Foreword

New Directions in the Economics and Integrated Assessment of Global Climate Change

Eileen Claussen, President, Pew Center on Global Climate Change

This report elaborates on four issues - technological innovation, the behavior of firms, intergenerational equity, and climate "surprises" - that have profound implications for the modelers and makers of climate policy. Computer models that integrate climate science, policy, and economic research have become essential to climate change policy discussions. These "integrated assessment" (IA) models are extremely useful for several reasons: they assess specific climate change policies, coordinate the multiple issues in a systematic framework, and provide an analytical method for comparing climate policies to other, non-climate related policies. However, most IA is based largely on economic theories whose simplifications are not always applicable to climate change policy. This paper examines four kinds of assumptions that underlie most IA models, and shows how different approaches more in line with the latest research might change our view of the economics of the climate problem.

The first paper, by Alan Sanstad, focuses on technological innovation and its treatment in IA models. Most models do not incorporate a realistic assessment of how market forces drive innovation. While innovation would clearly lower the costs of addressing climate change, many modelers focus on the opportunity cost of encouraging technological progress on climate-friendly technology. The fear is that climate-related R&D will "crowd out" other kinds of R&D. Sanstad's work examines this question, taking into account that the economy systematically underinvests in R&D, and shows that policies promoting climate-related R&D may simultaneously encourage, not discourage, R&D in other sectors.

The second paper by Stephen DeCanio discusses how IA models characterize the behavior of firms by assuming they do no more than maximize profits, and that they always succeed perfectly in doing so. This often leads to misunderstandings about: (1) how firms innovate, and (2) the trade-offs firms must make between environmental and economic performance. DeCanio's model describes firms as information networks with multiple objectives, which leads to a more complete picture of how firms innovate. The model also shows that both superior economic and environmental performance can be achieved through technological and organizational innovation.

The third paper by Richard Howarth addresses how future generations are depicted in most IA models. Models typically use a single, simple discount rate to make intertemporal comparisons for anywhere from 50 years to sometimes 300 years into the future. But over very long periods of time, these comparisons involve different generations of people. Howarth accounts for these differences using the so-called "overlapping generations" models - a model that incorporates the detail of IA models while providing a more realistic assessment of each generation's spending and savings behavior. This work indicates that policies inclined towards climate stabilization provide an "insurance" policy that protects future generations against potentially catastrophic costs. Even if damage costs turn out to be moderate, Howarth finds, emissions control is still consistent with maintaining long-term economic well-being.

Stephen Schneider and Starley Thompson, in the final paper, provide a new model to explore the causes and consequences of one major type of "climate surprise" - the collapse of the "conveyor belt" circulation of the North Atlantic Ocean. Climate "surprises" are the low-probability but high-consequence scenarios driving much of the international concern about climate change. Currently, most IA models assume the climate responds slowly and predictably. The authors find IA models that ignore the implications of rapid, non-linear climatic changes or surprises are likely to overestimate the capacity of humans to adapt to climatic change and underestimate the optimal control rate for GHG emissions. The conclusion is that it is critical that the full range of plausible climatic states become part of IA policy analysis.

This report is the latest in the Pew Center's economics series. As with the rest of the series, these reports will help to demystify the models and explain what type of questions they can (and cannot) answer. But whereas until now we have focused on what has been done in the past, we now begin to focus on what needs to be done in the future. This report includes four critiques of the assumptions underlying IA, and suggests ways in which new and improved models could provide greater insights into what policies would be most efficient and effective in reducing greenhouse gas emissions:

• IA models that more accurately portray innovation will help policy-makers answer questions such as the following: Should the government subsidize climate-friendly R&D? Will increasing carbon prices alone drive sufficient innovation to solve the GHG problem? How should we time and phase emission reductions to take maximal advantage of technological progress?
  
• IA models that more realistically portray businesses will make it clear that the challenge for policy-makers is to find ways to encourage businesses to innovate in multiple dimensions to meet multiple objectives.
  
• IA models that take into account the standpoint of future generations will enable policy-makers to explicitly consider the implications of policy for equity as well as efficiency.
  
• IA models that can explore the causes and consequences of "climate surprises" will help policy-makers to understand the implications of speeding up or slowing down the rate of greenhouse gas build-up, which may turn out to be as important as the size of the build-up.
  

Earlier versions of the papers in this report were first presented during the Pew Center's July 1999 economics workshop, which convened leading experts to discuss potential improvements to current IA modeling methods. The insights of participants in that workshop were invaluable.

This report benefited greatly from the comments and input from several individuals. The Pew Center and authors would like to thank Kenneth Arrow, Larry Goulder, Robert Lind, Klaus Hasselman, and Bruce Haddad. Special thanks are also due to Ev Ehrlich and Judi Greenwald for serving as consultants on this project.