Summary

• I think decreasing greenhouse gas (GHG) emissions has benefits to humans of 0.00957 DALY/tCO₂eq, of which:
  • 68.8 % are strictly linked to decreasing GHG emissions.
  • 31.2 % are linked to decreasing air pollution from fossil fuels.
• I calculated the cost-effectiveness of:
  • Founders Pledge’s Climate Change Fund (CCF) is 0.0326 DALY/$, with a plausible range of 0.00174 to 0.300 DALY/$.
  • GiveWell’s Top Charities Fund (TCF) is 0.00994 DALY/$.
  • Corporate campaigns for chicken welfare, such as the ones supported by The Humane League (THL), is 15.0 DALY/$.
• I estimated the cost-effectiveness of CCF is:
  • 3.28 times that of TCF, with a plausible range of 0.175 to 30.2 times. So it is unclear to me whether donors interested in improving nearterm human welfare had better donate to GiveWell’s funds or CCF.
  • 0.217 % that of corporate campaigns for chicken welfare, with a plausible range of 0.0116 % to 2.00 %. Consequently, I recommend donors who value 1 unit of nearterm welfare the same regardless of whether it is experienced by humans or animals to donate to the best animal welfare interventions, such as the ones supported by the Animal Welfare Fund (AWF).
• I concluded the harm caused to humans by the annual GHG emissions of a random person is 0.0660 DALY, and that caused to farmed animals by their annual food consumption is 4.04 DALY, i.e. 61.2 times as much. In my mind, this implies one should overwhelmingly focus on minimising animal suffering in the context of food consumption.

Calculations

I describe my calculations below. You are welcome to make a copy of this Sheet to use your own numbers.

Benefits to humans of decreasing greenhouse gas emissions

I think decreasing GHG emissions has benefits to humans of 0.00957 DALY/tCO2eq (= 0.00658 + 0.00299), adding:

• 0.00658 DALY/tCO2eq strictly linked to decreasing GHG emissions, which comprises 68.8 % (= 0.00658/0.00957) of the total.
• 0.00299 DALY/tCO2eq linked to decreasing air pollution from fossil fuels, which comprises 31.2 % (= 0.00299/0.00957) of the total.

I calculated a component strictly linked to decreasing GHG emissions of 0.00658 DALY/tCO2eq (= 0.0394*10^-3*167), multiplying:

• A value of increasing economic growth of 0.0394 DALY/k$ (= 0.5/(12.7*10^3)). I computed this from the ratio between:
  • Open Philanthropy’s (OP’s) valuation of health of 0.5 DALYs per multiple of income (= 1/2).
  • The global gross domestic product (GDP) per capita in 2022 of 12.7 k$.
• A social cost of carbon (SSC) in 2020 of 167 $/tCO2eq (= 17.1*1.14*2.76*2.06*1.51). I determined this from the product between:

  • A partial SSC in 2020 representing just the effects on mortality of 17.1 2019-$/tCO2eq, as obtained in Carleton 2022 for the representative concentration pathway 4.5 (RCP 4.5), and their preferred discount rate of 2 %^[1] (see Table III).

    • Carleton 2022 got 36.6 2019-$/tCO2eq for RCP 8.5. Nevertheless, I considered the value for RCP 4.5 because this results in a global warming in 2100 of 2.5 to 3 ºC relative to the pre-industrial baseline, which is in agreement with Metaculus’ median community prediction on 11 March 2024 of 2.81 ºC relative to the 1951-1980 baseline. In contrast, RCP 8.5 leads to a global warming in 2100 of 5 ºC relative to the pre-industrial baseline.

    • Carleton 2022 says “a “full” SCC would encompass effects across all affected outcomes (and changes in mortality due to other features of climate change, like storms)”. However, I believe Carleton 2022’s estimates could be interpreted as encompassing all the impacts on mortality, as I guess additional deaths caused by GHG emissions through natural disasters are much smaller than those via changes in temperature. Based on data from The International Disaster Database (EM-DAT), the mean death rate from natural disasters decreased 97.6 % (= 1 - 0.64/26.5) from the 1920s to the 2010s^[2], while temperature increased 1.01 ºC^[3] (= 0.735 - (-0.274)). Additionally, the fraction of the damage of extreme weather events attributable to climate change does not appear to present a clear downwards/upwards trend.

  • 1.14 2022-$/2019-$, to adjust the partial SCC for inflation.
  • A factor of 2.76 (= 17.5*10^3/(6.35*10^3)) to account for climate change affecting more people with lower income. Carleton 2022 “allow[s] the VSL [value of a statistical life] to vary with income”, whereas I wanted 1 year of healthy life to always be worth the same (regardless of income). I obtained the factor from the ratio between:
    • 17.5 k 2017-$, which is the global real GDP per capita in 2022.

    • 6.35 k 2017-$ (= (19.1*1.41*10^3*18.2 + 10.1*333*64.6 + 62.7*1.42*10^3*7.11 + 376.0*236*5.38 + 248.5*171*6.26 - 4.7*447*46.0 + 131.8*1.21*10^3*3.77)*10^3/(19.1*1.41*10^3 + 10.1*333 + 62.7*1.42*10^3 + 376.0*236 + 248.5*171 - 4.7*447 + 131.8*1.21*10^3)), which is the real GDP per capita in 2022 of the countries mentioned in Table II of Carleton 2022 weighted by future deaths caused by GHG emissions in 2020^[4]. I got the deaths by multiplying the death rates in Table II by the population of the countries in 2022.

  • A ratio between the full SSC and the partial SSC of 2.06 (= 185/90), as in Rennert 2022 (see Fig. 3).
    • From Fig. 3, impacts on agriculture account for 45.4 % (= 84/185) of the cost, on energy consumption 4.86 % (= 9/185), on mortality 48.6 % (= 90/185), and on sea-level rise 1.08 % (= 2/185).
    • “The mortality damage functions are based on the results of Cromar et al. 202219, in which a panel of health experts was convened to conduct a meta-analysis of peer-reviewed research studying the impacts of temperature on all-cause mortality risk [including conflicts and natural disasters?], which includes human health risks related to a broad set of health outcomes including cardiovascular, respiratory and infectious disease categories. The meta-analysis combined studies to produce regionally disaggregated estimates of the effects on all-cause mortality of each degree of warming across a broad range of baseline temperatures, including both increased mortality risk at high temperatures and reduced risk at cooler temperatures”.
    • I did not directly use Rennert 2022’s full SSC of 185 $/tCO2eq because I agree with David Friedman’s critique of this study. Crucially, it assumes the same relationship between income and mortality until 2300 despite significant increases in income, thus underestimating mitigation (e.g. via greater ability to afford air conditioning). For context, according to data from the Global Burden of Disease study (GBD), the disease burden per capita from non-optimal temperature decreased 35.7 % (= 1 - 486/756) from 1990 to 2019. I guess part of this decrease is explained by an increase in mitigation measures enabled by higher incomes.

  • A factor of 1.51 (= 2.54/1.68) to integrate the effect of morbidity^[5]. I obtained this from the ratio between:

    • 2.54 billion DALYs in 2019.

    • 1.68 billion (= (2.54 - 0.861)*10^9) years of life lost (YLL) in 2019, which I got from the difference between the above and 0.861 billion years lived with disability (YLD) in 2019.

• A cost-effectiveness of decreasing GHG emissions of 3.41 tCO2eq/$, with a plausible range of 0.182 to 31.4 tCO2eq/$.
  • These were obtained in the context of Founders Pledge’s 2018 climate report (see section 3.2), and respected the future work of Clean Air Task Force (CATF), which is the organisation that has received the most money from CCF.
  • Johannes Ackva, who is the manager of CCF, “thinks this [3.41 tCO2eq/$] is in the right ballpark and not worth getting more precise for an analysis like this (where most parameters are much more uncertain)”.

I estimated a component linked to decreasing air pollution from fossil fuels of 0.00299 DALY/tCO2eq (= 164*10^6/(54.8*10^9)), dividing:

• The disease burden connected to air pollution from fossil fuels in 2019 of 164 MDALY (= 5.13*10^6*31.9). I obtained this multiplying:
  • 5.13 M deaths caused by air pollution from fossil fuels in 2019, in line with Lelieveld 2023.
  • 31.9 DALY/death (= 213*10^6/(6.67*10^6)), which I got from the ratio between the disease burden and deaths from air pollution in 2019 of 213 MDALY and 6.67 M.
• The GHG emissions in 2019 of 54.8 GtCO2eq.

Cost-effectiveness of the Climate Change Fund

I calculated the cost-effectiveness of CCF is 0.0326 DALY/$ (= 0.00957*3.41), with a plausible range of 0.00174 (= 0.00957*0.182) to 0.300 DALY/$ (= 0.00957*31.4), multiplying:

• The benefits of decreasing GHG emissions of 0.00957 DALY/tCO2eq (see previous section).
• A cost-effectiveness of decreasing GHG emissions of 3.41 tCO₂eq/$, with a plausible range of 0.182 to 31.4 tCO2eq/$.
  • These were obtained in the context of Founders Pledge’s 2018 climate report (see section 3.2), and respected the future work of Clean Air Task Force (CATF), which is the organisation that has received the most money from CCF.
  • Johannes Ackva, who is the manager of CCF, “thinks this [3.41 tCO2eq/$] is in the right ballpark and not worth getting more precise for an analysis like this (where most parameters are much more uncertain)”.

Cost-effectiveness of the Top Charities Fund

I concluded the cost-effectiveness of TCF is 0.00994 DALY/$ (= 51*0.195*10^-3), multiplying:

• GiveWell’s implicit valuation of saving lives of 51 DALY/life. According to OP, “GiveWell uses moral weights for child deaths that would be consistent with assuming 51 years of foregone life in the DALY framework (though that is not how they reach the conclusion)”.
• The mean reciprocal of the cost to save a life of GiveWell’s 4 top charities of 0.195 life/k$ (= (1/5 + 1/5.5 + 1/5 + 1/5)*10^-3/4).

Cost-effectiveness of corporate campaigns for chicken welfare

I arrived at a cost-effectiveness of corporate campaigns for chicken welfare of 15.0 DALY/$ (= 8.20*2.10*0.870), assuming:

• Campaigns affect 8.20 chicken-years per $ (= 41*1/5), multiplying:
  • Saulius Šimčikas’ estimate of 41 chicken-years per $.
  • An adjustment factor of 1/5, since OP thinks “the marginal FAW [farmed animal welfare] funding opportunity is ~1/5th as cost-effective as the average from Saulius’ analysis [which is linked just above]”.

• An improvement in chicken welfare per time of 2.10 times the intensity of the mean human experience, as I estimated for moving broilers from a conventional to a reformed scenario based on Rethink Priorities’ median welfare range for chickens of 0.332^[6].

• A ratio between humans’ healthy and total life expectancy at birth in 2016 of 87.0 % (= 63.1/72.5).

In light of the above, corporate campaigns for chicken welfare are 1.51 k (= 15.0/0.00994) times as cost-effective as TCF.

Discussion

Cost-effectiveness

I infer the cost-effectiveness of CCF is 3.28 (= 0.0326/0.00994) times that of TCF, with a plausible range of 0.175 (= 0.00174/0.00994) to 30.2 (= 0.300/0.00994) times. The actual plausible range is wider because I only modelled the uncertainty in the cost-effectiveness of CCF in tCO2eq/$. So it is unclear to me whether donors interested in improving nearterm human welfare had better donate to GiveWell’s funds or CCF^[7]. At the same time, I do not recommend Founders Pledge’s Global Health and Development Fund nor EA Funds’ Global Health and Development Fund.

Note CCF is mostly trying to minimise the damage strictly linked to GHG emissions, but interventions explicitly targeting decreasing deaths from air pollution, like those of OP’s area of South Asian air quality, may be more cost-effective. I established 31.2 % of the benefits of decreasing GHG emissions are linked to decreasing air pollution from fossil fuels, but the best interventions to mitigate the damage strictly linked to GHG emissions being the best to mitigate air pollution would be a little bit of a surprising and suspicious convergence. For reference, I was also expecting a greater fraction of the benefits to come from decreasing air pollution.

In addition, I conclude the cost-effectiveness of CCF is only 0.217 % (= 0.0326/15.0) that of corporate campaigns for chicken welfare, with a plausible range of 0.0116 % (= 0.00174/15.0) to 2.00 % (= 0.300/15.0). The actual plausible range is wider for the reasons I mentioned above, and the difference may be attenuated owing to indirect effects^[8], but I believe corporate campaigns will still come out on top given reasonable assumptions^[9]. Consequently, I recommend donors who value 1 unit of nearterm welfare the same regardless of whether it is experienced by humans or animals to donate to the best animal welfare interventions, such as the ones supported by AWF. As a side note, I currently also prefer these over ones explicitly targeting existential risk mitigation, like those supported by the Long-Term Future Fund^[10] (LTFF).

It can still make sense for Founders Pledge to recommend CCF to donors partial to climate change:

• Even if Founders Pledge’s researchers are mostly cause neutral, many people donating to CCF may be partial to climate change.
• The counterfactual donations of CCF’s major donors might go to less effective organisations in its absence, and supporting CCF might be a pathway towards moving to more pressing areas.

Nonetheless, I would say cause neutral organisations offering philanthropic advice would do well to make a serious effort to influence the areas their donors support, which may well be the greatest driver of impact (relatedly). Furthermore, I encourage such organisations to invest more resources into cause prioritisation, in order to better understand how to compare climate change, global health and development, and animal welfare interventions.

Carbon and farmed animal welfare footprint

For the global GHG emissions per capita in 2021 of 6.9 tCO2eq, my estimate for the benefits of decreasing GHG emissions of 0.00957 DALY/tCO2eq implies the annual personal emissions of a random person cause 0.0660 DALY (= 6.9*0.00957). Equivalently, given the healthy life expectancy in 2019 of 63.7 years, the annual GHG emissions of 965 (= 63.7/0.0660) random people cause an harm comparable to 1 human death.

I estimated the scale of the annual suffering of all farmed animals is 4.64 times the scale of the annual happiness of all humans. As a consequence, for a ratio of 87.0 % between human’ healthy and total life expectancy at birth (see previous section), I think the harm caused to farmed animals by the annual food consumption of a random person is 4.04 DALY (= 4.64*0.870). Equivalently, the annual food consumption of 15.8 (= 63.7/4.04) random people causes an harm comparable to 1 human death.

Based on the above, the harm caused to farmed animals by the annual food consumption of a random person is 61.2 (= 4.04/0.0660) times that caused to humans by their annual GHG emissions. In my mind, this implies one should overwhelmingly focus on minimising animal suffering in the context of food consumption.

One could neutralise:

• The harm caused to humans by the annual GHG emissions of a random person donating just 2.02 $ (= 6.9/3.41) to CCF, or 0.00440 $ (= 0.0660/15.0) to THL, which has been playing a major role in corporate campaigns for chicken welfare.
• The harm caused to farmed animals by the annual food consumption of a random person donating just 0.269 $ (= 4.04/15.0) to THL.

I see these as nice opportunities to donate more instead of just offsetting the impact of one’s emissions.

Wild animals

I estimated the scale of the welfare of wild animals is 10.9 M times that of farmed animals. Nonetheless, I have neglected the impact of GHG emissions on wild animals due to their very low resilience. According to Brian Tomasik:

On balance, I’m extremely uncertain about the net impact of climate change on wild-animal suffering; my probabilities are basically 50% net good vs. 50% net bad when just considering animal suffering on Earth in the next few centuries (ignoring side effects on humanity's very long-term future).

In particular, it is unclear whether wild animals have positive/negative welfare.

Acknowledgements

Thanks to Anonymous Person, Johannes Ackva, Matthew Rendall and Rafael Latham-Proença for feedback on the draft^[11].

1. ^{^}

    “Our preferred estimates use a discount rate of $\delta =2\%$”. This is 1.08 (= 0.02/0.0185) times the 1.85 % (= (17.5/9.72)^(1/(2022 - 1990)) - 1) annual growth rate of the global real GDP per capita from 1990 to 2022. The adequate growth rate may be higher due to transformative AI, or lower owing to stagnation. I did not want to go into these considerations, so I just used Carleton 2022’s mainstream value.

2. ^{^}

    TV adoption was pretty low during the early 20th century, which could explain the greater salience of natural disasters today.

3. ^{^}

    0.735 ºC (= (0.68 + 0.54 + 0.58 + 0.62 + 0.67 + 0.83 + 0.93 + 0.85 + 0.76 + 0.89)/10) is the mean temperature anomaly in the 2010s relative to the mean one during period from 1961 to 1990, and -0.274 ºC (= (-0.30 - 0.24 - 0.34 - 0.32 - 0.31 - 0.28 - 0.12 - 0.23 - 0.21 - 0.39)/10) is that in the 1920s relative to the same period.

4. ^{^}

    I set the real GDP per capita of Europe to that of the European Union (EU), which is the one provided by the World Bank. This assumption has little importance because Europe’s weight is only -0.515 % (= -4.7*447/(19.1*1.41*10^3 + 10.1*333 + 62.7*1.42*10^3 + 376.0*236 + 248.5*171 - 4.7*447 + 131.8*1.21*10^3)).

5. ^{^}

    Carlson 2023 called for quantifying the current disease burden of climate change. Cromar 2022 says the following about morbidity and mortality:

   Morbidity costs are generally lower [in agreement with the aforementioned factor being lower than 2] and more difficult to estimate than mortality costs due to the lack of data available at the country level (with the notable exception of birth outcomes). A single endpoint, such as health care expenditures, may be a useful metric in this regard in the short term for valuing morbidity outcomes that are difficult to estimate. Mortality impacts will continue to be of greater importance in economic modeling.

6. ^{^}

    Ideally, I would use a number encompassing both broiler and chicken-free campaigns.

7. ^{^}

    GiveWell has 3 funds, TCF, All Grants Fund and Unrestricted Fund, which should ideally have the same marginal cost-effectiveness.

8. ^{^}

    The CCF has not funded interventions aiming to decrease GHG emissions from animal agriculture, which could have significant effects on animal welfare.

9. ^{^}

    For example, using best guess welfare ranges 0.316 (= 10^-0.5) to 3.16 (= 10^0.5) times those from Rethink Priorities.

10. ^{^}

     For donors interested in interventions explicitly targeting existential risk mitigation, I recommend donating to LTFF, which mainly supports AI safety. I guess existential risk from climate change is smaller than that from nuclear war (relatedly), and estimated the nearterm annual risk of human extinction from nuclear war is 5.93*10^-12, whereas I guess that from AI is 10^-6.

11. ^{^}

     The names are ordered alphabetically.