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Calculating Effects of Climate Change with Macroeconomic Models

There are two types of methods for calculating the economic effects of climate change. The first can be called an “enumerative approach.”[1] In this approach, estimates of the “physical effects” of climate change are obtained from natural sciences based on some climate models, impact models, and laboratory experiments. Each physical impact is then assigned a price and added up. For agricultural products, results from models are used to predict the effect of climate on crop yield. Then, market prices are used to value the change in output as a consequence of climate change. The effect of the rise in sea level is calculated as the additional cost of coastal protection and land lost. These estimates are taken from the engineering studies. For goods and services that are not sold in the markets, such as the impact on health, different methods are needed. In this method, epidemiological results are used to calculate the aggregate impact by simply assigning some cost to additional lives lost or damage done to human bodies.

The second method can be called a “statistical approach.”[2] The economic impact of climate change is calculated based on estimates using observed variations. The observations are typically made within a given country or region over a span of time. This impact is then extrapolated to examine what would happen in other countries or regions if and when similar events take place. Thus, physical modeling of the underlying process of the economic phenomenon is completely eliminated.

Each calculation method has its own advantages and disadvantages. The enumeration method is based on actual physical phenomena. Thus, they are realistic. However, when they are extrapolated to the future on a much larger canvas, the errors get magnified. Perhaps more importantly, they do not take into account changes in human behavior. Changes in climate conditions will induce policy responses. The model does not take into account such changes. They are mechanical models.

The statistical models avoid such traps. These models are based on actual differences observed in the real world. The problem arises with these models because of biases arising from omitted variables. Specifically, if some changes in the relevant variables take place, such as income, almost always there are other variables that we have not measured that would have an impact on the income level, not just the climate variable. In addition, these models often rely on cross section data that do not exhibit large variation. On the other hand, the questions for which we want answers are spread out over decades or centuries. Hence, the changes in climate variables are much larger. Once again, if our statistical models have errors, they simply get amplified.

  • [1] This name was first suggested by W.R Cline, “The costs and benefits of greenhouse gasabatement. A guide to policy analysis” in OECD (ed.), The Economics of Climate Change (OECD,Paris 1994).
  • [2] This nomenclature is owed to Richard S.J. Tol, “The Economic Effects of Climate Change”(2009) 23 Journal of Economic Perspectives 29.
 
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