Evaluating the role of urban social networks in just energy transition of cities

With increasing urbanization it has become increasingly important to focus on reducing emissions at a residential level. Decisions of households to adopt emissions reducing technologies are influenced not just by economic factors, but by the type of social networks they are embedded in. There is however little insight into the relationship between underlying networks and effectiveness of policy interventions.

This thesis builds a Data-Driven Agent-based Model grounded in Theory of Planned Behavior, initialised with synthetic household population for a case-study of Albany County, (New York, USA). It uses network modelling to study how social network structures influence adoption rates of different communities.

Results show that flat tax-credit schemes can result in poor adoption rates in segregated networks, with performance worsening if the network size is small. Seeding policies and information agents, do not necessarily result in better adoption rates, highlighting the importance of trusted information sources than mass communicators.

The resulting understanding of social networks’ influence over adoption of energy-efficient technologies can assist policy-makers in designing energy policy interventions that improve access of clean energy technologies for all socioeconomic groups.