Quantifying Socio-Economic Uncertainty
This study combines two well-known computer modeling techniques to scope out the energy and technology choices needed over the coming decades to achieve desired environmental and economic outcomes. The first technique, Monte Carlo analysis, quantifies uncertainty levels for dozens of energy and economic indicators; feeds that information into a model of the world economy that captures the cross-sectoral impacts of energy transitions, and runs that model hundreds of times to estimate the likelihood of different outcomes. The second technique, scenario discovery, uses machine learning tools to screen databases of model simulations in order to identify outcomes of interest and their conditions for occurring. In a novel approach, this study combines these tools with the Monte Carlo analysis to explore how different outcomes are related to one another. This approach can also identify individual scenarios that result in specific combinations of outcomes of interest, and provide insight into their likelihood and the conditions needed for them to occur.
This unique approach shows that there are many pathways to a successful energy transition that benefits both the environment and economy, and quantifies multi-sector risks associated with those pathways. It can thus be used to guide decision-makers in government and industry to make sound energy and technology choices and avoid biases in perceptions of what “needs” to happen to achieve certain outcomes. It can also be used to home in on scenarios of interest for use in particular studies.
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. In this study, researchers at the MIT Joint Program on the Science and Policy of Global Change develop and use probability distributions to capture this uncertainty. They 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. They focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. They also apply machine learning approaches to find scenarios of interest from within the database of scenarios. They find that many patterns of energy and technology development are possible under a given long-term environmental pathway or economic outcome.