This reminds of me calorie tracking: you cannot perfectly capture the number of calories or macronutrients, but measuring does seem to help people loose weight. There are probably many loop holes where eating large amounts of certain food, with a certain margin of error, can leads to wildly incorrect estimates.

I wonder how much this analogy applies to carbon tracking? Does using a wide variety of foods help make the tracking more accurate because no single bad estimate becomes overrepresented? Can a similar approach be taken via a wide variety of cloud technologies being used?

Yea, I actually saw something similar in the early days of Infracost, when we didn't track that many price points. The % change and the directionality was really helpful for engineers. Then we iterated on the prices, added more coverage etc, and the accuracy increased to a point where people trust the output of Infracost more than the AWS pricing calculator. That was a cool learning moment for me.

>This reminds of me calorie tracking: you cannot perfectly capture the number of calories or macronutrients, but measuring does seem to help people loose weight.

This probably would explain the success of many fad diets if it were the increased awareness of the eating having an effect beyond the decision making about what to eat.