Simulation models are often used to explore future development pathways and their impacts on energy, emissions, economies and the environment. This requires making assumptions about various socio-economic conditions, such as how fast populations and economies will grow, the cost of technology options, or the amount of fossil fuels available. Different assumptions have significant impacts on model results, yet analyses typically only test a few alternatives.

Here, we develop and use probability distributions to capture this uncertainty. We draw samples from these distributions, run an energy-economic model hundreds of times, and quantify the resulting uncertainty in model outcomes, providing insight into their likelihood. We focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. We also apply approaches to find scenarios of interest from within the database of scenarios.

We find that many patterns of energy and technology development are possible under a given long-term environmental pathway (such as a 2C scenario) or a given economic outcome (such as high or low GDP). This approach can help identify biases in perceptions of what “needs” to happen to achieve certain outcomes, and shows that there are many pathways to a successful energy transition.