Modeling the technological adoption of solar energy neighborhoods: The case of Chile.

This paper presents an agent-based model of the technological adoption of solar panels, including potential adopters' characteristics and their interactions. This study analyzes the theoretical impact of seven social influence strategies on the adoption of solar panels by householders within a solar neighborhood in the Providencia district, Chile. In this case study, established adopters account for 0.9% of the population, opinion leaders for 1.3%, and individuals in the network for 97.8%. Considering the population's high-level ambition and tolerance of uncertainty, we found that householders are mainly characterized as optimizers (89.7%), followed by inquirers (7.6%), repeaters (2.3%), and imitators (0.3%) agents. Our agent-based simulation model evaluated the social influence strategies based on economic, environmental, and social benefits. The results show that social influence strategies lead to a 19.27% increase, on average, in the total number of adopters concerning the base case. Then, we performed a multi-objective analysis to select the best adoption strategy. These results show that selecting the most connected agents in the network is a robust strategy the decision-makers have environmental or financial concerns. However, if the decision-makers' primary interest is maximizing the diffusion scope of solar panel neighborhoods, random agent selection is the most advisable strategy, given its ease of implementation. [Display omitted] • Consumat approach and Campbell's paradigm were integrated into a simulation model. • The results from seven social influence strategies are compared. • Selective seeding and random seeding are found to be the most effective strategies. [ABSTRACT FROM AUTHOR]