Social Acceptability of Electric Cars: an Energy System Optimization-based Analysis in the European Case

Social acceptance of innovative technologies is often overlooked in the framework of energy system optimization modeling (ESOM), especially concerning the decarbonization of the transport sector. Neglecting social acceptance in ESOMs can translate into ineffective support to policy-making and sub-optimal strategies. The primary objective of this study is to identify the key criteria influencing social acceptance through an extensive literature review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to identify the main criteria and to classify them into three categories: a. social-psychological factors (i.e., level of education, gender, awareness of electric vehicles, income level, environmental concern) b. technological features (i.e., driving range, charging duration, charging rate, perceived risks), c. economic and market aspects (i.e., economic benefits, purchase cost, battery cost, incentive-based policies). Once identified the principal factors, five distinct scenarios were implemented using the TEMOA-Europe ESOM instance. The first one considers the respect of the full decarbonization target by 2050 envisaged in the European Green Deal; the second one, instead, considers no specific environmental constraints. In the three additional scenarios, any emission constraint is considered, but specific modifications are performed on the purchase costs of electric cars and the relative discount rates, respectively. As for the third scenario, the electric vehicle cost was considered comparable to conventional fuel vehicles (CFVs) cost from 2025 on. In the fourth scenario the discount rate for electric cars was decreased to 2% and the one of CFVs increased to 30% to capture the evolving nature of people’s risk perceptions over time, emphasizing the crucial linkage between techno-market criteria and socio-psychological factors. These two variables were chosen as key objects for translating the original qualitative criteria into quantitative objects modifiable on the model. In the fifth scenario, both criteria were included, in order to consider their combined role and influence on the adoption of electric cars. The results, in terms of costs and emissions, were compared against the environmentally unconstrainted scenario and against a decarbonization scenario. Preliminary findings highlighted the crucial role of hurdle rates and purchase costs in the process of adoption of green vehicles. Policies that reduce the risk of investing in electric cars improve at the same time their adoption with consequent impact on the total car sector costs.