Renewable energy adoption in urban residential communities in China: An agent-based model for assessing intervention impact

Designing effective policy interventions is an essential instrument to promote the widespread adoption of photovoltaic (PV) systems in the residential sector. Designing such policies and evaluating their effectiveness requires an approach that allows for simulation in the complex system setting of the built environment. In this study we applied Agent-Based Modelling to evaluate the effectiveness of two policies (i.e., information campaign and demonstration projects) and two community factors (i.e., community size and required agreement rate) to promote the adoption of residential community PV diffusion in Chinese cities. This model is developed based on the empirical results of a previous discrete choice experiment. The results show that lowering the required agreement rate for community decisions contributes to an increase in PV adoption, while community size has little impact on adoption diffusion. We found that combining the two policy interventions or combining them with a community factor (i.e., lowering the required agreement rate) can effectively promote the adoption of community PV. Policy intervention implications and suggestions are presented.