1 Intelligent Well Technology: Status and Opportunities for Developing Marginal Reserves SPE

Useful PARSIMONIOUS Long-term Energy Projections in the Face of Climate Change

Vanessa Schweizer, Carnegie Mellon University, Climate Decision Making Center, Department of Engineering and Public Policy, (412) 268-6922,

Overview

The problem of climate change demands a radical shift in societal energy supply and utilization. Considering the U.S. economy only, the electricity sector is the largest direct source of the nation’s carbon dioxide emissions, and electricity demand shows no signs of slowing. Yet estimates for necessary reductions in worldwide greenhouse gas emissions are as high as 80% below year 2000 levels by 2050. Because of time lags inherent in energy capital investment decisions, the political process, and climate systems, thinking about potential impacts that occur decades from now is necessary for making energy policy decisions today. Traditionally, scenario analysis and forecasting were used to think about the future on these timescales; however, energy demand forecasts have performed poorlyhistorically. Past energy demand forecasts have been overestimates, but in a warming world, there will be serious consequences for underestimates. Currently many decision-makers rely upon integrated assessment models to conceptualize future states of the world. However when detailed time-stepped models must be run decades into the future – where portions of their phase space are subject to discontinuities – submodels and, consequently, overall model results may become unreliable (Casman et al., 1999). This is particularly true for futures with impacts from climate change, as human societies will make significant social and technological changes in order to adapt. Casman et al. discuss several strategies that could be used to deal with this problem. In this study, initial steps are taken for constructing improved electricity demand projections under mid- to long-term uncertainty. The objective of this research is to identify methods that hold promise for calibrating expert judgments underlying scenarios in order to diminish surprise. This can be done by building as full a set as possible of developments that could appreciably influence a quantity of interest – in this case, future U.S. electricity demand – and by completing a bounding analysis for the discontinuous mid- to long-term future phase space (Morgan and Keith, 2008).

Methods

Probabilistic model switching (Casman et al., 1999) shows promise for handling the discontinuous mid- to long-term future phase space of sophisticated energy-economy models. However before subjective judgments of model failure can be elicited, the bounds of alternative parsimonious models must first be specified.

Using traditional parsimonious models such as the Kaya identity (Raupach et al., 2007), potential scenario discontinuities of interest can be identified to inform the set of possible developments that could appreciably influence the upper and lower bounds of a quantity of interest – in this case, future U.S. electricity demand.

Results

Examples of scenarios explored at the upper bound include how high U.S. electricity demand could be under a variety of circumstances including, but not limited to, (a) widespread use of plug-in hybrid electric vehicles, (b) widespread use of desalination due to increased water stress from climate change impacts and population pressure, and (c) widespread use of carbon capture technologies. Although lower-bound scenarios are still under development, they would explore how low U.S. electricity demand could be if national policies and practices were adopted resembling those of the E.U. or U.S. states that have successfully leveled electricity demand per capita such as California and Vermont. Preliminary results from the bounding analysis will be compared to U.S. electricity demand projections on comparable timescales published by the U.S. Energy Information Administration and other sources.

Conclusions

Scenarios for U.S. electricity demand referenced by U.S. policy-makers based upon detailed energy-economy models currently do not sufficiently consider many possible mid- and long-term social and technological developments that have important policy implications. As a result, these policy-makers lack visions of the future that would help broaden their thinking, or worse, that would aid cost-effective adaptive policy-making. For these reasons, bounding techniques that enable probabilistic model switching should be part of integrated assessment for climate policy.

References

Casman, E. A., Morgan, M. G. & Dowlatabadi, H. (1999) Mixed Levels of Uncertainty in Complex Policy Models. Risk Analysis, 19, 33-42.

Morgan, M. G. & Keith, D. W. (2008) Improving the way we think about projecting future energy use and emissions of carbon dioxide. Climatic Change, 90, 189-215.

Raupach, M. R., Marland, G., Ciais, P., Le Quéré, C., Canadell, J. G., Klepper, G. & Field, C. B. (2007) Global and regional drivers of accelerating CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 104, 10288-10293.