That’s why I only attributed half of the impact of total loss of international food trade. If I attributed all the impact, it would have been 4.43%+5.2% = 9.6% mortality. I don’t see how you are getting 5.67% mortality.
I was assuming 50 % reduction in international trade, and 50 % of that reduction being caused by climatic effects, so only 25 % (= 0.5^2) caused by climatic effects. I have changed “50 % of it” to “50 % of this loss” in my original reply to clarify.
My understanding is that you chose this piecewise linear function to be null at 11.3 Tg because that’s where the blue and gray dotted lines crossed, meaning that it appeared that the climate impacts did not kill anyone below 11.3 Tg.
Yes, that is quite close to what I did. The lines you describe intersect at 10.5 Tg, but I used 11.3 Tg because I believe Xia 2022 overestimates the duration of the climatic effects.
But what I’m arguing is that those two lines had different assumptions about feeding food to animals and waste, so the conclusion is not correct that there was no climate mortality below 11.3 Tg. And this is supported by the fact that there are currently under nutrition deaths
I was guessing this does not matter much because I think the famine deaths for 0 Tg for the following cases are similar:
No international food trade, and current food production. This matches the blue line of Fig. 5b I used to adjust the top line to include international food trade, and corresponds to 5.2 % famine deaths.
No international food trade, all livestock grain fed to humans, and no household food waste. This is the case I should ideally have used to adjust the top line, and corresponds to less than 5.2 % famine deaths.
Since the 2nd case has less famine deaths, I am overestimating the effect of having international food trade, thus underestimating famine deaths. My guess for the effect being small stems from, in Fig. 5b, the cases for which there are climatic effects (5 redish lines, and 2 greyish lines) all seemingly converging as the soot injected into the stratosphere tends to 0 Tg:
The convergence of the redish and greyish lines makes intuitive sense to me. If it was possible now to, without involving international food trade, decrease famine deaths by feeding livestock grain to humans or decreasing household food waste, I guess these would have already been done. I assume countries would prefer less famine deaths over greater animal consumption or household food waste.
there are currently under nutrition deaths, and any nonzero Tg is likely to increase those deaths.
I guess famine deaths due to the climatic effects are described by a logistic function, which is a strictly increasing function, so I agree with the above. However, I guess the increase will be pretty small for low levels of soot.
There are many ways that things could go worse than that scenario. As I have mentioned, there could be reductions in nonfood trade, such as fertilizers, pesticides, agricultural equipment, energy, etc. There could be further international conflict. There could be civil unrest in countries and a breakdown of the rule of law. If there is loss of cooperation outside of people known personally, it could mean a return to foraging, or ~99.9% mortality if we returned to the last time we were all hunter-gatherers. But it could be worse than this given the people initially would not be very good foragers, the climate would be worse, and we could cause a lot of extinctions during the collapse. The very worst case scenario is if there is insufficient food, if it were divided equally, everyone would starve to death.
There arereasons pointing in the other direction too. In general, I think further more empirical investigation usually leads to lower risk estimates (cf. John Halstead’s climate change and longtermism report). I am trying to update all the way now (relatedly), such that I do not (wrongly) expect risk to decrease (the rational thing is expecting best guesses to stay the same, although this is still compatible with higher than 50 % chance of the best guess decreasing).
As above, even if the baseline expectation were extinction, there could be high cost effectiveness of saving lives from resilient foods by shifting us away from that scenario, so I disagree with “The cost-effectiveness of saving lives is negligible for … sufficiently high mortality.”
I just meant the cost-effectiveness of saving lives tends to 0 as the expected population loss (accounting for preparation, response and resilience) tends to 100 %. An expected population loss of exactly 100 % means extinction with 100 % probability, in which case there is no room to save lives (nor to avoid extinction). Of course, this is a very extreme unrealistic case, but it illustrates cost-effectiveness will start decreasing at some point, so “I wonder what is the death rate for which cost-effectiveness is maximum”. On way of thinking about it is that, although importance always increases with mortality, the decrease in tractability after a certain point is sufficient for cost-effectiveness to decrease too.
I was assuming 50 % reduction in international trade, and 50 % of that reduction being caused by climatic effects, so only 25 % (= 0.5^2) caused by climatic effects. I have changed “50 % of it” to “50 % of this loss” in my original reply to clarify.
That makes sense. Thanks for putting the figure in!
I guess famine deaths due to the climatic effects are described by a logistic function, which is a strictly increasing function, so I agree with the above. However, I guess the increase will be pretty small for low levels of soot.
If it were linear starting at 10.5 Tg and going to 22.1 Tg, versus linear starting at 0 Tg and going to 22 Tg, then I think the integral (impact) would be about four times as much. But I agree if you are going linear from 10.4 Tg versus logistic from 0 Tg, the difference would not be as large. But it still could be a factor of two or three, so I think it’s good to run a sensitivity case.
If it were linear starting at 10.5 Tg and going to 22.1 Tg, versus linear starting at 0 Tg and going to 22 Tg, then I think the integral (impact) would be about four times as much.
You are right about that integral, but I do think that is the relevant BOTEC. What we care about is the mean death rate (for a given input soot distribition), not its integral. For example, for a uniform soot distribution ranging from 0 to 37.4 Tg (= 2*18.7), whose mean matches mine of 18.7 Tg[1], the middle points of the linear parts would be:
If the linear part started at 10.5 Tg, 7.27 % (= ((10.5 + 37.4)/2 − 10.5)/(18.7 − 10.5)*0.0443).
If the linear part started at 0 Tg, 10.1 % (= ((0 + 37.4)/2 − 0)/(18.7 − 10.5)*0.0443).
So the mean death rates would be:
If the linear part started at 10.5 Tg, 5.23 % (= (10.5*0 + (37.4 − 10.5)*0.0727)/37.4).
If the linear part started at 0 Tg, 10.1 %.
This suggests famine deaths due to the climatic effects would be 1.93 (= 0.101/0.0523) times as large if the linear part started at 0 Tg.
Another way of running the BOTEC is considering an effective soot level, equal to the soot level minus the value at which the linear part starts. My effective soot level is 8.20 Tg (= 18.7 − 10.5), whereas it would be 18.7 Tg if the linear part started at 0 Tg, which suggests deaths would be 1.78 (= 18.7/10.5) times as large in the latter case. Using a logistic function instead of a linear one, I think the factor would be quite close to 1.
But I agree if you are going linear from 10.4 Tg versus logistic from 0 Tg, the difference would not be as large. But it still could be a factor of two or three, so I think it’s good to run a sensitivity case.
The challenge here is that the logistic function f(x) = a + b/(1 + e^(-k(x—x_0))) has 4 parameters, but I only have 3 conditions, f(0) = 0, f(18.7) = 0.0443, f(+inf) = 1. I think this means I could define the 4th condition such that the logistic function stays near 0 until 10.5 Tg.
Ideally, I would define the logistic function for f(0) = 0 and f(+inf) = 1, but then finding its parameters fitting it to the 16, 27, 37, 47 and 150 Tg cases of Xia 2022 for international food trade, all livestock grain fed to humans, and no household food waste. Then I would use f(18.7) as the death rate. Even better, I would get a distribution for the soot, generate N samples (x_1, x_2, …, and x_N), and then use (f(x_1) + f(x_2) + … + f(x_N))/N as the death rate.
I was assuming 50 % reduction in international trade, and 50 % of that reduction being caused by climatic effects, so only 25 % (= 0.5^2) caused by climatic effects. I have changed “50 % of it” to “50 % of this loss” in my original reply to clarify.
Yes, that is quite close to what I did. The lines you describe intersect at 10.5 Tg, but I used 11.3 Tg because I believe Xia 2022 overestimates the duration of the climatic effects.
I was guessing this does not matter much because I think the famine deaths for 0 Tg for the following cases are similar:
No international food trade, and current food production. This matches the blue line of Fig. 5b I used to adjust the top line to include international food trade, and corresponds to 5.2 % famine deaths.
No international food trade, all livestock grain fed to humans, and no household food waste. This is the case I should ideally have used to adjust the top line, and corresponds to less than 5.2 % famine deaths.
Since the 2nd case has less famine deaths, I am overestimating the effect of having international food trade, thus underestimating famine deaths. My guess for the effect being small stems from, in Fig. 5b, the cases for which there are climatic effects (5 redish lines, and 2 greyish lines) all seemingly converging as the soot injected into the stratosphere tends to 0 Tg:
The convergence of the redish and greyish lines makes intuitive sense to me. If it was possible now to, without involving international food trade, decrease famine deaths by feeding livestock grain to humans or decreasing household food waste, I guess these would have already been done. I assume countries would prefer less famine deaths over greater animal consumption or household food waste.
I guess famine deaths due to the climatic effects are described by a logistic function, which is a strictly increasing function, so I agree with the above. However, I guess the increase will be pretty small for low levels of soot.
There are reasons pointing in the other direction too. In general, I think further more empirical investigation usually leads to lower risk estimates (cf. John Halstead’s climate change and longtermism report). I am trying to update all the way now (relatedly), such that I do not (wrongly) expect risk to decrease (the rational thing is expecting best guesses to stay the same, although this is still compatible with higher than 50 % chance of the best guess decreasing).
I just meant the cost-effectiveness of saving lives tends to 0 as the expected population loss (accounting for preparation, response and resilience) tends to 100 %. An expected population loss of exactly 100 % means extinction with 100 % probability, in which case there is no room to save lives (nor to avoid extinction). Of course, this is a very extreme unrealistic case, but it illustrates cost-effectiveness will start decreasing at some point, so “I wonder what is the death rate for which cost-effectiveness is maximum”. On way of thinking about it is that, although importance always increases with mortality, the decrease in tractability after a certain point is sufficient for cost-effectiveness to decrease too.
That makes sense. Thanks for putting the figure in!
If it were linear starting at 10.5 Tg and going to 22.1 Tg, versus linear starting at 0 Tg and going to 22 Tg, then I think the integral (impact) would be about four times as much. But I agree if you are going linear from 10.4 Tg versus logistic from 0 Tg, the difference would not be as large. But it still could be a factor of two or three, so I think it’s good to run a sensitivity case.
You are right about that integral, but I do think that is the relevant BOTEC. What we care about is the mean death rate (for a given input soot distribition), not its integral. For example, for a uniform soot distribution ranging from 0 to 37.4 Tg (= 2*18.7), whose mean matches mine of 18.7 Tg[1], the middle points of the linear parts would be:
If the linear part started at 10.5 Tg, 7.27 % (= ((10.5 + 37.4)/2 − 10.5)/(18.7 − 10.5)*0.0443).
If the linear part started at 0 Tg, 10.1 % (= ((0 + 37.4)/2 − 0)/(18.7 − 10.5)*0.0443).
So the mean death rates would be:
If the linear part started at 10.5 Tg, 5.23 % (= (10.5*0 + (37.4 − 10.5)*0.0727)/37.4).
If the linear part started at 0 Tg, 10.1 %.
This suggests famine deaths due to the climatic effects would be 1.93 (= 0.101/0.0523) times as large if the linear part started at 0 Tg.
Another way of running the BOTEC is considering an effective soot level, equal to the soot level minus the value at which the linear part starts. My effective soot level is 8.20 Tg (= 18.7 − 10.5), whereas it would be 18.7 Tg if the linear part started at 0 Tg, which suggests deaths would be 1.78 (= 18.7/10.5) times as large in the latter case. Using a logistic function instead of a linear one, I think the factor would be quite close to 1.
The challenge here is that the logistic function f(x) = a + b/(1 + e^(-k(x—x_0))) has 4 parameters, but I only have 3 conditions, f(0) = 0, f(18.7) = 0.0443, f(+inf) = 1. I think this means I could define the 4th condition such that the logistic function stays near 0 until 10.5 Tg.
Ideally, I would define the logistic function for f(0) = 0 and f(+inf) = 1, but then finding its parameters fitting it to the 16, 27, 37, 47 and 150 Tg cases of Xia 2022 for international food trade, all livestock grain fed to humans, and no household food waste. Then I would use f(18.7) as the death rate. Even better, I would get a distribution for the soot, generate N samples (x_1, x_2, …, and x_N), and then use (f(x_1) + f(x_2) + … + f(x_N))/N as the death rate.
18.7 Tg is the mean stratospheric soot until the end of year 2 corresponding to an initial injection of 22.1 Tg.