How Climate Models Underpin Climate Policy Despite Uncertainties and Why Scientific Plausibility Should Be Emphasized: Echoing Economics Professor Stephen Lewarne’s Piece in the Washington Examiner

     Stephen Leawarne, an economics professor at the Franciscan University of Steubenville, Ohio, wrote what I think is a well-explained article about climate models, how they influence climate policy, and how they are tweaked for political advantage. It was published today in the Washington Examiner.

     Leawarne first notes that it is widely acknowledged that climate policy has receded or been strongly toned down by the Trump administration. I might add that in the case of climate modeling, it too has been toned down by the recent CMIP-7 designations of the three highest emissions climate model scenarios as scientifically implausible. However, as I have noted elsewhere, those implausible models are still being cited and used, even in scientific papers. I should point out that Leawarne does not mention the CMIP-7 designations or the IPCC’s decision to abandon the three highest emissions scenarios.

     Leawarne writes:

“Over the past decade, climate models have become embedded throughout government, shaping regulatory analysis, infrastructure planning, financial supervision, and international commitments.”

     He sees them as entrenched in agency procedures, and if and when the administration changes to one more in favor of stronger climate policies, they will easily reemerge as policy supports.

     Below, he gives what I think is a very good, concise explanation of the intended functions, limitations, dependence on assumptions, and the main uncertainties that affect climate modeling. I bolded the three main uncertainties of climate models.

“Climate models are indispensable tools. They were designed not to predict the future but to explore possible futures under different assumptions. Every major climate model depends on several scientifically defensible assumptions whose precise values remain uncertain. Three are especially important: climate sensitivity (how much temperatures rise as atmospheric carbon dioxide doubles), aerosol forcing (the cooling effect of airborne particles), and cloud feedbacks (how clouds amplify or dampen warming). Small differences in these assumptions — well within ranges accepted by climate science — can produce materially different long-run projections. This is not a flaw. It is the unavoidable consequence of modeling an extraordinarily complex system.”

     Below, he continues about how those uncertainties can be exploited to show much different but still scientifically plausible conclusions that can have profound policy implications. The point is that the range of plausible conclusions is wide enough to allow promotion of quite different and widely ranging policy recommendations.

“These uncertainties create institutional opportunity. When several scientifically plausible parameter values exist, policymakers face a range of possible futures. Regulatory institutions need not depart from accepted science. They need only select among scientifically defensible assumptions. The resulting policies can still be described as science-based, even though materially different policy recommendations would have emerged from equally plausible assumptions.”

     Below, he notes that model assumptions underpin not only the models themselves, but also the policy decisions based on the models. The wide range in scientific plausibility simply allows for a wide range of policy decisions.

“Scientific uncertainty becomes institutional choice once one set of scientifically plausible assumptions is adopted for regulation. Differences in scientific assumptions become differences in public policy. The scientific question has not been settled; it has been institutionalized.”

     He goes on to explain in some detail how climate policy frameworks remain embedded regardless of which party is in power. These frameworks are utilized for planning in many government agencies. He also explains how carbon is quite amenable to being regulated.

“Governments have historically preferred tax bases that are broad, measurable, inexpensive to administer, and difficult to avoid. Carbon dioxide possesses all four characteristics. Nearly every household and business consumes energy directly or indirectly, and associated emissions can be estimated through existing fuel and energy reporting systems.”

     Next, he explains how climate models fit into this picture. He explains that carbon regulation is “institutionally attractive,” and why it became “a central organizing concept in modern environmental regulation.”

“Climate models, therefore, perform two functions. They estimate the long-run damages associated with emissions while also providing the analytical foundation for regulating — or taxing — one of the broadest potential tax bases available to modern governments. Unlike wealth taxes or financial transaction taxes, carbon emissions are closely tied to observable energy use, making administration comparatively straightforward and avoidance relatively difficult. That institutional attractiveness exists independently of the scientific debate itself. It helps explain why carbon became a central organizing concept in modern environmental regulation. Larger projected damages strengthen the apparent case for broader regulation and taxation.”

     With the loss of the three most implausible climate model scenarios, those “larger projected damages” will, or at least should, be much harder to justify and harder to make that "case for broader regulation and taxation.” Simply put, we should not be basing policy decisions on scientifically implausible assumptions.

     Below, he explains that he believes climate models have been misused, having been given “an authority they were never designed to possess.” This emphasizes that there can be a divide between science and policy that is often ignored.   

“None of this suggests abandoning climate models. They remain indispensable tools. The problem arises when exploratory models acquire an authority they were never designed to possess. Climate models simulate physical processes. They do not model political incentives, technological innovation, institutional adaptation, or changing human behavior with comparable confidence. Yet these factors often determine whether particular policies ultimately succeed.”

     In the paragraph below, he explains that basing regulatory decisions on the assumptions made by climate models without disclosing the wide range of uncertainty inherent in those assumptions is basically projecting a lack of transparency.  

“A more transparent approach would acknowledge both the power and the limits of climate modeling. Agencies should disclose how sensitive major regulatory decisions are to scientifically plausible alternative parameter choices. Policymakers should distinguish between conclusions that remain robust across many assumptions and those that depend heavily upon modeling choices. Such transparency would strengthen confidence by distinguishing genuine consensus from legitimate uncertainty.”      

     It is nice to see a well-written article about climate science and policy that is accurate, concise, detailed, non-biased, and that offers a better approach.

 

  

References:

 

Dial-a-crisis: How bureaucrats rig climate models for unlimited power. Stephen Lewarne, Washington Examiner. July 28, 2026. Dial-a-crisis: How bureaucrats rig climate models for unlimited power