- The pace and direction of the energy transition is shaped by feedback loops, which can amplify or resist change;
- Guide provides practitioners with useful mental models for managing fast-unfolding structural change.
Understanding six types of ‘virtuous’ and ‘vicious’ feedback loops can help policy-makers better manage the energy transition, researchers at the University of Oxford argue.
With governments across the world seeking to influence the rapidly unfolding transition, there has been growing demand to understand the processes that are driving, or resisting, this structural change.
A research team led by Max Collett has sought to provide policymakers with a framework for better understanding these processes, defining six ‘archetypes’ in a paper published in the journal Nature Reviews Clean Technology.
His team, which has a background in Complexity Economics, provide real-world examples of these feedback loops in effect and policy prescriptions for how to influence them.
Defining six feedback loop dynamics as system archetypes
The six archetypes outlined provide a guide for researchers and policymakers for understanding change and making decisions across diverse settings.
Archetype | Structure of feedback loop | Definition | Example | Policy Prescription |
Self-Amplifying Technological Growth | Reinforcing | The adoption of a technology drives innovation and cost reductions, promoting further deployment | Smaller and modular technologies such as solar, photovoltaics, wind turbines and batteries | Identify nascent clean technologies with cost-reduction potential, then provide support for deployment to kickstart and strengthen this virtuous cycle |
Technology synergies | Two interlinked reinforcing loops | There is a synergistic relationship between two technologies, driving growth in both. | Relationship between solar power and energy storage | Policy should aim to enable a co-evolution of technologies, ensuring that lagging deployment of one does not constrain deployment of the other |
Clean power technology revenue cannibalisation | Dampening | When the deployment of a technology undermines its own risk-adjusted returns, threatening future investment | Wind and solar power | De-risk projects by decoupling revenues from volatile wholesale markets, at least partially; accelerate energy storage deployment |
Path dependency and lock-in | Two interlinked reinforcing loops | When one of two competing technologies has an initial advantage that allows it to monopolise the resources needed for further growth, such as capital, social acceptance and political support. | Electric vehicles beating out fuel cell vehicles to become the dominant alternative to petrol cars; or petrol cars enjoying incumbency due to lock-in effects | Policies to create new markets and/or de-risk projects, such as capital subsidies or offtake agreements, coupled with research and development support to bring clean alternatives to maturity, can begin to shift investment |
Waterbed effects in carbon markets | Dampening | When policies that reduce emissions in one sector lead to emissions growth elsewhere in the system | Emissions Trading Schemes, especially when linked across sectors and/or geographies | Policies that prevent prices from falling too low, e.g. carbon price floor or market stability reserve |
The utility death spiral | Reinforcing | When mass movement of customers towards or away from centralised energy infrastructure impacts the per user network costs | Decommissioning of the UK gas network; Pakistan’s electricity grid | Manage network decommissioning by devising clear plans and timelines, setting appropriate asset depreciation rates and supporting vulnerable ratepayers by funding disconnections |
Effective decisions ‘must be sensitive to feedback effects'
Lead author Max Collett of The Institute for New Economic Thinking at the Oxford Martin School said that researchers, analysts and policymakers need to start using these powerful heuristics to improve their shared understanding and drive rapid progress in the energy transition.
“Effective decisions and analysis in the energy transition must be sensitive to feedback effects that drive, or resist, structural change. The feedback loops described above capture patterns of system behaviour that occur across different sectors, technologies and geographies.
“These analytical tools are flexible — they can be used in communication, in evaluation and to directly inform policymaking, modelling and other analyses"
Co-author Pete Barbrook Johnson, Lecturer at UCL, added:
“The feedback loops in the energy transition are extremely powerful but far too often are missing from analysts’ models and decision-makers’ thinking. This comment seeks to put these dynamics front and centre.”
Key Links
The paper is co-authored by Max Collett a former MSc Environmental Change and Management (ECM) alumnus of ECI, who now works across energy and innovation research, including at the Institute for New Economic Thinking (INET), the Institute for Sustainable Resources at University College London (UCL), and S-Curve Economics CIC; Professor Jan Rosenow, Professor of Energy and Climate Policy at the Environmental Change Institute; and Dr Pete Barbrook-Johnson, a teaching associate at ECI, associate at INET and lecturer at UCL.
