In the start-up phase of the project, we reviewed the literature on social tipping dynamics, made an inventory of existing models and put out surveys to gather additional data. The results were described in a joint scientific paper were we identify energy communities as a promising environment for tipping dynamics to occur. The paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to “make them stick”. Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems.
A second paper looks into tipping dynamics in mobility, specifically heavy transport. Here, we present a systems-level learning model for electric trucks to illustrate how this can be done. Focussing on Europe, we use an approach based on learning curves for eTruck drivetrain and battery pack design; battery developments in cost, durability and composition; energy efficiency and CO2 emissions; weights of all components; electricity and diesel costs; charging costs in different scenarios; and the use of an eTruck fleet with different ranges. Our model shows several tipping points that can lead to fast eTruck adoption. Policies could leverage these tipping points by rewarding longer range, faster charging, vehicle-to-grid capabilities, and an open and interoperable network of eTruck fast-chargers to drive a rapid and cost-effective transition to eTrucks.
Finally, we also concluded the first round of a survey in three countries (NL, DE, UK) to identify the potential for tipping dynamics.
