Revealing drivers of green technology adoption through explainable Artificial Intelligence

Effective governance of energy system transformation away from fossil resources requires a quantitative understanding of the diffusion of green technologies and its key influencing factors. In this article, we propose a novel machine learning approach to diffusion research focusing on actual decisions and spatial aspects complementing research on intentions and temporal dynamics. We develop machine learning models that predict regional differences in the accumulated peak power of household-scale photovoltaic systems and the share of battery electric vehicles from a large set of demographic, geographic, political, and socio-economic features. Tools from explainable artificial intelligence enable a consistent identification of the key influencing factors and quantify their impact. Focusing on data from German municipal associations, we identify common themes and differences in the adoption of green technologies. Specifically, the adoption of battery electric vehicles is strongly associated with income and election results, while the adoption of photovoltaic systems correlates with the prevalence of large dwellings and levels of global solar radiation. • Explainable Machine Learning Model for regional differences in technology adoption. • SHapley Additive exPlanations values quantify importance of influencing factors. • Strong differences in the key factors for photovoltaics and e-mobility in Germany. • Changing feature importances for different stages of diffusion of photovoltaics. • Income and political preferences most important in the model for e-mobility.