Search
White Paper

Adapting for Uncertainty: A Scenario Analysis of U.S. Technology Energy Futures

January 1, 2006
facebooktwitterlinkedInemail
top-down" models) rely on a highly stylized but limited characterization of technology that requires large price increases to reduce energy demand and their associated externalities. These various price mechanisms, including energy taxes or some form of a carbon charge, tend to show negative impacts on the economy as a result of those higher prices. Yet, transitioning from current business-as-usual growth patterns to sustainable development paths need not imply lower standards of living. Rather, it may imply an alternative combination of different and more efficient technologies and energy resources as well as a change in industrial and household practices. It can also reflect shifting consumer preferences and a different mix of sector growth rates (Hanson and Laitner 2004; and Laitner et al. 2005).

This article is intended to illustrate how investment decisions can be represented in the modeling of energy and climate policies. We use algorithms within the Argonne National Laboratory's AMIGA modeling system to illustrate this perspective (Hanson and Laitner 2006a). The problem addressed here is separated into three distinct parts: (1) An overview of the AMIGA Modeling System, a hybrid (i.e., technology-rich) computable general equilibrium (CGE) model of both the U.S. and world economies; (2) the appropriate representation of the set of technology choices in large energy models of the U.S. or other economies; and (3) the presentation and discussion of an exercise which illustrates the shift of investments and energy flows within a single sector of the economy as a response to both price and non-price policies and programs. We conclude with a discussion of the methodology as it preserves the essential character of energy end use technologies within a hierarchical CGE structure.

White Paper

Adapting for Uncertainty: A Scenario Analysis of U.S. Technology Energy Futures

© 2020 ACEEE All rights reserved.