Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.Learning; Uncertainty; Climate change; Decision analysis

Alistair Ulph

Andreas Lange

Arnulf Grübler

Brian C. O'neill

Charles Kolstad

Chris Wilson

Jonathan Koomey

Klaus Keller

Michael Obersteiner

Michael Oppenheimer

Michael Schlesinger

Minh Ha-Duong

Mort Webster

Nicolas Treich

Paul Crutzen

Warren Sanderson

William Pepper

English

Research Papers in Economics

  1
Submitted to Climate Policy as a Commentary 
September 8, 2006 
 
 
Learning and Climate Change 
 
Brian C. O’Neill,
1,2 Paul Crutzen,
3 Arnulf Grübler,
1 Minh Ha Duong,
4 Klaus Keller,
5 
Charles Kolstad,
6 Jonathan Koomey,
7 Andreas Lange,
8 Michael Obersteiner,
1 Michael 
Oppenheimer,
9 William Pepper,
10 Warren Sanderson,
11 Michael Schlesinger,
12 Nicolas 
Treich,
13 Alistair Ulph,
14 Mort Webster,
15 Chris Wilson
1 
 
 
1 International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria, 
Tel: +43 2236 807 380, fax: +43 2236 71 313, e-mail:oneill@iiasa.ac.at 
2 Watson Institute for International Studies, Brown University, Providence, RI, USA 
3 Max Planck Institute for Chemistry, Mainz, Germany 
4 CIRED/CNRS, Paris, France 
5 Pennsylvania State University, Pennsylvania, USA 
6 Donald Bren School of Environmental Science & Management,  
UC Santa Barbara, USA 
7 Stanford University, Stanford, California, USA 
8 AREC, University of Maryland, Maryland, USA 
9 Woodrow Wilson School and Department of Geosciences, Princeton University, New 
Jersey, USA 
10 ICF Consulting, Virginia, USA 
11 Department of Economics, SUNY Stony Brook, USA 
12 University of Illinois, Urbana-Champaign, Illinois, USA 
13 INRA, Toulouse, France 
14 Manchester University, Manchester, United Kingdom 
15 University of North Carolina, Chapel Hill, USA
   2
Abstract 
Learning – i.e., the acquisition of new information that leads to changes in our 
assessment of uncertainty – plays a prominent role in the international climate policy 
debate.  For example, the view that we should postpone actions until we know more 
continues to be influential.  The latest work on learning and climate change includes new 
theoretical models, better informed simulations of how learning affects the optimal 
timing of emissions reductions, analyses of how new information could affect the 
prospects for reaching and maintaining political agreements and for adapting to climate 
change, and explorations of how learning could lead us astray rather than closer to the 
truth.  Despite the diversity of this new work, a clear consensus on a central point is that 
the prospect of learning does not support the postponement of emissions reductions today. 
 
Keywords: learning, uncertainty, climate change, decision analysis 
   3
It is now widely recognized that the inclusion of uncertainty is necessary in any analysis 
of future climate change and response options.  There is still much to learn about how 
climate might change, what its consequences for ecosystems and society would be, and 
how much it would cost to reduce emissions.   
In contrast, there has been far less attention to the learning process itself. How much, 
and how fast, might we realistically expect to learn about particular elements of the 
climate change issue?  Will new information reduce uncertainties or increase them?  How 
should the expectation of future learning affect today’s policy decisions? 
Answers to these questions are not merely of academic interest, because despite its 
relatively thin treatment in the scientific literature, learning plays a prominent role in the 
international climate policy debate.  The view that we should postpone actions until we 
know more – one of the motivations for U.S. withdrawal from the Kyoto Protocol – 
continues to be influential, not least because of its intuitive appeal: shouldn’t we be able 
to make better decisions in the future with more information? 
Recently,
∗ an interdisciplinary group of climate scientists, economists, demographers 
and energy analysts met to present and consider the latest work on learning and climate 
change.  New theoretical models and better informed simulations of learning are being 
applied to the question of how learning should affect the timing of emissions reductions.  
Research is also expanding to engage a wider range of learning-related questions.  For 
example, new information could affect the prospects for reaching and maintaining 
political agreements on climate change and it will play an important role in the process of 
                                                 
∗ Conference on “Learning and Climate Change,” International Institute for Applied Systems Analysis, 
Laxenburg, Austria, April 10-11, 2006.   4
adapting to climate change.  However, in some cases learning could lead us astray rather 
than closer to the truth.  Despite the diversity of this new work, a clear consensus on a 
central point emerged from the papers presented: the prospect of learning does not 
support the postponement of emissions reductions today. 
 
What Might We Learn? 
One set of presentations focused on how learning – i.e., the acquisition of new 
information that leads to changes in our assessment of uncertainty – is being addressed 
with respect to the carbon cycle, climate sensitivity, and threshold events.  For example, 
observations of anthropogenic emissions and atmospheric concentrations of CO2 over the 
next few decades could allow us to reduce uncertainty in the response of the carbon cycle 
to emissions over the longer term (Melnikov and O’Neill, 2006). Simulations also 
suggest that future observations of temperature change and atmospheric composition 
might narrow substantially the range of uncertainty in climate sensitivity – a crucial 
property of climate models (Schlesinger and Andronova, 2003).  The possibility of 
anticipating threshold-type impacts is under study, a particularly relevant issue given 
recent observations of variations in the meridional overturning circulation of the Atlantic 
Ocean (Bryden et al., 2005) and rapid ice sheet melting (Rignot and Kanagaratnam, 2006; 
Thomas et al., 2004; Rapley, 2006).  
Most work on learning and economic aspects of the climate issue has focused on the 
role of various forms of learning-by-doing in models of endogenous technological change 
and its influence on lowering future mitigation costs.  While cost reductions could be   5
substantial (Grubb et al., 2006), they are also highly uncertain since development of both 
high- and low-emission technologies could benefit from learning (Gritsevskyi and 
Nakicenovic, 2000). The costs of climate change impacts are equally important. These 
costs are often assessed by comparing conditions under a changed climate to conditions 
today.  But recent work shows that costs may be largely determined by the transition 
period, when adaptation decisions must be made while agents learn about changing 
outlooks for future climate. 
 
Incorporating Learning In Decision Analytic Models 
Understanding the implications of learning about the science and economics of climate 
change requires incorporating these processes in formal decision analytic models.  Early 
analyses established that the prospect that we will learn more in the future – even if we 
don’t know exactly what we will learn – can affect today’s decisions because it increases 
the value of maintaining options for responding to new information (Arrow and Fischer, 
1974; Manne and Richels, 1992; Kolstad, 1996).  The more options we have, and the 
fewer irreversible commitments we have made, the more fully we can take advantage of 
whatever new information we obtain. 
Recent work has also shown that the effect of learning depends in particular on the 
attitude of the decision-maker towards risk and uncertainty, as well as towards the way 
future generations' preferences are considered. New decision theories can account for 
uncertainty using imprecise or multiple probability distributions to reflect differences in 
probabilistic predictions across alternative climate-economy models. Learning can also   6
play a key role in another kind of decision: whether to join, or to remain in, an 
international environmental agreement. 
Historical experience with other global issues also indicates that new models must be 
adapted to allow for alternative learning trajectories.  Those experiences can belie the 
intuitive expectation that learning will progress uniformly toward reduced uncertainty 
and toward the true answer over time.  For example, the ozone issue provides a classic 
case of what one of the presentations called negative learning, in which uncertainty about 
stratospheric photochemical processes appeared to decrease from the late 1970s through 
the mid-1980s, but predictions of total ozone depletion were actually narrowing in on 
what turned out to be the wrong answer.  The later discovery of the ozone hole pointed to 
processes missing from the earlier models and substantially increased the projected ozone 
depletion. 
Unanticipated surprises are not unique to the ozone issue.  The OPEC crisis and the 
oil price spikes of the 1970s and 1980s made most previous energy projections irrelevant.  
New projections made in the early 1980s then assumed that high oil prices would be a 
permanent feature of the energy system, and in a few years were themselves proven 
incorrect when prices fell sharply in the middle of the decade.  Similarly, population 
projections failed to foresee the rapid decline of fertility in developing countries that 
began in the late 1960s, and more recently the decline of fertility to very low levels in 
many industrialized countries.  
   7
Why Not Wait? 
Certain aspects of learning discussed above have been incorporated into decision models 
(e.g., Keller et al., 2004; Dumas and Ha-Duong, 2005).  In some cases, the incorporation 
of learning implies making larger emissions reductions in the near term than would be 
optimal under uncertainty in the absence of learning.  In others, it implies making smaller 
reductions.  But as presentations demonstrated, in no case does the learning effect offset 
the benefits of near-term emissions reductions entirely.   
Learning is a crucial feature of the climate change issue. Analyses of learning can 
inform research priorities (Keller et al., in press) by identifying not only which 
uncertainties in the climate system would, if reduced, yield the largest benefits to today’s 
decisions (Nordhaus and Popp, 1997), but also by identifying the kinds of actions that 
might be taken to facilitate such learning (Baehr et al., in press).  In some cases, 
observations of key elements of the system are needed to provide advance warning of 
potential thresholds or irreversibilities, such as ocean circulation changes, or to keep 
abreast of rapidly changing situations, such as in population or energy.  In others, 
research is needed that can best resolve uncertainty in fundamental processes, such as 
terrestrial carbon sinks or the dynamics of ice sheets. Model and theory development may 
be necessary to avoid misleading learning from flawed models. Because the effect of 
learning on near-term mitigation decisions depends on the balance between the long-term 
implications of emissions for the climate system and of emissions reductions for the 
economic system, analyses that integrate learning about both these effects simultaneously 
are needed.   8
Learning also applies to the development of institutions to respond to the climate 
issue, including international agreements, economic instruments such as tradeable 
permits or taxes, and legal frameworks to support them (Nordhaus, 2001; Victor et al., 
2005).  Investing in such institutional development now will allow faster and more 
flexible policy responses to new information in the future. The same argument applies to 
new technologies—if we do not invest in different options we may well preclude them. 
Regarding mitigation policy, learning can help to improve decisions not because it is 
a substitute for emissions reductions now, but because it will help us decide what to do in 
the future.  Reducing emissions now in the face of uncertainty has been likened to taking 
out insurance against the possibility of unpleasant outcomes (Yohe et al., 2004; and 
Schlesinger et al., 2005).  By learning whether the risks we face are likely to be more or 
less serious than we currently anticipate, we will know whether we should increase or 
decrease our insurance premiums over time. 
   9
References 
 
Baehr, J., Keller, K. and Marotzke, J., 2006. Detecting potential changes in the 
meridional overturning circulation at 26 degrees N in the Atlantic, Climatic Change, in 
press. 
 
Bryden, H.L., Longworth, H.R. and Cunningham, S.A., 2005. Slowing of the Atlantic 
meridional overturning circulation at 25 degrees N, Nature 438, 655. 
 
Dumas, P. and Ha-Duong, M., 2005, ”An Abrupt Stochastic Damage Function to Analyse 
Climate Policy Benefits”. In: Haurie, A. and Viguier, L. (ed.) The Coupling of Climate 
and Economic Dynamics. Essays on Integrated Assessment, Dordrecht, The Netherlands, 
Springer, 97-112. 
 
Gritsevskyi, A. and Nakicenovic, N., 2000. Modeling uncertainty of induced 
technological change, Energy Policy 28, 907-921. 
 
Grubb, M., Carraro, C. and Schellnhuber, J., 2006. Technological change for atmospheric 
stabilization: Introductory overview to the Innovation Modeling Comparison Project, 
Energy Journal, Endogenous Technological Change and the Economics of Atmospheric 
Stabilization Special Issue, 1-16. 
 
Keller, K., Bolker, B.M. and Bradford, D.F., 2004. Uncertain climate thresholds and 
optimal economic growth, Journal of Environmental Economics and Management, 48 
(1), 723-741. 
 
Keller, K., Schlesinger, M. and Yohe, G., 2006. Managing the risks of climate thresholds: 
Uncertainties and information needs (An editorial essay), Climatic Change, in press. 
 
Melnikov, N.B. and O'Neill, B.C., 2006. Learning about the carbon cycle from global 
budget data. Geophysical Research Letters 33, L02705, doi:10.1029/2005GL023935. 
 
Nordhaus, W.D., 2001. Global warming economics, Science 294, 1283-1284. 
 
Nordhaus, W.D., and Popp, D., 1997. What is the value of scientific knowledge? An 
application to global warming using the PRICE model, Energy Journal, 18 (1), 1-45. 
 
Rapley, C., 2006, “The Antarctic ice sheet and sea level rise”. In: Avoiding Dangerous 
Climate Change: A Scientific Symposium on Stabilisation of Greenhouse Gases, Exeter, 
U.K., 2006, U. K. Meteorological Office (Cambridge University Press), 25-27. 
 
Rignot, E. and Kanagaratnam, P., 2006. Changes in the velocity structure of the 
Greenland ice sheet, Science 311, 986-990.   10
 
Schlesinger, M.E. and Andronova, N.G, 2003, “Climate Sensitivity: Uncertainties and 
Learning”. In: Proceedings of World Climate Change Conference, Moscow, 2003, Y. 
Izrael et al. (ed.), 168-175. 
 
Schlesinger, M. E. et al., 2005, “Assessing the Risk of a Collapse of the Atlantic 
Thermohaline Circulation". In: Avoiding Dangerous Climate Change: A Scientific 
Symposium on Stabilisation of Greenhouse Gases, Exeter, U.K, 2005, U. K. 
Meteorological Office (Cambridge University Press). 
 
Thomas, R. et al., 2004. Accelerated sea-level rise from West Antarctica, Science 306, 
255-258. 
 
Victor, D.G., House, J.C. and Joy, S., 2005. A Madisonian approach to climate policy, 
Science 309, 1820-1821. 
 
Yohe, G., Andronova, N. and Schlesinger, M., 2004. To hedge or not against an uncertain 
future, Science 306, 416-417. 
 

A Madisonian approach to climate policy,

Accelerated sea-level rise from West Antarctica,

An Abrupt Stochastic Damage Function to Analyse Climate Policy Benefits”.

Assessing the Risk of a Collapse of the Atlantic Thermohaline Circulation&quot;. In: Avoiding Dangerous Climate Change: A Scientific

Changes in the velocity structure of the Greenland ice sheet,

Detecting potential changes in the meridional overturning circulation at 26 degrees N in the Atlantic, Climatic Change,

Global warming economics,

Learning about the carbon cycle from global budget data.

Managing the risks of climate thresholds: Uncertainties and information needs (An editorial essay), Climatic Change,

Modeling uncertainty of induced technological change,

Slowing of the Atlantic meridional overturning circulation at 25 degrees

Technological change for atmospheric stabilization: Introductory overview to the Innovation Modeling Comparison Project,

The Antarctic ice sheet and sea level rise”. In: Avoiding Dangerous Climate Change: A Scientific

To hedge or not against an uncertain future,

Uncertain climate thresholds and optimal economic growth,

What is the value of scientific knowledge? An application to global warming using the PRICE model,

Learning and climate change

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Learning and climate change

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