What participants actually do
In the World Climate Simulator, a participant chooses a real country to govern. They set policies such as carbon taxes, renewable subsidies, nuclear investment and efficiency measures, then move forward in five-year turns. Each turn weighs energy, emissions, economic output and public approval against each other, with research and development, diplomacy and crisis scenarios along the way. That is what the public landing page describes, and it is the extent of what we claim about it.
What makes this useful is friction. A popular subsidy strains the budget; a fast phase-out hurts approval; a crisis interrupts the plan. Learners stop treating climate policy as a list of good intentions and start treating it as a sequence of costly, connected choices.
A scenario is not a forecast
Forecasting models used in climate science are calibrated against observations, compared with other models and reviewed by specialists. An educational simulator is built for a different job: to make relationships visible and discussable. Its numbers are internally consistent within the game, but they are not predictions about any real country. We state this on our site and we repeat it here because it is easy to forget once a chart appears on screen.
Use the game to ask better questions. Use calibrated models and official data to answer them.
For real-world context, sources such as the European Environment Agency’s overview of climate change mitigation explain how emissions are actually tracked and reduced in Europe. They give a facilitator the material to connect a decision made in the game to the policy debate outside it. They do not validate the game, and we do not cite them as if they did.
Facilitation matters more than the software. In our experience of designing these sessions, the richest discussion tends to come after play, when groups compare strategies for the same country and discover that they disagreed about priorities rather than facts. A facilitator who knows the limits of the model can turn that disagreement into the real lesson: climate policy is a negotiation between legitimate goals, and every simplification hides a choice someone has made on your behalf.
Good questions for a workshop debrief
- Which decision did you regret, and when did you first notice its cost?
- Where did public approval and emissions pull in opposite directions?
- What would you need to know about a real country before trusting the same strategy?
- Which simplification in the game mattered most for your result?
A bounded research pilot
Researchers sometimes ask whether the simulator could support studies of decision-making. It could become part of one, but that requires new work agreed with partners. A responsible first pilot would be small and explicit:
- One research question, written before the sessions, for example how groups discuss trade-offs between approval and emissions.
- Consented, minimal logging of choices, designed with the partner’s ethics process.
- Documented model limits shared with participants before they play.
- Facilitated debriefs recorded as qualitative data alongside the logs.
- A written report that states what the pilot can and cannot conclude.
Regional variants built on partner data, or structured logging for research, are on our list of proposed extensions. They are not shipped features.
Where to go next
Read how the same two-layer thinking applies to learning in AI and simulation: learning through decisions, or see why health work starts somewhere else entirely in responsible AI in health research.
