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.

The policy consequences are profound.

Imagine two equally competent climate scientists constructing models using parameter values that both fall within the accepted literature. One selects assumptions implying relatively modest warming over the next century. The other adopts values toward the upper end of the plausible range. Both models represent scientifically defensible interpretations of an extraordinarily complex physical system. The uncertainty lies in the unavoidable need to specify parameters that cannot be known with precision decades into the future. Yet the resulting estimates of future damages, the social cost of carbon, and the scale of recommended regulation differ substantially. 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.

That distinction often disappears once models move from scientific inquiry into government. 

Regulatory agencies, financial supervisors, and international organizations require quantitative frameworks on which to base decisions. Conditional scenarios become planning assumptions; planning assumptions become regulatory baselines. Once embedded, these frameworks acquire institutional momentum through agency guidance, regulatory procedures, and cost-benefit methodologies. Future administrations inherit an established analytical architecture rather than a blank slate. Political leaders may alter priorities, but they rarely dismantle it.

This helps explain climate policy’s remarkable durability. Federal agencies and international organizations now routinely incorporate climate projections into planning, regulation, and financial oversight. Once embedded in administrative procedures, these frameworks persist even when political priorities change. 

An administration may narrow the regulatory treatment of carbon dioxide, revise the social cost of carbon, or limit the scope of climate-related rulemaking. But those decisions do not eliminate the analytical infrastructure supporting earlier policies. The analytical framework remains. A subsequent administration need only restore them under different policy assumptions.

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. 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.

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.

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.

APOCALYPSE NOW? CLIMATE DOOMSDAY GOES UP IN SMOKE

The real lesson is not about climate science but modern governance. Once analytical systems become embedded within the administrative state, they acquire an institutional life of their own. They survive elections and remain available for future expansion. The question is no longer whether climate models influence public policy. They plainly do. 

The models remain. So do the institutions built around them. For that reason, today’s regulatory retrenchment may prove temporary. Future administrations need not reconstruct the earlier analytical framework. They need only place greater weight on scientifically plausible assumptions that produce larger projected damages. Much of the earlier regulatory architecture can then be restored without rebuilding the machinery.

Stephen Lewarne is a professor of economics and finance at Franciscan University of Steubenville, Ohio.