Exploring business models for carbon emission reduction via post-consumer recycling infrastructures in Beijing: An agent-based modelling approach
Graphical abstract
The two dimensions for the upgrading of urban recycling sector. (MSW: Municipal Solid Waste).
Introduction
The potential for carbon emission avoidance through post-consumer recycling has been revealed at both product level (Gielen, 1997; Lauk et al., 2012) and economy-wide with detailed material flow analysis (Ohno et al., 2021). Since cities are increasingly becoming centers of consumption in the global network (Glaeser et al., 2001), the post-consumer recycling activities in a city have deep impacts on both the upstream supply chain and the downstream flows of discarded materials that flow beyond the territory of the city (Chen et al., 2020a). Therefore, the transformation of waste infrastructures has been identified as one of the key community-wide systems in cities in low carbon transitions (Ramaswami et al., 2021).
As the by-product of urban lifestyle, the amount of municipal solid waste (MSW), is growing much faster than the rate of urbanization. And, there is a positive correlation between waste generation and income level (World Bank, 2018). The effort to “decouple the waste from the wealth” has demonstrated that the window of opportunity for cities to find better solutions for this fundamental public service of modern cities is critical to achieve the Sustainable Development Goals (SDGs) in “making cities and human settlements inclusive, safe, resilient and sustainable”.
China has initiated an ambitious “Zero-waste city” plan that aims to minimize solid waste generation and maximize recycling in urban areas (Gu et al., 2021). A new wave of social movement to promote waste sorting at source in cities has been constructed as a "New Fashion"1 linking the desire for a better life of individual to the sustainable development of the whole society. In addition, China's pledge to reach peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060, highlights the potential for carbon emission reduction through a systematic optimization in waste management (Xiao et al., 2022). New technologies and business models are emerging to deal with the core challenges in waste reduction, which relate to the normative underpinnings of the current growth model, requiring substantial transition in the value and measurement of economic performance and social welfare. While various economic instruments are applied to provide the incentives for recycling, the social instruments are increasingly recognized in pursuing inclusivity and the environmental justice in this sector (Medina, 2010). How to redesign the urban recycling infrastructure to foster new business models that strengthen the cooperation among all stakeholders? How can the low carbon transition be incorporated with the community-level recycling efforts?
Section snippets
Literature review
Local community provides an important environment which can shape people's pro-environmental attitudes and behaviors (Junot et al., 2018; Zhang et al., 2020). Post-consumer recycling depends on consumers’ participation behaviors, which can be adapted to specific waste treatment infrastructures and structured recycling programs (Derksen and Gartrell, 1993). Existing research has identified key factors influencing the recycling behavior, and attempted to build models to predict the change of
Methods
Existing research has pointed out that the social and cultural aspects are crucial in low carbon transition (Sovacool and Griffiths, 2020). However, it is difficult to integrate the social aspects with the highly quantified studies in those material centric assessment of sustainability, such as circular economy (Walzberg et al., 2020). Agent-based Model (ABM) are increasingly used in recent years to study circular economy and low carbon transition scenarios in relation to consumer behaviors (
The TPB+ model of the community recycling behavior
The result shows that the community perception has significant positive effects on the classification behavior in a direct way without the mediation effects of the classification intention. The results of the model tested by SEM are shown in Fig. 4. The effects of the classical factors in TPB, including attitudes, subjective norms, and perceived behavior control, are confirmed. By adding the factor of Community Perception, the standard deviation that explained by the model can be increased from
Upgrading recycling infrastructures towards a low carbon city
Research has pointed out that waste sorting programs can be perceived as social interactions among many stakeholders, including the recycling sector, the households, and the government (Peng et al., 2021). Most studies dealt with various factors that shape the behavior of individuals in a way that is disconnected from the local context. Recent studies addressed place-specific factors focusing on psychological attachments to the living space which can theoretically influence the attitudes of
Conclusion
The potential to reduce carbon emissions through recycling has attracted the attention of regional recycling programs in China. This study reveals the significant potential for carbon emissions reduction by improving the participation rate of households in recycling activities. Changing the behavior of households is not only a problem of social influence, but also of infrastructure provision with proper business model which can facilitate recycling activities in communities. Even with widely
CRediT authorship contribution statement
Xin Tong: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. Haofan Yu: Data curation, Formal analysis. Ling Han: Conceptualization, Data curation. Tao Liu: Data curation, Formal analysis, Writing – review & editing. Liang Dong: Conceptualization, Funding acquisition. Filippos Zisopoulos: Conceptualization, Writing – review & editing. Benjamin Steuer: Conceptualization, Writing – review & editing. Martin de Jong: Supervision, Conceptualization, Funding
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We thank Prof. Mei Li and Miss.Hancong Ma from School of Earth and Space Sciences in Peking Unviersity for their professional help in computer programming. This research falls under one of the projects of the Erasmus Initiative: Dynamics of Inclusive Prosperity, a joint project funded by the Dutch Research Council (NWO) and the National Natural Science Foundation of China (NSFC): “Towards Inclusive Circular Economy: Transnational Network for Wise-waste Cities (IWWCs)” (NSFC project number:
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