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A new study in the scientific journal Review of Environmental Economics and Policy examines myriad reasons climate models should not be used to make public policies. The author, Robert S. Pindyck, of the Massachusetts Institute of Technology, writes integrated assessment models (IAMs)—models that consider the interaction of physics, demographics, and political and economic actions and responses that affect greenhouse gas emission scenarios in addition to the physical climate system—“have crucial flaws that make them close to useless as tools for policy analysis. IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory and can fool policymakers into thinking that the forecasts the models generate have some kind of scientific legitimacy.”
IAM’s critical weaknesses include:
- Critical inputs and foundational values are essentially arbitrary, assigned solely at the discretion of the modeler, yet these inputs or assumptions have huge effects on model projections. Among these arbitrary, but critical, inputs is the discount rate used to produce social costs of carbon projections.
- We know very little about climate sensitivity. Projections of future temperatures and climate projections are based on assumptions concerning the strength and response of various feedback mechanisms, assumptions that have not been proven or verified. Recent research indicates uncertainty about climate sensitivity has increased over the past decade, writes Pindyck.
- IAMs cannot account for “‘tail risk,’ i.e., the likelihood or possible impact of a catastrophic climate outcome, such as a temperature increase above 5 degrees Celsius, that has a very large impact on GDP. And yet it is the possibility of a climate catastrophe that is (or should be) the main driving force behind a stringent abatement policy,” Pindyck writes.
- Those who use IAMs to push policy prescriptions hide or are not forthcoming about these flaws or weaknesses, misleading policymakers and the general public into believing projections produced by IAMs have a scientifically verified basis and thus can be relied upon to predict climate harms and costs and the results of various policy responses to climate change.
These are just a few of the critical flaws and weaknesses inherent to IAMs that Pindyck discusses. Ultimately, Pindyck writes, “Models sometimes convey the impression that we know much more than we really do. They create a veneer of scientific legitimacy that can be used to bolster the argument for a particular policy” when, in fact, confidence in IAM projections is not warranted.