Exploring urban scenarios of individual residential waste sorting using a spatially explicit agent-based model

Area of Science:

• Environmental Science
• Urban Planning
• Computational Social Science

Background:

• Household waste management faces challenges due to diverse waste streams.
• Source waste sorting is a key strategy, but its effectiveness relies on resident participation and behavior.
• Existing models often neglect environmental factors, focusing mainly on psychological drivers of waste sorting behavior.

Purpose of the Study:

• To develop and apply a spatially explicit agent-based model (ABM) integrating the Theory of Planned Behaviour (TPB) to simulate residential waste sorting.
• To bridge the gap between individual waste sorting behavior and quantifiable waste management outcomes.
• To explore the impact of urban environmental factors on waste sorting performance.

Main Methods:

• An agent-based model (ABM) was developed where agents represent individual residents, incorporating TPB for behavioral architecture.
• The model was applied to simulate waste sorting in two urban areas with varying population densities.
• The study analyzed the influence of environmental factors, specifically waste bin placement and proximity, on sorting behavior.

Main Results:

• Changes in waste bin placement were demonstrated to significantly impact resident sorting behavior.
• Proximity to recycling bins was found to influence the correct sorting of residual waste.
• The model successfully linked individual agent actions to overall waste sorting metrics.

Conclusions:

• Integrating urban environmental factors into waste sorting models is crucial for understanding and improving waste management strategies.
• Agent-based modeling provides a powerful tool for exploring the complex interactions between urban design and resident behavior in waste sorting.
• Optimizing the spatial arrangement of waste bins can enhance household waste sorting performance and contribute to environmental sustainability.