BEIS Energy Trilemma

Contact:

Pete Barbrook-Johnson

Negotiating complexity in evaluation planning at BEIS: a participatory systems map of the energy trilemma.

The large number of programmes and policies at play in the ‘energy trilemma’ (i.e. the interaction in the energy system between sustainability and emissions, affordability and prices, and security of supply) has led to a crowded policy landscape with potential for complementary but also conflicting aims. In this case study, CECAN and the Department for Business, Energy and Industrial Strategy (BEIS) built a richer understanding of this complex area, by developing a participatory systems map of the energy trilemma.

Government policies that affect the energy trilemma include:

What were the aims of the case study?

The aim of the case study was to explore, via CECAN’s approach to participatory systems mapping, the energy trilemma policy landscape. Specifically, it was to map relevant policies, their interaction, context, and impact on the trilemma, and to highlight: the impacts of policies on the three ‘legs’ of the trilemma; potential common and/or contradictory aims and mechanisms amongst policies; and uncertainty and evidence gaps. 

This understanding is being used to support evaluation planning in BEIS, and make the case for, and to develop, proportionate evaluation(s) (i.e. evaluations which draw the right boundaries, and use the appropriate types and levels of evidence). The systems map created can also be used to feed into, or put in context, individual policy maps and theories of change.

The case study builds on existing methods (e.g. fuzzy cognitive mapping, dependency modelling) with a strong emphasis on a participatory approach to understanding complex systems. In practice the systems maps are:

  • Always built by as diverse a range of stakeholders as possible.
  • Designed to capture complexity rather than simplify it away (i.e. including feedbacks, context, and uncertainty).
  • Analysed using a ‘choose your own adventure’ approach, firmly rooted in combining network analysis and stakeholders’ beliefs about important, changeable, and controllable factors in a system.

A briefing note and full report on this case study are coming soon.