Introducing High Impact Athletes

I haven't looked into CoolEarth myself, but I think the standard view is that the analysis on the extreme cost effectiveness of this was faulty, based on very optimistic assumptions that are unlikely to be true (indirect protection of forests etc, I believe you could find posts on this searching the Forum) . 

We will discuss our findings on REDD in our upcoming report (Q1/21). I discuss it a bit here (last question): 

Introducing High Impact Athletes

Offsets are at least 15x worse than high impact charity on climate, I recently re-did the numbers on this and even on very conservative assumptions came out with the most effective work of CATF at something like 10 cents/t (https://youtu.be/TCretlmREXk?t=773). This is their best work and certainly they will not always be that cost effective so we can multiply it by 10 to get to USD1 but the best offsets are probably at USD 15 or so (the analysis on BURN by Giving Green mentions that they don't expect 1 offset to really express 1 t).

So whenever you include offsets in a portfolio of options alongside high impact options, maybe because they are more tangible, one needs to ask "how much more money does this crowd in?" compared to "how much does this crowd out from high impact options? " Because the impact differential is so large it can quite easily be the case that even a moderate dilution, say a 10% reduction in giving to high impact, leads to a net negative outcome because the additional crowding in of money is not sufficently large.

Apart from that offsets and the surrounding logic of compensation can possibly be quite bad for popularizing the goal of impact maximization, the idea of offsettting is incredibly unambitious compared to what we inspire people to strive for.

For those reasons I think offsets should have no place in a high impact portfolio.

Addendum: I guess one should always take the precise cost effectiveness estimates with a grain of salt, but it is easy to see from basic principles that offsets cannot be cost effective because offsets are always about direct interventions, whereas the world as a whole is spending hundreds of billions on climate and this is spending that can be affected by advocacy.

For offsets to be anywhere near the best advocacy charities, such as CATF and Carbon180, it would need to be true that there is almost nothing that can be done to improve societal resource allocation on climate.

This is deeply implausible because it is one of the most striking facts about climate how poor societal resource allocation is, leaving vast rooms for impact for charities that move the needle so that government budgets are spent more in line with global decarbonization priorities.

I discuss this in a lot more detail in our new report on implications of Biden win for high impact philanthropy: https://founderspledge.com/stories/the-implications-of-bidens-victory-for-impact-focused-climate-philanthropy

Addendum 2: Just to be clear 15x differential is not my best guess, but a very conservative guess biased towards finding offsets good. My best guess would be more in the range of high-impact charity focused on accelerating neglected technologies through advocacy is 100x-1000x better, but I know that this sometimes seems like a sales pitch or implausibly large, so the goal of my post was to give a bit more of the mechanics / underlying reasons and a very conservative estimate.

I would also add that we have revised our view on CfRN so that we don't think these numbers to be the case anymore, though those revisions were for reasons that do not affect the logic on the differential to expect (it is because of a different view on what they were advocating for, not on more pessimism on the potential of advocacy more generally).

[Linkpost] Global death rate from rising temperatures to exceed all infectious diseases combined in 2100

Thanks for the link, this is interesting!

A couple of quick thoughts from glancing at the paper (more hopefully later):

1) It is a first draft / working paper, not peer-reviewed (though the website frames it as if it was a finished study).

2) Some of the central assumptions seem quite selected for finding maximum impact, e.g. the 73 death per 100,000 people comes from a model run with RCP 8.5 which is currently seen as an extreme case, not business as usual but considerably worse, https://thebreakthrough.org/issues/energy/3c-world). The combination with SSP3 as the socio-economic scenario also seems to point in the direction of worst case assumption as this is a scenario of low/difficult adaptation, https://www.sciencedirect.com/science/article/pii/S0959378016300838 ). So, yes, 73 deaths per 100,000 from heat is possible but it is probably in the top 5% of the distribution of worst outcomes based on what we now think is realistic.

3) Something else that made me a bit worried about the bias in the direction of finding high impact was this statement "This projection accounts for adaptations to climate that populations are likely to make, given historical patterns of adaptation." One of the key features of expectable changes in the world to 2100, especially in high emissions scenarios, is that currently poor countries get a fair deal richer and use a lot more energy (in RCP 8.5 we could all burn lots of coal). So, it seems that the accounting for adaptation seems minimal compared to what one might expect, more adaptation beyond historical patterns as those countries get richer and have more resources available.

4) I am not saying it is a bad study and I am not really qualified to assess that on the deeper details, but I often find a lot of the climate-impact related literature to work with assumptions that seem very focused on finding maximal impacts (or with article titles exaggerating what the paper actually says, even in journals like Nature/Science etc.), a kind of publication bias that has made me quite skeptical of any single study.

Informational Lobbying: Theory and Effectiveness

Hi Matt,

1) I don't see incompleteness as an issue -- what is good for Journal Club is bringing in lots of interesting ideas which your post certainly does, updates you made and are working on are fine. So if that would work for you, I would suggest you as a speaker for Journal Club and we could see when it would fit over the next month or so?

2) My reading of your model -- which might be wrong -- was that you assumed independence between variables related to cost/effort and variables of success probability. It seems to me that when they are positively correlated rather than independent, cost efficiency would increase and become more narrow, because what this says is that worlds of high spending and success will be more likely to co-occur and worlds of high spending and no success less likely to occur than under independence. Does this make sense?

3) I think on money in politics my understanding is that a couple of intensely motivated politicians -- e.g. the representatives where headquarters of companies are -- can be quite sufficient for pork barrel style politics because they tend to fill committee positions important for their respective economic interests and they can easily bargain with other legislators.

Informational Lobbying: Theory and Effectiveness

Hi Matt,

Thanks for your reply and sorry for the slight delay!
Would you like to present your work in a session at Founders Pledge (we have a Journal Club where we discuss relevant research and having you as a guest speaker there sounds like a good idea)?

On the cost effectiveness model
On the model generality, I think my view is that even a general and simple model should not be unrealistic / systematically biased; because we trust the models more than our intuitions and generally fail -- I think -- to do intuitive adjustments on the model (e.g. we probably underestimate intuitively how much independence/dependence assumptions matter).

On the substance of that question, I am not sure I understand your reasoning (but see point above :)). To me it seems that when expenditure is positively correlated with success probability -- what seems to be implied by a view where actors are strategic and at least mildly successful at being so -- would that not (a) increase the cost effectiveness and (b) reduce the overall uncertainty?

Because we often trust models more than we should, I weakly lean towards having less models -- personally, for me the conclusion “this is a really fascinating piece and now we need to think about building models for these different situations and considerations” would be fine whereas with the very rough model I see the risk of incorrect updates, e.g. people not looking into it more because they think that the model fairly represents the uncertainty and under the given range it does not look attractive for some interventions (where the benefit is less large).

But these are just personal philosophical views on modeling, weakly held.

On Baumgartner

Thanks for this, this is really clarifying.

The rephrasing makes this a lot clearer, I had originally read this as “giving more money might be useless” which would undermine the whole case for investigating advocacy charities from an EA perspective, so I am glad I misunderstood that and that this is now clearer.

I would agree that one should not update too much from this correlational evidence. Mostly, because what seems to be the dependent variable here -- success probability -- is itself confounded by a strategic choice to engage with issues so it is not clear that the same success probability across different levels of resources expresses no difference in strength, rather than being confounded by smaller groups not trying harder things.

On money in politics

(1) On employees as explanation of influence, I do think this is a pretty strong explanation for a couple of reasons.

(i) While employees and employers might not see eye to eye with regards to such as issues as labor policy, they have essentially the same interests with regards to the companies they own/work for -- and this is where the lack of money paradox is focused, the lack of money in pork barrel political settings.

(ii) The argument does not rest on explicit voting intentions of employees, but can simply work with references to employment levels in districts, etc., it hands a very powerful argument to local business leaders vis-a-vis their political representatives.

(iii) Campaign finance as well as general influence of business leaders can lead to a situation where the political representatives are already “captured”, where there is no need for additional spending.

(iii) Empirically, it seems well-supported (or so I remember from my political science days, but I cannot find the paper so I might recall that wrongly).

(2) On lobbying equilibria, I am unsure who the relevant experts would be -- but I would trust the political science / political economy literature there more than people with very local “inside view” expertise (as it is a dynamic system-level feature, something that seems more accurately to observe with data than based on individual experience).

And just to conclude on this, I think there are many cases where lobbying is probably very good, I just think that the introduction overstates this in not fully considering explanations that make this less surprising and give reasons to think that there can also be many situations where additional money will not lead to additional influence, just higher spending.

Informational Lobbying: Theory and Effectiveness

Very impressive and interesting piece, thanks for this! I am a colleague of smclare at Founders Pledge and work a lot on modeling policy advocacy across charities. Would be great to have a chat.

It's a great and very useful summary of the literature, hugely valuable.

That being said, I am less convinced by the approach taken in the cost-effectiveness model which seems to somewhat contradict the prior analysis which stresses (a) contextuality and (b) strategic choice. As far as I can tell, the modeling tries to be entirely general (contradicting a)) and assumes (b) independence between variables that are very likely related (such as spending and success probability). Please let me know if I completely misunderstand what you are doing!

My sense from doing this kind of work in charity evaluation is that we would want to move towards a "suite" of models for different situations -- e.g. (i) pushing genuinely new ideas, (ii) opportunistically exploiting policy windows (such as stimulus season), (iii) pushing high-impact low probability policies, (iv) averting policy rollbacks etc. and that the cost effectiveness for these kinds of things will be very different and essentially unrelated to a generic estimate such as the one featured at the end of your piece.

To me it seems that this kind of classifying into the sub-models will be the only way to realistically bound parameters of interest and also that the dynamics underlying are sufficiently different that they should probably have their own models.

Some minor points/comments: Something I found missing is a bit are standard explanations for why there is so little money in politics, namely (1)because dominant firms in districts have lots of indirect power via employees voting so that they do not need to spend money. (2) It also seems that the report somewhat under-emphasizes the idea of lobbying equilibria where marginal increases by one side would be quickly countered, which would make it look like additional money could be effective when in effect it is not.

I also think that the conclusion which, I believe, mostly draws from Baumgaertner " (80%) Well-resourced interest groups are no more or less likely to achieve policy success, in general, than their less well-resourced opponents." is quite surprising and I would be curious to find out why you think that / in how far you trust that conclusion.

A portfolio approach towards effective environmentalism?

Yes we are, though not necessarily with a portfolio approach in the sense described above.

I think it is worth asking whether the EA community should pursue a portfolio approach to climate. Overall, EA funding levels of climate orgs are still quite low and one could make the argument that we should focus on funding the most effective organizations first before designing a broad portfolio, the latter seems more a task for a potential future where EA aligned donors are influencing a larger amount of climate funding.

That being said, we are looking at a broader portfolio of options and will certainly feature some diversity in the Climate Fund. But it will unlikely be distributed across all intervention areas since some seem much more effective than others.

The case for investing to give later

Thanks for writing this, this is fascinating!

To me, the assumptions around the issue of risk of loss seem quite optimistic for a couple of reasons:

  • From accounting for the fact that not only existential catastrophes but also catastrophic risk could cause expropriation you double the rate from 0.1% to 0.2%. But the universe of scenarios where existential catastrophe is avoided but there is enough destabilization vis-a-vis status quo to drive expropriation (or other ways in which the investment becomes unusuable) seems much larger than on the same order as existential catastrophes (which the mere doubling implies).
  • It is unclear to me how the exit-rate of non-profits is a relevant reference class here given a lot of the risk is not on the unit-level but on the systemic level, so things like economic upheavals / hyperinflation etc. seem a relevant consideration (e.g. "how many non-profit investors survived the Great Depression with their assets intact?")
  • As you write, property rights seem stable now, but that -- in its current level of stability more or less globally -- is a relatively new development and not necessarily a given.

From these considerations, 1% seems like a realistic guess, but it seems -- at least to me -- unlikely to be conservative in the sense of "with high likelihood being pessimistically biased against the argument".

A related "windows of wisdom" argument would be that ability to act in the future might be especially valuable in times where expropriation takes place / there is a certain turmoil, so investing in non-financial assets that do not require the current market order to persist could be relatively more valuable from that angle.

How hot will it get?

Re 2a, China, what matters is the degree to which it has influenced global average carbon intensity. It is difficult to think of an event as impactful on global average carbon intensity than the boom of the second largest country population wise fueled by coal, at least as long as the estimate of carbon intensity is global (population and gdp/capita matter here as time increasing weights for carbon intensity).

Re 2b, the state of strong local climate policy matters insofar as it gives reason for global carbon intensity decline going forward and the initiatives of California and EU countries on electric mobility and renewables have been very decisive changes begun the past 20 years but with most of their impact in the future.

Re 4, it seems pretty likely to me that we will figure out some negative emissions options that are cheap, there will be strong reasons to try, there are many natural and technological approaches and there is still time for progress on that. But can't offer you more than that as justification, I guess I just have a different prior for that.

Re 5, this might be more about semantics then. I agree it would not be natural to build this into the model (though the way you suggest would work) but I also think that for scenarios with more than 2 or 3 degrees of warming expectations about geoengineering will drive a significant part of the answer to your question of how hot it will get.

How hot will it get?

Hi John,

Thanks for the clarifications and responses!

Regarding your points:

1. Thanks for clarifying the meaning -- so it is not a worst case, but more a baseline where extra effort would be going beyond what we currently see.

It still seems to me what you model is significantly more pessimistic than that.

I think average marginal carbon prices are not a good proxy of overall climate policy effort, because carbon prices are usually not the (i) only climate policy, (ii) mostly not the dominant climate policy (possible exceptions of Sweden and British Columbia, but those are both negligible jurisdictions in terms of emissions) and (iii) other much stronger policies exist and drive carbon intensity reductions.

E.g. we both mention renewables, electric mobility and advanced nuclear as (potentially) important influences on carbon intensity trends, yet none of those has been brought about by carbon pricing policies, but by innovation and deployment policy. Across Europe, progressive states in the US, and China, we have fairly aggressive policies to stimulate low-carbon tech, often with implied carbon prices (technology specific and realized via subsidies) in the 100s USD/tCO2 range.

So, I think even without extra effort, there are significant efforts underway to drive cost differentials down, at least for electric power and light-duty transport, and that is very clearly the result of climate policy (plus air pollution policy).

This is far from enough, but I don’t think it is well-proxied by the state of average carbon pricing policy.


a. On China: Yes, the growth factor is in the growth parameter, but it is *also* in the intensity parameter as a weight, in the same period in which China rises quickly by burning lots of coal its economic importance also increases strongly (i.e. its weight in defining the trend).

I would agree that we should expect developing countries to escape poverty as cheaply as possible, though the other aspect there is that the sheer centralized action capacity and population size are anomalous for the Chinese case. Plus, availability and price of natural gas and renewables have somewhat changed since China’s decision to go all the way with coal.

b. Climate policy kicking off: I think we are talking about different things here. Yes, global climate policy is very weak and I would agree with you that we should, for example, not necessarily expect a change in trajectory from the Paris Agreement.

But despite that, strong climate policy exists in some places and will affect carbon intensity once championed technologies do scale. And this is new and this has not been reflected in carbon intensity yet but likely will.

c. Technologies in store: (I actually think the most significant technology for this to date will be electric mobility.) But even if it is solar and wind, I don’t think that “what solar and wind have done in Germany so far” is a good proxy for “what the technologies accelerated by some governments will do worldwide”, because (i) Germany isn’t very sunny, (ii) we phased out nuclear at the same time (genius, I know!), and (iii) we are already experiencing value deflation which most parts of the world will reach significantly later. (iv) Plus, the share of electrification and thereby the impact of low-carbon electric sources will already increase in a “no extra effort” case (v) And we are still in the beginning of seeing the impact of those technologies globally (the data from which you extrapolate the intensity ends in 2014).

d. New technologies in store: CCS and advanced nuclear both might or might not happen and I hope we can make them more likely to happen and happen faster, but at least for Europe and progressive parts of the US carbon prices in the range of USD 50 by 2030 (or comparable non-price policies) are part of my prediction of “no extra effort”. I agree with the relative evaluation of CCS and advanced nuclear.

e. Political coordination: I think both your and my “no extra effort” case assume essentially zero political coordination. When you assume carbon intensity trends going forward based on the last 30 years (and those end in 2014, i.e. pre-Paris), where there was very little coordination on emissions (in the grand scheme of things, Kyoto doesn’t really matter), there being even less coordination might be a plausible worst case, but just assuming continued no coordination should not change the estimate much. Likewise, I think your estimate is pessimistic not because I am more optimistic about global coordination, but because I think you underplay the non-coordinated-but-present efforts by some governments to change relative cost. If they have some effect, then carbon intensity declines in the future should be higher than in the last 30 years as a matter of default no-extra-effort-prediction.

g. Breakdown of cooperation / arms race: I agree with that. That should widen our range of estimates, not sure it should shift the median much (but the mean).

4. Negative emissions: As discussed above, I think also in the no-extra-effort scenario there is significant effort do enable low-carbon tech, and it seems a fairly pessimistic assumption that by the end of the century we will not have at least some cheap negative emissions tech (not necessarily enough to offset all emissions, but significantly more than having no effect in expectation). This is not the world I am seeing when I see what UK, EU, progressive governments in US are doing to further technological development. We are not in a world where no one is trying to make low-carbon solutions succeed and get cheaper.
And in particular, it seems hard to imagine a world with high climate sensitivity, high growth and no one attempting to bring down the cost of negative emissions approaches.

This seems quite at odds with typical dynamics of higher problem severity and higher capability driving a more active search for solutions, of which negative emissions are attractive because they can still work after we failed on having foresight early on and avoid some of the more unpredictable risks of geo-engineering.

On geo-engineering: You seem to answer a different question here, the value of geo-engineering. But if the question of the model is, “how hot will it get?”, then I think it makes sense to make an explicit assumption about when you would expect it being used based on empirical expectation.

In terms of conclusion

You write:

“All of this suggests that the estimates of carbon intensity decline might be biased a bit upwards. The most important factors seem to be decline in costs of renewables, electric cars and potentially advanced nuclear, as well as factors e and f.”

I think that downplays the issue and it conflates two distinct effects as if they affected the same variable (carbon intensity), which they do not.

From your list a-d (and g?) are responses to effects on carbon intensity (in my list the points under II).

From your list e-f and the issues under III in my list affect the probability that all four variables driving warming (population, GDP per capita, carbon intensity, climate sensitivity) vary in the same direction with regards to their effect on overall warming probabilities, which is probably less likely (we agree on that) and thereby will have an effect on expected warming quite different from the potential upward bias in carbon intensity.

This latter point is very different from arguing for a mean/median change in carbon intensity decline rate.

As you suggest, I will try to play around with the model a bit and see what the effects of these different assumptions are. Thanks for the good discussion!

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