Ok, so I think we converge pretty much then—essentially what I am saying is that people concerned about compounding risks would argue that these are not modeled correctly in GBD and that there is much more uncertainty there (and that the estimate is probably an underestimate, from the perspective of taking the compounding risk view seriously).
jackva
Thanks!
I think I remain confused as to what you mean with “all deaths from non-optimal temperature”.
It is clear that the data source you cite (GBD, focused on current deaths) will not feature nor proxy what people concerned about compounding risks from climate are concerned about.
So to me it seems you are saying “I don’t trust arguments about compounding risks and the data is evidence for that” whereas the data is inherently not set up to include that concern and does not really speak to the arguments that people most concerned about climate risk would make.
As said before, I think it is fine to say “I don’t trust arguments about compounding risks” and I am probably with you there to a large degree at least compared to people most concerned about this, but I don’t think the data from GBD is additional evidence for that mistrust, as far as I can tell.
By crude analogy, if you believed that COVID restrictions had a big toll on the young and this will affect long-run impacts somehow, pointing to few COVID deaths amongs this age cohort would not be evidence against this concern.
Something like this:
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I think an obvious risk to this strategy is that it would further polarize AI risk discourse and make it more partisan, given how strongly the climate movement is aligned with Democrats.
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I think pro-AI forces can reasonably claim that the long-term impacts of accelerated AI development are good for climate—increased tech acceleration & expanded industrial capacity to build clean energy faster—so I think the core substantive argument is actually quite weak and transparently so (I think one needs to have weird assumptions if one really believes short-term emissions from getting to AGI would matter from a climate perspective—e.g. if you believed the US would need to double emissions for a decade to get to AGI you would probably still want to bear that cost given how much easier it would make global decarbonization, even if you only looked at it from a climate maximalist lens).
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If one looks at how national security / competitiveness considerations regularly trump climate considerations and this was true even in a time that was more climate-focused than the next couple of years, then it seems hard to imagine this would really constrain things—I find it very hard to imagine a situation where a significant part of US policy makers decide they really need to get behind accelerating AGI, but then they don’t do it because some climate activists protest this.
So, to me, it seems like a very risky strategy with limited upside, but plenty of downside in terms of further polarization and calling a bluff on what is ultimately an easy-to-disarm argument.
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Thanks for this, Vasco, thought-provoking as always!
I do not have too much time to discuss this, but I want to point out that I am pretty unconvinced by the argument for why indirect effects should be easy to discount by the type of the argument you make. So, to be clear, I am not arguing that the conclusion is necessarily wrong (I think this would require stronger arguments), but rather that the argumentation strategy here does not work.
As far as I understand it your argument for “indirect effects cannot be very large” is something like:
1. Deaths from heat are very overstated and not that significant and they capture the most important direct effect.
2. A claim for strong indirect effects is then implausible because onto that direct effect you need to sequence a bunch of very uncertain additional effects with uncertain signs.
Insofar as is this a correct representation of your argument—please let me know—I think it has a couple of problems:
1.a. Dying from heat stress is a very extreme outcome and people will act in response to climate change much earlier than dying. For example, before people die from heat stress, they might abandon their livelihoods and migrate, maybe in large numbers.
b. More abstractly, the fact that an extreme impact outcome (heat death) is relatively rare is not evidence for low impact in general. Climate change pressures are not like a disease that kills you within days of exposure and otherwise has no consequence.
2.
a. You seem to suggest we are very uncertain about many of the effect signs. I think the basic argument why people concerned about climate change would argue that changes will be negative and that there be compounding risks is because natural and human systems are adapted to specific climate conditions. That doesn’t mean they cannot adapt at all, but that does mean that we should expect it is more likely that effects are negative, at least as short-term shocks, than positive for welfare. Insofar as you buy a story where people migrate because of climate impacts, for example, I don’t think it is unreasonable to say that increased migration pressures are more likely to increase tensions than to reduce them, etc.
b. I think a lot of the other arguments on the side of “indirect risks are low” you cite are ultimately of the form (i) “indirect effects in other causes are also large” or (ii) “pointing to indirect effects make things inscrutable and unverifiable”. (i) might be true but doesn’t matter, I think, for the question of whether warming is net-bad and (ii) is also true, but does nothing by itself on whether those indirect effects are real—we can live in a world where indirect effects are rhetorically abused and still exist and indeed dominate in certain situations!
I disagree with the substance, but I don’t understand why it gets downvoted.
Apologies if my comment was triggering the sense that I am questioning published climate science. I don’t. I think / hope we are mostly misunderstanding each other.
With “politicized” here I do not mean that the report says inaccurate things, but merely that the selection of what is shown and how things are being framed in the SMP is a highly political result.
And the climate scientists here are political agents as well, so comparing it with prior versions would not provide counter-evidence.
To make clear what I mean with “politicized”.
1. I do not think it is a coincidence that the fact that the graphic on climate impacts only shows very subtly that this assumes no adaptation.
2. And that the graph on higher impacts at lower levels of warming does not mention that since the last update of the IPCC report we also have now expectations of much lower warming.
These kind of things are presentational choices that are being made, omissions one would not make if the goal was to maximally clarify the situation because those choices are always made in ways justifying more action. This is what I mean with “politicized”, selectively presented and framed evidence.
[EDIT: This is a good reference from very respected IPCC authors that discusses the politicized process with many examples]
Sorry for the delay!
Here is a good summary of whether or not the recent warming should make us worried more: https://www.carbonbrief.org/factcheck-why-the-recent-acceleration-in-global-warming-is-what-scientists-expect/
It is nuanced, but I think the TLDR is that recent observations are within the expected range (the trend observed since 2009 is within the range expected by climate models, though the observations are noisy and uncertain, as are the models).
It is true that this is not true for the long-form summary of the science.
What I mean is that this graphic is out of the “Summary for Policymakers”, which is approved by policymakers and a fairly political document.
Less formalistically, all of the infographics in the Summary for Policymakers are carefully chosen and one goal of the Summary for Policymakers is clearly to give ammunition for action (e.g. the infographic right above the cited one displays impacts in scenarios without any additional adaptation by end of century, which seems like a very implausible assumption as a default and one that makes a lot more sense when the goal is to display gravity of climate impacts rather than making a best guess of climate impacts).
Thanks for this!
I think one important caveat to the climate picture is that part of the third of the three factors—climate impacts at given levels of warming—has also changed and that this change runs in the other direction.
Below is the graphic on this from the recent IPCC Synthesis Report (p. 18), which shows on the right hand side how the expected occurrence of reasons for concern has moved downwards between AR 5 (2014) and AR6 (2022). In other words, there is an evidential change that moves higher impacts towards lower temperatures and could potentially compensate the effect of lower expected temperature (eyeballing this, for some of the global reasons for concern it moves down by a degree).
I think one should treat the IPCC graphic with some skepticism—it is a bit suspicious that all the impact estimates move to lower temperatures at the same time as higher temperatures become less likely and IPCC reports are famously politicized documents—but it is still a data point worth taking into account.
I haven’t done the math on this, but I would expect that the effect of a lowered distribution of warming will still dominate, but it is something that moderates the picture and I think is worth including when giving complete accounts to avoid coming across as cherry-picking only the positive updates.
We looked into OpenAI’s revenue because financial information should be a strong indicator of the business decisions they make in the coming months, and hence an indicator of their research priorities
Is this really true? I am quite surprised by this given how much of the expected financial value of OpenAI (and valuation of AI companies more generally) is not in the next couple of months, but based on being at the frontier of a technology with enormous future potential.
Sure! Here is the unpacked version (trying real hard to sound like an LLM):
There are lots of complementarities (or also just similar effects applied to beef and chicken) that I think complicate a picture focused on short-term marginal consumption shifts:
(1) I think investor confidence / durable public support is the single-most important predictor of most “easy” technological transformations (APs aren’t like fusion, there isn’t really a world where APs fail for lack of technical feasibility, APs are modular technologies that can be made reliably cheaper and better if invested in).
(2) APs and climate regulation are not exactly having a great year and an ambitious carbon tax covering agriculture is a signal bucking this trend (if indeed passed), something that could give investors more confidence to continue investing in APs. Historically, Nordic carbon taxes have been a leading indicator and inspiration to ambitious climate jurisdiction so I also do not see this primarily as a Danish thing but as a broader signal where the ambitious climate policy coalition will move.
(3) If one believes the basic AP theory of victory – APs get cheap and competitive and this makes norm changes and regulatory policy feasible in the long run (and there is a mutually reinforcing dynamic or near-competitive technology and changing political feasibility as we are seeing with other forms of clean tech) – then I find it pretty hard to imagine that we end up in a world where we say “the Danish carbon tax increased suffering on net” based on short-term marginal dynamics. I find it pretty implausible that people at scale will say “I will eat AP beef, but this does not affect my view of animal suffering at all and I will continue to eat animal chicken”. I am generally pretty skeptical based on such arguments based on current techno-economic dynamics since I think getting serious about investing into technologies usually unveils lots of new opportunities to make things cheaper and easier.
(4) One should also not forget that there is a strong signal here for chicken as well, there is a clear signal that “yes, we will get more seriously about regulating animal agriculture”.
(5) By close analogy, the most important short-term effect of a carbon tax on electricity is usually to make natural gas more competitive than coal. But I think it would be mistaken to conclude that ambitious carbon taxes are good for the natural gas industry in the long run.
I haven’t done the math on this. My original comment was mostly motivated by the prior discussion being what I found too focused on short-term marginal changes that I think are likely to miss the big picture here. So, I mostly wanted to provide an alternative take and considerations not reflected here before.
I would expect the main effect of stronger regulation of agriculture to be the innovation signal for alternative proteins.
If passed, this would be a pretty strong carbon tax and a pretty clear signal that at least some countries will get serious about decarbonizing agriculture, so even if it leads to suffering-increasing short-term changes I would expect the long run effect for animals to be positive (essentially, it seems to me the complementarities for AP-innovation between chicken and beef seem larger than the short-term shifts one would expect—we do not get AP beef without ever getting AP chicken).
This has already been posted and discussed on the forum: https://forum.effectivealtruism.org/posts/seFH9jcH3saXHJqin/data-on-how-much-solutions-differ-in-effectiveness
There is still low-hanging fruit. Estimates for how effectively top RCT-tested interventions to generate net swing-state votes this election range from a few hundred to several thousand dollars per vote. Top non-RCT-able interventions are likely even better. Many potentially useful strategies have not been sufficiently explored.
This seems quite surprising given the sums and stakes involved.
Do you have sources for this?
Thanks for this, fascinating stuff!
I am wondering from many of the data that you present and also anecdotally: Isn’t it more that “climate change” is so strongly partisan, not environmental issues more broadly? And because climate change has become the dominant political environmentalist issue, “climate” and “environmentalism” become somewhat synonymous despite the underlying politics of other environmental issues being somewhat different?
Thanks, this updates me, I had cached something more skeptical on chicken welfare campaigns.
Do you have a sense of what “advocacy multiplier” this implies? Is this >1000x of helping animals directly?
I have the suspicion that the relative results between causes are—to a significant degree—not driven by cause-differences but by comfort with risk and the kind of multipliers that are expected to be feasible.
FWIW, I also do believe that marginal donations to help farmed animals will do more good than marginal climate donations.
While I think it is a mistake to motivate this estimate with a 2017 BOTEC (here we agree!), it is also mistaken to claim that such a range – spanning more than two OOMs and high and fairly low cost-effectiveness – is implausible as a quite uncertain best guess.
As discussed many times, CCF grantmaking does not rely on 2017 BOTECs and neither does my best guess on cost-effectiveness (Vasco operationalized it one specific way I am not going to defend here, I am just defending a view that expected cost-effectiveness is roughly in the 0.1 USD/t to to 10 USD/t range).
Why an estimate in this range seems plausible
This seems plausible for many reasons, none of which depending on the specific BOTEC:
I. Outside-view multiplier reasoning(1) It is clearly possible to reduce tons of CO2eq for USD 100/t through direct and high-certainty action.
(2) If you only assumed a conventional advocacy multiplier – of the form that many EA orgs assume when modeling policy work (e.g. OP) and that is well-substantiated by empirical political science research and many studies on successes in philanthropy – you would assume a 10x multiplier.
(3) You now “only” need another 10x multiplier to get to USD 1/t and there seem many plausible mechanisms to get there – e.g. focusing on actions with transformative potential such as innovation, avoiding carbon lock-in etc. or – more meta – driving in additional funding from other donors / foundations when supporting early-stage organizations.
(4) Obviously, one also needs to discount for things like funding additionality, execution risk. Etc.
(5) This will result in a very uncertain range, but it is well-approximated by what Vasco has chosen to model this.
Note that these are overall quite weak assumptions and, crucially, if you do not buy them you should probably also not buy the cost-effectiveness analyses on corporate campaigns for chicken welfare.
II. Observations of grants and inside-view modeling(1) While I generally put less stock in them than in comparative analysis, we also do more inside-view cost-effectiveness analyses that often have a range close to USD 0.1-USD 10t/CO2e.
(2) While the CCF does not exist long enough to be confident in long-run emissions outcomes – we generally invest in theories of change that need time – there is a lot of reason to expect that some of those bets will pay off at the very high cost-effectiveness:
(a) Many of the charities supported – such as TerraPraxis, Future Cleantech Architects etc. – have crowded in multiples of the funding we allocated to them often as a direct result of our recommendation and/or organizational development we enabled with early grants.
(b) While hard to disentangle, they also play key roles in many policy changes – e.g. Carbon180 was a leading advocacy org on carbon removal in the IRA/IIJA window, two of our grantees are pushing a conversation on repowering with advanced heat sources (nuclear or geothermal) and one of our grantees (FCA) had several policy wins in the EU (not all they can talk about).
(c) More nascent work is focused electricity market liberalization to advance renewables in emerging economies (Energy for Growth), a stronger climate civil society on the right (DEPLOY/US), as well as geothermal innovation in Canada (Cascade).
(d) This is a diversified sets of bets that leverages different mechanisms, with the uniting theme of leveraging advocacy, the focus on actions / spaces that are neglected, that have the potential to change trajectories, and that have a risk-reducing quality (hedging).
III. Learning from other areas of philanthropyMost areas of philanthropy seem structured such that, when being alright with risk neutrality and leveraged theories of change, one can get significant multiplier.
For example, I am quite confident that the implied multiplier for the case of chicken welfare campaigns compared to direct action is likely similarly large for what we are assuming for the case of Climate Fund. I also do not think any nuclear risk grant-maker would find it implausible that they could reduce nuclear risk 100x more cost-effectively (in expectation) than whatever the direct action equivalent would be. Or a global health grant-maker that would expect that their grants are 100x more cost-effective by influencing advocacy to have government invest in vaccine RD&D rather than buying equipment for their local hospital.
Bottom line: This cost-effectiveness range as a risk-neutral best guess does not depend on a 2017 BOTEC, but rather can be motivated via different streams of reasoning and evidence.(I also think the critique of the 2017 BOTEC is way over-confident but this would be a separate comment)
@Vasco Grillo would be well-placed to do the math here, but I have the strong intuition that under most views giving some weight to animal welfare the marginal climate damage from additional beef consumption will be outweighed by animal suffering reduction by a large margin.
My sense is that it is not a big priority.
However, I would also caution against the view that expected climate risk has increased over the past years.
Even if impacts are faster than predicted, most GCR-climate risk does probably not come from developments in the 2020s, but on emissions paths over this century.
And the big story there is that the expected cumulative emissions have much decreased (see e.g. here).
As far as I know no one has done the math on this, but I would expect that the decrease in likelihood of high warming futures dominates somewhat higher-than-anticipated warming at lower level of emissions.
Thanks, spelling these kind of things out is what I was trying to get at, this could make the case stronger working through them.
I don’t have time to go through these points here one by one, but I think the one thing I would point out is that this strategy should be risk-reducing in those cases where the risk is real, i.e. not arguing from current public opinion etc.
I.e. in the worlds where we have the buy-in and commercial interest to scale up AI that much that it will meaningfully matter for electricity demand, I think in those worlds climate advocates will be side-lined. Essentially, I buy the Shulmanerian point that if the prize from AGI is observably really large then things that look inhibiting now—like NIMBYism and environmentalists—will not matter as much as one would think if one extrapolated from current political economy.