Disclaimer: this is not a project from Alliance to Feed the Earth in Disasters (ALLFED).

Summary

• Global warming increases the risk from climate change. This “has the potential to result in—and to some extent is already resulting in—increased natural disasters, increased water and food insecurity, and widespread species extinction and habitat loss”.
• However, I think global warming also decreases the risk from food shocks caused by abrupt sunlight reduction scenarios (ASRSs), which can be a nuclear winter, volcanic winter, or impact winter^[1]. In essence, because low temperature is a major driver for the decrease in crop yields that can lead to widespread starvation (see Xia 2022, and this post from Luisa Rodriguez).

• Factoring in both of the above, my best guess is that additional emissions of greenhouse gases (GHGs) are beneficial up to an optimal median global warming in 2100 relative to 1880 of 3.3 ºC, after which the increase in the risk from climate change outweighs the reduction in that from ASRSs. This suggests delaying decarbonisation is good at the margin if one trusts (on top of my assumptions!):
  • Metaculus’ community median prediction of 2.41 ºC.
  • Climate Action Tracker’s projections of 2.6 to 2.9 ºC for current policies and action.
• Nevertheless, I am not confident the above conclusion is resilient. My sensitivity analysis indicates the optimal median global warming can range from 0.1 to 4.3 ºC. So the takeaway for me is that we do not really know whether additional GHG emissions are good/bad.
• In any case, it looks like the effect of global warming on the risk from ASRSs is a crucial consideration, and therefore it must be investigated, especially because it is very neglected. Another potentially crucial consideration is that an energy system which relies more on renewables, and less on fossil fuels is less resilient to ASRSs.
• Robustly good actions would be:
  • Improving civilisation resilience.
  • Prioritising the risk from nuclear war over that from climate change (at the margin).
  • Keeping options open by:
    • Not massively decreasing/increasing GHG emissions.
    • Researching cost-effective ways to decrease/increase GHG emissions.
  • Learning more about the risks posed by ASRSs and climate change.

Introduction

In the sense that matters most for effective altruism, climate change refers to large-scale shifts in weather patterns that result from emissions of greenhouse gases such as carbon dioxide and methane largely from fossil fuel consumption. Climate change has the potential to result in—and to some extent is already resulting in—increased natural disasters, increased water and food insecurity, and widespread species extinction and habitat loss.

In What We Owe to the Future (WWOF), William MacAskill argues “decarbonisation [decreasing GHG emissions] is a proof of concept for longtermism”, describing it as a “win-win-win-win-win”. In addition to (supposedly) improving the longterm future:

• “Moving to clean energy has enormous benefits in terms of present-day human health. Burning fossil fuels pollutes the air with small particles that cause lung cancer, heart disease, and respiratory infections”.
• “By making energy cheaper [in the long run], clean energy innovation improves living standards in poorer countries”.
• “By helping keep fossil fuels in the ground, it guards against the risk of unrecovered collapse”.
• “By furthering technological progress, it reduces the risk of longterm stagnation”.

I agree decarbonisation will eventually be beneficial, but I am not sure decreasing GHG emissions is good at the margin now. As I said in my hot takes on counterproductive altruism:

• Mitigating global warming decreases the chances of crossing a tipping point which leads to a moist or runaway greenhouse effect [i.e. severe climate change], but increases the severity of ASRSs [which can be a nuclear winter, volcanic winter, or impact winter, and lead to widespread starvation; see Xia 2022, and this post from Luisa Rodriguez].
  • The major driver for the decrease in [crop] yields during an ASRS is the lower temperature, so starting from a higher baseline temperature would be helpful.
  • One might argue the severity of ASRSs is only a function of the temperature reduction, not of the final temperature, on the basis that yields are roughly directly proportional to temperature in ºC. However, this is not the case.
  • The typical base temperature of cool-season plants is 5 ºC. So, based on the heuristic of growing degree-days, a reduction from 10 ºC to 5 ºC leads to a 100 % reduction in yields, not 50 % as suggested by a direct proportionality between temperature in ºC and yields.

In this analysis, I estimated the optimal global warming to decrease the reduction in the value of the future due to both climate change and the food shocks caused by ASRSs. Note such warming may well not be optimal from the point of view of maximising gross world product (GWP) in the nearterm (e.g. in 2100).

Methods

I calculated the reduction in the value of the future as a function of the median global warming in 2100 relative to 1880 from the sum between:

• The reduction in the value of the future due to the food shocks caused by ASRSs between 2024 and 2100.
• The reduction in the value of the future due to climate change.

The data and calculations are in this Sheet (see tab “TOC”) and this Colab^[2]. You can change the variables scale_ASRS and scale_climate_change to scale the reduction in the value of the future due to ASRSs, and climate change for 2.41 ºC by a constant factor. For instance, setting those variables to 2 would make:

• ASRSs 2 times as bad for any median global warming.
• Climate change 2 times as bad for a median global warming of 2.41 ºC.

I modelled all variables as independent distributions, and ran a Monte Carlo simulation with 100 k samples per variable to get the results. Owing to the independence assumption, my model implicitly considers climate change does not impact the risk from ASRSs via increased risk from nuclear war. I believe this is about right, in agreement with Chapter 12 of John Halstead’s report on climate change and longtermism:

• Most of the indirect risk from climate change flows through unaligned artificial intelligence, engineered pandemics, and unforeseen anthropogenic risks, whose existential risk between 2021 and 2120 is guessed by Toby Ord in The Precicipe to be 100, 33.3, and 33.3 times that of nuclear war. Nevertheless, there is significant uncertainty around these estimates^[3].
• Conflicts between India and China/Pakistan are the major driver for the risk from climate change, but these only have 7.15 % (= (160 + 350 + 165)/9,440) of the global nuclear warheads according to these data from Our World in Data (OWID).

Abrupt sunlight reduction scenarios

I computed the reduction in the value of the future due to the food shocks caused by ASRSs between 2024 and 2100:

• Using this baseline model, for which:
  • The mean reduction is 37.2 basis points (bp) (5th to 95th percentile, -73.1 to 199). This is 4.83 (-9.50 to 25.9) times that of Toby given in Table 6.1 of The Precipice for the existential risk from nuclear war^[4].
  • The actual global warming relative to 1880 is 1 ºC (= 0.32 + 0.68) according to these data from OWID. This refers to the year of 2010 studied in Xia 2022, which I used to model the calorie production without adaptation in the worst year of the ASRSs (see details here).
• Inputting into the baseline model a soot ejected into the stratosphere equal to the maximum between 0 and the difference between the soot ejection in the baseline model and its effective reduction due to global warming beyond that of the baseline model^[5]. I estimated this reduction from the product between:
  • The soot ejection required to neutralise the median global warming in 2100 relative to 1880 beyond that of the baseline model, linearly interpolating between (see table below):
    • 0 Tg^[6] for 0 ºC.
    • 5, 16, 27, 37, 47 and 150 Tg for the cropland 2 m air temperature reduction given in Figure 1a of Xia 2022.
    • 15,000 Tg for the continental temperature reduction of 28 ºC mentioned in Bardeen 2017 for the impact winter of the Cretaceous–Paleogene extinction event (which extinguished the dinosaurs).
  • The effective reduction in the soot ejection as a fraction of the above, which I defined as a uniform distribution ranging from 0 to 1. Consequently, for example, for a median global warming beyond that of the baseline model of 2.36 ºC, and an ASRS of 10 Tg:
    • The effective reduction in the soot ejection due to global warming would be a uniform distribution ranging from 0 to 5 Tg, which is the soot ejection that results in a maximum temperature reduction of 2.36 ºC. At one extreme, the effective reduction in the soot ejection is null, which means the median global warming has no effect, and therefore the maximum temperature reduction caused by the ASRS is all that matters. At the other extreme, the coldest temperature of the ASRS is all that matters.
    • The soot ejection to be inputted into the baseline model would be 10 Tg minus the uniform distribution of 0 to 5 Tg regarding the effective soot reduction. This equals a uniform distribution ranging from 5 to 10 Tg.

My methodology relies on the results of the climate and crop models of Xia 2022 for a single level of global warming (1 ºC), and then adjusts them via an effective soot reduction. Ideally, one should run the climate and crop models for each level of global warming, since the climate response caused by ASRSs depends on the pre-catastrophe global mean temperature. As an example of why this might be relevant, I do not know whether there is a good symmetry between the regional effects of global cooling and warming.

Climate change

I obtained the reduction in the value of the future due to climate change from a logistic function (S-curve), which:

• Tends to 1 as the median global warming in 2100 relative to 1880 increases to infinity.
• Equals 0.5 for a median global warming in 2100 relative to 1880 represented by a normal distribution with 5th and 95th percentiles equal to 10 and 20 ºC.
  • I would have ideally used a point in the right tail, for which the reduction is higher than the 0.5 of the inflection point, to achieve a better fit (see Sandberg 2021). However, that would make the calculation of the parameters of the logistic function way more computationally costly^[7].
  • For context, the global mean temperature was about 15 ºC (the mean of the above) higher than the current one 90 and 250 million years ago (see Figures 19 to 21 of Scotese 2021).
  • What worries me the most is that, according to Schneider 2019 (FAQ here), “stratocumulus decks become unstable and break up into scattered clouds when CO2 levels rise above 1,200 ppm”. “This instability triggers a surface warming of about 8 K [warming of 8 ºC] globally and 10 K in the subtropics”.
• Has a logistic growth rate defined based on the above, and setting the reduction in the value of the future due to climate change for a median global warming in 2100 relative to 1880 of 2.41 ºC to a lognormal distribution with:
  • Mean of 0.368 bp, which I got aggregating 3 forecasts with the geometric mean of odds (as recommended by default by Jaime Sevilla):
    • The order of magnitude of John’s best guess for the indirect risk of existential catastrophe due to climate change of 0.1 bp^[8] (search for “the indirect risk of” here), which is also John’s upper bound for the “direct extinction risk”.
    • 10 % of 80,000 Hours’ upper bound for the contribution of climate change to other existential risks, which is “something like 1 in 10,000 [1 bp]”. The best guess being 10 % of the upper bound feels reasonable, and results in an estimate of 0.1 bp, as estimated by John.
    • Probability of climate change being a cause of human extinction by 2300 according to Good Judgment Inc’s climate superforecasting, which is “0.05%” considering the median prediction between 0 and 1 %. I assumed the 5 forecasts (out of 26) above 1 % to be poorly calibrated outliers (which Jaime recommends excluding).
  • 95th percentile 100 times as high as the 5th percentile, since 2 orders of magnitude between an optimistic and pessimistic estimate feels reasonable.

Note the reduction in the value of the future due to climate change for an actual global warming of 2.41 ºC would be much lower than the aforementioned 0.368 bp, but a similar median warming allows for higher levels of actual warming, which are the driver for the overall risk. I used 2.41 ºC as the reference median warming in line with this Metaculus’ community prediction (on 11 April 2023).

There is significant uncertainty about the shape of the damage from climate change, but there is consensus that it increases more than linearly with warming^[9] before ceiling effects, and therefore relying on an S-curve seems appropriate. However, since my logistic function is always positive:

• There will still be a reduction in the value of the future for a null median global warming in 2100 relative to 1880 of zero, which might seem strange. However, the actual warming can still be high even if there is a 50 % chance that it will be negative/positive.
• It implies cooling the Earth to absolute zero only leads to marginal reduction in the value of the future, which is arguably not the case!

One factor which makes my logistic function underestimate the risk from climate change is assuming it is impossible for it to be beneficial. In reality, I think it can, at least for low levels of median global warming, maybe because of carbon dioxide fertilisation (which I have ignored). Note:

• There is a 1/3 chance of the food shocks caused by ASRSs being beneficial in my baseline model, which assumes a median global warming of 1 ºC.
• The lower estimate of Bressler 2021 for the 2020 mortality cost of carbon is -75.7 % (= -1.71/2.26) the mean.

Results

The key results are in the table below, and illustrated in the following figures. After these, I also present a short sensitivity analysis. The results plotted in the figures are in this Sheet (see tab “TOC”).

Key results

In this table, median global warming refers to the one in 2100 relative to 1880.

Climate change

Note the above curves refer to a reduction in the longterm future potential, not in the GWP.

Abrupt sunlight reduction scenarios

The sharp variation starting at 31.8 ºC of median global warming in 2100 results from the soot ejected into the stratosphere as a function of the maximum temperature reduction increasing much faster after 150 Tg (which causes a maximum temperature reduction of 15.8 ºC; see last table here). This leads to a sharp increase in the effective reduction in the soot ejection, and therefore the reduction in the value of the future due to food shocks caused by ASRSs quickly approaches 0.

Global warming

The figures below refer to the reduction in the value of the future due to both food shocks caused by ASRSs between 2024 and 2100, and climate change.

Sensitivity analysis

Discussion

Optimal median global warming and crucial considerations

My best guess is that additional GHG emissions are beneficial up to an optimal median global warming in 2100 relative to 1880 of 3.3 ºC, after which the increase in the risk from climate change outweighs the reduction in that from ASRSs. This suggests delaying decarbonisation is good at the margin if one trusts (on top of my assumptions!):

• Metaculus’ community median prediction of 2.41 ºC.
• Climate Action Tracker’s projections of 2.6 to 2.9 ºC for current policies and action.

Nevertheless, I am not confident the above conclusion is resilient. My sensitivity analysis indicates the optimal median global warming can range from 0.1 to 4.3 ºC, after which the reduction in the value of the future due to climate change starts to be material. The higher bound for the expected optimal median global warming would be lower/higher if the risk from climate change increased faster/slower than the exponential implied by my logistic function (for low levels of median global warming). The takeaway for me is that we do not really know whether additional GHG emissions are good/bad.

Note the cost-effectiveness of decreasing GHG emissions would be null for the optimal median global warming (by definition). The higher the cost-effectiveness bar, the more the median global warming would have to rise above the optimal value for the reduction in GHG emissions to be sufficiently effective.

In any case, it looks like the effect of global warming on the risk from ASRSs is a crucial consideration, and therefore it must be investigated, especially because it is very neglected. It is not mentioned in Kemp 2022, Founders Pledge’s report on climate philanthropy, nor John’s book-length report on climate change and longtermism. I am not sure whether the crucial consideration falls outside of the scope of these pieces, but I believe it should be addressed somewhere.

Another potentially crucial consideration is that an energy system which relies more on renewables, and less on fossil fuels is less resilient to ASRSs.

• According to Metaculus’ median community predictions, the share of the world’s primary energy coming from fossil fuels will be 33.7 % and 8.89 % in 2052 and 2122, which are much lower than the 82.3 % of 2021.
• This matters because solar radiation and precipitation would decrease during ASRSs, as plotted in Figure 1 of Xia 2022, which means there would be less solar energy, and probably less hydropower.
• Additionally, the global wind patterns might change such that there is less wind overall, or the windy regions move, or become too cold for wind turbines to operate.
• Geothermal and nuclear energy would not be impacted, so I like that these tend to be more supported than renewables (at the margin) by Founders Pledge, but I do not know whether the benefits are enough to outweigh the unclear consequences of mitigating global warming.

Implications

In Chapter 10 of WWOF, William suggests 3 rules of thumb for acting under uncertainty:

• “Take actions that we can be comparatively confident are good”.
• “Try to increase the number of options open to us”.
• “Try to learn more”.

I believe decreasing GHG emissions would be robustly good if the median global warming in 2100 relative to 1880 were much higher than 3.3 ºC (my best guess for the optimal value), but this is far from true. My analysis is not anything close to definite, but the fact it ignores many factors arguably implies I am underestimating the uncertainty of the matter, in which case the plausible range for the optimal median global warming should be even wider. One can reject this conclusion, and argue that decarbonising faster is good by postulating a strong prior that the optimal median global warming is lower than around 2.4 ºC, but would that really be reasonable? I do not think so, because reality just seems too complex for one to be that confident.

Robustly good actions would be:

• Improving civilisation resilience, which I am comparatively confident is good^[10]. For example, via research and development of resilient foods. Solar geoengineering may be a way of getting the best of both worlds too, although it requires careful implementation.
  • It can:
    • Selectively cool down the regions adversely affected by climate change induced by global warming.
    • Quickly be interrupted in the event of an ASRS to counter the decrease in temperature caused by it.
  • However:
    • If it is stopped because of another global catastrophe like a pandemic, temperature would rapidly increase, thus causing a double catastrophe as discussed in Baum 2013. This “demonstrates the value of integrative, systems-based global catastrophic risk analysis”.
    • Schneider 2020 argues it “is not a fail-safe option to prevent global warming because it does not mitigate risks to the climate system that arise from direct effects of greenhouse gases on cloud cover”.
  • Further discussion is in Tang 2021, and this episode of the 80,000 Hours podcast with Kelly Wanser, who “founded SilverLining — a nonprofit organization that advocates research into climate interventions, such as seeding or brightening clouds, to ensure that we maintain a safe climate”.
  • Not the relevant comparison, but careful solar geoengineering would be better to counter climate change than intentionally causing a nuclear winter!
• Prioritising the risk from nuclear war, which is the major driver for the risk from ASRSs, over that from climate change (at the margin). For instance, lobbying for arsenal limitation looks like a really cost-effective intervention (pragmatic limits are discussed in Pearce 2018).
• Keeping options open by:
  • Not massively decreasing/increasing GHG emissions. This does not actually require any action, as the current best guesses for the median global warming in 2100 relative to 1880 fall well within the plausible range for the optimal value.
  • Researching cost-effective ways to decrease/increase GHG emissions. I think Founders Pledge’s report on climate philanthropy can be useful for both options. It was written with the goal of decreasing the risk from climate change in mind, but preventing cost-effective reductions of GHG emissions would be a way of cost-effectively increasing them.
• Learning more about the risks posed by ASRSs and climate change. For example, to better quantify the reduction in the value of the future due to:
  • The food shocks caused by ASRSs, study the climate and agricultural response to soot ejections into the stratosphere as a function of the initial mean global temperature, using climate and crop models.
  • Climate change, explicitly model how a higher mean global temperature would increase the risk from unaligned artificial intelligence, engineered pandemics, and unforeseen anthropogenic risks.

Acknowledgements

Thanks to David Denkenberger, Johannes Ackva, John Halstead, and Alexey Turchin for feedback on the draft^[11].

1. ^{^}

    Technically, a nuclear/volcanic/impact winter is a type of climate change too, but the term climate change throughout my text refers to the adverse effects of global warming.

2. ^{^}

    For me, the running time is 3 min.

3. ^{^}

    As mentioned, my estimate for the risk from ASRSs is 4.83 (-9.50 to 25.9) times that of Toby for the existential risk from nuclear war.

4. ^{^}

    Converting Toby’s estimate to the period of 2024 to 2100 assuming constant annual risk.

5. ^{^}

    I computed the global warming beyond that of the baseline model from the maximum between 0 and the difference between the median global warming in 2062 (= (2024 + 2100)/2) relative to 1880 and 1 ºC. 2062 is the year in the middle of the period from 2024 to 2100 for which I studied the risk from ASRSs. 1 ºC is the actual global warming in my baseline model (2010) relative to 1880.

6. ^{^}

    1 Tg equals 1 million tonnes.

7. ^{^}

    If I had defined the logistic function without using its inflection point, I would have to solve a nonlinear system of 2 equations for each Monte Carlo sample to get the logistic growth rate, and median global warming for which there is a 50 % reduction in the value of the future (T0). Since I defined this a priori, I was able to directly obtain the logistic growth rate from ln(1/“reference reduction in the value of the future” - 1)/(T0 - “reference median global warming”).

8. ^{^}

    From here, John “assume[s] that all of the risk stems from the India v Pakistan and India v China conflicts, and in turn that most of the risk of existential catastrophe stems from AI, biorisk and currently unforeseen technological risks [as stated by Toby Ord in The Precipice]”.

9. ^{^}

    See figure in section “Social cost” of Revesz 2014, which I came across via Founders Pledge’s report on climate philanthropy (search for “non-linearity of climate damage”).

10. ^{^}

     Although there is a 1/3 chance of mitigating the food shocks caused by ASRSs being harmful in my baseline model, which assumes a median global warming of 1 ºC.

11. ^{^}

     Names ordered by descending relevance of contributions.