2. Reviews of models
What are the strengths and limitations of the models used in climate economics? How do the assumptions used in economic models shape their policy recommendations? Are there critical assumptions and relationships that should be added to standard models?
Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE
Frank Ackerman, Elizabeth A. Stanton, and Ramón Bueno
Ecological Economics (2010) 69(9): 1657-1665.
The problem of low-probability, catastrophic risk is increasingly central to discussion of climate science and policy, but integrated assessment models (IAMs) in climate economics rarely incorporate this possibility. What modifications are needed to analyze catastrophic economic risks in an IAM? This article explores the question using DICE, a well-known IAM, examining the implications of a fat-tailed probability distribution for the climate sensitivity parameter, a focus of recent work by Martin Weitzman, and the shape of the damage function, one of the issues raised by the Stern Review.
Limitations of integrated assessment models of climate change
Frank Ackerman, Stephen J. DeCanio, Richard B. Howarth and Kristen A. Sheeran
Climatic Change (2009) 95: 297-315.
Integrated assessment models (IAMs) used by economists to analyze climate change frequently suggest that the “optimal” policy is to go slowly and to do relatively little in the near term to reduce greenhouse gas emissions. This article traces this finding to contestable assumptions of IAMs, such as discounting future climate impacts at relatively high rates. IAMs also assign monetary values to the benefits of climate mitigation on the basis of incomplete information and sometimes speculative judgments concerning the monetary worth of human lives and ecosystems, while downplaying scientific uncertainty about the extent of expected damages. In addition, IAMs may exaggerate mitigation costs by failing to reflect the socially determined, path-dependent nature of technical change.
A better approach to climate policy, drawing on recent research on the economics of uncertainty, would reframe the problem as buying insurance against catastrophic, low-probability events. Policy decisions should be based on a judgment concerning the maximum tolerable increase in temperature and/or carbon dioxide levels given the state of scientific understanding. The appropriate role for economists would then be to determine the least-cost global strategy to achieve that target. While this remains a demanding and complex problem, it is far more tractable and defensible than the cost-benefit comparisons attempted by most IAMs.
Descriptive or conceptual models? Contributions of economics to the climate policy debate
Stephen J. DeCanio
International Environmental Agreements (2005) 5: 415-427.
Economists have brought two distinct modeling styles to the debate on climate policy. Some attempt to forecast the effects of policy decisions by constructing models that purport to be ‘‘descriptive’’ of the global economic system, while others offer a ‘‘conceptual’’ focus on particular economic or environmental issues. The descriptive models typically offer numerical comparisons of policy scenarios to a baseline, while the conceptual modelers often seek to provide insight into the ethical foundations or implications of different assumptions. These different modeling styles exhibit both contrasts and areas of overlap in their policy implications.
Induced technological change: Exploring its implications for the economics of atmospheric stabilization: Synthesis report from the Innovation Modeling Comparison Project
Ottmar Edenhofer, Kai Lessmann, Claudia Kemfert, Michael Grubb and Jonathan Köhler
The Energy Journal (2006) Special Issue: Endogenous Technological Change and the Economics of Atmospheric Stabilisation.
This paper summarizes results from ten global economy-energy-environment models implementing mechanisms of endogenous technological change. Different CO2 stabilization goals are imposed, and the contribution of induced technological change to meeting the goals is assessed. Climate policy induces additional technological change, in some models substantially. Its effect is a reduction of abatement costs in all participating models. Most models calculate abatement costs below 1 percent of aggregate gross world product for the period 2000–2100. The models predict different dynamics for carbon costs, with some showing a decline in carbon costs toward the end of the century. There are four major drivers of differences in results between models. First, the extent of the necessary CO2 reduction, which depends mainly on predicted baseline emissions, determines how much a model is challenged to comply with climate policy. Second, when climate policy can offset market distortions, some models show that not costs but benefits accrue from climate policy. Third, assumptions about long-term investment behavior, e.g. foresight of actors and number of available investment options, exert a major influence. Finally, whether and how options for carbon-free energy are implemented (backstop and end-of-the-pipe technologies) strongly affects both the mitigation strategy and the abatement costs.
Integrating economic analysis and the science of climate instability
Darwin C. Hall and Richard J. Behl
Ecological Economics (2006) 57: 442-465.
Scientific understanding of climate change and climate instability has undergone a
revolution in the past decade with the discovery of numerous past climate transitions so
rapid, and so unlike the expectation of smooth climate changes, that they would have
previously been unbelievable to the scientific community. Models commonly used by
economists to assess the wisdom of adapting to human-induced climate change, rather
than averting it, lack the ability to incorporate this new scientific knowledge. Here, we
identify and explain the nature of recent scientific advances, and describe the key ways in
which failure to reflect new knowledge in economic analysis skews the results of that
analysis. This includes the understanding that economic optimization models reliant on
convexity are inherently unable to determine an optimal policy solution. It incumbent
on economists to understand and to incorporate the new science in their models, and on
climatologists. and other scientists to understand the basis of economic models so that they can assist in this essential effort.
Technological learning in energy-environment-economy modelling: A survey
Sondes Kahouli-Brahmi
Energy Policy (2008) 36(1): 138-162.
This paper aims at providing an overview and a critical analysis of the technological learning concept and its incorporation in energy-environment-economy models. A special emphasis is put on surveying and discussing, through the so-called learning curve, both studies estimating learning rates in the energy field and studies incorporating endogenous technological learning in bottom-up and top-down models. The survey of learning rate estimations gives special attention to interpreting and explaining the sources of variability of estimated rates, which is shown to be mainly inherent in R&D expenditures, the problem of omitted variable bias, the endogeneity relationship and the role of spillovers. Large-scale models survey show that, despite some methodological and computational complexity related to the non-linearity and the non-convexity associated with the learning curve incorporation, results of the numerous modelling experiments give several new insights with regard to the analysis of the prospects of specific technological options and their cost decrease potential (bottom-up models), and with regard to the analysis of strategic considerations, especially inherent in the innovation and energy diffusion process, in particular the energy sector's endogenous responses to environment policy instruments (top-down models).
Room for improvement: increasing the value of energy modeling for policy analysis
J.A. Laitner, S.J. DeCanio, J.G. Koomey and A.H. Sanstad
Utilities Policy (2003) 11: 87-94.
There are expanding national discussions on energy-related issues ranging from the importance of reducing air pollution and greenhouse gas emissions to enhancing the nation’s energy security and moving toward a competitive electric utility industry. These issues have motivated the development of many energy-economic models to assist policy makers in framing appropriate policy directions. But how much do these models really inform the debate? The record of US model-based energy forecasting shows that such models provide biased estimates that tend to reinforce the status quo, inadequately inform policy-makers about new market potential, and serve to constrain the development of innovative policies. This paper reviews some of the reasons for this conclusion and explores the extent to which energy-economic models may reflect a more dynamic technological diffusion process that encourages new policy development.
Inside the integrated assessment models: Four issues in climate economics
Elizabeth A. Stanton, Frank Ackerman and Sivan Kartha
Climate and Development (2009) (accepted for publication).
Good climate policy requires the best possible understanding of how climatic change will impact on human lives and livelihoods in both industrialized and developing counties. Our review of the recent climate-economics literature assesses 30 existing integrated assessment models in terms of four key aspects of the nexus of climate and the economy: the connection between the model structure and the type of results produced; uncertainty in climate outcomes and the projection of future damages; equity across time and space; and abatement costs and the endogeneity of technological change. Differences in treatment of these issues are substantial, and directly affect model results and their implied policy prescriptions. Much can be learned about climate economics and modeling technique from the best practices in these areas; there is unfortunately no existing model that incorporates the best practices on all or most of the questions we examine.