Some of this reasoning about social impacts, nonzero probability of severe collapse, dynamic effects, etc, applies equally well to many other issues. Your comment on S-risks—you could tell a similar story for just about any cause area. And everyone has their own opinion on what kind of biases EAs have. So a basic GDP-loss estimate is not a very bad way to approach things for comparative purposes. You are right though that the expected costs are a lot more than 2% or something tiny like that.
In Candidate Scoring System I gave rough weights to political issues on the basis of long run impact from ideal US federal policy. I expected the global GDP costs of future GHG emissions at 26% by 2090, and used that to give climate change a weight of 2.9. Compare to animal farming (15.6), existential risks from emerging technologies (15), immigration (9), zoning policy (1.5), and nuclear security (1.2).
Whether climate adaptation could also be potentially high value for EAs
For the same game theoretic reasons that make climate change a problem in the first place, I would expect polities to put too much emphasis on adaptation as opposed to prevention.
If the Burke et al. article that you’re largely basing the 26% number on is accurate (which I strongly doubt), it seems like trying to cause economic activity to move to more moderate climates might be an extremely effective intervention.
If the claims made here from p.13 on are true, it seems like the model can’t be reliable. This also disagrees. In general, it seems intuitively like it would be extremely hard to do this kind of statistics and extrapolate to the future with any serious confidence or rely on it for an estimate without a lot more thought. (I haven’t tried to look for critiques of the critiques and don’t claim to have a rigorous argument.)
Economic activity already goes to wherever it will be the most profitable. I don’t see why we would expect companies to predictably err.
I was thinking if climate has effects on growth rate, companies may not be capturing the full costs/benefits from that. My intuition that it could be extremely effective was something like “if an extremely blunt tool like global average temperature can have big effects on growth through improving local temperature in more places than it worsens local temperature, you can probably get much bigger effects by optimizing local temperature in a fine grained way through changing the locations of things.” Maybe that’s wrong, I don’t know.
OK, CSS5 will address this by looking more broadly at the literature and the articles you cite, or maybe I will just focus more on the economist survey.
Some of this reasoning about social impacts, nonzero probability of severe collapse, dynamic effects, etc, applies equally well to many other issues. Your comment on S-risks—you could tell a similar story for just about any cause area. And everyone has their own opinion on what kind of biases EAs have. So a basic GDP-loss estimate is not a very bad way to approach things for comparative purposes. You are right though that the expected costs are a lot more than 2% or something tiny like that.
In Candidate Scoring System I gave rough weights to political issues on the basis of long run impact from ideal US federal policy. I expected the global GDP costs of future GHG emissions at 26% by 2090, and used that to give climate change a weight of 2.9. Compare to animal farming (15.6), existential risks from emerging technologies (15), immigration (9), zoning policy (1.5), and nuclear security (1.2).
For the same game theoretic reasons that make climate change a problem in the first place, I would expect polities to put too much emphasis on adaptation as opposed to prevention.
If the Burke et al. article that you’re largely basing the 26% number on is accurate (which I strongly doubt), it seems like trying to cause economic activity to move to more moderate climates might be an extremely effective intervention.
What is wrong with it?
Economic activity already goes to wherever it will be the most profitable. I don’t see why we would expect companies to predictably err.
And, even if so, I don’t share the intuition that it might be extremely effective.
If the claims made here from p.13 on are true, it seems like the model can’t be reliable. This also disagrees. In general, it seems intuitively like it would be extremely hard to do this kind of statistics and extrapolate to the future with any serious confidence or rely on it for an estimate without a lot more thought. (I haven’t tried to look for critiques of the critiques and don’t claim to have a rigorous argument.)
I was thinking if climate has effects on growth rate, companies may not be capturing the full costs/benefits from that. My intuition that it could be extremely effective was something like “if an extremely blunt tool like global average temperature can have big effects on growth through improving local temperature in more places than it worsens local temperature, you can probably get much bigger effects by optimizing local temperature in a fine grained way through changing the locations of things.” Maybe that’s wrong, I don’t know.
OK, CSS5 will address this by looking more broadly at the literature and the articles you cite, or maybe I will just focus more on the economist survey.