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Pastperformancenoguidetofuturereturns v2

Pastperformancenoguidetofuturereturns v2






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    Pastperformancenoguidetofuturereturns v2 Pastperformancenoguidetofuturereturns v2 Presentation Transcript

    • Past performance is no guideto 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.comPresented at the Energy & Resources Group Colloquium September 28, 2011 Copyright  Jonathan  G.  Koomey  2011   1  
    • 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 Copyright  Jonathan  G.  Koomey  2011   2  
    • Cost-benefit analysis: the standard approach9/27/11   3  
    • True or False?: If only we had enough…•  Time•  Money•  Graduate Students•  Coffee we could accurately predict the cost of energy technologies in 2050 Copyright  Jonathan  G.  Koomey  2011   4  
    • Widespread modeling practiceimplies that the answer is “True” Copyright  Jonathan  G.  Koomey  2011   5  
    • Based on my experience and reviews of historicalretrospectives on forecasting, I say “No way” Copyright  Jonathan  G.  Koomey  2011   6  
    • Aside: Many of the best modelersacknowledge the difficulties in the pursuit of accurate forecasts, but in their heart of hearts they stillbelieve they can predict accurately with greater effort Copyright  Jonathan  G.  Koomey  2011   7  
    • 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.   Copyright  Jonathan  G.  Koomey  2011   8  
    • 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. Copyright  Jonathan  G.  Koomey  2011   9  
    • One example: 1970s projectionsof 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. Copyright  Jonathan  G.  Koomey  2011   10  
    • Another   example:   Oil  price  projec3ons   by  U.S.   DOE,  AEO   1982   through   AEO  2000   Copyright  Jonathan  G.  Koomey  2011   11  
    • Not  any   beEer  aFer  2000:   Oil  price  projec3ons   by  U.S.   DOE,  AEO   2000   through   AEO  2007   Copyright  Jonathan  G.  Koomey  2011   12  
    • Another example: NERC fanUS  electricity  genera?on  BkWh/year   Copyright  Jonathan  G.  Koomey  2011   13  
    • Why Are Long-term EnergyForecasts 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. Copyright  Jonathan  G.  Koomey  2011   14  
    • 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 modelSource: Hodges, James S., and James A. Dewar. 1992. Is it you or your model talking? A framework for modelvalidation. Santa Monica, CA: RAND. ISBN 0-8330-1223-1. Copyright  Jonathan  G.  Koomey  2011   15  
    • ∑: Accurate forecasts requirestructural constancy and no surprises Copyright  Jonathan  G.  Koomey  2011   16  
    • Market structure can change fast Source:    Scher  and  Koomey  2010.   Copyright  Jonathan  G.  Koomey  2011   17  
    • Fast changing markets #2: US electricity consumption hWp://www.koomey.com/post/6868835852   Copyright  Jonathan  G.  Koomey  2011   18  
    • Surprises can be big: U.S. nuclear busbar costs Projected cost range from Tybout 1957Source: Koomey and Hultman 2007. Assumes 7% real discount rate. Copyright  Jonathan  G.  Koomey  2011   19  
    • 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 Copyright  Jonathan  G.  Koomey  2011   20  
    • ∑: Economics ≠ Physics Copyright  Jonathan  G.  Koomey  2011   21  
    • So no matter how many $, coffee cups, months, or graduatestudents you have, accurate long- run forecasting of technology costs is impossible Copyright  Jonathan  G.  Koomey  2011   22  
    • Two senses of the word “impossible”: Practically and TheoreticallyEither way, the net result is the same: inaccurate forecasts Copyright  Jonathan  G.  Koomey  2011   23  
    • So what does this result implyfor predictions of the costs of energy technologies? Copyright  Jonathan  G.  Koomey  2011   24  
    • 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 Copyright  Jonathan  G.  Koomey  2011   25  
    • Nuke costs: here we go again?Source: Koomey and Hultman 2007. Copyright  Jonathan  G.  Koomey  2011   26  
    • “No battle plan survives contact with theenemy.” –Helmuth von Moltke the elder Copyright  Jonathan  G.  Koomey  2011   27  
    • 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 Copyright  Jonathan  G.  Koomey  2011   28  
    • 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. Copyright  Jonathan  G.  Koomey  2011   29  
    • 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) Copyright  Jonathan  G.  Koomey  2011   30  
    • “The best way to predict the future is toinvent it.” –Alan Kay Copyright  Jonathan  G.  Koomey  2011   31  
    • 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). Copyright  Jonathan  G.  Koomey  2011   32  
    • 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.•  ONeill, 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. Copyright  Jonathan  G.  Koomey  2011   33