Resilience to Nuclear & Volcanic Winter
This is a summary. The full CEARCH report can be found here.
Key points
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.
We estimate that an advocacy campaign costing ~$1million would avert 6,000 deaths in expectation. Incorporating the full mortality, morbidity and economic effects, the intervention would provide a marginal expected value of 24,000 DALYs per $100,000. This is around 30x the cost-effectiveness of a typical GiveWell-recommended charity.
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.
GCR policy experts were broadly optimistic about the value of further work in this area. On average, they estimated that a two-person, five-year advocacy effort would have a 25% probability of triggering a significant policy breakthrough in one country. Experts emphasized the importance of multi-year funding to enable policy advocates to build strategic relationships. Some experts suggested that food resilience is a better framing than ASRS (cooling catastrophe) resilience for policies that protect against global agricultural shortfall.
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.
Executive Summary
This report addresses Abrupt Sunlight Reduction Scenarios (ASRSs) - catastrophic global cooling events triggered by large volcanic eruptions or nuclear conflicts—and interventions that may increase global resilience to such catastrophes. Cooling catastrophes can severely disrupt agricultural production worldwide, potentially leading to devastating famines. We evaluate the probability of such events, model their expected impacts under various response scenarios, and identify the most promising interventions to increase global resilience.
Volcanoes are the main source of risk according to our model, although we expect nuclear cooling events to be more damaging.
We estimate that the annual probability of an ASRS causing at least 1°C of average cooling over land is around 1 in 400, or a 20% per-century risk. Most of the threat comes from large volcanic eruptions injecting sun-blocking particles into the upper atmosphere. While the probability of a severe “nuclear winter” scenario is lower, such an event could potentially comprise a substantial portion of the expected overall burden. This is due to the compounding effects of nuclear conflict undermining the international cooperation and social stability required for an effective humanitarian response.
The annualized burden of cooling catastrophes by scenario. Much of the expected burden comes from mild and moderate cooling scenarios.
Notably, the majority of the projected burden from cooling catastrophes comes from mild and moderate events in the range of 1-4°C cooling over land. We find that in these scenarios, pragmatic policy measures and interventions could significantly decrease the risk of mass famine. Such measures include diverting animal feed towards human consumption, minimizing food waste, and facilitating efficient international agricultural trade and food aid delivery.
A hierarchy of human responses to an ASRS[1].
However, the ability to implement such measures hinges on maintaining public order, economic complexity, and international cooperation—prerequisites that may break down during more extreme cooling events as domestic food shortages increase tensions between nations. To address these risks, we identify policy advocacy aimed at raising awareness and preparedness among governments as the most promising avenue. An investment of around $1 million in advocacy efforts could plausibly catalyze major policy breakthroughs in one or more countries. Potential outcomes include government funding for research into resilient alternative food sources, comprehensive national food security risk assessments, and the development of national response plans for catastrophes that threaten global food supply.
Based on expert surveys and modeling, we estimate that such an advocacy campaign could avert the equivalent of approximately 25,000 disability-adjusted life years (DALYs) per $100,000 spent in expectation. This figure accounts for the combined mortality, morbidity, and economic impacts projected across a range of cooling scenarios and response effectiveness.
While the report acknowledges that resilient alternative food sources like mass-produced greenhouses or cellular agriculture may prove vital in worst-case scenarios, it cautions that the scaling potential of such solutions is inherently limited without broader resilience measures. On the margin we recommend expanding policy advocacy efforts as a higher-leverage approach to increase the chances of an effective, coordinated government response capable of averting mass famine during the next global cooling catastrophe. We identify a number of organizations that could perform policy advocacy in this space, including ALLFED, which specializes in post-catastrophe food resilience, and Global Shield, which is pushing for GCR policy in the US.
We emphasize that despite the uncertainties involved, investing in food system resilience through pragmatic policy advocacy represents one of the most robust strategies available for addressing the risk from global catastrophes.
Quick links to key sections of the full report
This report was created by the Centre for Exploratory Altruism Research (CEARCH) as part of our cause-prioritization work. We also undertake research commissions.
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The lower stages are high-priority, as they enable conventional agriculture to continue to function. “Adaptation” includes food efficiency measures such as redirecting animal feed to humans, rationing, and reducing waste. To some extent, adaptation would be an inevitable consequence of food scarcity.
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.
Hi Mike,
Firstly, thanks to you and all of ALLFED for your willingness to let me prod and poke at your work in the past year.
You make some excellent points and I think they will help readers to decide where they stand on the important cruxes here.
For anyone wanting to get up to speed on my nuclear winter model, plus a quick intro to why nuclear cooling is so uncertain, see my just-released nuclear winter post.
Weightings of Metaculus, XPT vs individual estimates: we do place high weightings on individual’s estimates, which is not ideal. The main reason for this is that the Metaculus and XPT estimates are not calibrated to a 100+ detonation nuclear conflict involving US and/or Russia.
The Metaculus estimate still forms around 20% of the total weight. Overall, it seems credible that my final estimate (0.10%) is around a third of the Metaculus estimate (0.30%) for 100+ detonations, given that most experts I spoke to considered India/Pakistan to be one of the main threats of a 100+ detonation nuclear exchange.
The XPT estimate only forms around 2% of the total weight.
Distribution of the number of detonations in a 100+ detonation conflict. I have done my best to incorporate different sources of evidence. One expert I spoke to broadly agreed with you, Mike, and guessed that in a 100+ conflict involving US/Russia there would be 80-90% risk of over 1000 detonations. Another expert was less pessimistic and placed higher weight on the possibility of ‘moderate’ escalation involving hundreds of weapons.
I re-ran the numbers just looking at conflicts with over 1000 detonations and found that the cooling levels increase only modestly: the 75th-percentile cooling level increases from 0.66 degrees (100+ detonations) to 1.24 degrees (1000+ detonations). The initial assumption about soot volumes is far more important (see below)
Detonation-soot relationship: Yes, the results are extremely sensitive to the initial assumption about how much soot is produced in a small nuclear exchange targeting cities. When I re-ran the numbers under the more pessimistic assumption that soot levels were 10%-100% those predicted by Toon et al., 75th-percentile cooling increased from 0.66 degrees to 3 degrees.
I don’t exclude supereruptions; I estimate that the right tail of my volcanic cooling model already accounts for them.
I want to be clear that I recommend that funders prioritize policy advocacy over R&D on the margin at this point in time. I totally agree that advocacy on such an uncertain topic can only be effective if it is grounded in research, and that ALLFED’s research will very likely form the foundations of policy work in this area for years to come.
One key takeaway from my analysis is that mild and moderate scenarios form a larger proportion of the threat than the lore of nuclear winter might suggest. Resilient foods would likely have a role to play in these scenarios, but I think the calories at stake in distribution and adaptation are likely to be more pivotal.
One reason for my focus on resilient food pilot studies is that they are a possible next step for ALLFED if it were to receive a funding boost. ALLFED has been ticking along with modest but reliable core funding for some time now, and perhaps I am guilty of taking its theoretical research for granted.
Feel free to set the record straight and give some indication of the kinds of work ALLFED might be interested in accepting funding for.
Michael—I agree with your assessment here, both that the CEARCH report is very helpful and informative, but also that their estimated likelihood of nuclear (only 10% per century) seems much lower than seems reasonable, and much lower than other expert estimates that I’ve seen.
Just as a lot can happen in a century of AI development, a lot can happen over the next century that could increase the likelihood of nuclear war.
Hi Geoffrey,
I just wanted to clarify your “likelihood of nuclear (only 10% per century [9.44 % = 1 - (1 − 9.91*10^-4)^100])” refers to a nuclear conflict with at least 100 nuclear detonations involving China, the US and Russia, not just to the chance of at least 1 nuclear detonation (which would be higher).
Vasco—understood. The estimate still seems much lower than most other credible estimates I’ve seen. And much lower than it felt when we were living through the 70s and 80s, and the Cold War was still very much a thing.
Hi Mike,
To clarify, CEARCH’s estimated a probability of a conflict involving at least 100 nuclear detonations in the US, Russia or China of 0.0991 % per year, which is 9.91 % of the 1 % you mention. However, this refers to the probability of nuclear launch:
The US or Russia launching a nuclear weapon does not have to lead to 100 nuclear detonations in the US, China and Russia. The US or Russia could be attacking other countries, or the nuclear conflict fall short of escalating to at least 100 nuclear detonations. So I do not think the above estimates are obviously at odds with CEARCH’s.
I think the median particpant of The Existential Risk Persuasion Tournament (XPT) is very pessimistic about nuclear risk. The annual nuclear extinction risk from 2023 to 2050 respecting the median superforecaster and expert is 602 k and 7.23 M times mine of 5.93*10^-12.
It is also worth noting XPT’s forecasters varied a lot in their predictions. 6.21 % (10/161) forecasted a nuclear extinction risk from 2023 to 2100 of exactly 0 (which is obviously too low and wrong, but still illustrates my point).
ALLFED’s estimate suggest dividing by 46.9 (= 1⁄0.0213)?
David Denkenberger (ALLFED’s co-founder and research director) modelled the “percent of combustible material that burns that turns into soot” as a lognormal distribution with 2.5th and 97.5th percentiles equal to 1 % and 4 % (see Table 2), whose mean is 2.13 %.
Reisner 2018 consider the above fraction to be 1 (emphasis mine):
“Further, because the current version of FIRETEC assumes BC production to be inversely proportional to oxygen depletion (no soot model was employed), that is, all the carbon in the fuel participated in the reaction and was turned into BC, the estimates, which represent upper bounds for the given fuel loadings, are higher (worst case) than they would be if a detailed chemical combustion model was used for soot production”.
For readers’ reference, I have explained why I think Mike’s nuclear winter analysis is too pessimistic.
From the report:
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.
Good point.
I looked at WTO agreements early in the research process and eventually decided that WTO advocacy was probably not, on the margin, the best way forward.
Food stockholding
I focused on the consequences of the AoA for food stockpiling (known as “stockholding”), the most urgent concern being that it may dissuade countries from stockholding as much as they otherwise would (as suggested by Adin Richards here). Although food reserves would never be big enough to get us through a full catastrophe, they would buy time for countries to adapt to the cooling shock.
The feedback I got from someone with experience at the WTO since the 1990′s was
The AoA probably isn’t holding countries back from stockholding. It does not make any restrictions on how much a country can hold, only on how much a country can spend on subsidizing domestically-produced stocks [while technically true, a lot of authors certainly seem to believe that the AoA affects stockholding, eg. here].
This AoA appears to be counterfactually reducing subsidies for only a handful of products in a few countries. India is the only country to have notified exceeding its limit, and only for rice.
The AoA seems intractable. The G33, a coalition of LMICs, have pushed for change with no success
On the other hand, basic amendment to the AoA seems obviously needed (imho). The original agreement does not properly allow for inflation. It does not make adequate exceptions for very low-income countries (whose market share is so low that allowing them to subsidize farmers would not be very distortionary).
Overall the WTO seems deadlocked at the moment and suffering a crisis of legitimacy. Tit-for-tat between US and China has led to a breakdown of trust in the organization.
If the US was on-side for AoA amendment, it is possible that the other dissenting countries would fall in line. But it is not clear that the US can be influenced on this. The US is doing fine with the system as it currently is, and has other ways of subsidizing domestic agriculture.
Other WTO theories of change
I don’t know much about the implications of the AoA beyond stockholding.
The most important things is that we ensure trade continues in a catastrophe, which seems congruent with the AoA. The second most important thing is that countries are able to quickly adapt their food systems in a crisis. In a major catastrophe I think all WTO rules would go out of the window. But could the AoA prevent countries from preparing?
It may hold them back from stockholding, or producing much more food than the market demands. But these measures would likely be way too expensive for governments to support in the name of preparing for a 1-in-400 event, anyway.
It may prevent them from using agri subsidies to foster emerging, resilient food sources that are not economically viable in normal times. But these food sources are at an early stage of development—pilot studies at most—and my guess is that they could be pursued under academic or innovation budgets.
I may well be missing something. Are there other ways the AoA could frustrate resilience work?
Thanks a lot for this answer
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:
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:
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).
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):
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:
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.
Hi Vasco. Thanks for all of your help giving feedback on the report and the modeling underpinning the CEA.
I am going to focus on the main points that you make. I hope to explain why I chose not to adopt the changes you mention in your comment and also to highlight some key weaknesses and limitations of my model.
Points I address (paraphrasing what you said):
By asking domain experts you probably got an overestimate for “probability that advocacy succeeds”. You should have also asked people in other fields.
Although you mention fungibility, you don’t account for it in cost-effectiveness estimates. You should be more explicit that fungibility undermines cost-effectiveness of grants that perform other, less effective interventions.
You overestimate the mortality effects of mild cooling events: if we apply your model to the 1815 eruption, we get higher mortality rates than actually occurred.
Poverty is a strong predictor of famine mortality. If your persistence estimate relied only on poverty & malnutrition burden indicators, the full-term benefits of policy advocacy would be significantly lower
By asking domain experts you probably got an overestimate for “probability that advocacy succeeds”. You should have also asked people in other fields.
I agree that domain experts are likely to overestimate the probability of successful policy advocacy in their space.
In my defence, only two of the seven experts I consulted for estimates worked in food resilience specifically. The geometric mean of their estimates was 30%; only slightly higher than the group average of 24%. The other experts would be best classified as GCR experts (so still likely to be overly optimistic)
The difficulty is that people who are not domain experts are (by definition) not well-informed. I don’t think people at GiveWell will have an accurate understanding of prospects for ASRS resilience policy advocacy. Especially because this is a very small field.
Although you mention fungibility, you don’t account for it in cost-effectiveness estimates. You should be more explicit that fungibility undermines cost-effectiveness of grants that perform other, less effective interventions.
I think that in ideal circumstances, fungibility should be accounted for in cost-effectiveness analysis. But since it depends on the organization receiving the funding, I decided not to do quantitative estimates of fungibility effects in this report. Maybe we will do so when we evaluate specific grants in this area.
I agree that funding to orgs who only do one highly cost-effective thing is generally less fungible.
You overestimate the mortality effects of mild cooling events: if we apply your model to the 1815 eruption, we get higher mortality rates than actually occurred.
I love this analysis, thanks for doing it.
First, let me say that yes, my model is very sensitive to mortality estimates in mild cooling scenarios. My estimate may be too high, but I believe there are compelling reasons not to be confident of this.
To illustrate my model for mortality in a mild cooling event (1-2.65 degrees cooling):
2% probability of 6% mortality (no adaptation, no food trade)
18% probability of 3% mortality (no food trade)
80% probability of no mortality
This gives an average of approximately 0.6% mortality
My counterarguments are as follows:
On a broad level, I think that a ‘panic’ scenario could include countries banning food exports to secure domestic supplies. This would be catastrophic for some food-importing countries even in normal climate conditions. The same cannot be said for the world of 1815, where almost all food was consumed locally and very few people lived far from agricultural areas.
I think the comparison with 1815 is well worth doing. However, there are a number of reasons why the validity of the comparison is limited:
A global 1% mortality event in 1815 may not have even been noticed. We have to patch together estimates of famine mortality in 1815 because there was almost no systematic documentation at the time. People were not aware of any global phenomenon, so nobody was trying to “join up the dots” and tally the full famine impact that year. Especially if much of the effects were felt in South Asia, East Asia or Africa, it is possible that a major-yet-distributed famine could have gone unnoticed
Relatively few people in the world of 1815 relied on food imports, as mentioned above[1]. Breakdown in international food trade is the main famine mechanism in modern-day agricultural catastrophes, but it barely applies to the agrarian economies of 1815.
Local famine effects may not have been worse in Indonesia. Undoubtedly, the effects of ash blanketing crops would have been worse near the eruption. But stratospheric soot quickly circulates around the world. Furthermore, Indonesia has a warm climate and would not have been at risk from completely failed harvests through unseasonal frost. Multiple annual harvests are common in the tropics; in higher latitudes, a failed crop leaves farmers without food for almost a year.
Cooling damage is highly superlinear. The Pinatubo eruption of 1991 caused 0.5 degrees of cooling and is not associated with important declines in agricultural productivity. Thus we might expect the expected burden of an 1815-level cooling event (0.8 to 1.3 degrees cooling) to be far lower than a 1-2.65 degree cooling event[2].
Poverty is a strong predictor of famine mortality. If your persistence estimate relied only on poverty & malnutrition burden indicators, the full-term benefits of policy advocacy would be significantly lower.
To push back:
A world with no extreme poverty and no nutritional diseases would still be vulnerable to global agricultural catastrophes. The advantage of my “grain production per capita” metric is that it has no such edge-case problems: more grain is always good for food security.
The famine/poverty relationship is strong in normal times. There are a number of reasons that it may become less strong in an agricultural catastrophe
Many of the global poor are subsistence farmers in warm countries. This demographic will be close to food supplies and away from the risk of frosts etc.. They may be better-positioned to survive than their middle-class compatriots in cities.
People at the bottom of the pile are the most at-risk of post-catastrophe famine, regardless of whether they are in absolute poverty. A wealthier world would simply have higher food prices, leaving the poorest without enough to eat.
As described above, the main cause of famine in my model is the breakdown of food trade. It is not clear that progress against poverty and nutritional diseases is an indication that populations are less dependent on food trade. If anything, development is probably associated with increased reliance on trade.
Thanks again for the detailed feedback!!
Admittedly, this would have made some people more vulnerable as it was difficult to relieve famine-stricken areas.
A counterargument could be that Western Europe appears to have had particularly bad summer cooling in 1816 - as well as better record-keeping than much of the world—and their famines were not so bad. On the other hand, spring cooling may be more important, as late frosts can ruin harvests of wheat, potatoes etc.
Thanks for the clarifying comment, Stan! I strongly upvoted it.
As a preliminary note, I think it makes a lot of sense to give feedback on analyses like yours privately, but I wonder whether it is worth for me to invest significant time in writing comments like mine above. It seems that they are often downvoted, and that I can sometimes tell before hand when this is going to be case. So, to the extent karma is a good proxy for what people value, I wonder whether I am just spending signicant time on doing something which has little value. In this particular case, I am still guessing it was worth it because, even if it had negligible value to the public, it was still relevant for my own cause prioritisation (and making it public had little cost).
For what is worth, I was already aware of the arguments you mentioned, and directionally agree with all the points you make. I just think their effect is not as strong as you do, so I maintain my adjustments are warranted.
In any case:
Could you comment on the 2 points above?
Agreed, so I adjusted for underreporting in my calculations. I considered the actual mortality to be 13.9 (= 1⁄0.0721) times as high as the reported one.
Agreed, so I adjusted less strongly your mortality rates for more severe coolings:
I am sad to see your comment getting downvotes as I do think it contributes a lot of value to the discussion.
I can guess why you might be getting them. You often respond to cause-prio posts with “what about corporate campaigns for chicken welfare?”, and many people now probably switch off and downvote when they see this. Maybe just keep the chicken comparison to one line and link to your original post/comment?
Also, you comment is 3200 words long—about 3x longer than the actual post. I think a 200-word summary-of-the-comment with bullet points would be really useful for readers who have only read this post and are unable to pick up the finer points of your modeling critique.
On animal welfare
I think that if you adopt RP’s moral weight estimates and reject speciesism, it is almost inevitable that the most cost-effective interventions to improve wellbeing will be animal welfare interventions.
My understanding is that CEARCH is not against evaluating animal welfare interventions in principle, but in practice we are not doing so while comparisons between human and animal welfare remain so shaky. Our research direction is also partly driven by the value of information, ie. how much resources we can plausibly redirect and the impact this will have. Maybe this is too deterministic of me, but I feel that banging the drum about corporate chicken campaigns will only open so many wallets.
Thanks for the feedback on the votes and animal welfare comparison!