Dear Stan,

Thanks for your work here, and it’s always great to see people doing a deep dive on nuclear winter and abrupt sunlight reduction scenarios (ASRS). As Alliance to Feed the Earth in Disasters (ALLFED) we are highly concerned about these issues and certainly feel that they are neglected, and our analysis also suggests that the field is high impact and cost effective to mitigate.

However, there are a number of points we would like to raise, where we differ, at least in part, with your analysis:

• We assign a higher probability that a nuclear conflict occurs compared to your estimates, and also assume that conditional on a nuclear conflict occurring that higher detonation totals are likely. This raises the likelihood and severity of nuclear winters versus your estimates.
   
  • The weightings of the commenters vs Metaculus is your choice, but we would suggest that a prediction market result should have a higher weighting, due to its aggregation of a large amount of expert opinions. 
  • Your probability analysis excludes some high quality work (such as peer reviewed publications) which have a higher probability of nuclear conflict, potentially at 1% annually. This would primarily be via the risk of an inadvertent exchange, however the dynamics of an inadvertent exchange can be more damaging versus a deliberate conflict, as a response to an enemy launch is likely to focus more on critical infrastructure rather than weapon sites (which are assumed to have just fired at you).
  • The threshold for a catastrophic nuclear war in the XPT was very high - causing at least 10% of humanity to die over 5 years or less - and so should be considered as the probability of a nuclear conflict killing at least 800 million people, rather than a nuclear exchange.
  • However, your readers can also adjust this themselves in their heads reasonably easily if they wish, with a similar scale adjustment to the impact factor.
  • In terms of expected weapon detonations given at least 100, we also feel like your estimate of a uniform distribution is too low, and the logic of nuclear warfare suggests that “use it or lose it” would apply for at least the vulnerable land based weapon systems (including bombers). This pushes the distribution of detonations for a future NATO/Russia/China exchange towards the upper point of deployed weaponry, rather than a more uniform/skewed towards lower values distribution. This raises the expected severity of the following nuclear winter, if one occurs.
  • Soot lofting is very complicated and has serious uncertainties, but our estimates are that far higher levels are possible versus your estimates. Dividing Reisner 2018 (which cannot replicate real world firestorms) by 30 may be driving this, as well as your lower assumptions for detonations in a nuclear conflict.
• We estimate that the expected mortality from supervolcanic eruptions (VEI 8+) would be comparable to VEI 7 eruptions, so their inclusion could increase cost effectiveness significantly.
• We feel that you are selling short the importance of research in building resilience to nuclear winters in particular and ASRSs in general (page 27, and page 41 onwards), possibly by conflating research with just one of its subsets (pilot schemes and field tests of resilient food technologies). 
   
  • Research covers many activities, and a good amount of the analysis you link to in the report is based upon fundamental research of the likely dynamics of food consumption, production and trade, which did not exist before organizations like ALLFED started working on them.
  • These issues are highly complex and understudied, there is a significant risk of ineffective or even counterproductive actions if one rushes in without proper consideration, so new policy advocacy and engagement should result from careful consideration. 
  • Research at ALLFED covers many different fields, for example analyzing nutrition and diets in these scenarios, the likely production/yields of these sources under extreme conditions, the cost of their production and the likely dynamics of trade, accessibility, pricing and storage. In addition, we are proposing gathering some experimental data or carrying out pilot studies in cases where it would generate useful insights or build capacity, but this is only part of the story.
  • The impact on the long-term future is likely to be relatively larger from the most extreme catastrophes than the mortality, which is a further reason that we focus on the larger scenarios. Of course some of this work could provide tangible benefits for tackling smaller scale events too.
  • For example, you highlight uncertainty about the impact of novel resilient foods in all but the largest scenarios, as they can only provide around 19% of global calories in a no-international food trade scenario. Research is a way of bridging this gap in understanding by getting to the core of where they could be useful. For example, where might prices go in a variety of scenarios? How resilient are the different food sources to the different shocks, and how much would they cost to produce? Can they integrate into the food system as feed or biofuels to free up human edible foods? A 1% shock to output leads to around a 7% rise in prices, being able to produce 19% more food at short notice is not a trivial factor in many crises of varying severity, and resilience like that could save millions of lives. 
  • Some resilient foods are already cost effective for small quantities, such as seaweed and greenhouses, so they would be scaled up in lesser shocks. Also, we think of crop relocation to existing planted areas and crop area expansion as resilient foods, and these are likely to be a big part of the response in lesser catastrophes (and these are not included in the 19% figure).
  • Overall, we see research as the foundation on which you then build the policy work and other actions. Broadening and strengthening this foundation is therefore vital in allowing the work that finally effects change to occur - it isn’t an either/or. Now that there is a solid enough research base, it is possible to take some policy action, hence ALLFED’s expansion into this area of work, but more research will allow better and additional resilience-building in the future.

Thanks again for your work, and the openness with which it was conducted. It’s important to talk about and dig deep into these issues, and we hope others will do the same.

From the report:

The experts we spoke to favored unilateral advocacy (targeting individual countries) over advocacy in multilateral organizations such as the UN. This was on the basis that domestic policy and priorities usually take precedence over international agreements, especially in a catastrophe.

I tend to agree, because there are many low-hanging fruits in in-country advocacy. However,  I wonder if some food security policies could face obstacles from WTO's Agreement on Agriculture, so that amending it could be highly effective.

Thanks for all your work on this, and asking me for feedback on your analysis, Stan!

My recommendation for donors

Your results imply the cost-effectiveness of ASRS policy advocacy is 0.242 DALY/$ (= 32.9*0.00737). I explain below why I think this too high, but, even if not, I estimate it is only 1.61 % (= 0.242/15.0) as cost-effective as corporate campaigns for chicken welfare, such as the ones supported by The Humane League (THL). So I think donors who value 1 unit of welfare in humans as much as 1 unit of welfare in animals (i.e. who reject speciesism) had better donate to THL instead of an organisation doing ASRS policy advocacy.

I would be curious to know CEARCH's position on animal welfare. I noted there are 0 animal welfare causes in your long list of 588. Their absence is especially surprising given the presence of causes like sporting excellence and freedom of hobby.

Points of agreement

Here are some points I agree with from the sections "Key points" and "Executive Summary" of your report:

• Policy advocacy, targeted at a few key countries, is the most promising way to increase resilience to global agricultural crises. Advocacy should focus on increasing the degree to which governments respond with effective food distribution measures, continued trade, and adaptations to the agricultural sector.

[...]

• Compared to other interventions addressing Global Catastrophic Risk (GCR), the evidence is unusually robust:
  • We know that the threat is real: volcanic cooling is confirmed by the historical and geological record.
  • We know this is neglected: current food resilience policy focuses on protecting farmers and consumers from price changes, regional agricultural shortfalls or from small global shocks. There is very little being done to prepare for a significant global agricultural shortfall.
  • We are uncertain about the effect size: we have significant uncertainty about the extent to which governments and the international community would step up to the challenge of a global agricultural shortfall. There is little evidence on the scale of the effect that a policy breakthrough would have on the human response.

[...]

• We identify two main sources of downside risk. (1) Increasing resilience to nuclear winter could reduce countries’ reluctance to use nuclear weapons. (2) nuclear winter resilience efforts could be seen by other nuclear-armed states as preparation for war, thereby increasing tensions. However, these risks are unlikely to apply to broader food resilience efforts.

[...]

Our analysis suggests that uncertainties about the validity of nuclear winter theory are often overlooked, leading some authors to overstate the risk of nuclear cooling [I agree]. [...]

[...]

However, because simple adaptations and international food trade & aid are sufficient to feed everyone in many scenarios, resilient foods would only be pivotal in averting mass famine if food trade is severely inhibited, or in the most severe cooling scenarios [agreed]. We believe that these very conditions [which involve large infrastructure given thousands of nuclear detonations] would likely restrict the scaling-up of resilient foods, which require a high degree of complexity (see Human Response for justification).

Points of disagreement

I believe your cost-effectiveness should be 12.4 % (= 1/3*0.505*0.736) as large based on the adjustments below, i.e. 4.08 (= 32.9*0.124) times as cost-effective as GiveWell's top charities, or 0.200 % (= 0.0161*0.124) as cost-effective as corporate campaigns for chicken welfare. In other words, I conclude these are 500 (= 1/0.00200) times as cost-effective as ASRS policy advocacy.

Remaining regression to lower cost-effectiveness (I guess this makes your cost-effectiveness 1/3 as large)

Your cost-effectiveness has become around 10 % as large over the course of my review, so I guess there is still some remaining regression left which would make it 1/3 as large. In particular:

• I suspect an advocacy campaign of 1 M$ leading to a reduction in the disease burden of cooling events of 0.272 % (or 0.35 % in your case) with 24.2 % probability is optimistic. To estimate this probability, you gave a weight of 76.9 % (= 70/91) to an expert survey, and experts tend to overestimate the cost-effectiveness of work in their area.
• Relatedly, your report was reviewed by many people working to decrease nuclear and volcanic risk, which are naturally inclined to overestimate cost-effectiveness due to selection effects (see example in the context of AI risk), but not by people sceptical of such work?
  • By sceptical, I roughly mean people who think the interventions is less cost-effective than GiveWell's top charities. My adjustments imply it is 4.08 times as cost-effective as them, so I do not qualify.
  • Did you have the chance to contact people at GiveWell, which I do not think has funded work specifically targeting cooling events?
• I believe funging decreases the cost-effectiveness of your potential grants.

On the last point, you say in the report that:

All grants in this space have the potential to be fungible. Both ALLFED [Alliance to Feed the Earth in Disasters] and RCG [Observatorio de Riesgos Catastróficos Globales] conduct research alongside policy advocacy, and so even a policy-restricted grant could displace other funding towards research and other work, which we believe to be less effective on the margin. Funding other GCR-focused orgs to perform ASRS resilience work could pull them away from highly-effective work in other domains.

We do not have strong beliefs about which funding options are least fungible: those considering making an ASRS policy grant should be careful to ensure that their donations lead to a counterfactual increase in advocacy efforts.

There would be no worries about funging if you considered all the activities of the supported organisation similarly cost-effective (at the margin). Conversely, even if you do not have strong beliefs about which funding options are least fungible, you can still be especially worried about funding organisations pursuing activities that you do not consider cost-effective.

In particular, your model implies the benefits coming from resilient foods, by which I think you mean ways of increasing calorie production via new (or massively scaled up) food sectors, are negligible as a 1st approximation. Assuming the policy breakthrough only affects resilient foods (not adaptation nor trade) makes the proportion of deaths averted by it 0.805 % (= 2.84*10^-5/0.00353) as large. I am sympathetic to this view, although I would guess a higher contribution, but it implies funding organisations doing research on or advocating for new (or massively scaled up) food sectors is less promising. Importantly:

• A significant fraction of ALLFED's work has been research on new (or massively scaled up) food sectors, like greenhouse crop production, lignocellulosic sugar, methane single cell protein and seaweed.
• Other organisations in the space may be significantly influenced by ALLFED's work. As you say in the report:
  • "The Alliance to Feed the Earth in Disasters (ALLFED) is the only organization dedicated to food resilience in global catastrophes, and very little seems to happen in this space without some sort of ALLFED involvement".
  • "In such a small domain, this means that many of the experts in the field are either a member of, or have previously worked with, ALLFED. It also means that any new initiative in ASRS food resilience, if not run by ALLFED, would probably collaborate with them".

Overestimation of the mortality of mild cooling events (my correction for this makes your cost-effectiveness 50.5 % as large)

I think your global mortality rate of 0.6 % for a cropland cooling in the worst 12 months of 1.5 ºC is way too high. Stoffel 2015, which you use in your volcanic winter model, suggests the 1815 eruption of Mount Tambora caused a cropland cooling of 1.05 ºC (= (0.8 + 1.3)/2).

Our [Stoffel 2015's] tree-ring reconstructions and climate simulations are in agreement, with a mean Northern Hemisphere extra-tropical summer cooling over land of 0.8 to 1.3 ◦C for these eruptions [" in AD 1257 and 1815"]

In 1815, trade efficiency would arguably be negligible, and there would not be resilient foods, but there would still be adaptation, so I guess you would predict cropland coolings of 1.5 and 3.8 ºC then would have global mortality rates of 3 % and 5 %. Linearly extrapolating, I estimate you would predict a global mortality rate then of 2.61 % (= 0.03 + (0.05 - 0.03)/(3.8 - 1.5)*(1.05 - 1.5)), which would be 27.4 M global deaths (= 0.0261*1.05*10^9). Wikipedia's section on the fatalities caused by the 1815 eruption of Mount Tambora suggests a much lower death toll (emphasis mine):

The number of fatalities has been estimated by various sources since the nineteenth century. Swiss botanist Heinrich Zollinger traveled to Sumbawa [Indonesian island] in 1847 and recollected witness accounts about the 1815 eruption of Tambora. In 1855, he published estimates of directly killed people at 10,100, mostly from pyroclastic flows. A further 37,825 were numbered having died from starvation on Sumbawa island.^[39] On Lombok [Indonesian island], another 10,000 died from disease and hunger.^[40] Petroeschevsky (1949) estimated that about 48,000 and 44,000 people were killed on Sumbawa and Lombok, respectively.^[41] Several authors have used Petroeschevsky's figures, such as Stothers (1984), who estimated 88,000 deaths in total.^[29] However, Tanguy et al. (1998) considered Petroeschevsky's figures based on untraceable sources, so developed an estimate based solely on two primary sources: Zollinger, who spent several months on Sumbawa after the eruption, and the notes of Sir Stamford Raffles,^[30] Governor-General of the Dutch East Indies during the event. Tanguy pointed out that there may have been additional victims on Bali [province of Indonesia] and East Java [Java is Indonesia's capital city] because of famine and disease, and estimated 11,000 deaths from direct volcanic action and 49,000 from post-eruption famine and epidemics.^[42] Oppenheimer (2003) estimated at least 71,000 deaths,^[3] and numbers as high as 117,000 have been proposed.^[36]

All the estimates above refer to deaths in Indonesia, where Mount Tambora is located. Local effects are more severe than global ones, so dividing the above estimates by Indonesia's population in 1815 would overestimate the global mortality rate. Wikipedia's list of famines says Tambora's eruption caused 65 k deaths in Europe (see below), i.e. 0.0304 % (= 65*10^3/(214*10^6)) of Europe's population in 1815.

I would say the aforementioned 65 k deaths should not be fully attributed to Tambora's eruption. From Wikipedia's page on the Year Without the Summer, respecting the volcanic winter caused by Tambora's eruption, they were also the result of earlier volcanic eruptions and the Napoleonic Wars, which ended in 1815:

As a result of the series of volcanic eruptions in the 1810s, crops had been poor for several years; the final blow came in 1815 with the eruption of Tambora. Europe, still recuperating from the Napoleonic Wars, suffered from widespread food shortages, resulting in its worst famine of the century.^{[22][23][24][25]} Low temperatures and heavy rains resulted in failed harvests in Great Britain and Ireland. Famine was prevalent in north and southwest Ireland, following the failure of wheat, oat, and potato harvests. Food prices rose sharply throughout Europe.^[26] With the cause of the problems unknown, hungry people demonstrated in front of grain markets and bakeries. Food riots took place in many European cities. Though riots were common during times of hunger, the food riots of 1816 and 1817 were the most violent period on the continent since the French Revolution.^[23]

Between 1816 and 1819, major typhus epidemics occurred in parts of Europe, including Ireland, Italy, Switzerland, and Scotland, precipitated by the famine. More than 65,000 people died as the disease spread out of Ireland.^[22][23]

I speculate only half of the 65 k deaths were caused by Tambora's eruption, i.e. 32.5 k (= 0.5*65*10^3). However, I guess this only accounts for the effects of protein-energy malnutrition (the more visible starvation), whose mortality in 2019 was only 7.21 % (= 212*10^3/(2.94*10^6)) of that from child and maternal malnutrition. So I estimate Tambora's eruption caused 451 k (= 32.5*10^3/0.0721) deaths in Europe (accounting for non-reported deaths), respecting a mortality rate of 0.211 % (= 451*10^3/(214*10^6)). This is 7.03 % (= 0.00211/0.03) of the 3 % I understand your model would predict.

The death rate above is for an eruption 209 years (= 2024 - 1815) ago. Poverty has been a major risk factor for famines, and the global real gross domestic product (real GDP) per capita in 2022 was 14.8 (= 16.7*10^3/(1.13*10^3)) times that in 1820, so I think Tambora's eruption today would be way less deadly. Here are the death rate from protein-energy malnutrition (arguably proportional to the death rate from child and maternal malnutrition) and real GDP per capita in 2017-$ by country:

Eyeballing the graph above, the death rate from protein-energy malnutrition weighted by population is:

• For the real GDP per capita in 1820 of 1.30 k 2017-$, 2*10^-4.
• For the real GDP per capita in 2022 of 17.5 k 2017-$, 1*10^-5.

So I guess the increase in death rate caused by Tambora's eruption today would be 5 % (= 1*10^-5/(2*10^-4)) of my estimate for 1815 of 0.211 %, i.e. 0.0106 % (= 0.05*0.00211). For context, the deaths from child and maternal malnutrition in 2019 as a fraction of the global population were 0.0326 %. So I predict the increase in death rate caused by Tambora's eruption today would correspond to 32.5 % (= 1.06*10^-4/(3.26*10^-4)) of that. This sounds reasonable:

• I estimate global cooling is 54.2 % (= 1.3/2.4) of cropland cooling:
  • From Fig. 1a of Xia 2022, the maximum cropland cooling is 2.4 ºC for 5 Tg.
  • From Fig. 3a of Toon 2014, the maximum global cooling for 1.3 ºC for 5 Tg.
• So Tambora's 1.05 ºC of cropland cooling would respect a global cooling of only 0.569 ºC (= 0.542*1.05).
• Global temperature often has annual variations about half as large as the above, so I would be surprised if it increased famine a lot.

You estimate cropland coolings of 1.5 and 3.8 ºC today would have global mortality rates of 0.6 % and 3.3 %. Linearly extrapolating, I estimate you would predict a global mortality rate today of 0.0717 % (= 0.006 + (0.033 - 0.006)/(3.8 - 1.5)*(1.05 - 1.5)). So, in light of the above, I would say your mortality rate for a cropland cooling of 1.05 ºC should be 14.8 % (= 1.06*10^-4/(7.17*10^-4)) as high.

I suppose the mortality rate adjustment factor increases linearly with cropland cooling, from the 14.8 % mentioned just above for 1.05 ºC, to 1 (no adjustment) for 8 ºC, which respects an injection of soot into the stratosphere of 47 Tg. From Fig. 5a of Xia 2022, this is roughly the amount of soot below which there are enough calories to feed everyone given equitable distribution, no household food waste, no inefficient consumption of animals, and no other adaptations. So I multiplied your mortality rates for a cropland cooling of:

• 1.5 ºC by 20.3 % (= 0.148 + (1 - 0.148)/(8 - 1.05)*(1.5 - 1.05)).
• 3.8 ºC by 48.5 % (= 0.148 + (1 - 0.148)/(8 - 1.05)*(3.8 - 1.05)).
• 5.5 ºC by 69.4 % (= 0.148 + (1 - 0.148)/(8 - 1.05)*(5.5 - 1.05)).
• 7 ºC by 87.7 % (= 0.148 + (1 - 0.148)/(8 - 1.05)*(7 - 1.05)).

For 8 and 14.5 ºC (the most severe cooling you considered), I used your values. My updated mortality rates imply a proportion of deaths averted by a policy breakthrough of 0.277 %, and a global mortality rate for a cooling event of 0.819 %. These make my tractability 78.6 % (= 6.72*10^-5/(8.55*10^-5)) as large as yours, and my expected burden from cooling events in 2024 64.2 % (= 7.26*10^6/(1.13*10^7)) as large as yours. So my updated mortality rates lead to a cost-effectiveness 50.5 % (= 0.786*0.642) as large as yours.

Overestimation of the persistence of the intervention (my correction for this makes your cost-effectiveness 73.6 % as large)

You estimate an annual reduction in food security from 2024 to 2100 of 1.76 %, which is the geometric mean between:

• 3.24 % annual reduction in the disease burden of nutritional deficiencies from 1990 to 2019.
• 1.45 % annual reduction in undernourishment from 2000 to 2022.
• 0.76 % annual increase in cereal production per capita from 1961 to 2022.
• 2.67 % annual reduction in extreme poverty from 1961 to 2018.

I think it is better to rely on the 1st and last of the above, and the annual reduction in the disease burden of child and maternal malnutrition from 1990 to 2019 of 2.76 % (= 1 - (295/665)^(1/29)). So I estimate an annual reduction in food security from 2024 to 2100 of 2.88 % (= (0.0324*0.0267*0.0276)^(1/3)), which makes the persistence and cost-effectiveness of the invervention 73.6 % (= 18.4/25) as large. Using the disease burden of nutritional deficiencies, and of child and maternal malnutrition makes sense to me given they are the cause and risk of the Global Burden of Disease Study (GBD) more closely connected to what you are predicting. I also agree with including extreme poverty for the 3 reasons below.

Firstly, the share of the population in extreme poverty has been a better predictor of the death rate from protein-energy malnutrition (R^2 of 0.941 for 30 points) than the share of the population that is undernourished (R^2 of 0.82 for 20 points) and cereal production per capita (R^2 of 0.297 for 30 points). I guess this would continue to hold for larger food shocks. Note the 2nd R^2 would tend to be lower if it referred to 30 points as the other 2. The graphs are below.

Secondly, poverty has been a major risk factor for famines.

Thirdly, we should expect people in extreme poverty to be especially vulnerable to increases in food prices on 1st principles. A calorie sufficient diet in low income countries in 2017 costed 0.86 2017-$/d/person, i.e. 40.0 % (= 0.86/2.15) of the maximum income of someone in extreme poverty. In contrast, someone earning e.g. 20 2017-$/h, or 160 2017-$/d for 8 h/d, can afford the same diet with just 0.538 % (= 0.86/160) of income. If food prices triple such that satisfying the caloric requirement requires 2.58 2017-$/d (= 0.86*3), someone earning 2.15 2017-$/d would only be able to afford 83.3 % (= 2.15/2.58) of the calorie sufficient diet even spending all income on it, so the person would be in trouble. In contrast, someone earning 160 2017-$/d would be able to afford the same diet with just 1.61 % (= 2.58/160) of income, so the person would be fine.