So much “painting by numbers” is done with numerical models. And the government is probably the largest consumer of such models. All models require assumptions, and as Commandment 2 in “Painting By Numbers” counsels, you must identify these assumptions to understand the results.

The need for assumptions gives policy-makers wide latitude to drive towards answers which support their policies. For example, the EPA under the Obama administration calculated the “social cost of carbon” as a value around $50/ton of carbon emitted. The EPA under the Trump administration managed to tweak the model so that the social cost of carbon (SCC) was more like $7/ton.

I wrote about this a while back in this space. Apparently, one thing you can do is select a different value for the internal rate of return (a financial parameter) in the model, according to a few references I read at the time.

Now here’s some fun: A paper I found surfing the web entitled “The Social Cost of Carbon Made Simple” shows one methodology for calculating it. By the way, this has got to be the most wrongly titled paper of 2010, the year it was published. There is nothing simple about it! Go on – click on it and read the first few pages. I dare you.

https://www.epa.gov/sites/production/files/2014-12/documents/the_social_cost_of_carbon_made_simple.pdf

But the paper does acknowledge that a “…meta-analysis…found that the distribution of published SCC estimates spans several orders of magnitude and is heavily right-skewed: for the full sample, the median was $12, the mean was $43, and the 95th percentile was $150…” Moreover, the spread was as low as $1/ton.

See what I mean? If you want to de-emphasize carbon in your economic policies, you pick a methodology that minimizes SCC. If you want to build your policies around climate change, you pick a method that maximizes it. To the credit of the Obama administration, they settled on something close to the mean.

The paper is provisional work and nine years old, so don’t take it for any kind of gospel. I use it simply to illustrate points that require of the paper neither absolute accuracy or timeliness.

In an article (New York Times, March 27, 2020)  titled “Trump’s Environmental Rollbacks Find Opposition From Within: Staff Scientists,” I read this: “In 2018, when the Environmental Protection Agency proposed reversing an Obama-era rule to limit climate-warming coal pollution, civil servants included analysis showing that by allowing more emissions, the new version of the rule would contribute to 1,400 premature deaths a year.”

I’m not going to dig deep and determine how they arrived at the number 1400, and anyway, the key to the sentence isn’t the number, it’s the word “contribute.” How many other factors “contribute to those premature deaths?

The article argues that Trump administration officials are not even trying to “tweak” the models, but instead have come in with a “repeal and replace” attitude “without relying on data, and science and facts.” It was reported that Obama’s head of the EPA, before she departed, had encouraged staffers to remain and make sure that EPA’s analyses have the “truth” put in there. 

Unfortunately, numerical models don’t cough up the truth, just someone’s version of it. Those who don’t take the time understand all of this become victims reduced to parroting others’ versions of the truth. On the other hand, not even being willing to consider data and science and facts is completely wrong-headed. That is ignorance, as any model of human behavior will tell you.