Summary

• This analysis estimates the cost-effectiveness of reducing existential risk via the Climate Change Fund (CCF) of Founders Pledge (FP) (see Methodology).
• The results were obtained with this Colab, and the key ones are summarised in the table below^[1] (for more, see Results and Discussion). Comments about how to interpret them are welcome.
• The cost-effectiveness bar for reducing existential risk (8 kt/$) is estimated to be 3 times as high as (an overestimate for?) the cost-effectiveness of CCF (2 kt/$) (see Discussion). This suggests the best interventions to fight climate change are not amongst the most effective ways of reducing existential risk.

Acknowledgements

Thanks to Alexey Turchin, Anonymous Person, David Denkenberger, Johannes Ackva, Luke Kemp, and Nuño Sempere.

Introduction

Removing greenhouse gas (GHG) emissions decreases heat-related deaths: Bressler 2021 estimated the 2020 mortality cost of carbon to be 2.26*10^-4 life/t. However, most of the benefits of reducing GHG emissions respect the decrease in existential risk due to climate change. 

This analysis estimates the existential risk reduction caused by removing GHG emissions, and the cost-effectiveness of CCF, which is compared with an estimate for the cost-effectiveness bar for reducing existential risk. Nonetheless, it should be noted that, according to this comment from Johannes Ackva^[2]:

The goal of the Climate [Change] Fund is to optimize spending that is not cause-neutral, to have as big as possible a positive impact given constraints, it is not intended or marketed as "the top bet on reducing existential risk" and we are careful to not crowd in resources that would otherwise go to areas we think of as higher marginal priority.

I encourage the readers to check this comment from Matt Lerner, FP's research director, to better understand FP's mission:

To be absolutely clear, FP's goal is to do the maximum possible amount of good, and to do so in a cause-neutral way.

Methodology

The (marginal) cost-effectiveness of CCF was estimated from the product between:

• The existential risk reduction caused by removing GHG emissions, which was estimated from the ratio between:
  • The existential risk due to climate change.
  • The cumulative GHG emissions between 2020 and 2100 assuming current climate policies^[3].
• The cost-effectiveness of removing GHG emissions via CCF, which was estimated from:
  • The reciprocal of the cost to remove GHG emissions via CCF.

The assumption of current climate policies regarding the cumulative GHG emissions is consistent with the current Metaculus' cummunity prediction of 2.6 ºC for how much greater (in ˚C) will the average global temperature in 2100 be than the average global temperature in 1880. According to the Climate Action Tracker (CAT), current climate policies are predicted to result in a global warming between 2.5 ºC and 2.9 ºC (interval which contains 2.6 ºC). The modelling of the cumulative GHG emissions would ideally consider more scenarios. The exclusion of the emissions after 2100 until net zero, after which the existential risk due to climate change is arguably negligible, tends to overestimate the cost-effectiveness.

Existential risk due to climate change

The existential risk due to climate change was modelled as a beta distribution with:

• Mean equal to 0.01 %, which was determined from the geometric mean between:
  • The 0.1 % guessed by Toby Ord in The Precipice for the next 100 years (2021 to 2120).
  • The upper bound of 0.01 % guessed by 80,000 Hours here^[4].
  • The 0.001 % respecting the John Halstead's best guess presented here^[5].
• Ratio between the mode and mean equal to that of the total existential risk considered in Denkenberger 2022, which is defined as a beta distribution with parameters alpha and beta of 1.5 and 8 (see section 2.3).

This led to parameters alpha and beta having values of 1.73 and 17.3 k^[6]. 

The distribution defined here should not be considered resilient. As discussed in Kemp 2022, bad-to-worst-case scenarios of climate change are underexplored.

Cumulative GHG emissions between 2020 and 2100

The cumulative GHG emissions between 2020 and 2100 assuming current climate policies were modelled as a lognormal distribution with 25th and 75th percentiles equal to 3.333 Tt and 4.136 Tt, which are the lower and upper estimates of CAT. This led to values in Tt for the mean and standard deviation of 3.73 and 0.595.

Cost to remove GHG emissions via CCF

The cost to remove GHG emissions via CCF was modelled as a lognormal distribution with 2.5th and 97.5th percentiles equal to the lower and upper bound of the 95 % confidence interval provided by Johannes Ackva (via personal communation): in $/t, 10^-4 to 10. These should be intended as informed guesses, not resilient estimates. FP is working to produce a more robust cost-effectiveness distribution.

The guesses were assumed to account for the value of removing emissions as a function of global warming. For example, assuming existential risk due to climate change increases quadratically with global warming, and that this increases linearly with cumulative emissions, removing 1 t at 4 ºC of global warming would be twice as valuable as removing 1 t at 2 ºC of global warming.

Results

The results are presented below for a Monte Carlo simulation with 10 M samples. I encourage the readers to make a copy of the Colab model, and select their preferred parameters. The model is fully commented (the inputs section is at the top), and could be run in 6 s for 10 M samples.

Discussion

The mean cost-effectiveness of removing GHG emissions via CCF of 2.34 kt/$ appears to be an overestimate:

• According to Table 2 of Gillingham 2018, it is^[7]:
  • 7 k times as high as the cost-effectiveness of 0.3 t/$ for "reforestation".
  • 50 k times as high as the cost-effectiveness of 0.04 t/$ for "wind energy subsidies".
  • 200 k times as high as the cost-effectiveness of 0.01 t/$ for "concentrating solar power expansion (China & India)", "renewable fuel subsidies", and "livestock management policies".
  • 1 M times as high as the cost-effectiveness of 0.002 t/$ for "solar photovoltaics subsidies".
• It is 2 k times as high as 1 t/$, which is arguably the bar FP considers.
• It is 70 times as high as the "optimistic" cost-effectiveness of 31.4 t/$ estimated by FP here for the future projects of Clean Air Task Force^[8] (CATF). However, Johannes thinks the 2018 cost-effectiveness analysis which produced this estimate "radically underestimated the real uncertainty in both directions".

However, the estimated mean cost-effectiveness of CCF is still smaller than most of the cost-effectiveness bars for reducing existential risk. These are summarised in the table below, and were taken from the answers to this question from Linchuan Zhang^[9], or provided via personal message^[10].

The mean cost-effectiveness of CCF of 0.6 bp/G$ only exceeds OP's conservative (lower) bar. The median cost-effectiveness bar of 2.17 bp/G$ is equivalent to 7.94 kt/$ (= 2.17/0.273), which is 3 times (= 2.17/0.640) as high as the estimated cost-effectiveness of CCF, and 8 k times as high as 1 t/$. 

Assuming interventions funded by CCF are amongst the best opportunities to remove GHG emissions, the above suggests interventions to fight climate change are not amongst the most effective ways of reducing existential risk.

In addition, it should be noted there seem to be opportunities whose cost-effectiveness is above the bar of 2 bp/G$. Denkenberger 2021 and Denkerberger 2022 estimate the following 5th and 95th percentiles^[14] (in bp/G$):

• Denkenberger 2021 (see Table 2):
  • "Far future potential increase per $ due to loss of industry preparation average over ~ $30 million model 1": 4 and 30 k.
  • "Far future potential increase per $ due to loss of industry preparation average over ~ $50 million model 2": 1 and 80.
  • "Far future potential increase per $ AGI safety research at the $3 billion margin (same for both models)": 0.08 and 50.
• Denkerberger 2022 (see Table 3):
  • "Far future potential increase per $ due to resilient foods average over ∼$100 million S model": 20 and 20 k.
  • "Far future potential increase per $ due to resilient foods average over ∼$100 million E model": 30 and 80 k.
  • "Far future potential increase per $ AGI safety research at the $3 billion margin (same for both models)": 0.2 and 100.

This conclusion would hardly change due to including effects of removing GHG emissions which do not lead to trajectory changes. For example, the direct benefits of reducing existential risk due to climate change which result from removing GHG emissions may well be over 100 k times as large as those from decreasing temperature-related mortality between 2020 and 2100, supposing:

• The benefits of reducing existential risk are 27.5 life/t, based on the following assumptions:
  • Existential risk reduction caused by removing GHG emissions of 0.275 bp/Tt (see Results).
  • Population size of 10 G (currently, it is 8 G).
  • Existence of 10 Gyear, which is given as a lower bound in Beckstead 2013 (search for “expected years of civilization ahead of us”).
  • Life expectancy of 100 year/life (currently, it is 70 year/life).
• The benefits of decreasing temperature-related mortality between 2020 and 2100 are 2.26*10^-4 life/t, as given by the mortality cost of carbon estimated in Bressler 2021.

1. ^{^}

   bp stands for basis point (0.01 pp), t for tonne of CO2e, k for thousand, G for billion, and T for trillion.

2. ^{^}

   Johannes leads FP's climate team, and is the first author of FP's latest climate report.

3. ^{^}

   In other words, the more GHG emissions are required to reach a given level of global warming, the smaller is their longterm impact.

4. ^{^}

   In the climate change problem profile from 80,000 Hours, Benjamin Hilton writes:

   That said, we [80,000 Hours] still think this risk is relatively low. If climate change poses something like a 1 in 1,000,000 risk of extinction by itself, our guess is that its contribution to other existential risks is at most a few orders of magnitude higher — so something like 1 in 10,000.

5. ^{^}

   John Halstead writes:

   With those caveats in my mind, my best guess estimate is that the indirect risk of existential catastrophe due to climate change is on the order of 1 in 100,000, and I struggle to get the risk above 1 in 1,000. Working directly on US-China, US-Russia, India-China, or India-Pakistan relations seems like a better way to reduce the risk of Great Power War than working on climate change.

6. ^{^}

   Based on the formulas for the mean ($\mu$) and mode ($m$) provided in Wikipedia, the parameters of the beta distribution are: $\alpha =(1-2m)/(1-m/\mu )$; $\beta =(1-1/\mu )\alpha$.

7. ^{^}

   The cost-effectiveness estimates were calculated from the reciprocal of the point estimates or the geometric mean of the lower and upper bounds of the values in $/t presented in Gillingham 2018.

8. ^{^}

   CATF has received two grants from the CCF (see table here): 850 k$ in December 2020; and 2 M$ in November 2021.

9. ^{^}

   The estimates of Kirsten Horton and Nuño Sempere were not included, as they were seemingly supposed to be underestimates of their cost-effectiveness bars.

10. ^{^}

    Anonymous Person's estimate was the only provided via personal message.

11. ^{^}

    This refers to OP's longtermist projects, and is based on the estimate of "$200 trillion per world saved" provided by Ajeya Cotra in this section of episode 90 of The 80,000 Hours Podcast. It concerns "meta R&D to make responses to new pathogens faster", and "[Open Philanthropy] were aiming for this to be conservative", meaning the actual cost-effectiveness bar is higher.

12. ^{^}

    Geometric mean between the lower and upper bound provided. "I currently think it's probably somewhere between $1-100 trillion per existential catastrophe averted (I find this framing more intuitive than bp/G$)".

13. ^{^}

    This is the median cost-effectiveness bar provided by Linchuan. According to "[Linchuan's] very fragile thoughts as of 2021/11/27": "I feel pretty bullish and comfortable saying that we should fund interventions that we have resilient estimates of reducing x-risk ~0.01% at a cost of ~$100M" (10 bp/G$); "I think for time-sensitive grants of an otherwise similar nature, I'd also lean optimistic about grants costing ~$300M/0.01% of xrisk, but if it's not time-sensitive I'd like to see more estimates and research done first" (3.33 bp/G$); "for work where we currently estimate ~$1B/0.01% of xrisk, I'd weakly lean against funding them right now, but think research and seed funding into those interventions is warranted" (1 bp/G$).

14. ^{^}

    The quantiles are expressed in 1/$ in the articles, but in bp/G$ here. However, for loss of industry preparation and resilent foods, they do not apply (as accurately) to investments of 1 G$, as they were computed for investments between 30 M$ and 100 M$.