Quantifying uncertainty in socio-economic outcomes can provide insight into the likelihood of future energy and emissions projections, thereby informing multi-sector risk assessment.
There are major uncertainties in human systems—economic, demographic, trade and technological processes—that affect how they evolve over time. This study undertakes an extensive assessment of uncertain human-system variables and formal uncertainty quantification of future global socio-economic outcomes. To that end, researchers at the MIT Joint Program on the Science and Policy of Global Change have developed probability distributions of key model parameters, sampling from the distributions and exploring the range and likelihoods of human-system model outcomes. This approach reveals how uncertainties in different socio-economic variables interact, and provides a way to gauge the likelihood of future outcomes in energy, emissions, economic sectors and other human-system projections.
This paper demonstrates how available information (historical data, scientific literature, expert judgment, etc.) can be used to develop probability distributions for important socio-economic uncertainties in human-system models. Researchers can then sample from those distributions, employing Monte Carlo simulation, in order to quantify the uncertainty in key human-system model outcomes. This uncertainty quantification approach provides information about risks that can aide decision-making about energy and technology choices and sectoral emission reduction strategies. It can also identify scenarios of interest, provide a better understanding of model responses, and offer insight into areas for further research and model development.
Future global socio-economic development pathways and their implications for the environment are highly uncertain, as are the technology mixes associated with different global environmental targets. To explore a range of possible future outcomes, the researchers develop probability distribution estimates for key input parameters to a model of global human activity. They then draw samples from the probability distributions for each uncertain input variable, including costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, population, and labor and capital productivity. The sampled values are simulated through a multi-sector, multi-region, recursively dynamic model of the global economy. The results are 400-member ensemble simulations describing future energy and technology mixes as well as GDP and emissions. The researchers find that many patterns of energy and technology development are consistent with various long-term environmental pathways. Finally, they combine uncertainty quantification and scenario discovery to investigate scenarios with similar values for one outcome and the range of other outcomes in those scenarios. This analysis illustrates how many combinations of conditions can be consistent with an outcome of interest. For example, many different technology mixes can be consistent with high or low economic growth.
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Jennifer Morris (email@example.com)
Research Scientist, MIT Joint Program on the Science and Policy of Global Change
The study was funded by the U.S. Department of Energy (DOE) Office of Science under the grant DE-FG02-94ER61937. The scenario discovery component of the work was supported by the DOE Office of Science, as part of research in the MultiSector Dynamics, Earth and Environmental System Modeling Program through a collaborative project between MIT and PNNL on Multi-Sector, Multi-Resource Interactions with Multiple Forcers (Contract #: 517296).
Morris, J.F., J.M. Reilly, S. Paltsev and A. Sokolov (2021), Representing Socio-Economic Uncertainty in Human System Models, MIT Joint Program Report 347
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Global GDP growth rate projections for the 5th, 50th and 95th percentiles in each time period. This study quantifies uncertainty in future GDP growth as well as other socio-economic outcomes.
Thumbnail image by Flickr user pierre9x6