeva and Brandt, 2013; Knickmeyer, 2020). According to the Value-Belief-Norm (VBN) paradigm, waste sorting is an environmental behavior motivated by moral norms (Xu et al., 2016). Loan et al. (2017) found empirical support for moral norms as a determinant of waste-sorting behavior. Wang et al. (2020) also stated in their study that residents with limited knowledge of waste sorting categories were less motivated to participate in waste sorting.

Contextual factors include environmental elements, such as policy, subjective norms, and convenience. Appropriate policy incentives, such as information disclosure, education, publicity, and role modeling, can help raise waste sorting levels (Schultz et al., 1995; Grazhdani, 2016). Social influence from family, neighbors, and friends encourages waste sorting and supports follow-up actions (Patrícia et al., 2004). As waste sorting by urban residents is a family behavior, internal family members may exhibit stronger behavioral linkages (Ting and Cheng, 2017). Xu et al. (2017) discovered that even minor modifications to enhance convenience can have a significant impact on behavior. Convenient, clean, and user-friendly waste collection stations promote waste classification by residents (Knickmeyer, 2020).

By reviewing studies on the factors associated with waste sorting behavior, it was found that many factors stem from multiple theories, which often identify associations but lack guidance for designing effective interventions from a theoretical standpoint. This highlights the need for a theoretical framework that integrates multiple factors and links them to interventions as the basis of analysis.

The COM-B model offers a comprehensive framework for integrating multiple theories and supporting behavioral intervention designs. It serves as the core behavioral layer of the Behavior Change Wheel Theory and connects to interventions in the peripheral intervention layer, to offer a concise, comprehensive, and logically coherent model for designing new behavioral interventions and describing existing ones (Olander et al., 2016).

The COM-B model states that behavior is the outcome of the interplay between capability, opportunity, and motivation, with capability and opportunity interacting with motivation (Michie et al., 2014; Ellis et al., 2019). Initially, the COM-B model was widely utilized in the medical industry, such as to investigate factors relevant to patient self-care behavior (Zou et al., 2017). Later, it was gradually introduced into other fields such as energy-saving behavior (Azizi et al., 2019), food safety (Thaivalappil et al., 2018), and invasive species management (Mcleod et al., 2015).

In recent years, several scholars have introduced the COM-B theory into the field of waste recycling behavior analysis. Non-quantitative questionnaires and interviews were commonly used in these research findings. The collected textual content was coded using the COM-B framework, which identified influential elements across three dimensions: capability, opportunity, and motivation. For example, Allison et al. (2021) and Cook et al. (2023) identified determinants of disposable paper cup use and hospital food waste recycling behavior. Fernandez-Alvarez et al. (2025) conducted quantitative research by developing a Spanish-language version of a COM-B framework-based factor quantification questionnaire for home waste recycling. The questionnaire passed reliability and validity testing, indicating acceptable psychometric qualities. Allison et al. (2022) conducted regression analysis to quantify associations between capability, opportunity, and motivation factors on household food waste recycling behavior.

Existing studies exploring associated factors with waste sorting behavior based on the COM-B framework have mostly focused on qualitative analysis, primarily identifying and describing potential associated factors. Even studies employing quantitative methods have largely been limited to investigating direct, static associations between factors and behavioral outcomes. This analytical perspective has two limitations: first, it fails to highlight the potential interacting processes among the numerous COM-B elements; second, it is unable to depict the dynamic evolution of waste sorting behavior and its associated factors over time. In real-world circumstances, changes in residents' behavior are dynamic and evolutionary, driven by a variety of factors. It is critical to investigate the mechanisms by which these factors interact and relate to behavioral evolution throughout time.

To address these limitations, this study will integrate the Structural Equation Modeling (SEM) and System Dynamics (SD) methods based on the COM-B model to analyze the static structural relationships among contributing factors, and then simulate the dynamic evolution of waste sorting behavior under the effect of multiple factors, thereby characterizing behavioral mechanisms from both static structural and dynamic evolutionary dimensions.

Capability is described as an individual's psychological and physical ability to engage in the action at hand, including the possession of essential knowledge and skills (Michie et al., 2011). Knowledge demonstrates a significant statistical association with waste-sorting behavior. According to Chai and Baudelaire (2015), knowledge may be divided into two categories: “know what” and "know how.” The former refers to understanding the significance of trash sorting, whereas the latter refers to knowing the process itself. In some developed countries such as Australia and the United Kingdom, waste is classified as 60 percent. In comparison, developing nations, such as Iran and Turkey, have waste classification rates of less than 10 percent (Pakpour et al., 2014; Fan et al., 2019). This is because in many developing nations limited resident knowledge about waste limits it's sorting (Zand et al., 2020). Therefore, this study proposes the following hypothesis:

H1: Knowledge is positively related to waste sorting behavior.

Opportunity is defined as all the external variables that enable or urge an activity. (Michie et al., 2011). Here, opportunity consists of the infrastructure provided by the physical environment and the subjective norms provided by the sociocultural environment. Indeed, the lack of easily accessible infrastructure could be one of the most significant hurdles to individual waste sorting efforts. The number, distance, and appearance of sorting infrastructures play crucial roles in determining how easily residents can utilize them (Thomas and Sharp, 2013; Varotto and Spagnolli, 2017). Residents may take more sorting actions if proper infrastructure is easily accessible.

Subjective norms are the degree to which people perceive societal expectations relevant to their behavior (Sarkis, 2017). If the people around them are trustworthy or have leadership qualities, their opinions may correlate with behavioral choices (Shin and Hancer, 2016). For example, Park and Ha (2014) found that residents were motivated when they observed their neighbors or friends sorting and recycling waste. As a result, when residents' family members, acquaintances, and neighbors sort waste, it has a modeling effect on them, increasing the likelihood that residents will act in a similar manner. In this context, the study proposes two more hypotheses:

Motivation refers to all brain processes that motivate and direct behavior, including habitual and emotional responses, as well as analytical decision-making processes (Michie et al., 2011). The change from the old waste disposal strategy of employing a single trash can to “automatic” waste sorting into various containers (Timlett and Williams, 2009) necessitates significant intrinsic incentives. The motivation for waste sorting can be divided into two types: habit that arises spontaneously and behavioral intention that emerges after considerable thought. Waste sorting, a daily practice conducted under comparable conditions, has the potential to become a habitual practice. Once waste sorting becomes a spontaneous habit, it becomes simple to develop repetitive behaviors; hence, habit is an important variable that supports long-term behaviors (Broers et al., 2021).

Behavioral intention is a precursor to actual conduct and is related to an individual's subjective judgment that particular behaviors are likely to occur in the future (Fishbein and Ajzen, 1977). Waste sorting intention has thus a significant predictive effect on behavior (Zhang et al., 2021; Lou et al., 2024). When the prerequisites for actual conduct are met, behavioral intention becomes actual behavior. Based on the above literature, this study proposes the following hypothesis:

According to the COM-B model, motivation mediates the relationship between capability, opportunity, and behavior. Prior studies have also provided evidence confirming the link between knowledge, subjective norms, habit, and intention (Li et al., 2017; Zhang et al., 2021). More hypotheses thus emerge from the suggestions by prior studies:

Given that opportunity factors demonstrate the statistically strongest association with behavior, strategies to improve them can produce higher behavioral facilitation results than capability and motivation components. The Behavior Change Wheel Theory provides a solid foundation for designing interventions in addressing the opportunity component. These interventions include reducing obstacles to using waste sorting facilities by expanding facility coverage, optimizing facility appearance design, improving ease of facility operation, and flexibly setting centralized drop-off times. Second, it minimizes residents' options for non-segregation from both hardware and software perspectives, such as prohibiting traditional waste cans as soon as it is feasible and organizing patrols to stop and advise residents on non-segregation behavior. Third, local government can select benchmark communities, model families, and individuals for waste sorting; provide publicity, commendation, and material rewards to the winners; maximize the effect of exemplary role models; and motivate other residents to participate in waste sorting.

This study has some limitations. It investigates only a part of the capability, opportunity, and motivational elements relevant to waste sorting among urban residents. Future research could thus expand the framework by incorporating additional components, such as personal energy under capacity and economic or ecological benefits under motivation factors. In addition, the study focuses on Shanghai, which is among the top cities in the country in terms of the degree of refinement and implementation of waste sorting policies. However, policy variability across cities can differentiate the mechanisms of residents' waste sorting behavior (Tian et al., 2022). The generalizability of the findings to cities with imperfect policies is questionable. Therefore, it is proposed that future studies select diverse city samples to assess the model's applicability across varying policy contexts and develop precisely behavior promotion programs. Finally, although a strict sampling method was used to collect the data, the limitations of the web-based survey resulted in a younger and more highly educated sample. Therefore, field sampling data can be supplemented in future work to improve sample representativeness.