Ensured Energy: A simulation game to elicit preferences around Swiss energy transition pathways
By: Toby Simpson , Saara Jones , Gracia Brückmann and more
Potential Business Impact:
Lets people play a game to help climate plans.
The 2015 Paris Agreement on global warming specifies national objectives for the reduction of greenhouse gas emissions. In support of Switzerland's energy and climate strategy for 2050, researchers investigate scenarios for the transition of energy systems towards a higher share of renewables, assessing their social, environmental and economic impact. Their results guide stakeholders and policy makers in designing resilient and sustainable systems. Political scientists use surveys to quantify public acceptance of energy policy, but the complexity and long time horizon of the subject creates difficulties, both for researchers in posing contextually relevant questions, and for respondents in assimilating enough information to give meaningful answers. A population survey was therefore augmented with an online serious game in which players experience an accurate simulation of current and future energy provision and manage transition towards a sustainable future. This interactive environment allows better informed and engaged decisions, and provides richer information on public opinion. In this paper we motivate and describe the design of the game and report initial findings on player characteristics and engagement. We show that a serious game can successfully attract participants from diverse societal groups and highlight the challenge of balancing complexity and entertainment.
Similar Papers
Policy Robustness & Uncertainty in Model-based Decision Support for the Energy Transition
Applications
Finds best ways to change power to clean energy.
Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions
Human-Computer Interaction
Helps countries cut pollution by finding key causes.
Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions
Machine Learning (CS)
Helps plan energy changes without hurting people or nature.