Climate models have become the backbone of modern climate policy—embedded in regulations, ESG mandates, and fiscal planning. Because CO₂ is universal and measurable, it makes the perfect base for taxation: link emissions to catastrophe through a model, and you create a perpetual revenue stream from daily life.
Every modern model rests on three adjustable assumptions: climate sensitivity, aerosol offsets, and cloud feedbacks. These are not fixed laws of physics but inputs—judgment calls embedded in code. Push any one of them toward pessimism, and you produce the catastrophic projections that justify regulation and taxation.
Major models—NASA’s GISS ModelE, the UK Met Office’s HadGEM3, NOAA’s GFDL CM4, and NCAR’s CESM2—share the same bias levers: high sensitivity, large aerosol cooling, and positive cloud feedbacks. European systems like IPSL-CM6 and MPI-ESM are similar. Together they shape CMIP6, the ensemble behind the IPCC scenarios that drive policy. Minor internal revisions—like GISS ModelE2.1-G’s moderated 3.0–3.5 °C sensitivity or CAM6.3’s reduced cloud feedback—barely dent a landscape still dominated by upper-end assumptions.
Climate sensitivity. Most policy-facing models still assume 3.5–5.5 °C warming for a CO₂ doubling, even though empirical observation-based estimates cluster closer to 1.8–2.5 °C—a gap that separates realistic adaptation from doomsday planning. Yet governments price carbon and set targets using the higher range.
Aerosols. Because mid-20th-century warming lagged behind CO₂, modelers introduced strong sulfate-aerosol cooling to make the data fit. Newer inventories show smaller cooling—especially as Asia cleans up sulfur emissions—meaning past models overstated sensitivity. CMIP6 now places mean aerosol effective radiative forcing near –1.1 W/m², while new studies show that declining aerosols have slightly increased net warming—evidence that the mid-century “fix” now inflates forecasts.
Clouds. The greatest uncertainty lies here. Most models assume positive cloud feedback, but satellite evidence is mixed. CAM6.3’s lower feedback relative to CAM6.0 shows how small parameter tweaks can swing results, yet ensemble averages treat these choices as constants, creating a false sense of certainty.
These technical choices shape real policy. In 2025, the Trump administration moved to rescind the EPA’s 2009 endangerment finding for greenhouse gases—the legal anchor for federal climate rules—and to withdraw GHG reporting and emissions standards. These steps, partly justified by skepticism of model-based risk, reopened the debate over model uncertainty. The Department of Energy’s Critical Review argued that climate damages were overstated and mitigation less cost-effective than claimed; the National Academies denounced it as biased, but the episode exposed the black-box nature of the models driving fiscal policy.
Canada went the opposite direction. Prime Minister Mark Carney’s 2025 reforms abolished the consumer carbon tax while retaining performance-based industrial pricing (OBPS) and launching the One Canadian Economy Act to streamline permitting. Gasoline prices fell and the rebate system was repealed. Canada now favors industrial carbon policy over retail taxation—still model-informed, but no longer a household levy.
Europe remains most committed to high-sensitivity modeling. The EU Green Deal, ETS, and sustainable-finance taxonomy rely on IPCC ensemble means dominated by upper-bound models. The ECB and national supervisors use these projections in bank stress tests and sovereign-risk assessments, effectively re-pricing credit and capital based on speculative feedbacks. Prudence is defensible—but treating model outputs as certainties risks misallocating trillions.
Developing nations are pushing back. At the 2025 Africa Climate Summit, Kenya’s President William Ruto accused Western governments of breaking a “climate blood pact” by imposing conditional climate rules without promised investment. The Addis Ababa Declaration called for financing tools favoring development and grant-based aid over debt-heavy, conditional loans. Across Asia and at the AIIB, borrowers warned that rigid “climate-compatibility” tests stifle sovereignty and growth. Even the AIIB now stresses transparency and context sensitivity—an implicit admission that one-size climate policy is failing.
The remedy is transparency and range disclosure:
- The U.S. Office of Management and Budget should require agencies to publish their sensitivity, aerosol, and cloud-feedback parameters, plus low-assumption runs showing how outputs change.
- EPA’s Office of Air and Radiation should convene independent panels to vet those parameters before embedding them in regulatory analyses.
- DOE should require federally funded models to release parameter files and open-source their code.
- Canada should apply the same disclosure rules to industrial pricing and project-screening models.
- European regulators should show how stress-test outcomes shift under lower-sensitivity and reduced-feedback runs—not just worst-case ensembles.
This is not climate denial. It is a demand that uncertainty be acknowledged and that fiscal consequences be transparent. Climate models no longer just forecast—they set prices, shape taxes, and direct global capital. When pessimistic assumptions are treated as settled fact, CO₂ becomes the perfect tax base: universal, measurable, and politically unresisting.
The solution is not to abolish models but to open them—publish assumptions, reveal parameter ranges, and treat their results as hypotheses, not truths. Only transparency can restore science to climate modeling and strip politics from the code that governs it.