{"id":366674,"date":"2025-02-18T08:52:55","date_gmt":"2025-02-18T07:52:55","guid":{"rendered":"https:\/\/climatescience.press\/?p=366674"},"modified":"2025-02-18T08:52:56","modified_gmt":"2025-02-18T07:52:56","slug":"the-social-cost-of-carbon-a-bureaucratic-boondoggle-exposed","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=366674","title":{"rendered":"The Social Cost of Carbon: A Bureaucratic Boondoggle Exposed"},"content":{"rendered":"\n
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From Watts Up With That?<\/a><\/p>\n\n\n\n

By Charles Rotter<\/a><\/p>\n\n\n

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https:\/\/www.nature.com\/articles\/s41598-025-90254-2<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n

The Biden administration put in a lot hard at work inflating the Social Cost of Carbon (SCC), using exaggerated agricultural damage estimates to justify sweeping climate policies. But in a new study, economist Ross McKitrick\u2014one of the sharpest minds in climate policy skepticism\u2014has taken apart the foundations of these inflated numbers, revealing what happens when climate alarmism meets real data.<\/p>\n\n\n\n

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The Biden Administration raised its Social Cost of Carbon (SCC) estimate about fivefold based in part on global crop yield decline projections estimated on a meta-analysis data base first published in 2014. The data set contains 1722 records but half were missing at least one variable (usually the change in CO2<\/sub>) so only 862 were available for multivariate regression modeling. By re-examining the underlying sources I was able to recover 360 records and increase the sample size to 1222. Reanalysis on the larger data set yields very different results. While the original smaller data set implies yield declines of all crop types even at low levels of warming, on the full data set global average yield changes are zero or positive even out to 5 \u00b0C warming.

https:\/\/www.nature.com\/articles\/s41598-025-90254-2<\/a><\/p>\n<\/blockquote>\n\n\n\n

Of course, the entire concept of the Social Cost of Carbon is a bureaucratic fantasy\u2014a number conjured up by modelers who can tweak discount rates, speculative damage functions, and climate projections until they get the politically convenient answer. It\u2019s an arbitrary metric, designed less to quantify real-world economic impacts and more to justify sweeping government interventions. That said, even within<\/em> this rigged framework, McKitrick\u2019s study exposes how the game is played: if you follow their own rules but use the full dataset, the SCC comes out far lower than the Biden administration\u2019s sky-high estimate. In other words, if SCC were a legitimate measure (it isn\u2019t), this paper shows they\u2019re still doing it wrong.<\/p>\n\n\n\n

McKitrick\u2019s study, Extended Crop Yield Meta-Analysis Data Do Not Support Upward SCC Revision<\/a><\/em>, exposes how bureaucrats have distorted agricultural yield projections to push for higher SCC estimates. His findings? The supposed climate-related crop damage is wildly overstated. In reality, global crop yields appear to remain stable\u2014or even increase\u2014under warming scenarios up to 5\u00b0C. This fundamentally undermines one of the key justifications for the massive SCC hikes driving Biden\u2019s climate policies.<\/p>\n\n\n\n

Let\u2019s break this down.<\/p>\n\n\n\n

How the SCC Got a Makeover<\/strong><\/h3>\n\n\n\n

The SCC is supposed to measure the economic damage per ton of carbon dioxide emissions. Under the Biden administration, the Environmental Protection Agency (EPA) boosted this estimate to an eye-popping $220 per ton\u2014a nearly fivefold increase from the first Trump administration\u2019s EPA\u2019s previous $51 per ton. A big chunk of this increase comes from projected agricultural losses, derived from the GIVE<\/em> damage model. Nearly half of the SCC estimate ($103 per ton) is based on assumed losses in global food production due to warming.<\/p>\n\n\n\n

But as McKitrick demonstrates, these assumptions are built on a foundation of bad data and flawed modeling.<\/p>\n\n\n\n

The Data Problem: When Missing Numbers Become Policy Drivers<\/strong><\/h3>\n\n\n\n

The SCC estimates for agriculture rely on two studies:<\/p>\n\n\n\n