This page shows how we determine an optimal mix of solutions as the target of our money distribution.
As a simple example, suppose there are five different solutions that reduce the amount of greenhouse gases in the atmosphere, and their cost effectiveness estimates are as shown in the table below. (“CO2-eq” is the amount of greenhouse gases expressed as carbon dioxide equivalents that are avoided or removed from the atmosphere.)
| Solution | CO2-eq per dollar (kg/US$) | Money distribution |
| A | 100 | ? % |
| B | 60 | ? % |
| C | 20 | ? % |
| D | 14 | ? % |
| E | 6 | ? % |
How would you distribute money to these solutions to maximize the total effect?
Top Performer Only
You might say, “That’s easy. Put all your money in the top performer only,” like this:
| Solution | CO2-eq per dollar (kg/US$) | Money distribution |
| A | 100 | 100% |
| B | 60 | 0% |
| C | 20 | 0% |
| D | 14 | 0% |
| E | 6 | 0% |
Yes, that’s correct, if the effect per dollar estimates are accurate for the present and the future. But they are not. It’s hard to get accurate estimates, and even if they were accurate for the present, unknown future improvements to the solutions would likely change the numbers. (If we knew the probability distributions of the estimates, we could find the mathematically best money distribution. But even so, we can’t accurately predict the future improvements.)
Distribute Evenly
Then, you might say, “I see. If these numbers aren’t reliable, then we should distribute the money evenly to all the solutions,” like this:
| Solution | CO2-eq per dollar (kg/US$) | Money distribution |
| A | 100 | 20% |
| B | 60 | 20% |
| C | 20 | 20% |
| D | 14 | 20% |
| E | 6 | 20% |
It seems a safe bet because it covers all possible changes. But this completely ignores the estimate numbers as if they were totally unreliable. They are not. Although the estimate numbers can’t be totally accurate, they are not totally unreliable, either.
Optimal Mix: Distribute Proportionally
Then, what would be the best distribution we could make? It would be between the two extremes above. In other words, we should take each solution’s effect per dollar estimate as indicative of how likely the solution will become the top performer over time. The more likely it is, the more money we should put.
It leads to the money distribution proportional to the effect per dollar estimates, like this:
| Solution | CO2-eq per dollar (kg/US$) | Money distribution |
| A | 100 | 50% |
| B | 60 | 30% |
| C | 20 | 10% |
| D | 14 | 7% |
| E | 6 | 3% |
We at No More CO2, Inc. use this distribution as the optimal mix. We consider more climate solutions than in the example above, and apply this proportional scheme to determine the optimal distribution of your donations.
For calculating the CO2-eq per dollar estimates, we use the CO2-eq estimates and cost estimates of the solutions from The Drawdown Review published by Project Drawdown. For your reference, “Assessing Solutions” on Page 73 of The Drawdown Review 2020 states that they “use conservative estimates of the financial cost and emissions impact for each solution. In other words, assumptions about costs fall on the high end, while assumptions about emissions reductions or sequestration rates fall on the low end.”
Let us maximize the power of your donations in fighting climate change.