Representing Socio‐Economic Uncertainty in Human System Models

Morris, Jennifer; Reilly, John; Paltsev, Sergey; Sokolov, Andrei; Cox, Kenneth

doi:10.1029/2021EF002239

Title: Representing Socio‐Economic Uncertainty in Human System Models

Journal Article · 06 April 2022 · Earth's Future

DOI: https://doi.org/10.1029/2021EF002239 · OSTI ID:1861924

^[1]; Reilly, John ^[1];

^[1];

^[1]; Cox, Kenneth ^[1]

MIT Joint Program on the Science and Policy of Global Change Massachusetts Institute of Technology Cambridge MA USA

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; FG02-94ER61937

OSTI ID:: 1861924

Alternate ID(s):: OSTI ID: 1866512; OSTI ID: 1978506

Journal Information:: Earth's Future, Journal Name: Earth's Future Journal Issue: 4 Vol. 10; ISSN 2328-4277

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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