I find this whole genre of post tedious and not very useful. If you think climate change is a good cause area, just write an actual cause prioritization analysis directly comparing it to other cause areas, and show how it’s better! If that’s beyond your reach, you can take an existing one and tweak it. This reads like academic turf warring, a demand that your cause area should get more prestige, instead of a serious attempt to help us decide which cause areas are actually most important.
1) There is a lack of evidence for the more severe impacts of climate change, rather than evidence that the impacts will not be severe.
OK, but I don’t know if anyone here was previously assuming that the impacts will definitely not be severe. The EA community has long recognized the risks of more severe impact. So this doesn’t seem like a point that challenges what we currently believe.
One of the central ideas in effective altruism is that some interventions are orders of magnitude more effective than others. There remain huge uncertainties and unknowns which make any attempt to compute the cost effectiveness of climate change extremely challenging. However, the estimates which have been completed so far don’t make a compelling case that mitigating climate change is actually order(s) of magnitude less effective compared to global health interventions, with many of the remaining uncertainties making it very plausible that climate change interventions are indeed much more effective.
I haven’t read those previous posts you’ve written, but the burden of argument is on showing that a cause is effective, not proving that it’s ineffective. We have many causes to choose from, and the Optimizer’s Curse means we must focus on ones where we have pretty reliable arguments. Merely speculating “what if climate change is worse than the best evidence suggests???” does nothing to show that we’ve neglected it. It just shows that further cause prioritization analysis could be warranted.
The EA importance, tractability, neglectedness (ITN) framework discounts climate change because it is not deemed to be neglected (e.g. scoring 2⁄12 on 80K Hours). I have previously disagreed with this position because it ignores whether the current level of action on climate change is anywhere close to what is actually required to solve the problem (it’s not).
This criticism doesn’t make sense to me. The mere fact that a problem will be unsolved doesn’t mean it’s more important for us to work on it. What matters is how much we can actually accomplish by trying to solve it.
The 80K Hours problem profile makes no mention of the concept of a carbon budget—the amount of of carbon which we can emit before we are committed to a particular level of warming.
That’s fine. Marginal/social cost of carbon is the superior way to think about the problem.
4) EA often ignores or downplays the impact of mainstream climate change, focusing on the tail risk instead
I’ve seen EAs talk about ‘mainstream’ costs many times. GWWC’s early analysis on climate change did this in detail. In any case, my estimate of the long-term economic costs of climate change (detailed writeup in Candidate Scoring System: http://bit.ly/ea-css ) aggregates over the various scenarios.
5) EA appears to dismiss climate change because it is not an x-risk
This phrasing suggests to me that you didn’t read, or perhaps don’t care, what is actually in many of the links that you’re citing. We do not believe that climate change is irrelevant because it’s not an x-risk. We do, however, believe that the arguments in favor of mitigating x-risks do not apply to climate change. So that provides one reason to prioritize x-risks over climate change. This is clearly a correct conclusion and you haven’t provided arguments to the contrary.
6) EA is in danger of making itself a niche cause by loudly focusing on topics like x-risk
If you think that people will like EA more when they see us addressing on climate change, why don’t you highlight all the examples of EAs actually addressing climate change (there are many examples) instead of writing (yet another, we’ve had many) post making the accusation that we neglect it?
7) EA tries to quantify problems using simple models, leading to undervaluing of action on climate change
Other problems have complex, far-reaching negative consequences too, so it’s not obvious that simplistic modeling leads to an under-prioritization of climate change. It is very easy to think of analogous secondary effects for things like poverty.
In any case, estimating the damages of climate change upon the human economy has already addressed by multiple economic metanalyses. Estimating the short- and medium-term deaths has been done by GWWC. Estimating the impacts on wildlife is generally sidelined because we have no idea if they are net positive or net negative for wild animal welfare.
Global health interventions have a climate footprint, which I’ve never seen accounted for in EA cost effectiveness calculations.
I briefly addressed it in Candidate Scoring System, and determined that it was very small. If you look at CO2 emissions per person and compare it to the social cost of carbon, you can see that it’s not much for a person in the United States, let alone for people in (much-lower-emissions) developing countries.
Climate change is a problem which is getting worse with time and is expected to persist for centuries. Limiting warming to a certain level gets harder with every year that action is not taken. Many of the causes compared by EA don’t have the same property. For example, if we fail to treat malaria for another ten years, that won’t commit humanity to live with malaria for centuries to come. However, within less than a decade, limiting warming to 1.5C will become impossible.
Climate change being expected to persist for centuries is conditional upon the absence of major geoengineering. But we could quite plausibly see that in the later 21st century or anytime in the 22nd century.
Failing to limit warming to a certain level is a poor way of defining the problem. If we can’t stay under 1.5C, we might stay under 2.0C, which is not that much worse. The right way to frame the problem is to estimate how much accumulated damage will be caused by some additional GHGs hanging around the atmosphere for, probably, a century or more. That is indeed a long term cost.
But other cause areas also have major long-run impacts. There is plenty of evidence and arguments for long-run benefits of poverty relief, health improvements and economic growth.
10) Case study: Climate is visibly absent or downplayed within some key EA publications and initiatives
Pick another cause area that’s currently highlighted, compare it to climate change, and show how climate change is a more effective cause area.
That’s fine. Marginal/social cost of carbon is the superior way to think about the problem.
(A) Carbon budgets express an important idea about continued emissions committing us to particular levels of warming. This is particularly important when we are likely to exceed the 1.5C carbon budget in less than 10 years. (B) The 80K Hours problem problem also doesn’t mention marginal/social cost of carbon. (C) Social cost of carbon is usually computed from an IAM, a practice which has been described as such:
“IAMs can be misleading – and are inappropriate – as guides for policy, and yet they have been used by the government to estimate the social cost of carbon (SCC) and evaluate tax and abatement policies.” [Pindyck, 2017, The Use and Misuse of Models for Climate Policy]
In any case, my estimate of the long-term economic costs of climate change (detailed writeup in Candidate Scoring System: http://bit.ly/ea-css ) aggregates over the various scenarios.
I reviewed your writeup, focusing on pages 47 − 61 and I have some questions/comments.
Short term impacts (page 56):
I see that you use GWWC’s estimate of tonnes of CO2 per life saved. I critiqued GWWC’s approach in this previous post.
“40% of Earth’s population lives in the tropics, with 50% projected by 2050 (State of the Tropics 2014) so we estimate 6 billion people affected (climate impacts will last for multiple generations).”—The world population is expected to be ~10 billion by 2050, so 50% would be 5 billion. How are you accounting for multiple generations?
“We discount this to 2 billion to account for the meat eater problem”—What is the meat eater problem?
“If each of them suffers −1 QALY over their lifetime from climate change on average”—why did you choose −1 QALY?
″… the total impact would be 550,000 QALYs per year. However, the reality could be worse because of increases in temperature variance. And we must add to this the other short-run costs of air pollution. They seem plausibly greater than the short-run costs of climate change, especially for the American middle and upper classes. So as a very rough guess, we estimate a weight of 2.15million QALYs per year.”—Why did you choose to multiply 550 by ~3.9?
Long run growth (page 56, 57)
“yields an expected cost of 11% of GWP from climate change (driven substantially by tail risks of extreme climate change with the assumption of quadratic damages)”—Why did you choose to use the “expected cost”? Is there a way to compute the probability of impact >= 20%, or >= 30%? That would help me understand the likelihood of the more severe outcomes.
As a general comment, this section seems to rely heavily on economic models which rely on IAMs. See my comment above for reasons to be skeptical about the results of IAMs.
If you think that people will like EA more when they see us addressing on climate change, why don’t you highlight all the examples of EAs actually addressing climate change (there are many examples)
Appendix 1 lists many of the more recent EA engagements with climate change. I agree that EA has not ignored climate change. However, the point of this post was to discuss some trends which I have observed in how EA engages with climate change.
In any case, estimating the damages of climate change upon the human economy has already addressed by multiple economic metanalyses. Estimating the short- and medium-term deaths has been done by GWWC.
There are legitimate causes for concern about both of the sources you cite—the first relies on IAMs, GWWC relies on one 2014 WHO publication which has some important limitations.
Climate change being expected to persist for centuries is conditional upon the absence of major geoengineering. But we could quite plausibly see that in the later 21st century or anytime in the 22nd century.
I agree that this is a plausible possibility, but not one which I’d like to have to rely on.
If we can’t stay under 1.5C, we might stay under 2.0C, which is not that much worse.
That depends on what you count as “not that much worse”. The IPCC SR15 report predicts that hundreds of millions more people will be severely impacted at 2.0C versus 1.5C.
I find this whole genre of post tedious and not very useful. If you think climate change is a good cause area, just write an actual cause prioritization analysis directly comparing it to other cause areas, and show how it’s better! If that’s beyond your reach, you can take an existing one and tweak it.
[...]
I haven’t read those previous posts you’ve written
[...]
Pick another cause area that’s currently highlighted, compare it to climate change, and show how climate change is a more effective cause area.
(C) Social cost of carbon is usually computed from an IAM, a practice which has been described as such:
“IAMs can be misleading – and are inappropriate – as guides for policy, and yet they have been used by the government to estimate the social cost of carbon (SCC) and evaluate tax and abatement policies.” [Pindyck, 2017, The Use and Misuse of Models for Climate Policy]
In any case I think that picking a threshold (based on what exactly??) and doing whatever it takes to get there will have more problems than IAMs do.
I see that you use GWWC’s estimate of tonnes of CO2 per life saved. I critiqued GWWC’s approach in this previous post.
Nice, that looks like a good noteworthy post. I will look at it in more detail (would take a while). Until then I’m revising from 258,000 tons down to 40,000 (geometric mean of their estimate and your 15,620 but biased a little towards you).
“40% of Earth’s population lives in the tropics, with 50% projected by 2050 (State of the Tropics 2014) so we estimate 6 billion people affected (climate impacts will last for multiple generations).”—The world population is expected to be ~10 billion by 2050, so 50% would be 5 billion. How are you accounting for multiple generations?
I figured many people will be wealthy and industrialized enough to generally avoid serious direct impacts, so it wasn’t an estimate of how many people will live in warming tropical conditions. But looking at it now, I think that’s the wrong way to estimate it because of the ambiguity that you raise. I’m switching to all people potentially affected (12 billion), with a lower average QALY loss.
“We discount this to 2 billion to account for the meat eater problem”—What is the meat eater problem?
Described in “short-run, robust welfare” section of “issue weight metrics,” it’s the fact that increases in wealth for middle-income consumers may be net neutral or harmful in the short run because they increase their meat consumption.
“If each of them suffers −1 QALY over their lifetime from climate change on average”—why did you choose −1 QALY?
Subjective guess. Do you think it is too high or too low? Severely too high, severely too low?
Why did you choose to multiply 550 by ~3.9?
Arbitrary guess based on the quoted factors. Do you feel that is too low or too high.
I agree that this is a plausible possibility, but not one which I’d like to have to rely on.
I’m not saying to rely on it. I’m saying your estimates of climate damages cannot rely on geoengineering not happening. The chance that we see “full” geoengineering by 2100 (restoring the globe to optimal or preindustrial temperature levels) is, hmm 25%? Higher probability for less ambitious measures.
If we were in in the 1980s it would be improper to write a model which assumed that cheap renewable energy would never be developed.
Based on these changes I’ve increased the weight of air pollution from 15.2 to 16. (It’s not much because most of the weight comes from the long run damage, not the short run robust impacts. I’ve increased short run impact from 2.15 million QALYs to 3 million.)
I find this whole genre of post tedious and not very useful. If you think climate change is a good cause area, just write an actual cause prioritization analysis directly comparing it to other cause areas, and show how it’s better! If that’s beyond your reach, you can take an existing one and tweak it. This reads like academic turf warring, a demand that your cause area should get more prestige, instead of a serious attempt to help us decide which cause areas are actually most important.
OK, but I don’t know if anyone here was previously assuming that the impacts will definitely not be severe. The EA community has long recognized the risks of more severe impact. So this doesn’t seem like a point that challenges what we currently believe.
I haven’t read those previous posts you’ve written, but the burden of argument is on showing that a cause is effective, not proving that it’s ineffective. We have many causes to choose from, and the Optimizer’s Curse means we must focus on ones where we have pretty reliable arguments. Merely speculating “what if climate change is worse than the best evidence suggests???” does nothing to show that we’ve neglected it. It just shows that further cause prioritization analysis could be warranted.
This criticism doesn’t make sense to me. The mere fact that a problem will be unsolved doesn’t mean it’s more important for us to work on it. What matters is how much we can actually accomplish by trying to solve it.
That’s fine. Marginal/social cost of carbon is the superior way to think about the problem.
I’ve seen EAs talk about ‘mainstream’ costs many times. GWWC’s early analysis on climate change did this in detail. In any case, my estimate of the long-term economic costs of climate change (detailed writeup in Candidate Scoring System: http://bit.ly/ea-css ) aggregates over the various scenarios.
This phrasing suggests to me that you didn’t read, or perhaps don’t care, what is actually in many of the links that you’re citing. We do not believe that climate change is irrelevant because it’s not an x-risk. We do, however, believe that the arguments in favor of mitigating x-risks do not apply to climate change. So that provides one reason to prioritize x-risks over climate change. This is clearly a correct conclusion and you haven’t provided arguments to the contrary.
If you think that people will like EA more when they see us addressing on climate change, why don’t you highlight all the examples of EAs actually addressing climate change (there are many examples) instead of writing (yet another, we’ve had many) post making the accusation that we neglect it?
Other problems have complex, far-reaching negative consequences too, so it’s not obvious that simplistic modeling leads to an under-prioritization of climate change. It is very easy to think of analogous secondary effects for things like poverty.
In any case, estimating the damages of climate change upon the human economy has already addressed by multiple economic metanalyses. Estimating the short- and medium-term deaths has been done by GWWC. Estimating the impacts on wildlife is generally sidelined because we have no idea if they are net positive or net negative for wild animal welfare.
I briefly addressed it in Candidate Scoring System, and determined that it was very small. If you look at CO2 emissions per person and compare it to the social cost of carbon, you can see that it’s not much for a person in the United States, let alone for people in (much-lower-emissions) developing countries.
Climate change being expected to persist for centuries is conditional upon the absence of major geoengineering. But we could quite plausibly see that in the later 21st century or anytime in the 22nd century.
Failing to limit warming to a certain level is a poor way of defining the problem. If we can’t stay under 1.5C, we might stay under 2.0C, which is not that much worse. The right way to frame the problem is to estimate how much accumulated damage will be caused by some additional GHGs hanging around the atmosphere for, probably, a century or more. That is indeed a long term cost.
But other cause areas also have major long-run impacts. There is plenty of evidence and arguments for long-run benefits of poverty relief, health improvements and economic growth.
Pick another cause area that’s currently highlighted, compare it to climate change, and show how climate change is a more effective cause area.
I often disagree with kbog, and I think this comment was pretty harsh, but I agree with his criticisms of the post
I concur; I disagree with the tone of kbog’s comment but broadly agree with the content.
(A) Carbon budgets express an important idea about continued emissions committing us to particular levels of warming. This is particularly important when we are likely to exceed the 1.5C carbon budget in less than 10 years. (B) The 80K Hours problem problem also doesn’t mention marginal/social cost of carbon. (C) Social cost of carbon is usually computed from an IAM, a practice which has been described as such:
“IAMs can be misleading – and are inappropriate – as guides for policy, and yet they have been used by the government to estimate the social cost of carbon (SCC) and evaluate tax and abatement policies.” [Pindyck, 2017, The Use and Misuse of Models for Climate Policy]
I reviewed your writeup, focusing on pages 47 − 61 and I have some questions/comments.
Short term impacts (page 56):
I see that you use GWWC’s estimate of tonnes of CO2 per life saved. I critiqued GWWC’s approach in this previous post.
“40% of Earth’s population lives in the tropics, with 50% projected by 2050 (State of the Tropics 2014) so we estimate 6 billion people affected (climate impacts will last for multiple generations).”—The world population is expected to be ~10 billion by 2050, so 50% would be 5 billion. How are you accounting for multiple generations?
“We discount this to 2 billion to account for the meat eater problem”—What is the meat eater problem?
“If each of them suffers −1 QALY over their lifetime from climate change on average”—why did you choose −1 QALY?
″… the total impact would be 550,000 QALYs per year. However, the reality could be worse because of increases in temperature variance. And we must add to this the other short-run costs of air pollution. They seem plausibly greater than the short-run costs of climate change, especially for the American middle and upper classes. So as a very rough guess, we estimate a weight of 2.15million QALYs per year.”—Why did you choose to multiply 550 by ~3.9?
Long run growth (page 56, 57)
“yields an expected cost of 11% of GWP from climate change (driven substantially by tail risks of extreme climate change with the assumption of quadratic damages)”—Why did you choose to use the “expected cost”? Is there a way to compute the probability of impact >= 20%, or >= 30%? That would help me understand the likelihood of the more severe outcomes.
As a general comment, this section seems to rely heavily on economic models which rely on IAMs. See my comment above for reasons to be skeptical about the results of IAMs.
Appendix 1 lists many of the more recent EA engagements with climate change. I agree that EA has not ignored climate change. However, the point of this post was to discuss some trends which I have observed in how EA engages with climate change.
There are legitimate causes for concern about both of the sources you cite—the first relies on IAMs, GWWC relies on one 2014 WHO publication which has some important limitations.
I agree that this is a plausible possibility, but not one which I’d like to have to rely on.
That depends on what you count as “not that much worse”. The IPCC SR15 report predicts that hundreds of millions more people will be severely impacted at 2.0C versus 1.5C.
I already did that: “Review of Climate Cost-Effectiveness Analyses”. I would love to get your feedback on that post.
You can also use economists’ subjective estimates ( https://policyintegrity.org/files/publications/ExpertConsensusReport.pdf ) or model cross validation ( https://www.rff.org/publications/working-papers/the-gdp-temperature-relationship-implications-for-climate-change-damages/ ) and the results are not dissimilar to the IAMs by Nordhaus and Howard & Sterner. (it’s 2-10% of GWP for about three degrees of warming regardless.)
In any case I think that picking a threshold (based on what exactly??) and doing whatever it takes to get there will have more problems than IAMs do.
Nice, that looks like a good noteworthy post. I will look at it in more detail (would take a while). Until then I’m revising from 258,000 tons down to 40,000 (geometric mean of their estimate and your 15,620 but biased a little towards you).
“40% of Earth’s population lives in the tropics, with 50% projected by 2050 (State of the Tropics 2014) so we estimate 6 billion people affected (climate impacts will last for multiple generations).”—The world population is expected to be ~10 billion by 2050, so 50% would be 5 billion. How are you accounting for multiple generations?
I figured many people will be wealthy and industrialized enough to generally avoid serious direct impacts, so it wasn’t an estimate of how many people will live in warming tropical conditions. But looking at it now, I think that’s the wrong way to estimate it because of the ambiguity that you raise. I’m switching to all people potentially affected (12 billion), with a lower average QALY loss.
Described in “short-run, robust welfare” section of “issue weight metrics,” it’s the fact that increases in wealth for middle-income consumers may be net neutral or harmful in the short run because they increase their meat consumption.
Subjective guess. Do you think it is too high or too low? Severely too high, severely too low?
Arbitrary guess based on the quoted factors. Do you feel that is too low or too high.
I’m not saying to rely on it. I’m saying your estimates of climate damages cannot rely on geoengineering not happening. The chance that we see “full” geoengineering by 2100 (restoring the globe to optimal or preindustrial temperature levels) is, hmm 25%? Higher probability for less ambitious measures.
If we were in in the 1980s it would be improper to write a model which assumed that cheap renewable energy would never be developed.
Based on these changes I’ve increased the weight of air pollution from 15.2 to 16. (It’s not much because most of the weight comes from the long run damage, not the short run robust impacts. I’ve increased short run impact from 2.15 million QALYs to 3 million.)
Yes I will look into that and update things accordingly.