Green choices in rural settings: analyzing community adoption of eco-friendly shopping alternatives through agent-based modeling

To address the high CO 2 emissions from private car use in rural areas, largely due to limited infrastructure and few public transport options, there is a need for innovative, locally accessible services that support sustainable practices. Understanding what drives rural communities to adopt such alternatives is essential for effective intervention. This study demonstrates the development of a toolkit, employing a multi-agent model to analyze how a rural community reacts to the introduction of a local, eco-friendly shopping alternative—a container store—compared to a common grocery store facility. We integrate the diffusion of innovation theory, the theory of planned behavior, and a spatially and socially explicit agent-based model (ABM) to simulate individual and collective decision-making processes regarding this new shopping method. We illustrate how the theoretical foundations are operationalized within the model. Our experiments explore the effects of store attributes, location, and initial adopter groups on community adoption rates. Results across scenarios reveal variations in adoption rates, illustrating how the applied toolkit effectively captures the influence of store attributes, location accessibility, and community network structures on sustainable consumer behaviors.