Periodic Reporting for period 1 - FAST (Fostering social tipping dynamics to Accelerate Sustainability Transitions)
Berichtszeitraum: 2022-09-01 bis 2025-02-28
Staying within a maximum global warming of 1.5 degrees requires an acceleration of the transition to a low carbon society. Current theory views such sustainability transitions as inevitably slow, as they require interdependent changes throughout socio-technical systems. The observation that energy transition scenarios systematically underestimate the diffusion of key technologies challenges this view.
Existing models that form the basis for energy transition scenarios do not include the positive feedbacks resulting from the interactions between civil society and energy transitions. Yet these interactions have been identified as important drivers and barriers of energy transitions and as crucial in triggering the social tipping dynamics that can accelerate the energy transition. Social tipping dynamics in energy transitions occur when a small change or intervention has a large effect on emission reductions. To date, some examples of social tipping dynamics have been identified, but both a systematic understanding of the mechanisms underlying social tipping dynamics and an evaluation of their potential policy leverage is missing.
The overall objective of the FAST project is to explain and model social tipping dynamics and interventions in energy transitions. This requires a model that explains how the social factors influencing sustainability transitions scale up to realize social tipping dynamics.a
Existing models that form the basis for energy transition scenarios do not include the positive feedbacks resulting from the interactions between civil society and energy transitions. Yet these interactions have been identified as important drivers and barriers of energy transitions and as crucial in triggering the social tipping dynamics that can accelerate the energy transition. Social tipping dynamics in energy transitions occur when a small change or intervention has a large effect on emission reductions. To date, some examples of social tipping dynamics have been identified, but both a systematic understanding of the mechanisms underlying social tipping dynamics and an evaluation of their potential policy leverage is missing.
The overall objective of the FAST project is to explain and model social tipping dynamics and interventions in energy transitions. This requires a model that explains how the social factors influencing sustainability transitions scale up to realize social tipping dynamics.a
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.
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.
The figure below illustrates how energy communities can turn balancing feedbacks into reinforcing feedbacks for RE adoption. Blue arrows indicate new mechanisms arising from the institutional context of renewable energy. (see https://esd.copernicus.org/articles/15/485/2024/(öffnet in neuem Fenster) for full paper)