Pastperformancenoguidetofuturereturns v2

Past performance is no guide to future returns: What can we really say about the future of

economic, social, and technological systems?

Jonathan Koomey, Ph.D. Consulting Professor, Stanford University

http://www.koomey.com Presented at the Energy & Resources Group Colloquium

September 28, 2011

1 Copyright Jonathan G. Koomey 2011

My background

•  Founded LBNL’s End-Use Forecasting group and led that group for more than 11 years.

•  Peer reviewed articles and books on – Forecasting methodology – Economics of greenhouse gas mitigation – Critical thinking skills –  Information technology and resource use


9/27/11 3

Cost-benefit analysis: the standard approach

True or False?: If only we had enough…

•  Time •  Money •  Graduate Students •  Coffee

we could accurately predict the cost of energy technologies in

2050 4 Copyright Jonathan G. Koomey 2011

Widespread modeling practice implies that the answer is “True”


Based on my experience and reviews of historical

retrospectives on forecasting, I say “No way”


Aside: Many of the best modelers acknowledge the difficulties in the pursuit of accurate forecasts, but in their heart of hearts they still

believe they can predict accurately with greater effort


Uncertainty affects even physical systems

Es?mates of Planck’s constant "h" over ?me. In this physical system researchers repeatedly underes?mated the error in their determina?ons. At each stage uncertain?es existed of which the researchers were unaware. The problem of error es?ma?on is far greater in long-‐range energy forecas?ng. Taken from Koomey et al. 2003.


Forecasting Accuracy: The Models Have Done Badly

•  Energy forecasting models have little or no ability to accurately predict future energy prices and demand (Craig et al. 2002)

•  Even the sign of the impacts of proposed policies is a function of key assumptions (Repetto and Austin 1997)

•  The dismal accuracy and inherent limitations of these models should make modelers modest in the conclusions they draw (Decanio 2003) Craig, P., A. Gadgil, and J. Koomey (2002). “What Can History Teach Us? A Retrospective Analysis of Long-term Energy Forecasts for the U.S.” Annual Review of Energy and the Environment 2002. R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA, Annual Reviews, Inc. (also LBNL-50498). 27: 83-118.

Repetto, R. and D. Austin (1997). The Costs of Climate Protection: A Guide for the Perplexed. Washington, DC, World Resources Institute.

DeCanio, S. J. (2003). Economic Models of Climate Change: A Critique. Basingstoke, UK, Palgrave-Macmillan. 9 Copyright Jonathan G. Koomey 2011

One example: 1970s projections of year 2000 U.S. primary energy

Source: Craig, Paul, Ashok Gadgil, and Jonathan Koomey. 2002. "What Can History Teach Us?: A Retrospective Analysis of Long-term Energy Forecasts for the U.S." In Annual Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. (also LBNL-50498). pp. 83-118.


Another example: Oil price

projec3ons by U.S.

DOE, AEO 1982

through AEO 2000


Not any beEer

aFer 2000: Oil price

projec3ons by U.S.

DOE, AEO 2000

through AEO 2007


Another example: NERC fan

Copyright Jonathan G. Koomey 2011 13

US electricity genera?on BkWh/year

Why Are Long-term Energy Forecasts Almost Always Wrong?

•  Core data and assumptions, which drive results, are based on historical experience, which can be misleading if structural conditions change

•  The exact timing and character of pivotal events and technology changes cannot be predicted

Laitner, J.A., S.J. DeCanio, J.G. Koomey, A.H. Sanstad. (2003) “Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis.” Utilities Policy, 11, pp. 87-94.


Conditions for Model Accuracy •  Hodges and Dewar: models can be

accurate when they describe systems that – are observable and permit collection of

ample and accurate data – exhibit constancy of structure over time – exhibit constancy across variations in

conditions not specified in the model

Source: Hodges, James S., and James A. Dewar. 1992. Is it you or your model talking? A framework for model validation. Santa Monica, CA: RAND. ISBN 0-8330-1223-1.


∑: Accurate forecasts require structural constancy and no

surprises


Market structure can change fast

Source: Scher and Koomey 2010.


Fast changing markets #2: US electricity consumption


hWp://www.koomey.com/post/6868835852

Surprises can be big: U.S. nuclear busbar costs

Source: Koomey and Hultman 2007. Assumes 7% real discount rate.

Projected cost range from Tybout 1957


Implications for long-term energy forecasting

•  Forecasting models describing well-defined physical systems using correct parameters can be accurate because physical laws are geographically and temporally invariant (as long as there are no surprises)

•  Economic, social, and technological systems do not exhibit the required structural constancy, so models forecasting the future of these systems are doomed to be inaccurate. Four big sources of inconstancy –  Pivotal events (like Sept. 11th or the 1970s oil shocks) –  Technological innovation –  Institutional change –  Policy choices


∑: Economics ≠ Physics


So no matter how many $, coffee cups, months, or graduate

students you have, accurate long-run forecasting of technology

costs is impossible


Two senses of the word “impossible”:

Practically and

Theoretically

Either way, the net result is the same: inaccurate forecasts


So what does this result imply for predictions of the costs of

energy technologies?


Some lessons •  The world is evolutionary and path dependent

–  Increasing returns, transaction costs, information asymmetries, bounded rationality, prospect theory

–  Our actions now affect our options later (so do surprises!)

•  Experimentation is the order of the day •  Use real data to prove results

–  For nuclear power, we’re in the “show me” stage. Cost projections are no longer enough

•  Prefer technologies that –  are mass produced vs. site-built –  have short lead times vs. longer lead times


Nuke costs: here we go again?

Source: Koomey and Hultman 2007. 26 Copyright Jonathan G. Koomey 2011

“No battle plan survives contact with the enemy.” –Helmuth von Moltke the elder


More lessons •  Use physical and technological constraints to

define bounding cases. Examples: –  2 degrees Celsius warming limit implies a carbon

budget, which implies a certain rate of implementation of non-fossil energy sources to avoid worst effects of climate change.

– Certain technologies use materials that are in limited supply. Working backwards from a goal can help identify resource constraints.

–  Lifetime of power generation technologies and buildings limits penetration of new technologies unless we scrap existing capital


Reconsidering benefit-cost analysis for climate

•  "A corollary is that it is fruitless to attempt to determine the "optimal" carbon tax. If neither the costs nor the benefits can be known with any precision, just about the only thing that can be said with certainty about the welfare maximizing price of carbon emissions is that it is greater than zero. Economists have a great deal to say about how to implement such a tax efficiently and effectively, about the similarities and differences between a tax and a system of tradable carbon emissions permits, about about the best way to recycle the revenue from such a tax or permit system. And, as we have seen above, the distributional consequences of such a tax or permit auction plan will affect other economic variables through system-wide feedbacks. However, any attempt to specify the exact level of the "optimal" tax is less an exercise in scientific calculation than a manifestation of the analyst’s willingness to step beyond the limits of established economic knowledge."

•  –DeCanio, Stephen J. 2003. Economic Models of Climate Change: A Critique. Basingstoke, UK: Palgrave-Macmillan. p.157.


Conclusions •  It is impossible to accurately forecast energy

technology characteristics because of –  structural inconstancy and –  pivotal events

•  Forecasting community has yet to absorb the implications of this insight

•  To cope we need new ways to think about the future –  Experimental approach to implementation (try many things,

fail fast, learn quickly, try again) –  Rely on physical and technological constraints to create

bounding cases –  Embrace path dependence (there is no optimal solution,

just lots of possible pathways of roughly similar costs)


“The best way to predict the future is to invent it.” –Alan Kay


Some Key References •  Craig, Paul, Ashok Gadgil, and Jonathan Koomey. 2002. "What Can History

Teach Us?: A Retrospective Analysis of Long-term Energy Forecasts for the U.S." In Annual Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. pp. 83-118.

•  Ghanadan, Rebecca, and Jonathan Koomey. 2005. "Using Energy Scenarios to Explore Alternative Energy Pathways in California." Energy Policy. vol. 33, no. 9. June. pp. 1117-1142.

•  Koomey, Jonathan. 2001. Turning Numbers into Knowledge: Mastering the Art of Problem Solving. Oakland, CA: Analytics Press. (2d Printing, 2004). <http://www.analyticspress.com>

•  Koomey, Jonathan. 2002. "From My Perspective: Avoiding "The Big Mistake" in Forecasting Technology Adoption." Technological Forecasting and Social Change. vol. 69, no. 5. June. pp. 511-518.

•  Koomey, Jonathan G., Paul Craig, Ashok Gadgil, and David Lorenzetti. 2003. "Improving long-range energy modeling: A plea for historical retrospectives." The Energy Journal (also LBNL-52448). vol. 24, no. 4. October. pp. 75-92.

•  Laitner, J.A., S.J. DeCanio, J.G. Koomey, A.H. Sanstad. (2003) “Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis.” Utilities Policy, vol. 11, no. 2. June. pp. 87-94.

•  Scher, Irene, and Jonathan G. Koomey. 2010. "Is Accurate Forecasting of Economic Systems Possible?" Climatic Change (forthcoming).


More Key References •  Armstrong, J. Scott, ed. 2001. Principles of Forecasting: A Handbook for Researchers

and Practitioners. Norwell, MA: Kluwer Academic Publishers. •  Ascher, William. 1978. Forecasting: An Appraisal for Policy Makers and Planners.

Baltimore, MD: Johns Hopkins University Press. •  Cohn, Steve. 1991. "Paradigm Debates in Nuclear Cost Forecasting." Technological

Forecasting and Social Change. vol. 40, no. 2. September. pp. 103-130. •  Grubler, Arnulf, Nebojsa Nakicenovic, and David G. Victor. 1999. "Dynamics of energy

technologies and global change." Energy Policy. vol. 27, no. 5. May. pp. 247-280. •  Hodges, James S., and James A. Dewar. 1992. Is it you or your model talking? A

framework for model validation. Santa Monica, CA: RAND. ISBN 0-8330-1223-1. •  Huntington, Hillard G. 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?"

The Energy Journal. vol. 15, no. 2. pp. 1-22. •  Huss, William R. 1985. "Can Electric Utilities Improve Their Forecast Accuracy? The

Historical Perspective." In Public Utilities Fortnightly. December 26, 1985. pp. 3-8. •  Landsberg, Hans H. 1985. "Energy in Transition: A View from 1960." The Energy Journal.

vol. 6, pp. 1-18. •  O'Neill, Brian C., and Mausami Desai. 2005. "Accuracy of past projections of U.S. energy

consumption." Energy Policy. vol. 33, no. 8. May. pp. 979-993. •  Tetlock, Philip E. 2005. Expert Political Judgment: How Good Is It? How Can We Know?

Princeton, NJ: Princeton University Press."•  Tybout, Richard A. 1957. "The Economics of Nuclear Power." American Economic

Review. vol. 47, no. 2. May. pp. 351-360.


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