I think this piece could be more effective if it more clearly spells out the relationship between human life expectancy, land use, and invertebrate welfare. E.g. when you say "I estimate it increases the welfare of humans, and soil nematodes, mites, and springtails by 10.5 k QALY/$" I'm having trouble understanding what the relationship is between humans eating a healthier diet and invertebrate welfare.
Ok, thanks, that does make it easier to follow the argument.
Whatever one's goals, I'd caution against taking quick micromort estimates literally. E.g. I think the "walking on public roads is bad for you" data only includes your risk of getting hit by a car, and doesn't include the health benefits of walking, nor that pedestrian deaths are disproportionately at night and the victims are often intoxicated. Daytime walking while sober is overall good for longevity.
David and Mike do not say what is included in the risk of death from walking on public roads in the UK. I guess it does not include health benefits.
The reductions in life expectancy apply to random exposure covered in the estimation of the risk. For example, a random 1 km of walking on public roads in the UK considered in David and Mike's estimations, which should include walking at night and intoxicated. However, I agree it makes sense to assume a lower risk if one's exposure avoids the conditions where the risk is concentrated.
Reduction in life expectancy, and agricultural land due to consuming sugar-sweetened beverages, sodium, and unprocessed red meat
I recommended funding the Centre for Exploratory Altruism Research’s (CEARCH’s) High Impact Philanthropy Fund (HIPF) to increase the welfare of soil nematodes, mites, and springtailsvia increasing agricultural land as a result of decreasing human mortality. However, I think CEARCH’s grants decrease not only human mortality, but also calorie consumption. I had not covered this effect which decreases agricultural land. Accounting for both effects, I estimate funding HIPF increases agricultural land 78.5 % as cost-effectively as I had calculated, corresponding to 1.29 k m2-year/$. In addition, I estimate it increases the welfare of humans, and soil nematodes, mites, and springtails by 10.5 k QALY/$, 16.5 times as cost-effectively as the Shrimp Welfare Project’s (SWP’s) Humane Slaughter Initiative (HSI) has increased the welfare of shrimp.
I get the following (expected) reductions in life expectancy:
For the consumption of 1 g of sodium, 0.558 person-min.
For walking 1 km on public roads in the United Kingdom (UK), 0.855 person-min.
For the consumption of 100 mL of sugar-sweetened beverages (SSBs), 5.47 person-min.
For driving a car 100 km in the UK, 6.62 person-min.
For the consumption of 100 g of unprocessed red meat in the United States (US), 9.06 person-min.
For a man (woman) consuming 10 g of alcohol, 16.6 (33.2) person-min.
For a man (woman) smoking 1 cigarette, 17 (22) person-min.
I maintain my recommendation of funding HIPF. In addition, I encourage effective givinginitiatives (EGIs) to advocate for donating to organisations saving human lives cost-effectively over ones targeting animals. The cost-effectiveness of advocating for an intervention is the cost-effectiveness of the intervention times the money moved to the intervention as a fraction of the spending advocating for it. I expect funding HIPF or GiveWell’s funds to be roughly as cost-effective as the organisations I like working on invertebrate welfare, but I believe EGIs can move much more money to organisations targeting humans.
Introduction
I recommended funding CEARCH’s HIPF to increase the welfare of soil nematodes, mites, and springtails via increasing agricultural land as a result of decreasing human mortality, and therefore decreasing the living time of those soil animals, which I guess have negative lives. However, I think CEARCH’s grants until 27 May 2025 of 63 k$ to decrease the consumption of SSBs, and 150 k$ to decrease the consumption of sodium (in salt) decrease not only human mortality, but also calorie consumption. I had not covered this effect which decreases agricultural land. In this post, I estimate the reduction in life expectancy, and change in agricultural land accounting for both of the aforementioned effects due to consuming SSBs, sodium, and unprocessed red meat. For context, I also calculate the reduction in life expectancy due to drinking alcohol, driving a car, smoking tobacco, and walking on public roads. Here are my calculations.
SSBs
The deaths as a fraction of the global population in 2019 were 0.736 %. So the risk of death in 2019 was 0.00202 % per person-day.
Based on Wang et al. (2022), I consider consuming 250 mL/person-day more of SSBs is associated with a 7 % higher risk of death. I emailed the 1st and corresponding authors on 5 July 2025 asking about a guess for which fraction of this association is causal, but I have not heard back. Using Gemini 2.5 Pro’s best guess of 60 %, I conclude each 100 mL/person-day more of SSBs increases the risk of death by 1.68 %. Multiplying this by the above risk of death in 2019, I infer consuming SSBs increases the risk of death by 3.39*10^-7 per 100 mL.
There were 1.75 billionyears of life lost, and 57.0 M deaths in 2019, which imply 30.7 years of life lost per death in 2019. This times the increase in the risk of death due to consuming SSBs equals 5.47 person-min less of life expectancy per 100 mL.
The agricultural land per capita in 2022 was 6.0 k m2-year/person-year. This times the increase in the risk of death due to consuming SSBs leads to 0.0624 m2-year/(100 mL) less of agricultural land from reduced life expectancy.
Coca-Cola, arguably the most famous SSB, has 240 kcal per 20 fl oz, 40.0 kcal/(100 mL). Its calories come from high-fructose corn syrup (HFCS), which has 2.81 kcal/HFCS-g. So Coca-Cola has 14.2 HFCS-g/(100 mL). 25 kg of corn are needed to produce 15.1 kg of corn syrup, 1.66 corn-g/HFCS-g. Consequently, Coca-Cola needs 23.6 corn-g/(100 mL). Corn yield in 2023 was 0.596 corn-kg/m2-year. Dividing the corn needed by this, I determine consuming SSBs increases agricultural land by 0.0395 m2-year/(100 mL) without accounting for that of what replaces them, and their effect on life expectancy. I suppose SSBs are replaced with water, and sugar-free versions of the SSBs, which require a negligible amount of agricultural land. So I calculate consuming SSBs increases agricultural land by 0.0395 m2-year/(100 mL) without accounting for their effect on life expectancy.
From the above, I conclude consuming SSBs decreases agricultural land by 0.0228 m2-year/(100 mL), 36.6 % of the reduction in it from the reduction in life expectancy. So I estimate decreasing the consumption of SSBs increases agricultural land 36.6 % as cost-effectively as it would if it only increased human-years.
Sodium
The deaths from high systolic blood pressure (HSBP) as a fraction of the global population in 2019 were 0.136 %. So the risk of death from HSBP in 2019 was 3.72*10^-6 per person-day.
From CEARCH’s cost-effectiveness analysis of advocacy for decreasing the consumption of sodium, 1 sodium-g/person-day less decreases the disease burden of HSBP by 1.46 %. I assume this matches the relative reduction in the years of life lost due to HSBP. Multiplying it by the above risk of death from HSBP in 2019, I infer consuming sodium increases the risk of death by 5.44*10^-8 per sodium-g.
HSBP caused 205 M years of life lost, and 10.5 M deaths in 2019, which imply 19.5 years of life lost per death from HSBP in 2019. This times the increase in the risk of death due to consuming sodium equals 0.558 person-min less of life expectancy per sodium-g.
The agricultural land per capita in 2022 times the increase in the risk of death due to consuming sodium leads to 0.00637 m2-year/sodium-g less of agricultural land from reduced life expectancy.
Based on Table 2 of Grimes et al. (2020), I consider consuming 0.393 g more of sodium is associated with an additional consumption of SSBs of 1 g. For the density of Coca-Cola of 1.026 SSB-g/mL, this is 2.48 mL/sodium-g. I emailed the 1st and corresponding author on 5 July 2025 asking about a guess for which fraction of the association is causal, but I have not heard back. Using Gemini 2.5 Pro’s best guess of 25 %, I conclude each 1 g more of sodium increases the consumption of SSBs by 0.620 mL. Combining this with my estimate for the reduction in life expectancy due to consuming SSBs, I calculate a reduction in life expectancy due to increasing the consumption of these as a result of consuming sodium of 0.0339 person-min/sodium-g, 6.07 % of the above total reduction in life expectancy.
I suppose consuming sodium only increases agricultural land due to resulting in an increased consumption of SSBs. As a result, I obtain an increase in agricultural land due to consuming sodium without accounting for that of what replaces it, and its effect on life expectancy of 2.45*10^-4 m2-year/sodium-g, and this same value accounting for what replaces it, but without accounting for its effect on life expectancy.
From the above, I conclude consuming sodium decreases agricultural land by 0.00612 m2-year/(100 mL), 96.2 % of the reduction in it from the reduction in life expectancy. So I estimate decreasing the consumption of sodium increases agricultural land 96.2 % as cost-effectively as it would if it only increased human-years.
Unprocessed red meat
Based on Wang et al. (2015), I consider consuming 1 portion/person-day more of unprocessed red meat in the US is associated with a 15 % higher risk of death. I emailed the corresponding authors on 6 July 2025 asking about a guess for which fraction of this association is causal, but I have not heard back. Using Gemini 2.5 Pro’s best guess of 15 %, I conclude each 1 portion/person-day more of unprocessed red meat in the US increases the risk of death by 2.25 %.
The consumption of beef, and buffalo in the US was 56.0 % of that of beef, buffalo, and pork, and the consumption of pork in the US covered the other 44.0 %. I estimated 1 portion of beef has 73.7 g, and 1 portion of pork has 89.8 g. I weight these by the aforementioned fractions to compute a mass of 80.8 meat-g per portion of unprocessed red meat. Dividing the above relative increase in the risk of death by this, I get a relative increase in the risk of death due to consuming unprocessed red meat in the US of 2.19 % per 100 meat-g/person-day. Multiplying this by the risk of death in 2019, I infer consuming unprocessed red meat in the US increases the risk of death by 5.61*10^-7 per 100 meat-g.
The years of life lost per death in 2019 times the increase in the risk of death due to consuming unprocessed red meat in the US equals 9.06 person-min less of life expectancy per 100 meat-g.
The agricultural land per capita in 2022 times the increase in the risk of death due to consuming unprocessed red meat in the US leads to 0.103 m2-year/(100 meat-g) less of agricultural land from reduced life expectancy.
Weighting the agricultural land of beef (beef herd) and pig meat of 336 and 17.4 m2-year/(100 meat-g) by the above fractions of 56.0 % and 44 %, I arrive to 190 m2-year/(100 meat-g) of agricultural land for unprocessed red meat in the US. I suppose this is replaced by poultry meat taking 12.2 m2-year/(100 meat-g). So I calculate consuming unprocessed red meat in the US increases agricultural land by 178 m2-year/(100 meat-g) without accounting for its effect on life expectancy.
From the above, I conclude consuming unprocessed red meat in the US increases agricultural land by 178 m2-year/(100 meat-g), 1.72 k times the reduction in it from the reduction in life expectancy.
Other estimates of reductions in life expectancy
Drinking alcohol
Based on Table 1 of Spiegelhalter (2012), I consider men (women) consuming 10 g/person-day more of alcohol have a 6 % (12 %) higher risk of death. Using Gemini 2.5 Pro’s best guess that 85 % of this association is causal, I conclude men (women) consuming 10 g/person-day more of alcohol increases their risk of death by 5.10 % (10.2 %). Multiplying this by the risk of death in 2019, I infer men (women) consuming alcohol increase their risk of death by 1.03*10^-6 (2.06*10^-6) per 10 alcohol-g.
The years of life lost per death in 2019 times the increase in the risk of death due to men (women) consuming alcohol equals 16.6 (33.2) person-min less of life expectancy per 10 alcohol-g.
Driving a car
David Spiegelhalter and Mike Pearson calculated driving a car in the UK for 250 mi increases one’s risk of death by 1 micromort, which is 2.48*10^-7 per 100 km.
Motor vehicle road injuries (excluding motorcyclists’) caused 22.1 M years of life lost, and 436 k deaths in 2019, which imply 50.7 years of life lost per death from motor vehicle road injuries in 2019. This times the increase in the risk of death due to driving a car in the UK equals 6.62 person-min less of life expectancy per 100 km.
Smoking tobacco
From Jackson et al. (2024), smoking tobacco decreases the life expectancy of men and women by 17 and 22 person-min/cigarette.
Walking on public roads
David and Mike calculated walking on public roads in the UK for 17 mi increases one’s risk of death by 1 micromort, which is 3.65*10^-8 per km.
Pedestrian road injuries (excluding motorcyclists’) caused 20.2 M years of life lost, and 454 k deaths in 2019, which imply 44.5 years of life lost per death from pedestrian road injuries in 2019. This times the increase in the risk of death due to walking on public roads in the UK equals 0.855 person-min less of life expectancy per km.
Reduction in life expectancy, and agricultural land
Below are my estimates for the reduction in life expectancy, and agricultural land. I listed the activities by ascending reduction in life expectancy. Wikipedia has a page with estimates for how lifestyle and demographic risk factors correlate with, not cause, reductions in life expectancy.
Activity
Reduction in life expectancy (person-min)
Reduction in agricultural land (m²-year)
Consuming 1 g sodium
0.558
0.00612
Walking 1 km on public roads in the UK
0.855
Consuming 100 mL of SSBs
5.47
0.0228
Driving a car 100 km in the UK
6.62
Consuming 100 g of unprocessed red meat in the US
9.06
178
Man (woman) consuming 10 g of alcohol
16.6 (33.2)
Man (woman) smoking 1 cigarette
17 (22)
Cost-effectiveness of funding HIPF
I estimated decreasing the consumption of SSBs and sodium increases agricultural land 36.6 % and 96.2 % as cost-effectively as it would if it only increased human-years. Based on the information about CEARCH’s grants I shared in the introduction, among the 213 k$ CEARCH granted to decrease the consumption of SSBs and sodium, 29.6 % went to decreasing SSBs, and 70.4 % to decreasing sodium. So I assume 29.6 % and 70.4 % of the marginal funding of HIPF decreases the consumption of SSBs and sodium. Consequently, I estimate funding HIPF increases agricultural land 78.5 % as cost-effectively as I had calculated, corresponding to 1.29 k m2-year/$. In addition, I estimate it increases the welfare of humans, and soil nematodes, mites, and springtails by 10.5 k QALY/$, 16.5 times as cost-effectively as SWP’s HSI has increased the welfare of shrimp.
My recommendations
I maintain my recommendation of funding HIPF. In addition, I encourage EGIs to advocate for donating to organisations saving human lives cost-effectively without increasing calorie consumption over ones targeting animals. The cost-effectiveness of advocating for an intervention is the cost-effectiveness of the intervention times the money moved to the intervention as a fraction of the spending advocating for it. I expect funding HIPF or GiveWell’s funds to be roughly as cost-effective as the organisations I like working on invertebrate welfare, but I believe EGIs can move much more money to organisations targeting humans.
Sharing information about the risks of supposedly harmful consumption is generally better to increase human welfare than taxing it?
I often tell friends and family about the loss in life expectancy caused by the consumption of alcohol, and unprocessed red meat, and smoking tobacco. My personal experience is that people do not update their consumption much as a result. I feel this is explained by some scepticism about the risks (particularly of unprocessed red meat), discounting of future welfare (which is egoistic), but also by genuinely different preferences. Sharing information about the risks of supposedly harmful consumption empowers people to make the best choices for them. So I think it is generally better to increase human welfare than taxing such consumption.
CEARCH’s cost-effectiveness analyses of political work to decrease the consumption of SSBs and sodium model the harm from lower freedom of choice based on a survey of 16 people. I have not reviewed this, but I guess the harm has been underestimated due to these people caring more about health than the target consumers, and some other issues pointed out in the comments of CEARCH’s cost-effectiveness analysis of political work to decrease the consumption of SSBs.
Acknowledgements
Thanks to Anonymous Person for noting HIPF’s interventions may decrease calorie consumption. The views expressed in the post are my own.
I used AI to fix transcription errors, rerrarange the ideas, and suggest tweaks to the title and some sentences.
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This post presents the executive summary from Giving What We Can’s impact evaluation for 2025. At the end of this post we share links to more information, including the full report and...
I think this piece could be more effective if it more clearly spells out the relationship between human life expectancy, land use, and invertebrate welfare. E.g. when you say "I estimate it increases the welfare of humans, and soil nematodes, mites, and springtails by 10.5 k QALY/$" I'm having trouble understanding what the relationship is between humans eating a healthier diet and invertebrate welfare.
Thanks for the comment, Julia! The 1st sentence of the post and introduction is supposed to clarify that.
I have now updated that sentence to the following.
Ok, thanks, that does make it easier to follow the argument.
Whatever one's goals, I'd caution against taking quick micromort estimates literally. E.g. I think the "walking on public roads is bad for you" data only includes your risk of getting hit by a car, and doesn't include the health benefits of walking, nor that pedestrian deaths are disproportionately at night and the victims are often intoxicated. Daytime walking while sober is overall good for longevity.
Thanks for the good points, Julia.
David and Mike do not say what is included in the risk of death from walking on public roads in the UK. I guess it does not include health benefits.
The reductions in life expectancy apply to random exposure covered in the estimation of the risk. For example, a random 1 km of walking on public roads in the UK considered in David and Mike's estimations, which should include walking at night and intoxicated. However, I agree it makes sense to assume a lower risk if one's exposure avoids the conditions where the risk is concentrated.