What are the potential costs of cutting greenhouse gas emissions? Can such reductions be achieved without sacrificing economic growth or the standard of living we have come to enjoy? These are important questions, and they come up again and again as the United States and other nations consider what actions are needed in response to climate change.
Many participants in the climate change debate — in government, industry, academia, and non-governmental organizations — have conducted economic assessments to determine the costs of taking various actions to address climate change, with the number of economic assessments increasing exponentially in recent years. Differences among their quality and predicted cost of action, or inaction, have also grown, making it difficult to have faith in any one analysis.
The primary example of varying model results can be seen among the numerous reports predicting the domestic costs of complying with the Kyoto Protocol. Some have concluded the United States can reduce its emissions significantly below its Kyoto target (7 percent below 1990 levels), with net economic savings. Others have predicted dire effects on the U.S. economy. The truth most likely lies somewhere in-between.
Behind each analysis is an economic model with its own set of assumptions, its own definitions of how the economy works, and its own data sets. Unfortunately, these models often seem to be impenetrable “black boxes” allowing only a select few to decipher and interpret their results.
Fortunately, along with the rise in economic modeling there has also been a focus on identifying the differences among models. Professor John Weyant of Stanford University, the author of this report, has been at the forefront of these efforts as Director of the Energy Modeling Forum of Stanford University (EMF). His EMF working group convenes the world’s leading energy and climate modelers to discuss and model current energy policy topics.
In this report, Professor Weyant identifies the five determinants that together explain the majority of differences in modeling cost estimates. This is great news for those engaged in the climate change policy arena who are consumers of economic modeling results. Five key questions can be raised to help policy-makers understand the projected costs of climate change policy: What level of greenhouse gas emissions are projected under current policies? What climate policies are assumed to be put in place to achieve emissions reductions? What assumptions are made about how advances in technology might affect these emissions? To what extent are environmental impacts of climate change included? And is the full set of choices that firms and consumers have when presented with rising energy prices accounted for?
This paper would not have been possible without the assistance of numerous individuals. The author and the Pew Center would like to thank Ev Ehrlich, Judi Greenwald, Larry Goulder, Henry Jacoby, Rich Richels, Dick Goettle, Bill Nordhaus, and Bob Shackelton for their thoughtful comments on previous drafts of this paper.
We acknowledge the use of material from a background paper prepared by Robert Repetto, Duncan Austin and Gwen Parker at World Resources Institute.
This paper is an introduction to the economics of climate change policy. The goal is to help the reader understand how analysts use computer models to make projections of mitigation costs and climate change impacts, and why projections made by different groups differ. In order to accomplish this goal, the paper will describe five key determinants of greenhouse gas (GHG) mitigation cost estimates.
The paper starts with a discussion of how the economy would adjust to restrictions on GHG emissions, especially carbon dioxide, the dominant, and easiest to measure GHG produced in the United States. Combustion of fossil fuels — oil, gas, and coal — produces large amounts of carbon dioxide. Central to this discussion is the role of energy price increases in providing the incentives for corporations and individuals to reduce their consumption of these fuels.
Energy price increases cause producers to substitute among the inputs they use to make goods and services, and consumers to substitute among the products they buy. Simultaneously, these price increases provide incentives for the development of new technologies that consume less energy in providing the goods and services that people desire. How a model represents these substitution and innovation responses of the economy are important determinants of the economic impacts of restrictions on GHGs.
Three other factors are crucial to economic impact projections.
First, the projected level of baseline GHG emissions (i.e., without any control policies) determines the amount of emissions that must be reduced in order to achieve a particular emissions target. Thus, other things being equal, the higher the level of base case emissions, the greater the economic impacts of achieving a specific emissions target. The level of base case emissions depends, in turn, on how population, economic output, the availability of energy fuels, and technologies are expected to evolve over time without climate change policies.
The second factor is the policy regime considered, i.e., the rules that govern the possible adjustments that the economy might make. International or domestic trading of GHG emissions rights, inter-gas trading among all GHGs, inclusion of tree planting and carbon sequestration as mitigation options, and complementary economic policies (e.g., using carbon tax revenues to reduce the most distortionary taxes in the economy) are all elements of the policy regime. Other things being equal, the more flexibility provided in the policy regime under consideration, the smaller the economic impacts of achieving a particular emissions target.
The third factor is whether the benefits of reducing GHG emissions are explicitly considered. An analyst may subtract such benefits from the mitigation cost projection to get a “net” cost figure or produce a “gross” cost figure that policy-makers can weigh against a benefit estimate. The kind of cost figure produced often depends on whether the analyst is trying to do a cost-benefit analysis or an analysis focused on minimizing the cost of reaching a particular emissions target.
Thus, this paper will describe the major input assumptions and model features to look for in interpreting and comparing the available model-based projections of the costs and benefits of GHG reductions. Two of the five key determinants — (1) substitution, and (2) innovation — are structural features of the economic models used to make emissions projections. The other three determinants are external factors, or assumptions. They are: (3) the base case projections, (4) the policy regime considered, and (5) the extent to which emissions reduction benefits are considered.
The results summarized in this paper illustrate the importance of these five determinants and the large role played by the external factors or assumptions. Cost projections for a given set of assumptions can vary by a factor of two or four across models because of differences in the models’ representation of substitution and innovation processes. However, for an individual model, differences in assumptions about the baseline, policy regime, and emissions reduction benefits can easily lead to a factor of ten or more difference in the cost estimates. Together these five determinants explain the majority of differences in economic modeling results of climate policy.