Review of Climate Cost-Effectiveness Analyses
This post was prompted by the comments on my proposed updated 80K Hours Climate Change Problem Profile.
It’s important to make it clear up front that the surprising truth is that there is genuinely very little quantitative research into the impacts of climate change of 4C and above. The research which does exist is necessarily limited in scope and makes a large number of assumptions—many of which will tend to undervalue the overall impact of climate change.
In this post I examine four previous attempts to examine aspects of the impact of climate change and/or the cost-effectiveness of climate change interventions. Full details of these analyses are included below, but their headline figures are summarised here:
2016 GWWC estimate − 2.8C temperature increase by 2100 produces mortality estimates that [with proposed model fixes applied by me] suggest Cool Earth can save a life for ~$6,000, compared to $3,461 to save a life with Against Malaria Foundation [with enormous uncertainty about this estimate]
2018 Halstead Extinction Risk - <1% − 3.5% extinction risk (>10C of warming)
2019 Bressler Mortality Estimate − 4.1C temperature increase by 2100 results in 76 million deaths [provisional results from an in-progress PhD]
2019 Hillebrandt Cost-Effectiveness − 2.2C temperature increase by 2100 produces a SCC that [with proposed model fixes applied by me] suggests Cool Earth is 1.15x more effective than global health interventions [Range: 0.0000003x − 4,041x]
Based on currently announced national commitments, greenhouse emissions are likely to lead to global temperature increases of 2.3ºC-3.7ºC by 2100 with a 25% chance of exceeding 4°C based on current national policies. This suggests that (1) and (4) are undervaluing action on climate change since they are based on much lower levels of projected warming. Furthermore, (1) and (4) both have very large flaws in their methodology which are likely to dramatically under-value climate action—see below for full details.
(3) projects 76 million deaths over the period 2020-2100. This is of a similar magnitude to the total deaths caused by the second world war (70-85 million people over 6 years). This is also of a similar magnitude to the largest famines seen in the 20th century (1-2M people/year). These kinds of numbers give an idea of the scale of impact which we can expect if climate change of 4C happens.
(2) computed an existential risk of <1% − 3.5%. This risk is not accounted for in any of the existing cost-effectiveness analyses which only focus on the average case along with a high/low estimate of impact.
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.
Moreover, this result is reached when only considering the impact of deaths attributed to climate change. This seems like an enormously narrow lens through which to consider a problem which risks displacing hundreds of millions of people, threatening global food systems, causing massive species extinction, and could trigger climate tipping points that amplify all of these projected impacts. Given all of this, it seems extremely likely that climate change mitigation is actually at least an order of magnitude more cost-effective than the best available global health interventions.
1. Background
1.1. Discounting
Discounting of future values is a common practice in economics which has a huge impact on the forecast impact of climate change. Climate change is already impacting the world today and, if emissions continue, the impacts are expected to continue to get much worse. Many forecasts only choose to consider impacts within the 21st century, and hence the worst of these impacts will be at the end of that period. Taking a couple of exemplar years − 2050 and 2100 - these are roughly 30 and 80 years away. The impact of different levels of discounting is as follows:
1% → 30 years: 74%, 80 years → 45%
2% → 30 years: 55%, 80 years → 20%
3% → 30 years: 40%, 80 years → 9%
This means that if you choose to discount the future by 2%/year, then you are choosing to value impacts in 2100 as only 20% as important as if they were happening today. Therefore it’s important to ask what level of discounting is being applied when you look at climate impact forecasts.
If you believe that lives in the future are also valuable, perhaps even just as valuable as lives today, then you may choose a very low or even zero discount rate and this will have a very large impact on your resulting valuation of climate change impact.
1.2. Global Mortality
Some of the estimates below are expressed in terms of number of deaths per year. To put these numbers in context, it’s useful to have a few points of comparison.
Globally, there are currently ~60 million deaths/year across all causes, including those related to age related deaths. This is forecast to grow to ~120 million deaths/year by 2100 due to population growth and an aging population [source]
In the 20th century, the largest famines killed 10-20M people/decade, so 1-2M people/year, all of which happened when the world had fewer than 4 billion people [source]
Since the 1960s, wars have killed at most 300K people/year [source]
World War II killed 70-85 million people over 6 years, which is 11.7-14.2 million people/year, at a time when the world population was ~2.3 billion [source]
In 2017, 437K people died from Malaria [source]
1.3. IAM Validity Concerns
Two of the estimates below are based on Integrated Assessment Models (IAMs). Serious concerns have been raised with the use of these models.
“In a recent article, I argued that integrated assessment models (IAMs) “have crucial flaws that make them close to useless as tools for policy analysis.” In fact, I would argue that calling these models “close to useless” is generous: IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory, and can fool policy-makers into thinking that the forecasts the models generate have some kind of scientific legitimacy. 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. What are the crucial flaws that make IAMs so unsuitable for policy analysis? They are discussed in detail in Pindyck (2013b), but the most important ones can be briefly summarized as follows:
1. Certain inputs – functional forms and parameter values – are arbitrary, but have huge effects on the results the models produce. An example is the discount rate. There is no consensus among economists as to the “correct” discount rate, but different rates will yield wildly different estimates of the SCC and the optimal amount of abatement that any IAM generates. For example, these differences in inputs largely explain why the IAM based analyses of Nordhaus (2008) and Stern (2007) come to such strikingly different conclusions regarding optimal abatement. Because the modeler has so much freedom in choosing functional forms, parameter values, and other inputs, the model can be used to obtain almost any result one desires, and thereby legitimize what is essentially a subjective opinion about climate policy.
2. We know very little about climate sensitivity, i.e., the temperature increase that would eventually result from a doubling of the atmospheric CO2 concentration, but this is a key input to any IAM. The problem is that the physical mechanisms that determine climate sensitivity involve crucial feedback loops, and the parameter values that determine the strength (and even the sign) of those feedback loops are largely unknown, and are likely to remain unknown for the foreseeable future.
3. One of the most important parts of an IAM is the damage function, i.e., the relationship between an increase in temperature and GDP (or the growth rate of GDP). When assessing climate sensitivity, we can at least draw on the underlying physical science and argue coherently about the relevant probability distributions. But when it comes to the damage function, we know virtually nothing – there is no theory and no data that we can draw from.
4. IAMs can tell us nothing about the likelihood or possible impact of a catastrophic climate outcome, e.g., a temperature increase above 5°C that has a very large impact on GDP. And yet it is the possibility of a climate catastrophe that is (or should be) the main driving force behind a stringent abatement policy.”
[Pindyck, 2017, The Use and Misuse of Models for Climate Policy]
Further relevant criticism can be read in [Weitzman, 2011, Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change].
2. Climate Change Impact / Cost-Effectiveness Estimates
2.1. 2016 Giving What We Can Cost-Effectiveness
2.1.1. Approach
Giving What We Can (GWWC) describe their approach and results here. The approach can be summarised as follows.
Social Cost of Carbon (SCC) rejected as an appropriate measure of impacts.
WHO’s 2014 report “Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s” [source] selected as key source of mortality estimates. This report estimates incremental climate change related mortality in 2030 and 2050 for heat-related mortality; coastal flood mortality; diarrhoeal disease; malaria; Dengue fever; and undernutrition. The A1B emissions scenario is used which predicts 2.8C temperature increase by 2100 [source].
The estimates in 2030 and 2050 are assumed to define a linear relationship between year and number of deaths. The central value is an increase of 201.2 extra deaths/year on top of a baseline of 241K/year incremental deaths in 2030.
The causes of deaths in the WHO report only account for 5.1% of total mortality, so as a conservative estimate, all causes of death are assumed to scale by the same amount, so 201.2*(100/5.1)=3931 extra total deaths/year.
Reducing emissions in a year delays some fraction of these extra deaths/year.
Hence, the cost of an emissions reduction can be multiplied through to reach a cost per life saved. The central estimate is $97,300, and the most generous estimate is $32,700.
No discounting is applied in the final reported figures, although the spreadsheet allows this to be added on at the end.
2.1.2. Comments
2.1.2.1. WHO Report Limitations
The projections in the WHO report [source] come with a large number of limitations and caveats. Many of these are described in the report itself—the authors are clearly aware of the great difficulty involved in producing these kinds of estimates. However, these limitations are so severe that the resulting numbers must be used with extreme caution. Let’s consider a couple of the sections.
Malnutrition
Malnutrition modeling doesn’t account for: increases in extreme weather events, sea-level rise, changes in water demand, increases in pests and diseases, loss of income from land which becomes unproductive. [WHO 2014, p70]
“We believe our estimates should be considered very conservative for the following two reasons [...] our modelling does not include the impact of shocks; it considers stunting due only to expected average conditions” [WHO 2014, p96]
Coastal Flooding
Assumptions:
There is no change in storm-surge frequency and intensity from baseline (but floodwaters are deeper with sea-level rise).
People flooded on average once a year autonomously leave the area and are not at risk of flooding and hence mortality
Sea level rise: Average global warming was 2.4°C by the 2050s and 3.8°C by the 2090s. This corresponds to global mean sea-level rises of 0.22 m by 2050 and 0.37 m by 2080.
This last assumption is clearly out of date with the IPCC forecasting 0.52m of sea level rise in a 1.5C of warming world by 2100 - “Model-based projections of global mean sea level rise (relative to 1986–2005) suggest an indicative range of 0.26 to 0.77 m by 2100 for 1.5°C of global warming” [IPPC SR15 - Summary for Policymakers]
The GWWC estimate doesn’t use the coastal flooding mortality estimates as the WHO report only forecasts these within broad bands (e.g. 10K-30K) [WHO 2014, p35] and the estimates don’t turn out to change between bands between 2030 and 2080. Given more recent estimates of much greater sea level rise, this no longer seems plausible.
2.1.2.2. Linear Assumption
The GWWC model relies on the assumption that the point estimates given for mortality in 2030 and 2050 can be extrapolated into a linear relationship. This seems like a deeply flawed assumption which is contrary to academic work such as this 2015 nature paper—Global non-linear effect of temperature on economic production [slides]. It also fails some basic sanity tests as the presented numbers claim that climate change is causing excess malaria and diarrhoeal disease deaths, but that as climate change worsens, it causes fewer of these deaths.
2.1.2.3. Expanding To All Causes Of Death
The GWWC model asserts “we can quite roughly estimate that mortality due to climate change might grow proportionally to current levels of mortality—that is, that these diseases which currently make up 5.117% of global mortality will make up 5.117% of additional mortality due to climate change and, hence, that deaths due to climate change are 19.54 times higher than estimated in the WHO’s assessment.”
This assertion is weak as the resulting estimate is dependent on the five estimates taken from the WHO report. The two largest terms are (1) “excessive heat”, rising at 2851 deaths/year between 2030 and 2050, from a baseline of 37K in 2030, (2) “malaria”, declining at 1369 deaths/year between 2030 and 2050, from a baseline of 60K in 2030. If the GWWC estimate had not included Malaria (by choice, or if Malaria had not been in the 2014 WHO report), then the change in deaths/year between 2030 and 2050 would have risen from 201/year to 1571/year. There were 435K malaria deaths in 2017 [source], which is ~0.7% of global deaths. 1571*100/(5.117-0.7) = 35.6K/year, rather than the original 201/year estimate. So the estimate of change in deaths/year is very sensitive to the choice of estimates to include before multiplying out.
Finally, it seems wrong to count projected reductions in malaria deaths against climate change action when the reduction in deaths is presumably primarily because of direct action against malaria. If the climate was not warming, you would expect malaria to be declining more rapidly, but the GWWC model seems to imply the reverse. In fact, there is a campaign to eliminate malaria by 2040 [source], that if successful, would further invalidate the GWWC model which attributes malaria death reductions to climate change until long after this date.
2.1.2.4. Lives Are Saved Every Year
This appears to be one of the biggest flaws with the GWWC estimate. The GWWC estimate works on the basis that reducing emissions saves some fraction of the increase in deaths that would have happened as a result of those emissions. However, this saving actually applies for every year after the emissions were reduced.
The world currently emits 37Gt CO2/year. Ignoring longer term CO2 absorption processes, assuming these emissions continued at that rate indefinitely, if emissions are reduced by 1Gt in one year, then atmospheric CO2 concentrations will be lower every subsequent year than they would have been otherwise.
So the question is, how many years of saved lives should be included in the calculation? In theory the correct number should be the time until a given emission of CO2 has later been recaptured and sequestered. We don’t expect to be able to recapture most emitted CO2, so a very conservative value to use would be to attribute 50 years of increased deaths to each emission. Hence, this increases the estimate of lives saved by a factor of 50x. This also ignores any other impacts of a given CO2 emission, some of which are actually or effectively irreversible, such as triggering climate tipping points, species extinction, and sea level rise.
2.1.2.5. Use Of Central WHO Estimates
Cells C46 - E50 contain the estimates of lives saved for a given emissions reduction. These cells follow the same format as the rest of the sheet, with a central, low, and high estimate. However, these estimated are all based on the central WHO estimates. The only variation comes from use of a (central, low, high) estimate for the cost per acre of land protected by Cool Earth and the downward effect of adaption.
2.1.2.6. Handling Of Projected Declines
In the areas of Undernutrition, Malaria, and Diarrhoeal deaths, the WHO estimates showed declining climate change attributed mortality between 2030 and 2050. Cells C48-C50 reverses the sign of these estimates, which means they add to the lives saved rather than subtracting from them. I can’t see any rationale for this.
2.1.3. Updated Estimate
I have attempted to produce an updated estimate with the following changes:
I have removed consideration of malaria deaths which may have been entirely eliminated by 2040 and have adjusted the “Percentage of total deaths” figures downward by the approx 0.7% of global deaths caused by malaria today.
Number of lives saved are taken to be 50x the reduction in per-year incremental climate attributed deaths.
I have updated the low/high estimates to actually use the low/high estimates of climate mortality.
Removing sign reversal from Undernutrition, and Diarrhoeal deaths.
The resulting central estimate is $5,886 per life saved, which is the same order of magnitude as the $3,461 quoted to save a life by the Against Malaria Foundation.
The low and high estimates end up being weird due to the methodology used in the original estimate. For example, the low estimates for malnutrition are that in 2030 there are 119,807 fewer deaths, which drops to 29,203 fewer deaths by 2050. This produces a “low” estimate that climate change increases malnutrition mortality by 4530 lives a year, compared to the median estimate of climate change reducing malnutrition mortality by 524 lives a year. These kinds of numbers lead to a revised range of cost per life saved of between a low of $3,819/life saved and a high of -$701/life saved. This seems entirely nonsensical to me.
My updated model is available here.
2.2. 2018 Halstead Extinction Risk
2.2.1. Approach
Halstead posted to the EA forums about his 2018 paper “Stratospheric aerosol injection research and existential risk”. This paper estimates the risk of human extinction from climate change by combining the following estimates.
10C of warming is chosen as the threshold above which climate change will cause human extinction.
Table 1 - Atmospheric CO2 Concentration in 2100 → Probability
400 − 1%
500 − 5%
600 − 20%
700 − 30%
800 − 20%
900 − 15%
Note, the probabilities don’t sum to 100% - the 9% chance of >900 is ignored. The paper doesn’t explain why.
Table 2 - Probability of warming >10C, at each CO2 concentration → Probability
400 − 0.2%
500 − 0.83%
600 − 1.9%
700 − 3.2%
800 − 4.5%
900 − 6.6%
Deducing from the estimates in Tables 1 and 2, the unconditional probability of existential catastrophe-level warming is ∼3.5%. I use Weitzman’s estimate of climate sensitivity because it attempts to account for climate feedbacks which are important from the point of view of existential risk reduction. However, Weitzman’s ECS estimate is highly controversial, and there are a few reasons to think it may be too high. Nordhaus (2011a, 2011b) has criticised Weitzman’s analysis of the sample of IPCC model probability distributions across ECS. Weitzman (2009a) has defended his approach and noted that even if Nordhaus’ approach is correct, the probabilities in Table 2 would be reduced by around 60%, which still suggests that the risk of existential catastrophe is ∼1.5%.
[...]
Thus, the headline estimate I have produced in this section is highly controversial and some lines of argument suggest that the existential risks of climate change are (much) lower, plausibly < 1%. This controversy should be borne in mind in what follows.
[Halstead, 2018, p5]
So the range <1% − 3.5% is the existential risk predicted by this paper.
2.2.2. Comments
The probabilities in the tables above come from a 2011 Weitzman paper “Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change”. This paper also included estimates of the probability of >5C of warming.
Table 3 - Probability of warming >5C, at each CO2 concentration → Probability
400 − 1.5%
500 − 6.5%
600 − 15%
700 − 25%
800 − 38%
900 − 52%
Multiplying this through in the same way as before gives a 26.2% chance of greater than 5C of temperature increase. This is reassuring as it is (very roughly) in line with the 25% chance of greater than 4C temperature increase predicted here.
2.3. 2019 Bressler Mortality Estimate
2.3.1. Approach
Bressler is a Sustainable Development PhD candidate who is working on accounting for climate mortality in the calculation of the Social Cost of Carbon (SCC). This work extends William Nordhaus’ DICE Integrated Assessment Model (IAM). Bressler gave a public talk with some early results from his work in July 2019 and posted it to the EA forum.
This estimate is based on examining how climate change impacts global mortality in a future with 4.1C of temperature increase by 2100. The model predicts that over the next 80 years, 76 million cumulative additional deaths are caused. These are deaths from health impacts, increased murder, and intergroup conflict response. I have reached out to Bressler to find out more details about what specifics are included/excluded from these estimates. Accounting for these deaths triples the SCC estimate. It should be noted that these are all preliminary numbers.
The video shows a bar chart with the total deaths in each 5 year period between 2020 and 2100. The death rate is projected to have reached 2.18 million deaths/5 years by 2100.
2.4. 2019 Hillebrandt Cost-Effectiveness
2.4.1. Approach
Hillebrandt posted this estimate to the EA forum in October 2019. After posting, the estimate underwent a major update which changed the conclusion. I will only be discussing the updated version.
The model takes as an input an estimate of the SCC from a 2018 paper “Country-level social cost of carbon” of US$417 per tonne of CO2 (66% CI: US$177–805). This is computed on the basis of a 2% pure time preference discounting rate along with a 1.5% elasticity of marginal utility [See this for details on growth-adjusted discounting]. The paper uses RCP6.0 which is projected to result in 2.2C of warming by 2100.
The SCC is then normalised by the relative utility of $1 in a poor country versus the US—using a range of three values (13,610x, 1,260x, 120x). The result is multiplied further by a range of three values for the relative effectiveness of the very best interventions versus direct cash transfers (17.5x, 7.95x, 0.83x). Finally, the range of costs for offsetting/reducing emissions is taken to be ($232, $10, $0.02) based on a selection of scalable solutions.
The result is that climate change interventions are predicted to be X times as effective than global development: (0.0000003x, 0.004x, 4,041x).
2.4.2. Comments
2.4.2.1. Use of IAM based SCC
As per section 1.3. there are serious validity concerns with the IAM models which underly estimates of the SCC. It’s unclear to me whether these concerns apply entirely to the 2018 paper underlying this analysis as it implements its IAM differently.
“we used country-level climate projections taken directly from gridded ensemble climate model simulation data as well as country-level economic damage rela-tionships taken directly from empirical macroeconomic analyses. As climate and economic quantities are empirical in this analysis, these uncertainties are probabilistic in our output.”
The post on the EA forum does note in Appendix 3 that the validity of IAM models are questioned. However, the median SCC used from this paper of $477 is actually higher than other estimates which are often in the $50-$200 range.
2.4.2.2. SCC Excluded Costs
Appendix 1 of the EA forum post notes that the SCC used in the calculation excludes a number of factors which may turn out to be very important, such as tipping points, ocean acidification, sea level rise, and biodiversity loss. This is used to justify the use of a 10x higher SCC in the “pessimistic” case.
2.4.2.3. Use of High Cost Per Tonne of CO2 Averted
The cost per tonne of CO2 averted is taken from a sample of highly scalable interventions, with the lowest cost being $0.02. The median case is taken to be $10. However, this seems like a surprising choice given that an individual choosing to donate their money towards climate change today would definitely be able to find an intervention which was cheaper than this. The 2016 GWWC estimate used a cost $0.38/tonne for donations to Cool Earth.
In the section about the cost of abatement, the forum post quotes the v2.0 GHG abatement which was published in 2009 by McKinsey. The latest version is v2.1 from 2010, which is still very old at this point. A more recent paper from 2018, The Cost of Reducing Greenhouse Gas Emissions, computes an updated estimate. This paper says:
One sobering insight from the estimates in Table 2 is that many of the least-expensive interventions cover a small amount of CO2 reductions, whereas the scalable technologies that are at the center of discussions about a transformation to a low-carbon economy—electric vehicles, solar photovoltaic panels, and offshore wind turbines—are among the most expensive on the list.
However, the paper goes on to examine two case studies of solar power and electric cars and proposes that the initially high costs come down dramatically with deployment scale, and so using today’s prices is misleading.
Another datapoint to consider is the Drawdown Project, described on wikipedia
Project Drawdown is a climate change mitigation project initiated by Paul Hawken and climate activist Amanda Joy Ravenhill. Central to the project is the compilation of a list of the “100 most substantive solutions to global warming.” The list, encompassing only technologically viable, existing solutions, was compiled by a team of over 200 scholars, scientists, policymakers, business leaders and activists; The team measured and modeled each solution’s carbon impact through the year 2050, its total and net cost to society, and its total lifetime savings.
The results were published in a 2017 book and all the writeups for the solutions are available online. The 80 solutions that it examined that use established technology, have an overall cost/tonne of $28.61. However, the estimated savings are $71.87/tonne, for a net saving/tonne of $43.25. The savings largely come from lower operating costs, so financing will likely be required to cover the initial capital costs of these solutions, which will in many cases pay for themselves over time.
Finally, a May 2019 EA forum post promoted research by “Let’s Fund” which promoted funding a thinktank to advocate for increasing government funding for clean energy R&D. The median projected financial return calculated by their fermi estimate was 28x.
2.4.3. Updated Estimate
I have produced an updated estimate with the following assumptions:
Median SCC: $477 - no adjustment for over/under-estimation
Income adjustment: 120x—this is conservative about how much more valuable $1 is in a developing country
Cost per tonne: $0.38 - this is taken from the 2016 GWWC estimate
GiveDirectly vs. global health interventions: 7.95x—Median Givewell charity effectiveness vs. cash
On the basis of these assumptions, climate change intervention is 1.15x more effective than global health intervention.
There is clearly considerable uncertainty in this result, given that the original estimate had a range of 0.0000003x − 4,041x, which is 10 orders of magnitude. However, I claim that the title claim of the original EA forum post, that “Global development interventions are generally more effective than Climate change interventions” is far too strongly worded.
My updated model is available here.
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Nuclear winter would be approximately 8°C change in only one year, and this is unlikely to cause extinction. 10°C climate warming over a century would be much lower impact, because there is time to relocate infrastructure and people (and nuclear winter also reduces solar radiation). So I have put it in the intensity category of an abrupt 10% agricultural shortfall. Based on a survey of GCR researchers, this has a mean long-term reduction in far future potential of approximately 5%. This combined with a probability of about 2% gives about a 0.1% reduction in the far future potential. Full scale nuclear war is estimated to have a 17% reduction in long term future potential. There is great uncertainty in the probability of full-scale nuclear war, but I think 0.1% per year or 10% in the next 100 years is reasonably conservative.* Therefore, full scale nuclear war is more likely than extreme climate change and also significantly greater consequences if it were to happen. But then the question is how much would it cost to significantly mitigate the problems. Since solar radiation management is risky, the present value of the cost of largely solving the climate change problem by reducing emissions is around $10 trillion (there was an EA forum post on value of information of this, but I can’t seem to find it). I have researched both energy efficiency and renewable energy for years, and I do think there is still some low hanging fruit of energy efficiency that pays for itself. However, to actually solve the problem will cost a lot of money. On the other hand, reducing the far future impact of nuclear winter by about 17% would cost around $100 million by investing in response plans and research and development of alternative foods. Therefore, since alternative foods address a roughly 15 times bigger problem, at 100,000 times lower cost and with 1⁄5 the threat reduction (if we assume the $10 trillion on emissions reductions completely solves the problem), this works out to approximately 300,000 times higher cost effectiveness for alternative foods versus emissions reductions.
Fortunately, alternative foods also mitigates climate related catastrophes such as abrupt regional climate change, coincident extreme weather on multiple continents, and slow 10°C change (which makes the cost effectiveness of alternative foods even higher than the numbers calculated above). There may be other low hanging fruit that address climate change such as Cool Earth (though see this criticism) and energy efficiency (though even if energy efficiency pays for itself, it still costs donor money to advocate for it). But even at a cost of $0.38 per ton CO2, it is still a few orders of magnitude lower cost effectiveness than alternative foods or artificial general intelligence safety from the perspective of the long-term future. Of course it is better to do this probabilistically, which is why I have encouraged you to add climate change to an existing cost-effectiveness model of alternative foods and artificial intelligence.
Hopefully we can direct tens of billions of dollars more to EA, and then we can work our way further down the marginal cost effectiveness curves of existential risk mitigation, but I don’t think that reducing greenhouse gas emissions should be a priority for EA at this point.
*For the alternative food analysis, we only used at few decades effective time horizon but higher probability of nuclear war from here.
I don’t actually see a detailed calculation of human impacts in that paper. I agree that full extinction seems unlikely, but hugely catastrophic impacts seem very plausible. Also, a temperature decrease is definitely not guaranteed to have a symmetric impact with a temperature increase, so the comparison doesn’t seem entirely valid.
This is a statement which quickly points out a difference in our ethics. 10C of warming would likely require the evacuation of huge areas of land around the equator. That’s not the same as extinction, but I still consider it to be a deeply unacceptable outcome. Survival alone isn’t good enough for me. I’m not sure how to formalize this viewpoint within utilitarian calculations.
Also, less abstractly, I’m not confident that the natural ecosystem which we rely on would be able to adapt to 10C in warming over a century. This suggests to me that we would see a huge amount of species being pushed into extinction, and again I consider this to be an extremely negative consequence, even if we are able to figure out ways to feed ourselves from a limited number of crops that we still manage to cultivate.
It seems to me like a huge leap of faith is required to believe that the global impact of 10C of warming (over a century) is on the same order of magnitude as an abrupt 10% agricultural shortfall. You’d need to lay out much more of an argument for me to believe that. As it stands, I think you are either predicting a much lower impact from climate change than I am, or putting more faith in technological/economic growth to mitigate the impacts. In either case, it’s clear we disagree.
This only considers the impact of 10C of warming. If we don’t have 10C of warming, we are still going to have an amount less than that. There’s a currently a >25% of >4C of warming, without fully accounting for climate tipping points. 4C of warming is already expected to have serious consequences. However, these haven’t yet been well quantified because even 4C of warming has impacts on so many aspects of the world society, economy, and ecology, that it’s incredibly difficult to model.
This presumes that mitigating climate change through alternative foods is a morally acceptable outcome. As per my statement above, for me, it isn’t.
This statement actually very neatly encapsulates my main objection to long-termism. It feels very much like a case of Pascal’s Mugging. Over a long enough view of the potential future of humanity (e.g. 10s of millions of years), none of today’s problems really matter.
Can you clarify what exactly you mean by “from the perspective of the long-term future”? What time-horizon do you have in mind, and what kind of discount rate are you applying, if any?
Thanks very much for the thoughtful engagement.
Correct—the estimate of long term future impact was from the survey cited here.
I agree that it is not necessarily symmetric-cooling is generally worse for plants than warming. Also, we would have roughly 50% reduction in solar radiation in the nuclear winter case, which further makes it worse. Furthermore, I believe more people die from the cold than from the heat.
I agree that 10°C warming over a century would be bad. But would you agree that 8°C cooling and 50% reduction in solar radiation in one year would be worse?
It is important to note that 10°C global warming would have less than 10°C warming near the equator. This says that plants were doing pretty well near the equator when the Earth was about 12°C warmer. But as I note below, evolution takes a long time, so species would need to be relocated. As for humans, I’m not sure that a lot of relocation would be required. It is true that humans would not be able to go outside very long without technology. But I would say that is true at 40° latitude in the winter now. It is true that the technology of an insulating coat is fairly simple. But if it is hot, we could use the fairly simple technology of an ice vest like this. More complicated technology could involve a system which burns fuel and then uses absorption chilling to cool the body if one needed to stay cool for many hours. Of course this technology would not be affordable by many people in the tropics now, but 100 years out, I think the situation will be different.
Though life was doing well when the earth was 10°C warmer than now, it is true that a change over 100 years is very short considering evolutionary timescales. In order to avoid a mass extinction, I think humans would need to help relocate plants (and indeed people are working on this now) (animals can generally move quickly enough, though still might need help in some circumstances). Also, relocation would not save all species, because some would no longer have a habitat cool enough, and could only be saved with captivity. I personally think it is unlikely that humans would allow the climate to warm 10°C, and instead would do solar radiation management. However, solar radiation management has its own risks, such as a double catastrophe where some other catastrophe hits us and then we are not able to maintain solar radiation management, and then we have two problems. I think it is possible to have a more robust solar radiation management to avoid this problem. But it is possible we do not use solar radiation management. Overall, I agree that there is intrinsic value in biodiversity, but that would require another discussion to find the most cost-effective ways of preserving it.
I am focusing my analysis on the impact on the long term future, which means the reduction in the long term potential of humanity (out thousands or millions of years). But I think it is a reasonable proxy to look at the mortality. In this paper, I estimated the expected mortality of an abrupt 10% food shortfall from something like India Pakistan nuclear war was about 500 million. Technically speaking, adaptation and relocation in response to a century long 10°C rise should involve the loss of many fewer lives, but it could go very badly, even up to including full-scale nuclear war, which could kill billions of people. So I think it is in the same order of magnitude in expectation as an abrupt 10% food shortfall. What would your estimate be of the expected mortality a century long 10°C rise?
It is true that this analysis is not taking into account the smaller warmings, but these are less likely to have an impact on the long-term future, so I think they are unlikely to change the order of magnitude of the result. I am concerned about a possible tipping point that would be a runaway greenhouse effect. But since the earth was about 14°C warmer about 50 million years ago, and the sun’s radiation is not that much higher than it was then, I think we only have to start worrying about this at over ~10°C warming.
I agree it would be better to prevent climate change rather than just mitigate some of the impacts if we had unlimited resources. But EA is about prioritizing-we have limited resources and we want to make the largest impact possible. We have other competing demands I have not talked about, such as reducing the risk from natural and engineered pandemics. I do hope that we can bring many more resources to EA and then we can work our way further down the existential risk mitigation curve to climate emissions mitigation.
I don’t think the comparison I am making is Pascal’s Mugging. I think Pascal’s Mugging could be considering the immense potential value of the long-term future and then demanding some sacrifice now. However, since I am looking at the reduction in the long-term future due to climate change and due to nuclear winter, they are on equal footing and do not depend on the precise value of the long-term future.
As for prioritizing the present generation, my analysis indicates that prioritizing current global poverty is a couple orders of magnitude more effective than reducing emissions at carbon costs required to solve the whole problem (largely because the current poor will likely be richer when the main climate change impacts hit). However, if you believe the Cool Earth numbers (not counting opportunity costs of the value of the land for farming) and if you don’t think they will be taken by someone else, then it could be competitive. However, I think alternative foods are even better from the present generation perspective.
I think the only discount rate I would apply would be that due to existential risk. So it is something like maximizing the total expected utility of sentient beings as long as we can keep them going. But other long-termists would say it differently (different ethical theories than consequentialism could still result in highly valuing the longterm future). Also note that even if one has a few percent discount rate, if one puts a non-negligible probability mass on some sort of technological singularity within a century or two with the potential for a huge number of computer consciousnesses, that is another way of getting at the overwhelming importance of making it through to the longterm.
From your original comment (emphasis added by me to highlight what jumped out at me):
From my reply:
From your reply:
Also from your reply:
It looks like I misread your original comment a bit. When you said “much lower impact”, I didn’t realize that you had predicted 500 million deaths from a 10% agricultural shortfall. I have now read your paper, and am entirely comfortable to agree that: (1) 8°C cooling and 50% reduction in solar radiation in one year would likely be much worse than 10°C warming over a century, (2) expecting ~500 million deaths from a century long 10°C rise seems like roughly the right order of magnitude.
Having said that, some of the circumstances that would lead to a 10% agricultural shortfall (e.g. extreme weather in several breadbaskets in one year) wouldn’t also come with all the other costs of climate change (mass migration, species extinction etc).
This may well be true, but this is another case where I’d consider adaption as unacceptable. I don’t want to create a world where we need ice vests, or worse yet—something which needs to burn fuel, to be comfortable outside—particularly when that outcome is entirely avoidable.
I’m worried about tipping points that accelerate and amplify warming much sooner than 10°C of warming. Page 21 of this suggests that anything above 3C is extremely concerning.
As per my comment on HaukeHillebrandt’s comment below—The trouble with these estimates is that I’m not convinced they do a good job of considering how costs change as a technology is scaled. For example, we’ve seen this with solar—http://solarsouthwest.co.uk/solar-panel-cost/. Do you have a recommended source which does somehow take account of these effects? If not, we’re not really comparing costs properly.
Also, I want to specifically comment on this. Unless you believe that very large scale CO2 air-capture is going to be economically/technologically/land-use viable, we don’t have time to wait for people to get richer. The CO2 being emitted today, is committing humanity to a particular temperature rise for centuries to come. The cheapest time to deal with that is right now, to avoid putting the CO2 in the atmosphere in the first place.
In this post I wasn’t trying to look at the long-term value of climate change—I was mostly considering the impacts by 2100. Your response dismisses the concerns about smaller warmings because they are less likely to impact the long term potential of humanity. I still care about these impacts, because they will still kill people in the current century, even if those people don’t matter as much on a long-term basis because human civilization will be fine without them. In my own ethics, I value life which exists today and in the near future more highly than in the far future. The main reason I’m willing to extend my horizon to 2100, is that I have a strong belief that the economic system which I’m living in today will very directly impact people in 2100, so they are not remote and detached from my choices—I bear some responsibility for the world they get to live in. This implies that I use a non-zero discount rate, but which I’m willing to reduce specifically for cases where there’s a strong causal link to actions being taken today.
At the same time, I rationally understand the argument for long-termism. If I had to pick between (A) a world with terrible climate change, but where human civilization ends up surviving and then thriving for 10 million years, or (B) a world where we avert climate change and then wipe ourselves out in 2110 with a synthetic virus, I would obviously pick (A). But that definitely feels like a mugging—accept climate change because that way some far future people will lead great lives.
The final thing which makes this all more complex, is that climate change is something which we are on a very well defined trajectory towards—where inaction results in terrible consequences. However, things like nuclear war are risks which may never materialize. If we invest effort into averting credible but potential risks, we’ll never be sure whether that investment actually mattered. If we invest effort in averting climate change, we’ll be much more sure that the effort was worthwhile.
It is true we generally see reduction in costs as cumulative production increases (this is called learning in economics). But then this means it might be cheaper to reduce CO2 emissions in the future (at least at the margin for EA, and even for the world as a whole if some of the learning occurs in related fields that does not require spending money on CO2 mitigation now). It is possible that renewable energy will become less expensive than fossil fuels in the near future, though usually the comparison is made with fossil fuel electricity. It is much more difficult for renewable energy to be lower cost than fuels used directly. Furthermore, if we want to go back to 350 PPM, we would need to do some form of air capture, which I think will be expensive for quite a while. So overall, with learning, it would reduce the cost of solving the problem, but I think it is harder to imagine it being less than $1 trillion present value with low discounting.
You are right that there is a trade off. If we spend money on saving lives at $3000 per life now with health interventions instead of reducing CO2 emissions, that means more CO2 in the atmosphere in 100 years. So the question is whether that harm to the relatively richer people in 100 years is greater than the harm you avert by spending money on global health now if your time horizon only extends about 100 years.
Full-scale nuclear war may very well not happen this century. However, when you include additional catastrophes such as extreme weather on multiple continents (which a UK government study estimated had an ~80% likelihood this century), regional nuclear war, etc., it appears to be more likely than not that we will have one of these catastrophes this century. But it is possible that we will not have one of these catastrophes. As I said in my 80,000 Hours interview, if you are someone who has paid for insurance where they have gotten no payout from it whether they have wasted their money, they say “no” because it makes sense to insure things we can’t afford. So I think of this as an insurance policy for the world. And actually in terms of probabilities, I would say one of these agricultural catastrophes is actually more likely than median or worse slow climate change, so the probability of the investment paying off is actually higher for alternate food preparedness.
I think the upper end of Halstead’s <1%-3.5% x-risk estimate is implausible for a few reasons:
1. As his paper notes and his climate x-risk writeup further discusses, extreme change would probably happen gradually instead of abruptly.
2. As his paper also notes, there’s a case that issues with priors and multiple lines of evidence imply the tails of equilibrium climate sensitivity are much less fat than those used by Weitzman. As I understand it, ECS > 10 would imply paleoclimate estimates are highly misleading and estimates based on the instrumental record are highly misleading and climate models are highly misleading. I don’t know how this sort of reasoning relates to Earth system feedbacks, but I guess the thresholds for them to become relevant would be less likely to be crossed.
3. Even if some of it were abrupt, a 10 degree rise would probably not be an existential disaster in the strict sense, though it would be horrible. (On the other hand, maybe a less than 10 degree rise would still have some risk of causing an existential disaster through some indirect effect on the stability of civilization.)
4. All estimates of the chance that a particular development will cause an existential disaster have to account for the possibility that some other development will have caused an existential disaster by that time and the possibility that some other development will have made humanity mostly immune to existential disasters.
Thanks for engaging with this critically!
Generally, my agenda was probably a bit simpler than people might have supposed. This was not intended to be the last word on whether climate change or development interventions are always better. Rather it’s a starting point and “choose your own adventure” model to help prioritizing between a concrete climate and a concrete development charities. Different situations call for the model to be adapted.
Note that there are four parameters that drive the results of this analysis (the SCC, the income adjustment eta, the cost to avert CO2, and the effectiveness of global dev/health vs. cash). For the first two, there really is a lot more uncertainty, but for the latter two, it’s more clear. This makes the model actually valuable and with action guiding potential.
For instance, if you’re a small donor and can’t decide between GiveDirectly and the Coalition for Rainforest Nations, then, if you believe that CfRN really has a cost-effectiveness of $0.02 / tCO2e averted, in many scenarios, especially the realistic one around which there is most consensus, it will often beat unconditional cash-transfers, even if you believe that social cost of carbon is quite low.
However, CfRN does lobbying, not a scalable intervention that one could invest a lot of money in. So, in contrast, if you’re a billionaire and are looking to decide between global development and climate change as a cause area for your foundation, then perhaps global development might be a better bet.
You write that there are very large flaws in my methodology, but because you then adopted the methodology, I think you actually have quarrels with the empirical estimates that I’ve plugged in, correct?
Some comments on the parameter estimates that you use in your model:
Your less $1/tCO2e averted figure for Cool Earth seems fair for small donors (deforestation prevention can probably absorb a few hundreds of millions—see “Redd+ agreement” and social impact bonds between Norway and Brazil).
However, for large foundations/governments it doesn’t seem quite as scalable in terms of absorbing very large amounts of money as many global development interventions. That’s why I used an intervention such as ocean alkalinity as an example because it might be a way to absorb large amounts of carbon up to 100 billion tonnes / year) for as little as $10 per tonne of CO₂ averted. https://iopscience.iop.org/article/10.1088/1748-9326/aabf9f/pdf
I thought this intervention was representative / similar order of magnitude (10s of $ / ton averted) as some of the bigger ones in the McKinsey report. The next order of magnitude, 100s of $ is for direct air capture, which as far as I understand could absorb most of the CO at scale, but is too expensive. I think this is why getting direct air capture costs down by one more order of magnitude is seen as a climate holy grail, where you can just pump money into and then it solves the whole problem.
But your model seems to be geared towards small donors deciding between to different charities, and then is inconsistent, because you used the median Givewell charity effectiveness (7.95) and not the most effective 17, comparing the best in class low risk offsetting with the median development charity. Even using your model parameters suggests small donors should donate to Deworming over Cool Earth.
Finally, can you say a bit more why you prefer the eta, marginal utility of consumption, to be equal to 1? I felt you you did not provide sufficient empirical justification for this.
See:
“To arrive at estimates of social discount rates consistent with these growth rates, it is necessary to obtain estimates of the elasticity of marginal utility [...] the survey by Groom and Maddison (2019) suggests estimates between 0.5 and 2.0.
The most substantial cross-country analysis available (Evans, 2005; with a focus on advanced economies) arrives at an estimate of 1.4, which we adopt. This estimate is consistent with a review of some 200 experts who have published on social discount rates, which returns a mean value of 1.35 (Drupp et al., 2018). This estimate—drawing on experts who have published on discount rates in highly ranked journals is not necessarily confined to advanced economies, though the authors acknowledge that expertise from developing countries might be underrepresented.”
“We present a quantitative survey of estimates of the elasticity of intertemporal substitution in what we believe is the largest metaanalysis conducted in economics. We collect 2735 estimates from 169 published studies and find that the mean elasticity is 0.5, but that the estimates vary greatly across countries and methods.” https://www.sciencedirect.com/science/article/abs/pii/S002219961500032X
“estimates of η are derived from the so-called Euler-equation although in the macroeconomics literature this information is normally presented in terms of the elasticity of intertemporal substitution (EIS) which is equal to 1/η.” https://link.springer.com/article/10.1007/s10640-018-0242-z
So the last study suggest the eta is equal 2.
This is why I plugged in 1.5 for the realistic case. But I haven’t looked into this in detail and I’d love to have more people look into it.
I hope this did not come across as too critical—I generally really enjoyed reading your treatment and synthesis of the issue.
That may have been your intention, but the title of your article is “Global development interventions are generally more effective than Climate change interventions” and your summary states “My spreadsheet model below shows that climate change interventions are only more effective than global development interventions, if and only if: [...] under quite pessimistic assumptions about climate change (if the social cost of carbon is higher than $1000 per tonne of carbon)”. I think that these things together would make it very easy for a reader to leave with the simple conclusion that climate change interventions are not cost effective, and I just don’t think the evidence exists to back up that simple conclusion.
I also don’t think that you need to make particularly pessimistic assumptions for the social cost of carbon to be much higher. At the very least, your chosen source of a social cost of carbon used an emission pathway (RCP6.0) which only results in 2.2C of warming by 2100. Based on currently announced national commitments, greenhouse emissions are likely to lead to global temperature increases of 2.3ºC-3.7ºC by 2100 with a 25% chance of exceeding 4°C based on current national policies.
I think the methodology is flawed in the sense that you are combining the low/mid/high estimates of several parameters to produce an estimate which is 10 orders of magnitude wide. That’s so wide as to be almost meaningless. I produced an updated estimate mainly to demonstrate that it’s possible to produce an estimate where climate change is better value than global health with some fairly plausible choices of parameters.
The trouble with these estimates is that I’m not convinced they do a good job of considering how costs change as a technology is scaled. For example, we’ve seen this with solar—http://solarsouthwest.co.uk/solar-panel-cost/. Do you have a recommended source which does somehow take account of these effects? If not, we’re not really comparing costs properly.
Additionally, it’s worth recognizing that the current economic model has huge climate externalities. I really hope we get a climate emissions tax at some point, at which point the fundamental incentive structures change, and I’m not sure how to properly price emissions reductions at that point. At least from a government perspective, the carbon tax could be considered “free”. Can you recommend any papers which have tried to come up with a cost/tonne for a carbon tax?
Honestly, it’s because I haven’t yet had a chance to read up on the marginal utility of consumption and it seemed implausible to me that the value of money would actually be 3 orders of magnitude higher in a cash transfer situation. I’m very much prepared to believe that I’m wrong, and I hope to find the time at some point to read the research you referenced and figure that out for myself. In my defense, I also think the SCC used in my update is perhaps an order of magnitude too small, so I could also have used a higher SCC and the 1260x income adjustment and come to the same conclusion.
Thank you for taking the time to reply! I’ve enjoyed responding to your points :)
Thanks for writing this up!
All four of the estimates you review appear to be (at least loosely) EA-affiliated. Were those the only ones you could find that satisfied your criteria? What were your criteria when deciding which estimates to review?
I’m only aware of (loosely) EA affiliated attempts to assess the cost effectiveness of climate change versus global health. I knew about the “2016 GWWC estimate” before I started work on this writeup, and I had seen the “2019 Hillebrandt Cost-Effectiveness” estimate posted on this forum very recently. I found “2018 Halstead Extinction Risk” and “2019 Bressler Mortality Estimate” by searching on this forum.
Let me know if you’re aware of any other relevant estimates.
Ahh, if you’re specifically looking for comparisons to global health, that makes sense that they’re all EA-affiliated.
Thanks for this. Have you seen the reports produced by BreakThrough https://www.breakthroughonline.org.au/
The authors argue that:
the IPCC reports and those based on them are overly conservative and under report the probability and impact of climate risks
the serious impacts that are often considered in 2100 scenarios will more likely come around 2050
I don’t have the expertise to describe how the calculations you’ve done above would be affected by this, but hopefully someone else will.
(edit: I no longer endorse this comment)
This seems to be the key disagreement between your estimate and GWWC’s. As I understand it, if we reduce emissions for the year X by 1%, different things happen in the two calculations:
In GWWC’s calculation, every year Y for decades, we prevent 1% of the deaths during the year Y that would have been prevented by a delay of all climate change for one year (corresponding to the year X)
In your calculation, every year for decades, we prevent 1% of the deaths that would have been caused by climate change during the year Y
There are two “per year”s at play, “per year of deaths” and “per year of emissions”, and the “per year of deaths” is canceled out by “years of deaths”, leaving only the “per year of emissions”. GWWC treats a one-year-long stop to all emissions (in the present) as equivalent to a delay of warming by one year (in the future). I don’t quite understand why that is, but the units seem right. So if I’m not mistaken, you were understandably confused by the numbers being implicitly “per year per year” rather than just “per year”, and the factor 50 shouldn’t be there.
edit: To be more concrete, if you’re multiplying by 50 years in cell C44 of the updated sheet, then cell C34 should do something like divide the averted emissions by the total emissions over decades rather than by the emissions for just the year 2016.
I’m sorry but I don’t follow your argument. I’ll try and explain my own logic and perhaps you can point out the key step where I’m going wrong.
The 2014 WHO paper provides an estimate for the number of climate attributed deaths in 2030 and 2050. Let’s imagine that these estimates were 30 deaths and 50 deaths. The GWWC approach then assumes a linear relationship between CO2 emissions and deaths, producing a straight line passing through these estimates. So 2030 sees 30 deaths, 2031 sees 31 deaths, 2032 sees 32 deaths etc. The GWWC approach then subtracts the 2030 estimate from the 2050 estimate to give the change per year in the climate attributes deaths. In this toy example, that would be a figure of 1 death / year / year.
Now imagine that global emissions drop to zero for a single year in 2030, and that climate response was instantaneous—then we’d expect to see 30 deaths in 2030, 30 deaths in 2031, 31 deaths in 2032, 32 deaths in 2033 etc. So over a 50 year period, we’d see 50 saved lives.
However, the original GWWC spreadsheet simply takes a fraction of the deaths / year / year figure, and declares that the resulting total is the number of deaths averted over all time.
Ah, it looks like I was myself confused by the “deaths/year” in line 20 and onward of the original, which represent an increase per year in the number of additional deaths per year. My apologies. At this point I don’t understand the GWWC article’s reasoning for not multiplying by years an additional time.
My prior was that, since economists argue over the relative value of mitigation (at least beyond low hanging fruit) and present consumption, and present consumption isn’t remotely competitive with global health interventions, a calculation that shows mitigation to be competitive with global health interventions is likely to be wrong. But after looking it over another time, I now think that’s accounted for mostly by:
1. The assumption that climate change increases all causes of death by the same percentage as the causes of death investigated here, which, as the article notes, seems very pessimistic. If 57 million people worldwide died in 2016 (and population is increasing but death rate is decreasing), then 5 million additional deaths per year in 2030-2050 seems implausibly large: almost one in ten deaths would be due to climate change.
2. Cool Earth being estimated here to be orders of magnitude more efficient than the kinds of mitigation that economists usually study. (I have no opinion on whether this is accurate.)
Do you have any particular sources in mind for this? My understanding is that economists are in strong agreement that action now is much cheaper than action in future.
Re: 1. I think it’s useful to consider concrete examples from history which have killed a large number of people. As per my writeup, in the 20th century, the largest famines killed 10-20M people/decade, so 1-2M people/year, all of which happened when the world had fewer than 4 billion people [source]. So if you think that 1-2M people is implausible, then you’re saying that climate change isn’t likely to cause the same kind of agricultural issues as we’ve previously faced, without serious climate issues.
I was thinking e.g. of Nordhaus’s result that a modest amount of mitigation is optimal. He’s often criticized for his assumptions about discount rate and extreme scenarios, but neither of those is causing the difference in estimates here.
According to your link, recent famines have killed about 1M per decade, so for climate change to kill 1-5M per year through famine, it would have to increase the problem by a factor of 10-50 despite advancing technology and increasing wealth. That seems clearly wrong as a central estimate. The spreadsheet based on the WHO report says 85k-95k additional deaths due to undernutrition, though as you mention, there are limitations to this estimate. (And I guess famine deaths are just a small subset of undernutrition deaths?) Halstead also discusses this issue under “crops”.