Regarding CCEI’s effect of shifting deploy$ to RD&D$:
Yes, in the Guesstimate model the confidence intervals went from 0.1% to 1% lognormally distributed, with a mean of ~0.4%
With UseCarlo I used a metalog distribution with parameters 0%, 0.1%, 2%, 10%, resulting in a mean of ~5%
So you’re right, there is indeed about an order of magnitude difference between the two estimates:
This is mostly driven by my assigning some credence to the possibility that CCEI might have had as much as a 10% influence, which I wouldn’t rule out entirely.
However, the confidence intervals of the two estimates are overlapping.
I agree this is the weakest part of the analysis. As I highlighted, it’s a guesstimate motivated by the qualitative analysis that CCEI is part of the coalition of key movers and shakers that shifted budget increases to energy RD&D.
I think both estimates are roughly valid given the information available. Without further analysis, I don’t have enough precision to zero in on the most likely value.
I lost access to UseCarlo during the writeup and the after the analysis was delayed for quite some time (I had initially pitched it to FTX as an Impact NFT).
I just wanted to get the post out rather than delay further. With more resources, one could certainly dig deeper and make the analysis more rigorous and detailed. But I hope it provides a useful starting point for discussion and further research.
One could further nuance this analysis e.g. by calculating marginal effect of our $1M on US climate policy philanthropy at the current ~$55M level vs. what it’s now.
Thanks also for the astute observation about estimating expected cost-effectiveness in t/$ vs $/t. You raise excellent points and I agree it would be more elegant to estimate it as t/$ for the reasons you outlined.
I really appreciate you taking the time to engage substantively with the post.
Great comment—thanks so much!
Regarding CCEI’s effect of shifting deploy$ to RD&D$:
Yes, in the Guesstimate model the confidence intervals went from 0.1% to 1% lognormally distributed, with a mean of ~0.4%
With UseCarlo I used a metalog distribution with parameters 0%, 0.1%, 2%, 10%, resulting in a mean of ~5%
So you’re right, there is indeed about an order of magnitude difference between the two estimates:
This is mostly driven by my assigning some credence to the possibility that CCEI might have had as much as a 10% influence, which I wouldn’t rule out entirely.
However, the confidence intervals of the two estimates are overlapping.
I agree this is the weakest part of the analysis. As I highlighted, it’s a guesstimate motivated by the qualitative analysis that CCEI is part of the coalition of key movers and shakers that shifted budget increases to energy RD&D.
I think both estimates are roughly valid given the information available. Without further analysis, I don’t have enough precision to zero in on the most likely value.
I lost access to UseCarlo during the writeup and the after the analysis was delayed for quite some time (I had initially pitched it to FTX as an Impact NFT).
I just wanted to get the post out rather than delay further. With more resources, one could certainly dig deeper and make the analysis more rigorous and detailed. But I hope it provides a useful starting point for discussion and further research.
One could further nuance this analysis e.g. by calculating marginal effect of our $1M on US climate policy philanthropy at the current ~$55M level vs. what it’s now.
Thanks also for the astute observation about estimating expected cost-effectiveness in t/$ vs $/t. You raise excellent points and I agree it would be more elegant to estimate it as t/$ for the reasons you outlined.
I really appreciate you taking the time to engage substantively with the post.