Climate Change & Longtermism
Author: John Halstead1

Executive Summary
In this report, I will evaluate the scale of climate change from a longtermist point of view.
Longtermism is the idea that influencing the long-term future, thousands of years into the
future and beyond, is a key moral priority of our time.
In economics, longtermism is embodied by the idea that we should have a zero rate of ‘puretime preference’: we should not discount the welfare of future people merely because it is in
the future. Economists who embrace a zero rate of pure time preference will tend to favour
more aggressive climate policy than those who discount future benefits.
Climate change is a proof of concept of longtermism. Every time we drive, fly, or flick a lightswitch, each of us causes CO2 to be released into the atmosphere. This changes the amountof CO2 that is in the atmosphere for a very long time: unless we suck the CO2 out of theatmosphere ourselves, concentrations only fall back to natural levels after hundreds ofthousands of years. The chart below shows long-term CO2 concentrations after different
amounts of cumulative carbon emissions.

Source: N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric
CO2 in an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): 2–17,
https://doi.org/10.1002/2014GB005074.

Some of the ecological effects of climate change get worse over time. The clearest exampleof this is sea level rise. On current policy, the most likely sea level rise this century is 75cm.
However, over 10,000 years, sea levels will rise by 10 metres. Over the long-term, the world
will look very different.

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If you have any comments on this report or notice any errors, please email
john.halstead309[at]gmail.com

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From a longtermist point of view, it is especially important to avoid outcomes that could havepersistent and significant effects. These include events like human extinction, societal
collapse, a permanent negative change in human values, or prolonged economic stagnation.
If we go extinct, then that would be the end of the human story, and there would be no future
generations at all. If civilisation collapses permanently, then future generations will be left
much worse off than they could have been, living lives full of suffering rather than ones offlourishing.

The anatomy of climate risk
The overall size of climate risk depends on the following factors:
1. Greenhouse gas emissions
2. The climate change we get from different levels of emissions
3. The impacts of different levels of climate change
There is uncertainty about all three factors. The main findings of this report are as follows.

Emissions are likely to be lower than once thought
Due to recent progress on clean technology and climate policy, we look likely to avoid theworst-case emissions scenario, known in the literature as ‘RCP8.5’. The most likely scenario
on current policy is now the medium-low emissions pathway known as ‘RCP4.5’. Moreover,climate policy is likely to strengthen in the future. For instance, as I was writing this report,
the US Senate passed the Inflation Reduction Act, the most significant piece of climate
legislation in American history.

2Source: Hausfather and Peters, ‘Emissions – the ‘business as usual’ story is misleading’, Nature,
2020.

Climate change is a great illustration of how society can make progress on a problem if
enough people are motivated to solve it. This does not mean that climate change is solved,
but there is significant momentum, and we are at least now moving in the right direction.
The amount of carbon we could burn in a worst-case scenario is also much lower than once
thought. Some of the literature assumes that there are 5 or even 10 trillion tonnes of carbon
remaining in fossil fuels, mostly in the form of coal. However, these estimates fail torecognise that not all fossil fuels resources are recoverable. Estimates of recoverable fossil
fuels range from 1 to 3 trillion tonnes of carbon.
It is difficult to come up with plausible scenarios on which we burn all of the recoverable
fossil fuels. Doing so would require (1) significant improvements in advanced coal extraction
technology which is not part of the energy conversation today, but (2) a dramatic slowdown
in progress in low carbon technologies that are already getting substantial policy support.

3Warming is likely to be lower than once thought
Warming will likely be lower than once feared, in part because of lower emissions and in part
because the scientific community has reduced uncertainty about climate sensitivity. Where
once current policy seemed likely to imply 4ºC of warming above pre-industrial levels, now
the most likely level of warming is around 2.7ºC, and the chance of 4ºC is around 5%.
Moreover, where once there seemed to be a >10% chance of 6ºC on current policy, the risk
now seems to be well below 1%.
On a worst-case scenario in which we burn all of the fossil fuels, the most likely level ofwarming is 7ºC, and there is a 1 in 6 chance of more than 9.5ºC.

Climate change will disproportionately harm the worst-off
The climate impacts literature suggests that climate change will impose disproportionate
costs on countries at low latitude, which are disproportionately low- and middle-income andhave done the least to contribute to climate change. People in Asia will have to deal withincreasing flooding due to rising sea levels. Climate change will damage agricultural output,
and cause droughts in countries reliant on rainfed agriculture. People in the tropics will facerising levels of heat stress. Fossil fuels also kill millions of people from air pollution in bothpoor and rich countries.
Many low- and middle-income countries have essentially never experienced sustained
improvements in living standards, and a significant fraction may be left worse-off than today
due to climate change. This undermines one common argument for discounting the future
costs of climate change - that future generations will be richer and so better able to adapt tothe effects of climate change.
We have a clear moral responsibility not to impose this harm, to reduce emissions, and toencourage economic development in poorer countries.

Average living standards will probably continue to rise
Climate-economy models confirm that the costs of climate change will fall disproportionatelyon poorer people, but most models also suggest that global average living standards in the
future will be higher than today, on plausible levels of warming. Income per person looks set
to increase by several hundred percent by the end of the century, notwithstanding the effects
of climate change.
‘Bottom-up’ climate-economy models included in the IPCC’s Sixth Assessment Report that
add up the effects of climate impacts in different sectors and plug them into modern
economic models suggest that warming of 4ºC would do damage equivalent to reducing
global GDP by around 5%. One recent model, Takakura et al (2019), includes the following
impacts:2
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Jun’ya Takakura et al., ‘Dependence of Economic Impacts of Climate Change on Anthropogenically
Directed Pathways’, Nature Climate Change 9, no. 10 (October 2019): 737–41,
https://doi.org/10.1038/s41558-019-0578-6.

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Fluvial flooding
Coastal inundation
Agriculture
Undernourishment
Heat-related excess mortality
Cooling/heating demand
Occupational-health costs
Hydroelectric generation capacity
Thermal power generation capacity

For instance, in agriculture, the message from the climate impacts literature is that althoughclimate change will damage food production, average food consumption per person will behigher than today, even for 4ºC of warming, due to progress in agricultural productivity andtechnology. This is illustrated on the chart below from van Dijk et al (2021), which shows per
capita food consumption on different socioeconomic pathways.

Source: Michiel van Dijk et al., ‘A Meta-Analysis of Projected Global Food Demand and Population at
Risk of Hunger for the Period 2010–2050’, Nature Food 2, no. 7 (July 2021): 494–501,
https://doi.org/10.1038/s43016-021-00322-9.

I have previously been critical of climate-economy models, but now believe they are morereliable than they once were. Until recently, a key determinant of aggregate impact
assessments was how to model the effects of >4.4ºC because the chance of that level ofwarming was so high. Estimates that models arrived at were unmotivated and arbitrary inpart because the literature on the impacts of >4.4ºC was sparse. However, warming of>4.4ºC now seems increasingly unlikely (<1% given likely trends in policy), and there is arich and voluminous literature on the impact of warming up to 4.4.ºC. This makes recent
bottom-up models more reliable.
However, even the best bottom-up climate-economy models underestimate the costs ofclimate change because they do not account for some important direct costs:●●

They do not include tipping points
They do not explicitly model the potential effects of climate change on economic
growth and technological progress

It is unclear how much these factors would increase the overall direct costs of climatechange; that is an important area of future research for climate economics. However, for5

levels of warming that now seem plausible, these effects seem unlikely to be large enough tooutweigh countervailing improvements in average living standards.
Bottom-up climate-economy models also do not account for indirect effects, such as conflict,which I discuss below.
‘Top-down’ climate-economy models try to directly measure the effects of climate change on
aggregate economic output, and some of these find much higher impacts from climatechange, on the order of a 25% reduction in GDP for 4ºC warming. However, these results
are highly model-dependent, rely on questionable econometric assumptions, and exclude
several important climate impacts. In my view, the best bottom-up studies are a more reliable
guide, notwithstanding their flaws.
Although average living standards are likely to continue to rise, we also need to consider the
possibility of societal collapse for other reasons, such as a pandemic or nuclear war. If there
were to be a major global catastrophe, then future living standards may not actually behigher than today. Future generations trying to rebuild society would have to do so in a lesshospitable climate.

Some tipping points could have very bad effects
In my view, the most concerning tipping points highlighted in the literature are rapid cloud
feedbacks, collapse of the Atlantic Meridional Overturning Circulation and collapse of theWest Antarctic Ice Sheet.
Some models suggest that if CO2 concentrations pass 1,200ppm (compared to 415ppm
today), cloud feedbacks could cause 8ºC of additional warming over the course of years to
decades, on top of the 5ºC we would already have experienced. The impacts of this sort of
extreme warming have not been studied, but it seems plausible that hundreds of millions ofpeople would die. Moreover, people would be stuck with an extreme greenhouse world formillennia. This would extend the ‘time of perils’: the period in which we have the technology
to destroy ourselves, but lack the political institutions necessary to manage that technology.
It would also make it much harder to recover from a civilisational collapse caused bysomething else (such as a pandemic or nuclear war). However, given progress onemissions, it is now difficult to come up with plausible scenarios on which CO2
concentrations rise to 1,200ppm.
Collapse of the Atlantic Meridional Overturning Circulation would cause cooling and drying
around the North Atlantic, and more importantly would probably weaken the Indian
monsoons and the West African monsoons, with potentially dire humanitarian implications.
For 4ºC, models suggest that the chance of collapse is 1-5%, though they probably
understate the risk.
There is deep uncertainty about potential sea level rise once warming passes 3ºC. For
higher levels of warming, there is a risk of non-linear tipping points, such as collapse of theWest Antarctic Ice Sheet, which would cause sea levels to rise by around 5 metres over 100
years, which would probably cause flooding of numerous highly populated cities, especiallyin Asia.
6Due to progress on emissions, these tipping points now look less likely than they did ten
years ago, but their expected costs (impact weighted by probability) may still be large.
Furthermore, our understanding of the climate system is imperfect, and there may be otherdamaging tipping points that we do not yet know about.
All this being said, contra some prominent research, the evidence from models and thepaleoclimate (the deep climate history of the Earth) suggests that it is not the case that, once
warming passes 2ºC-4ºC, runaway feedback loops will kick in that make the world
uninhabitable.

Direct impacts fall well short of human extinction
Given progress in emissions, the risk of human extinction from the direct effects of climatechange now seems extremely small. The most plausible route to human extinction is via
runaway feedback loops. However, models and evidence from the paleoclimate suggest that
it is impossible to trigger such runaway effects with fossil fuel burning. Models suggest that
we could only trigger a runaway greenhouse if CO2 concentrations pass 3,000ppm (at thevery least), which is out of reach on revised estimates of recoverable fossil fuels.
Moreover, global average temperatures have been upwards of 17ºC higher several times in
the past without triggering runaway feedback loops that killed all life on Earth. Indeed, since
the Cretaceous, 145 million years ago, periods of high temperatures and/or rapid warming
have not been associated with ecological disaster. However, prior to the Cretaceous, climatechange was linked to ecological disaster. In the report, I discuss the theory that this was
because of ecological and geographical factors unique to the pre-Cretaceous period.
I construct several models of the direct extinction risk from climate change but struggle to get
the risk above 1 in 100,000 over all time.
One argument that climate change could directly cause civilisational collapse is that it could
be a contributing factor (along with deforestation, human predation, and pollution) toecosystem collapse, which could in turn cause the collapse of global agriculture. I argue inthe main report that this risk is minimal.

Indirect risks are under-researched but now seem fairly low
Because interstate war has become increasingly rare since the end of World War II, most of
the literature on climate change and conflict has focused on the connection between climate
and civil conflict: conflicts between a government and its citizens in which more than 25
people are killed.
Scholars in the field agree that, so far, climate-related factors have been a much weaker
driver of civil conflict than other factors such as socioeconomic development and state
capacity. However, there is strong disagreement in the field about how important climatechange will be in the future. It is widely agreed that the risk of climate-induced conflict is

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greatest in low- and middle-income countries, and that the most important mechanism isdamage to agriculture.
The potential impact of climate change on the risk of interstate, rather than civil, war ispotentially much more important but also much less studied. Among interstate conflicts,
conflicts between the major powers pose by far the largest risk to humanity. This is because
the major powers have far more destructive weaponry and have the capacity to alter the
trajectory of humanity in other ways.
The most plausible way that climate change could affect the risk of interstate war is bycausing agricultural disruption, which causes civil conflict, which in turn causes interstate
conflict. Indeed, there is some evidence that countries embroiled in civil conflict are more
likely to engage in military disputes with other countries.
It is difficult to see how climate change could be an important driver of some of the mostpotentially consequential conflicts this century - between the US and Russia, and the US andChina. It is more plausible that climate change could play a larger role in driving conflict
between India and Pakistan and also India and China. However, for plausible levels ofwarming, other drivers of this conflict seem much more important.
It is extremely difficult to provide reliable quantitative estimates of the risk of Great Power
War caused by climate change. Nonetheless, I have built a model that attempts to put somenumbers on the key considerations. I think this is valuable for several reasons. Firstly, itclarifies the cruxes of disagreements and allows focused discussion on those cruxes.
Secondly, it allows us to prioritise different problems. If we do not quantify, we will still have
judgments about how important different considerations are. Models make these
considerations precise.
The downside of quantitative models is that they can cause false precision and anchor
readers, even if the model is not good and has not been subject to scrutiny. Many of theconsiderations I have discussed are very difficult to quantify because there is essentially noliterature on them.
With those caveats in my mind, my best guess estimate is that the indirect risk of existential
catastrophe due to climate change is on the order of 1 in 100,000, and I struggle to get therisk above 1 in 1,000. Working directly on US-China, US-Russia, India-China, orIndia-Pakistan relations seems like a better way to reduce the risk of Great Power War thanworking on climate change.

My personal thoughts on prioritising climate change relative toother problems
My primary goal in this report is to help people to answer the following question:
If your goal is to make the greatest possible positive impact on the world, whatshould you do with your time and money right now, given how the rest of society is
spending its resources?

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Crucially, this question is about what people should do on the margin. It is about what people
should do given how society allocates its resources, not about how society as a wholeshould allocate its resources. Thus, when I say that working on some other problems, suchas nuclear war or biosecurity, will have greater impact, this doesn’t mean that society as awhole should spend nothing on climate change and everything on nuclear war andbiosecurity. Rather, it is a claim about what we should do with our resources given how otherresources are currently spent.
Moreover, the question I am trying to answer in this report is specifically about how to makethe greatest possible impact on the world. This is the highest possible bar. In my view,climate change is one of the most important problems in the world, but other problems,
including engineered viruses, advanced artificial intelligence and nuclear war, are morepressing on the margin because they are so neglected. One can visualise this in thefollowing way. Green projects are beneficial on the margin, and red projects are harmful on
the margin. Deeper green projects are more beneficial whereas deeper red projects are
more harmful on the margin.

To emphasise, we should not confuse the claim that other problems are more pressing thanclimate change with the claim that climate change doesn’t matter at all. I am glad that9

climate change is a top priority for millions of young people and for many of the world’s
smartest scientists, and I would like governments and the private sector to spend more on
climate change. I helped to set up the Founders Pledge Climate Change Fund (donate
here), which has helped to move millions of dollars to effective climate change charities. The
point is that I would like other global catastrophic risks to receive comparable attention, notthat I would like climate change to receive less than it does today.
Imagine that only a few hundred people in the world thought that climate change is an
important problem (rather than at least tens of millions), that philanthropists worldwide spenta few million dollars a year on climate (rather than $10 billion), that society as a whole spenta million dollars on the problem (rather than $1 trillion), and that the international institutions
trying to tackle the problem either don’t exist or have a similar budget to a McDonald’s
restaurant. How bad would climate change be? This is how bad things are for the otherglobal catastrophic risks, and then some.
The final important piece of context is as follows: although I am taking a longtermist
perspective in this report, my conclusions about the priority of climate change relative toother global catastrophic risks are also true if you think only current generations matter.
In my view, the risks from AI, biorisk and nuclear war this century are much higher thancommonly recognised.
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AI: Forecasters on the community forecasting platform Metaculus think that artificial
intelligent systems that are better than humans at all relevant tasks will be created in
2042. The most sophisticated attempt to forecast transformative AI is by Ajeya Cotra,
a researcher at the Open Philanthropy Project and her model now suggests that it ismost likely to be developed in 2040. A 2017 survey of hundreds of leading AI
researchers found that the median judgments implied that there is around a 4%
chance of human extinction caused by AI before the end of the century.
Biorisk: Combined forecasts on Metaculus imply that the chance of synthetic biology
killing more than 10% of the world population by 2100 is around 7%. The impliedchance of synthetic biology killing more than 95% of the world population before
2100 is around 0.7%.
Nuclear war: Forecasters on the community forecasting platform Metaculus thinkthat there is an 8% chance of thermonuclear war by 2070.

These risks are not speculative possibilities, and the case for working on them is notcontingent on ignoring the suffering of the current generation for the sake of a tiny probabilityof techno-catastrophe. I think it highly likely that my daughter will have to live throughnuclear war, pandemics created by engineered viruses, and/or the emergence oftransformative AI systems that will radically alter society. It is deeply unfortunate that few
people acknowledge these problems, and that many people who are aware of them dismissthem as sci-fi fantasies without attempting to engage with the arguments, or grappling withthe fact that many people working in these fields agree that the risks are large.
Although, I contend, my conclusions follow on both neartermist and longtermist perspectives,
it is important to reiterate that, in my view, a longtermist ethical point of view is the correctone. I see no compelling arguments for ignoring the welfare of future generations, and an10

ethical system that does ignore them is obviously difficult to square with concern aboutclimate change.
While many people accept that the direct risks of climate change are lower than these otherrisks, some argue that the indirect effects of climate change may be large enough to makethe total risk of climate change comparable. I do not think this is plausible. As discussed
above, my rough models suggest that the total risk of climate change falls well short of thedirect risk posed by the other global catastrophic risks. Moreover, the other risks also haveindirect effects. As a rule, we should expect greater direct risks to have greater indirect
effects. For instance, the indirect effects of trends in biotechnology seem to me much larger
than the indirect effects of climate change. If biotechnology does democratise the creation of
weapons of mass destruction, the indirect effects for the global economy and geopolitics are
hard to fathom but seem enormous.
Overall, because other global catastrophic risks are so much more neglected than climatechange, I think they are more pressing to work on, on the margin. Nonetheless, climatechange remains one of the most important problems from a longtermist perspective. If
progress stalls and emissions are much higher than we expect, then there is a non-negligible
chance of highly damaging tipping points. Moreover, climate change is a stressor of political
upheaval and conflict, which can in turn increase other global catastrophic risks. Finally,
extreme climate change would make recovery from civilisational collapse more difficult.

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Acknowledgments
I am very grateful to the following expert reviewers for their comments and criticism. Any
mistakes are my own, and the views expressed here are my own.
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Matthew Huber, Professor, Dept. of Earth, Atmospheric and Planetary Sciences,
Purdue University
Dan Lunt, Professor of Climate Science, Bristol University
Jochen Hinkel, Head of Department of Adaptation and Social Learning at the Global
Climate Forum
R. Daniel Bressler, PhD Candidate in Economics at Columbia
Cullen Hendrix, Professor at the Korbel School of International Studies, University ofDenverAndrew Watson, Royal Society Research Professor at the University of ExeterPeter Kareiva, Pritzker Distinguished Professor in Environment & Sustainability,
UCLA
Christina Schädel, Assistant Research Professor Center for Ecosystem Sciences andSociety, Department of Biological Sciences, Northern Arizona University
Joshua Horton, Research Director, Geoengineering, Keith Group, Harvard
Laura Jackson, UK Met Office
Keith Wiebe, Senior Research Fellow at the International Food Policy ResearchInstitute
Matthew Burgess, Assistant Professor, Department of Environmental Studies,
University of Colorado BoulderDavid Denkenberger, Assistant Professor of Mechanical Engineering at University of
Alaska Fairbanks
Peter Watson, Senior Research Fellow and Proleptic Senior Lecturer, School of
Geographical Sciences, Cabot Institute for the Environment, University of Bristol
Goodwin Gibbins, Research Fellow, Future of Humanity Institute, University of Oxford
Linus Blomqvist, Senior Fellow at Breakthrough Institute, PhD candidate inEnvironmental Economics and Science at UC Santa Barbara
Luca Righetti, Research Fellow, Open Philanthropy Project
Johannes Ackva, Climate research lead, Founders Pledge
James Ozden, Extinction Rebellion

For helpful advice and discussion, thanks to Prof. Tapio Schneider, Dr. Roman Hoffman, Prof
Colin Goldblatt, Dr. Zeke Hausfather, Prof. Paul Wignall, and Prof. Ramses Ramirez. Finally,
I am grateful to my colleagues Max Daniel and Stephen Clare for their comments.

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Table of Contents
Executive Summary
The anatomy of climate risk
Emissions are likely to be lower than once thought
Warming is likely to be lower than once thought
Climate change will disproportionately harm the worst-off
Average living standards will probably continue to rise
Some tipping points could have very bad effects
Direct impacts fall well short of human extinction
Indirect risks are under-researched but now seem fairly low
My personal thoughts on prioritising climate change relative to other problems
Acknowledgments
Table of Contents
Introduction
1. How much will we emit?
1.1. Emissions so far
1.2. Future emissions
1.2.1. Climate progress
1.2.2. Assumptions about coal use
1.2.3. Assumptions about economic growth
1.3. Recent projections of emissions
1.3.1. International Energy Agency
1.3.2. Liu and Raftery (2021)
1.3.3. Climate Action Tracker
1.3.4. Integrated Assessment Models
1.3.5. Meinshausen et al (2020)
1.3.6. Summary
1.4. Will renewables and batteries solve climate change on their own?
1.5. Worst-case emissions scenarios
1.5.1. How high could fossil fuel emissions be?
Will we try and extract all of the coal?
Ultimately recoverable resources
1.5.2. Explosive economic growth
1.5.3. Indefinitely stalled decarbonisation
Excessive regulation
Low hanging fruit
Breakdown in international coordination
Overall assessment
1.6. Can we change course?
1.6.1. The physics

131.6.2. How hard is changing course?
Emissions reduction should be the priority
1.6.3. Crash direct air capture
1.6.4. How likely is crash direct air capture?
Observed willingness to pay to abate
How might willingness to pay change in the future?
2. How hot will it get?
2.1. Climate sensitivity metrics
2.1.1. Advantages and disadvantages of different metrics
Equilibrium metrics
Transient Climate Response to Cumulative Emissions
2.2. Estimates of climate sensitivity
2.2.1. Equilibrium Climate Sensitivity
2.2.3. Transient Climate Response to Cumulative Emissions
2.3. Warming on different emissions scenarios
2.3.1. Current policy
2.3.2. All fossil fuels without underground coal gasification
2.3.3. The worst-case scenario
2.4. Structural uncertainty
3. Our future in paleoclimatic context
3.1. Our climate
3.2. Hothouses and rapid warming
3.3. Were past greenhouses inhospitable?
3.3.1. Pre-Cretaceous warming and mass extinctions
Warming at the Permian-Triassic boundary
Volcanic kill mechanisms
3.3.2. Biodiversity after Pangea
Cretaceous warming
The Paleocene-Eocene Thermal Maximum and Eocene Climatic Optimum
Miocene Climatic Optimum
Mid-Pliocene Warm Period
The Last Interglacial
Transition into the Holocene from last glacial
The Quaternary Megafaunal Extinction
3.4. Hominid flourishing and climate change
3.5. Why did things change after Pangea?
3.5.1. Pangea
3.5.2. What about prior to Pangea?
3.6. Summarising lessons from the paleoclimate
4. Economic and demographic trends
4.1. Economic trends
4.1.1. Global growth
4.1.2. Regional growth14Regional growth and discounting
4.1.3. Growth, impacts, adaptation and course correction
Subsequent collapse
4.2. Demographic trends
5. Agriculture
5.1. Context and trends in agriculture
5.1.1. Food production
5.1.2. Agriculture and the economy
5.1.3. Historical trends in water use and irrigation
Water use
Irrigation
5.2. By what mechanism does climate change affect food production?
5.3. What is the projected effect of climate change on agricultural production?
5.3.1. Crop yields
Correlated yield declines
5.3.2. Crop migration
5.3.3. Water stress and agriculture
The effect of climate change on precipitation
The effect of climate change on drought
What effect will climate-induced water stress have on agriculture and theeconomy?
Irrigation is an effective adaptation measure
Water management is poor
Desalination
What are the economic costs of water stress?
5.3.4. Heat stress for agricultural workers
5.4. Future agricultural progress
5.5. Overall judgement on the effects of climate change on agriculture
5.6. Extreme warming and agriculture
5.6.1. Lethal limits
At what level of warming would plants pass lethal limits?
EuropeNorth America
China
RussiaIndia
5.6.2. Extreme heat stress and agricultural labour
5.6.3. Summary of extreme warming and agriculture
5.7. Ecosystem collapse and threats to agriculture
5.7.1. Trends in biodiversity
Species extinctions
Population losses
Speciation and hybridisation
5.7.2. Drivers of species extinctions

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The Species-Area Relationship
Climate change as a driver
5.7.3. Could ecosystem collapse destroy agriculture?
From human intervention to the destruction of agricultural ecosystem services
Extinctions have mainly occurred on islands
Forest cover is increasing in many regions
Local species richness is stable
Track record of predictions based on species area relationships
Models vs the paleoclimate
Global species loss and agriculture
Clarifying the argument
Closeness to pre-modern ecology and living standards are negatively correlated
Overall judgement on ecosystem collapse and agriculture
6. Heat stress6.1. Metrics
6.2. Heat stress today
6.3. What are high levels of heat stress actually like?
6.4. Impacts
6.4.1. The scope for adaptation
6.4.2. Death estimates
6.5. The future human climate niche
7. Sea level rise
7.1. Past trends in relative sea level rise and flooding
7.1.2. Vertical land movement
7.1.3. Floods
7.2. How much will sea level rise due to global warming?
7.3. What is the scope to adapt to relative sea level rise?
7.3.1. Adaptation options
7.3.2. Future adaptation prospects
Up to 2 metres over 80 years
Relative sea level rise of 5 metres over 100 years
Past experience with subsidence and living below sea level
Future projections of adaptation
7.3.3. Effects on 21st Century sea level rise on land loss and migration
7.3.4. Economic effects of sea level rise
7.4. Overall verdict on sea level rise
8. Tipping points
8.1. Permafrost carbon release
8.2. Methane clathrates
8.2.1. Methane emissions so far
8.2.2. Most methane would not reach the atmosphere8.2.3. The paleoclimate record
8.2.4. Expert estimates of emissions from methane hydrates
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8.3. Amazon forest dieback
8.4. Boreal forest dieback
8.5. Collapse of the Atlantic Meridional Overturning Circulation
8.5.1. Climatic effects of AMOC collapse
8.5.2. How would AMOC collapse affect human society?
8.5.3. The probability of AMOC collapse
8.6. Changes to the West African monsoon
8.7. Indian monsoon shift
8.8. Cloud feedbacks
8.8.1. How plausible are rapid cloud feedbacks?
8.8.2. What effects would rapid cloud feedbacks have on human society?
8.9. Hothouse Earth
9. Greenhouses and habitability
9.1. The long-term habitability of Earth9.2. Could humans cause a moist greenhouse or runaway greenhouse?
9.3. How might a moist or runaway greenhouse kill us?
9.4. Quantifying the direct extinction risk of climate change
10. Economic costs
10.1. Context and trends
10.2. The social cost of carbon
10.3. Estimating climate damages
10.4. Bottom-up studies
10.4.1. Microeconomic evidence of how temperature affects productivity
10.4.2. Included sectors and impacts
10.4.3. Tipping points
AMOC collapse
West African Monsoons
Indian monsoon shift
Collapse of the West Antarctic Ice Sheet
Rapid cloud feedbacks
How do tipping points affect the economic costs of climate change?
10.4.4. Indirect effects
10.4.5. Regional or global aggregation
10.4.6. Growth or levels?
10.4.7. Overall judgement on bottom-up studies
10.5. Top-down studies
10.5.1. Cross-sectional correlations
Cross-country correlations
The cross-country correlation between temperature and per capita GDP
The cross-country correlation between temperature and growth rates
Methodological issues with cross-country regressions
Cross-sectional relationship within countries
Methodological issues with within-country cross-sectional regressions

17

Other problems with cross-sectional evidence
Overall judgement on the cross-sectional evidence
10.5.2. Weather and economic performance
Methodological drawbacks of weather studies
Weather is different to climate change
Noisy proxies
Non-market impacts
Findings of weather studies
GDP growth or GDP levels?
Adaptation
Model uncertainty
Overall judgement on weather studies
10.6. Expert elicitation
10.6.1. Methodological problems with expert elicitation
10.6.2. Findings of expert elicitation studies
10.6.3. Overall judgement on expert elicitation studies
10.7. Overall view on climate damages
10.8. Climate economics and the long-term
10.8.1. Risk of direct extinction or collapse
10.8.2. Size of indirect risks
10.8.3. Long-run stagnation
10.8.4. The relative geopolitical power of different countries
10.8.5. What is the sign of the effect of economic growth on the long-term?
11. Displacement and migration
11.1. Definitions and trends
11.1.1. Displacement
11.1.2. Migration
International migrants
Internal migrants
Migration to coastal regions
11.1.3. The character of migration and displacement
11.2. How will climate change affect displacement?
11.2.1. Coastal displacements from storms and floods
11.2.2. River flood displacements
11.2.3. Overall displacements from floods and storms
11.3. How will climate change affect migration?
Myers estimates
Other estimates
Summary of migration estimates
11.4. Summary of climate change, migration and displacement
12. Conflict
12.1. Context and trends
12.1.1. Definitions18

12.1.2. Trends
12.1.3. Drivers of the Long Peace
12.2. Climate change and pre-industrial strife
12.3. The contemporary relationship between climate change and conflict
12.3.1. The correlation
12.3.2. Overview of the literature
12.3.4. Effect sizes found in the literature
12.3.5. A review of Burke et al (2009)
Failure to predict decline in civil conflict incidence
Sensitivity to civil war cut-off
Aggregated spatial and temporal scales
Qualitative analysis
Overall view
12.3.6. A review of the Burke et al (2015b) meta-analysis
Intergroup conflict includes a lot
Study inclusion criteria
Study inclusion and coding decisions
Reporting bias
Implausibly precise estimates
Africa focus
Caruso et al (2016) - Climate Change, Weather Shocks and Violent Conflict:
A Critical Look at the Evidence
Dell et al (2012) - Temperature Shocks and Economic Growth: Evidence fromthe Last Half Century’, American Economic Journal: Macroeconomics
Hsiang et al (2011) - Civil conflicts are associated with the global climate,
NatureOther issues with panel studies
Overall judgement on these estimates
Implications of the estimates
Burke et al estimates taken at face value
My favoured estimates
Mechanisms of impact
12.3.7. Climate less important than other factors
Future conflict predictions
Hegre et al (2016) ‘Forecasting Civil Conflict along the Shared
Socioeconomic Pathways’, Environmental Research Letters
Witmer et al (2017) ‘Subnational violent conflict forecasts for sub-SaharanAfrica, 2015–65, using climate-sensitive models’, Journal of Peace Research
12.4. Concluding thoughts on climate change and civil conflict
12.5. Climate, conflict and Great Power War
12.5.1. How would Great Power War affect the long-term future?
12.5.2. Theories of interstate war
Realism
Rational states?
Liberalism

19Constructivism
Public choice theory
12.5.3. How might climate change affect the risk of Great Power War?
Conflict over water resources
Economic costs
Civil conflict and upheaval
Mass migration, displacement and conflict
The mechanism
The size of the effect on conflict
12.5.4. The main drivers of interstate war
War usually involves contiguous neighbours
A large proportion of wars involve enduring or strategic rivals
Security dilemma and conflict spiral
Unresolved territorial disputes
Misperceptions about relative power
Power transitions
12.5.4. The influence of climate change on the risk of Great Power War
The US and China
The US and Russia
India and Pakistan
Water wars
Economic performance
Mass migration and civil conflict
India and China
12.5.5. Which conflicts would do the most damage?
AI
Biorisk
Nuclear weapons
Other technological risks
12.5.6. Quantifying the indirect risks of climate change
12.5.7. Overall judgement on climate change and Great Power War
13. Interpersonal conflict, aggression and violence
13.1. Context, base rates and trends
13.2. Cross-sectional relationship13.3. Experimental and quasi-experimental evidence
13.3.1. Methodological issues
Weather different to climate
Heavy US focus
Implausible precision
Reporting bias
13.4. Mechanisms
13.5. Effect size
Appendix 1. The WCRP estimate of climate sensitivity

20

Prior selection
Using multiple lines of evidence
Formally producing a posterior distribution

21

Introduction
In this report, I will evaluate the scale of climate change from a longtermist point of view.
Longtermism is the idea that influencing the long-term future, thousands of years into the
future and beyond, is a key moral priority of our time.
The basic case for longtermism is as follows. First, future sentient life matters. Our lives
surely matter just as much as those lived thousands of years ago — so why shouldn’t the
lives of people living thousands of years hence matter equally? Second, the future could bevast. Absent catastrophe, most people who will ever live have not yet been born. Third, ouractions may predictably influence how well this long-term future goes. In sum, we have a
responsibility to ensure that future generations get to survive and flourish.
Climate change is a proof of concept of longtermism. Every time we drive, fly, or flick a lightswitch, each of us causes CO2 to be released into the atmosphere. This changes the amountof CO2 that is in the atmosphere for a very long time: unless we suck the CO2 out of theatmosphere ourselves, concentrations only fall back to natural levels after hundreds ofthousands of years. The chart below shows long-term CO2 concentrations after different
amounts of cumulative carbon emissions.

Source: N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric
CO2 in an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): 2–17,
https://doi.org/10.1002/2014GB005074.

Because CO2 is a greenhouse gas, it traps heat leaving the Earth’s surface, causing theplanet to warm.
Some of the ecological effects of climate change get worse over time. The clearest exampleof this is sea level rise. On current policy, the most likely sea level this century is 75cm.
However, over 10,000 years, sea levels will rise by 10 metres. Over the long-term, the world
will look very different.
Climate change is a long-term problem in which we are all implicated; by emitting CO2, we
contribute to a problem that imposes costs on our descendants for generations to come.

22

Some economists think it is appropriate to discount climate impacts that occur in the future.
They do this for two reasons. Firstly, they assume that future generations will be richer andso will be better able to deal with climate impacts. It is not clear that this is justified. Climate
impacts will be especially bad for developing countries at low latitude, which will account fora large fraction of the human population by the end of the century. Historically, many
countries, especially African ones, have had low rates of economic growth, so it is not clearthat they will be much richer in the future. Even though average living standards will improve,these improvements are likely to be concentrated in countries outside of Africa, and asubstantial fraction of people may not be much better off.
Secondly, some economists defend positive pure time preference, which is the view that weshould value the welfare that occurs in the future less because it is in the future. For
example, Nordhaus (2008) uses a 1% rate of pure time preference.3 This implies that one
death today is worth more than 100 deaths in 500 years. Economists defend pure time
preference on the basis that, as shown in people’s saving and consumption behaviour,people in the real world are impatient and prefer a benefit today to one in a year’s time. It isdifficult to see why people’s impatience about their own welfare is relevant to how muchweight to put on the welfare of other people who will live in the future. It is, for instance,
exactly analogous to arguing that my child’s future welfare is worth less than my own on thesole basis that I have frittered away their inheritance at a theme park.
Disagreements about the weight to put on the welfare of future generations can have crucial
importance for policy. The more weight you put on the welfare of future generations, themore likely you are to favour aggressive climate policy. Indeed, disagreement about thediscount rate largely explains why the UK government’s Stern Review favoured much moreaggressive climate policy than Nordhaus’ prominent DICE model.
From a longtermist point of view, it is especially important to avoid outcomes that could havepersistent and significant effects. These include events like human extinction, societal
collapse, a permanent negative change in human values, or prolonged economic stagnation.
If we go extinct, then that would be the end of the human story, and there would be no future
generations at all. If civilisation collapses permanently, then future generations will be left
much worse off than they could have been, living lives full of suffering rather than ones offlourishing.

3

Nordhaus, W. (2008). A question of balance. New Haven, CT: Yale University Press.

23

1. How much will we emit?
As Stefan Schubert argues in his piece on Sleepwalk bias, there are two different kinds ofprediction:
1. What I bet will happen
2. Predictions as warnings - if we don’t do x (get our act together), then y will happen.
There are few probabilistic assessments of the emissions pathways (type 1 above) and a lotof the scenarios are more like warnings, which are conditional probabilities of different kinds,such as:1. If current policy never strengthens, this is where we could go
2. If current pledges and promises are followed, this is where we could go
3. If trends continue as they have been doing, this is where emissions could go.
From the perspective of risk management, we are more interested in the bets than thewarnings. We want to know how likely different emissions scenarios are, not what emissions
scenarios are most likely conditional on an improbable set of assumptions about technology
and policy.

1.1. Emissions so far
James Watt’s patent for the steam engine in 1769 marked the start of the Industrial
Revolution and centuries of almost unchecked fossil fuel burning. Prior to COVID, we were
putting about 36 billion tonnes of CO2 into the atmosphere every year.

24Despite increasing attention to climate change, more than half of all CO2 ever emitted was
released after 1990. Since the 1980s, much of the growth in emissions has been driven by
emerging economies in Asia. In the future, most energy demand and emissions growth islikely to come from outside Europe and North America.
Between 1750 and 2017, cumulative emissions from fossil fuels and cement were 1,580
billion tonnes of CO2 (gigatonnes of CO2 (GtCO2)). This is 431 GtC (because 1 GtC = 3.667
GtCO2).4 Note that a gigatonne is the same as a petagram (Pg), another metric sometimes
used in climate science.
Unless we eventually remove CO2 from the atmosphere ourselves, CO2 accumulates in theatmosphere for millennia. This means that as long as CO2 emissions are positive,
concentrations of CO2 in the atmosphere will continue to rise.
Immediately prior to the Industrial Revolution, CO2 concentrations were 278 parts per million.
They are now at 415ppm

4Note that this does not include cumulative emissions from land use change.

25

At current emissions rates, CO2 concentrations are rising at about 2.5 ppm each year.

1.2. Future emissions
The latest IPCC report outlines a range of emissions scenarios known as ‘Representative
Concentration Pathways’ (RCPs), as well as a range of ‘Shared Socioeconomic Pathways’ socioeconomic narratives about how the world will develop in key areas such as population,
income, inequality and education. There are 5 Shared Socioeconomic Pathways.
SSP1: Sustainability (Taking the Green Road)
SSP2: Middle of the Road
SSP3: Regional Rivalry (A Rocky Road)
SSP4: Inequality (A Road divided)
SSP5: Fossil-fueled Development (Taking the Highway)
These diagrams show some key assumptions of the shared socioeconomic pathways.

26

Source: Keywan Riahi et al., ‘The Shared Socioeconomic Pathways and Their Energy, Land Use, andGreenhouse Gas Emissions Implications: An Overview’, Global Environmental Change 42 (1 January2017): 153–68, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

SSP2 is meant to be the ‘current trends continue’ scenario. SSP5 is a scenario involving
rapid growth in energy demand and in GDP. You can read the narratives in full here. These
scenarios are not all meant to have equal probability so we shouldn’t see the spread across
SSPs as a probability range.
The chart below shows regional GDP growth on the different SSPs:

27

Source: Håvard Hegre et al., ‘Forecasting Civil Conflict along the Shared Socioeconomic Pathways’,
Environmental Research Letters 11, no. 5 (April 2016):
https://doi.org/10.1088/1748-9326/11/5/054002.

As I discuss in chapter 5, given historical trends, the most plausible SSP seems to be SSP3
or SSP4. The other SSPs predict much higher growth in Africa than is warranted byhistorical experience so far.
Each Shared Socioeconomic Pathway can be combined with a range of different
Representative Concentration Pathways. For instance, the high growth Shared
Socioeconomic Pathway 5 (SSP5) can be combined with modest and also very highemissions, depending on assumptions about the strength of climate policy.
The chart below shows a subset of shared socioeconomic pathways. The scenario labels
combine the SSP with the Representative Concentration Pathway. So, for instance, SSP5
combined with RCP8.5, is called ‘SSP5-8.5’, while SSP2 combined with RCP4.5 is called‘SSP2-4.5’. The number of each RCP refers to the radiative forcing in Watts per square
metre at 2100 on the different emissions scenarios: the higher the number, the greater thewarming.

28

IPCC, Climate Change 2021: The Physical Science Basis, Assessment Review 6, Summary for
Policymakers: Figure SPM.4

There are also ‘baseline’ SSPs which provide a “description of future developments inabsence of new climate policies beyond those in place today”, i.e. on current policy.(riahi
155)

29

Source: Keywan Riahi et al., ‘The Shared Socioeconomic Pathways and Their Energy, Land Use, andGreenhouse Gas Emissions Implications: An Overview’, Global Environmental Change 42 (1 January2017): Fig 5, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

For SSP2, 3 and 4 the baseline scenario is around RCP6 to RCP7. For SSP1, it is below
RCP6, while for SSP5, it is RCP8.5
To understand the risk posed by climate change, what we would like to know is theprobability of different emissions pathways. The IPCC does not put a probability on future
emissions scenarios and probabilistic assessments are thin on the ground in the wider
literature. However, some recent studies suggest that there is now more cause for optimism
than there ever has been before. While many previously assumed that RCP6 or RCP8.5 was
the most likely outcome on current policy, it now looks like RCP4.5 is more likely, and thatclimate policy is going to strengthen in the future.

1.2.1. Climate progress
Since the Industrial Revolution, emissions from fossil fuel burning and cement increased at
upwards of 3% per year, on average, and the historical trend fits an exponential closely. If
this trend were to continue, emissions would be extremely high by the end of the century,
well in excess of SSP5-8.5:

30

Source: Our World in Data

Between 2000 and 2010, emissions grew at 2.8% per year. However, the picture on climatechange looks more optimistic than it once did. Emissions growth slowed down dramaticallybetween 2010 and 2019, to only around 1%. This decline was (as explained here) due toimprovements in energy efficiency, in the emissions intensity of energy (i.e. switching to lesspolluting sources of energy), and due to slower than expected economic growth.5
Many recent studies suggest that the highest emissions scenarios now look much less likely.
There are several reasons for this. Firstly, the costs of renewables and batteries havedeclined extremely quickly. Historically, models have been too pessimistic on cost declines
for solar, wind and batteries: out of nearly 3,000 Integrated Assessment Models, none
projected that solar investment costs would decline by more than 6% per year between 2010
and 2020. In fact, they declined by 15% per year.6 This chart from Our World in Data shows
the decline in the cost of solar module costs, with costs falling by more than 99% over 43
years:
5

Matthew G. Burgess et al., ‘IPCC Baseline Scenarios Have Over-Projected CO2 Emissions andEconomic Growth’, Environmental Research Letters 16, no. 1 (December 2020): 014016,
https://doi.org/10.1088/1748-9326/abcdd2
6“Sound energy investments require reliable forecasts. As illustrated in Figure 2(a), past projectionsof present renewable energy costs by influential energy-economy models have consistently beenmuch too high. (“Projections” are forecasts conditional on scenarios, so we use the terms
interchangeably.) The inset of the figure gives a histogram of 2,905 projections by integrated
assessment models, which are perhaps the most widely used type of global energy-economy
models19,20,21,22, for the annual rate at which solar PV system investment costs would fall between
2010 and 202019. The mean value of these projected cost reductions was 2.6%, and all were lessthan 6%. In stark contrast, during this period solar PV costs actually fell by 15% per year. Suchmodels have consistently failed to produce results in line with past trends3,23” Way et al, ‘Empiricallygrounded technology forecasts and the energy transition’, Oxford Martin School, 2021: p.3.

31

Fundamentally, existing mainstream economic models of climate change consistently fail tomodel exponential cost declines, as shown on the chart below. The left pane below shows
historical declines in solar costs compared to Integrated Assessment Model projections ofcosts. The pane on the right shows the cost of solar compared to Integrated Assessment
Model assessments of ‘floor costs’ for solar systems - the lowest that solar could go. Realworld solar system costs have consistently smashed through these supposed floors.

32

Source: Way et al, ‘Empirically grounded technology forecasts and the energy transition’, Oxford
Martin School, 2021.

Secondly, climate policy has strengthened substantially over the last few years. Countries
representing 66% of global CO2 emissions have committed to achieving net-zero emissions
by 2050. Most importantly, China has pledged to get to net zero by 2060. Historically, suchtargets have not been met, but some of them, such as that of the UK, are enshrined in law.
As a consequence of this and other factors such as declining natural gas costs, many
countries have recently absolutely decoupled GDP growth and consumption-based CO2
emissions. Since the emissions are consumption-based, the decline does not merely reflect
offshoring of emissions intensive industries like steel. In a piece for the BreakthroughInstitute, Zeke Hausfather shows that over the past 15 years, 32 countries with more than 1
million people have seen rising incomes but lower emissions, even when we adjust foremissions embedded in trade.

33

It seems likely that this will be true for more and more countries in the future.
This does not necessarily mean that there is a Kuznets Curve relationship between GDP
and CO2 emissions. On the Kuznets Curve, once income per head increases past a certain
point, pollutants decline because environmental protection is a luxury good.

It is unclear how exactly to define the Kuznets Curve, and identifying one for income andemissions raises some difficult econometric problems. Many studies find there to be an

34

Environmental Kuznets Curve for carbon, but many don’t, and there have been some sharp
criticisms of the econometrics used in the studies finding a positive effect.7
It is especially difficult to disentangle time effects from income effects in Kuznets Curvestudies. Emissions per head are declining over time in many rich countries (which are also
experiencing economic growth), but this may be due to the passage of time rather thaneconomic growth. For instance, there might be technological improvements that don’t
improve growth proportionately, such as less polluting cars that are otherwise identical, or
switching from coal to gas, solar or wind, without reducing costs.
To understand what this means - imagine if in Britain, economic growth was expected to be
2% in the coming year and instead it turned out to be 3%. Then we should expect emissions
in Britain to be higher than were initially expected. If Britain was past the turning point on the
curve, the Environmental Kuznets Curve theory would expect them to be lower. This doesn't
contradict that emissions might fall in Britain. On one interpretation of the Kuznets Curvetheory they'd fall more if there was more growth and on the opposing theory they would fallless if there was more growth.
As an illustration of this, Italy has had zero income growth for twenty years, but emissions
per person have dropped a lot over that time. It would be easy to confuse this time effectwith an income effect if Italy had indeed experienced income growth.
It is unclear how one should specify the correct lag from growth to the Kuznets Curve turning
point, i.e. whether we should expect emissions to decline instantaneously, or only a few
years later.
For all this, the evidence of absolute decoupling is encouraging.

1.2.2. Assumptions about coal use
To understand SSP ‘current policy’ models better, it is useful to investigate their assumptions
about coal use. Many SSPs assume that there will be a large increase in per capita coal use.
This looks very unlikely in part due to the decline in the cost of renewables and theabundance of natural gas driven by hydraulic fracturing, and in part because countries tend
to transition away from coal as they get richer. For example, here is the increase in percapita coal use on different baseline SSPs:

7

David I. Stern, ‘The Environmental Kuznets Curve after 25 Years’, Journal of Bioeconomics 19, no. 1(1 April 2017): 7–28, https://doi.org/10.1007/s10818-017-9243-1; Herman RJ Vollebergh, Bertrand
Melenberg, and Elbert Dijkgraaf, ‘Identifying Reduced-Form Relations with Panel Data: The Case of
Pollution and Income’, Journal of Environmental Economics and Management 58, no. 1 (2009):
27–42; Martin Wagner, ‘The Environmental Kuznets Curve, Cointegration and Nonlinearity’, Journal ofApplied Econometrics 30, no. 6 (2015): 948–67, https://doi.org/10.1002/jae.2421.

35

Source: Justin Ritchie and Hadi Dowlatabadi, ‘Why Do Climate Change Scenarios Return to Coal?’,Energy 140 (1 December 2017): fig. 3, https://doi.org/10.1016/j.energy.2017.08.083.

For comparison, China burned what was widely seen to be a prodigious amount of coal from2000 onwards, but that is dwarfed by the increase in coal use projected on SSP5-8.5.
Ritchie and Dowlatabadi (2017) argue that the SSPs rely on a coal cost projection model,
which assumes that costs decline as we extract more. This has been invalidated by thehistorical data.8
This is illustrated in the figure below from Burgess et al (2020), which shows theassumptions of MESSAGE, a leading integrated assessment model, about solar costs
compared to coal costs over the next 30 years. As this shows, MESSAGE assumes that coalcapital costs will be threefold cheaper than solar for the next thirty years, even though solar
costs are already lower.

8“To understand possibilities for energy resources in this context, the research community draws from
Rogner (1997) which proposes a theory of learning-by-extracting (LBE). The LBE hypothesis
conceptualizes total geologic occurrences of oil, gas, and coal with a learning model of productivity
that has yet to be empirically assessed.
This paper finds climate change scenarios anticipate a transition toward coal because of systematic
errors in fossil production outlooks based on total geologic assessments like the LBE model. Suchblind spots have distorted uncertainty ranges for long-run primary energy since the 1970s andcontinue to influence the levels of future climate change selected for the SSP-RCP scenario
framework. Accounting for this bias indicates RCP8.5 and other ‘business-as-usual scenarios’
consistent with high CO2 forcing from vast future coal combustion are exceptionally unlikely.
Therefore, SSP5-RCP8.5 should not be a priority for future scientific research or a benchmark forpolicy studies.” Justin Ritchie and Hadi Dowlatabadi, ‘Why Do Climate Change Scenarios Return to
Coal?’, Energy 140 (1 December 2017):

36

Source: Matthew G. Burgess et al., ‘IPCC Baseline Scenarios Have Over-Projected CO2 Emissions
and Economic Growth’, Environmental Research Letters 16, no. 1 (December 2020): Fig 5b.,https://doi.org/10.1088/1748-9326/abcdd2

One concern might be that we discover a novel and cheap way to extract fossil fuels which
could outcompete low carbon technology, in turn driving strong emissions growth. The
historical trends suggest that this is unlikely. In the long-run, fossil fuel prices have been
fairly stable, and have hovered around the same order of magnitude for more than a century.
Way et al (2021) construct a probabilistic forecast using autoregression from past trends,
which suggests that fossil fuel prices will be flat in the future.

Source: Way et al, ‘Empirically grounded technology forecasts and the energy transition’, Oxford
Martin School, 2021, Fig. 3.

These forecasts account for large structural changes in fossil fuel extraction technology,
such as the discovery of fracking.
The reason that fossil fuel prices will probably be fairly flat in the future is that there is a race
between progress in extraction technology and use of the cheapest and most promising
deposits. For example, in the early 2000s we discovered fracking technology, which allows
us access to cheap gas. The best deposits are used up such that we then have to extract37

from less promising and more expensive sites, so that the longer-term trend in natural gas
prices is fairly flat.
The increase in coal use seems especially unlikely given the assumptions made in the SSPs
about economic growth. For instance, in order for us to follow SSP5-RCP8.5, there would
have to be very fast economic growth and technological progress, but meagre progress on
low carbon technologies. This is implausible. In order to reproduce SSP5-8.5 with newer
models, the models had to assume that average global income per person will rise to$140,000 by 2100 and also that we would burn large amounts of coal.

Keywan Riahi et al., ‘The Shared Socioeconomic Pathways and Their Energy, Land Use, andGreenhouse Gas Emissions Implications: An Overview’, Global Environmental Change 42 (1 January2017): fig. 2.

It is difficult to imagine that in such a cornucopia, there would not also be a lot of progress on
low carbon technology, and that countries would have greatly increased willingness to pay toprotect the environment. The same argument applies to the other relatively high growth
futures, such as SSP1 and SSP2.

1.2.3. Assumptions about economic growth
Economic growth is a key driver of emissions growth. The SSPs make assumptions about
future economic growth that are crucial to future emissions projections. The historical trackrecord of the projections of these models to date is poor and tends to overestimate both
economic growth and emissions. The figure below shows the error in the Fifth Assessment
Report (the 2013-14 IPCC report) and in the Shared Socioeconomic Pathways relative toobserved growth:38

Source: Matthew G. Burgess et al., ‘IPCC Baseline Scenarios Have Over-Projected CO2 Emissions
and Economic Growth’, Environmental Research Letters 16, no. 1 (December 2020): 014016,
https://doi.org/10.1088/1748-9326/abcdd2.

The error in projecting emissions growth is largely attributable to the overestimation of GDP
per capita growth between 2005 and 2017.
These future projections of these models are also likely to be biased upwards. I discuss
potential future growth trajectories in section 1.5.

1.3. Recent projections of emissions
Due to improving low carbon technology, strengthening climate policy, and the implausibilityof a return to coal, recent models suggest that very high emissions scenarios are extremely
unlikely and that we are most likely to follow a medium-low emissions pathway - around
RCP4.5.99

For an overview, see Hausfather, ‘Flattening the Curve of Future Emissions’, Breakthrough Institute,
2021.

39

1.3.1. International Energy Agency
The International Energy Agency produces influential energy systems models that project
likely future emissions, given certain socioeconomic, technological and climate policy
assumptions. In 2015, the International Energy Agency projected that we were most likely tofollow RCP6, on current policy.10 More recent estimates suggest that we are now most likely
to follow RCP4.5, on current policy.
The chart below from Hausfather and Peters (2020) shows emissions on current policies andpledged policies, according to the International Energy Agency.

Source: Hausfather and Peters, ‘Emissions – the ‘business as usual’ story is misleading’, Nature,
2020.

The chart relies on IEA models of future energy systems. These are still probably too
pessimistic on renewables. You can find the IEA’s cost assumptions here. They show thelevelised cost of solar falling by 40% between now and 2030. But if historical trends
continue, we should actually expect costs to decline by 89%. Trends may not continue,
perhaps because we may be reaching saturation for renewables capacity additions, which
10

Wagner and Weitzman, Climate Shock, p. 31.

40

drive cost declines. But the default assumption should be that cost declines will continue.
Going against this assumption has a poor track record, as noted above. The IEA revised
down its short-term emissions projections in 2019, 2020 and 2021.11
Moreover, climate policy will probably strengthen in the future. The IEA retired their current
policies scenario after their 2019 report, arguing that the world was moving too quickly for acurrent policy scenario to be of much use. The latest IEA report projects emissions on
near-term stated policies such as Paris commitments up to 2030 (STEPS), and theannounced pledges scenario (APS 2021). Emissions on these scenarios are lower than
RCP4.5.

Pielke Jnr et al (2022) assess the plausibility of future emissions scenarios according to theircompatibility with (1) historical fossil fuel emissions between 2005 and 2020 and (2) IEA
projections to 2050. They rule out as implausible models that have a divergence of
0.3%/year on historical emissions and on IEA future emissions projections. Their results for
future emissions are as follows:11“If IEA’s projections of near-term emissions continue to be revised down— as they have in recent
years (IEA 2019, 2020, 2021)...” Roger Pielke Jr, Matthew G. Burgess, and Justin Ritchie, ‘Plausible
2005–2050 Emissions Scenarios Project between 2 °C and 3 °C of Warming by 2100’, Environmental
Research Letters 17, no. 2 (February 2022): 3, https://doi.org/10.1088/1748-9326/ac4ebf.

41

Source: Roger Pielke Jr, Matthew G. Burgess, and Justin Ritchie, ‘Plausible 2005–2050 Emissions
Scenarios Project between 2 °C and 3 °C of Warming by 2100’, Environmental Research Letters 17,
no. 2 (February 2022): 3, https://doi.org/10.1088/1748-9326/ac4ebf.

As this shows, their ‘best guess’ scenario projects that we would decarbonise in around2090.
The chart below shows plausible scenarios according to Pielke Jnr. et al (2022) compared to
SSP baselines. All of the SSP baselines are classed as implausible on their criteria; only
those diagonally left of the SSP baseline rectangle are plausible.

42

Since IEA projections are likely too pessimistic on renewables, these classifications areprobably overly pessimistic.

1.3.2. Liu and Raftery (2021)
In 2017, Raftery et al estimated the following probability distribution across emissions:43Source: Adrian E. Raftery et al., ‘Less than 2 °C Warming by 2100 Unlikely’, Nature Climate Change
7, no. 9 (September 2017): Fig 3a, https://doi.org/10.1038/nclimate3352.

This suggests that the most likely pathway is around RCP6 and that there is around 1-5%
chance of RCP8.5. However, Liu and Raftery (2021) updated the estimates with more recent
data up to 2015, which produced a more optimistic picture.

44

Peiran R. Liu and Adrian E. Raftery, ‘Country-Based Rate of Emissions Reductions Should Increaseby 80% beyond Nationally Determined Contributions to Meet the 2 °C Target’, Nature
Communications Earth & Environment 2, no. 1 (9 February 2021): Figure 1.

The most likely level of emissions is now between RCP4.5 and RCP6, and the chance of
RCP8.5 is now well below 1%. I suspect that if their model were updated again using more
recent data, projected emissions would be lower still.
Liu and Raftery et al (2021) combine their emissions estimates with the ensemble of CMIP5
climate models (which were used in the 2013-14 IPCC Fifth Assessment Report) to calculate
the probability of different levels of warming:

45

Due to progress on emissions, likely warming estimated by Liu and Raftery (2021) has fallen
relative to Raftery et al (2017). Liu and Raftery (2021) estimate that the 90% confidence
interval spans from 2ºC to 3.9ºC. Relative to Raftery et al (2017), the median is 0.4ºC lower
and the 95th percentile is 1ºC lower.12
These emissions estimates are produced in the following way. Raftery et al (2017) combinescountry-level estimates of future emissions using the Kaya identity.13
Emissions = People * $ per person * emissions per $They use UN population projections at the country-level. The model projects GDP per capita
by assuming that other countries systematically converge towards the frontier level of GDP
per capita, at a country-specific rate.
They model the logarithm of carbon intensity as following a linear trend, with randomness.14
At the global level, carbon intensity is actually declining linearly, but modelling the logarithm
captures the idea that emissions reduction will get more difficult as we make more progress.

12“The median forecast for 2100 is 2.8 °C, with likely range (90% prediction interval) [2.1, 3.9] °C. The
median is 0.4 °C lower than that of Raftery et al., the upper bound is 1.0 °C lower, while the lower
bound is 0.1 °C higher. The tighter interval reflects the additional 5 years of data and the improved
model.” Peiran R. Liu and Adrian E. Raftery, ‘Country-Based Rate of Emissions Reductions Should
Increase by 80% beyond Nationally Determined Contributions to Meet the 2 °C Target’, Nature
Communications Earth & Environment 2, no. 1 (9 February 2021)
13
Adrian E. Raftery et al., ‘Less than 2 °C Warming by 2100 Unlikely’, Nature Climate Change 7, no. 9(September 2017): 637–41, https://doi.org/10.1038/nclimate3352.
14“We used the UN’s official 2015 population projections for all countries”; “There is a world frontier of
GDP per capita, for which we use the United States as a proxy, and the GDP per capita of othercountries converges to this world frontier at a country-specific rate.”; “We projected carbon intensity
on the logarithmic scale for each country. We model the logarithm of carbon intensity as following alinear trend plus a first-order autoregressive process for each country.” Raftery et al., ‘Less than 2 °C
Warming by 2100 Unlikely’.

46

The virtue of this estimate is that it is simple and more transparent than integrated
assessment models, which often make use of opaque but implausible assumptions. It is also
a great virtue that they give a 90% confidence interval, which is rarely done in the literature.
However, their approach has some drawbacks. Firstly, the assumption of independence
between parameters is not true in practice and this likely systematically biases the estimate.
Most importantly, their approach fails to capture the demographic transition - that higher
economic growth will tend to lead to lower population growth. This biases their estimates
upwards.
Secondly, Raftery et al use a ‘assumption-light’ approach to estimating the decline inemissions intensity that extrapolates the country-level trend in emissions intensity. The
drawback of this approach is illustrated by the fact that they produced a different estimate
with newer data: it turns out that the 2017 estimate was not a reliable estimate of future
emissions. Sometimes, we have reasons to think that a trend will not continue into the future.
Technological changes can bring non-linear changes in emissions, especially when the costsof key low carbon technologies are declining exponentially. Raftery et al’s model does not
capture such processes well.
Nonetheless, Liu and Raftery (2021) remains a very useful source on likely future emissions.

1.3.3. Climate Action Tracker
In 2015, Climate Action Tracker estimated that 4°C was the most likely scenario, on current
policy.

47

Climate Action Tracker now suggests that RCP4.5 is now the most likely scenario - this
scenario implies around 2.7ºC of warming.

As with the other studies, Climate Action Tracker finds marked progress on emissions.

1.3.4. Integrated Assessment Models
RCP8.5 was initially produced by Integrated Assessment Models called MESSAGE and
REMIND. However, these and other Integrated Assessment models now suggest thatsomething in the range of RCP4.5 to RCP7 is more plausible, on current policy.

It is difficult to assess how plausible such models are because they are complex andopaque. However, this does suggest that RCP8.5, on which emissions would reach 120 Gt
CO2 per year by 2100 now looks very unlikely. Moreover, as discussed above, these models
are probably too pessimistic about cost declines in renewables and batteries, and many ofthem seem to rest on erroneous assumptions about economic growth and coal consumption.

48

1.3.5. Meinshausen et al (2020)
For a long time, pledges made following the Paris Agreement were insufficient to meet theParis goals of limiting warming to 2°C.15 However, due to recent progress on climate policy, if
current pledges are met, we have a better than 50% chance of limiting warming to 2°C andthat the chance of RCP4.5 is less than 5%.

Source: Malte Meinshausen et al., ‘Realization of Paris Agreement Pledges May Limit Warming Just
below 2° C’, Nature 604, no. 7905 (2022): fig. 3a.

There seems a decent chance that many countries will not in fact meet their pledges. But
this does illustrate how climate ambition has increased over the last decade.

1.3.6. Summary
A range of recent studies suggest that something around RCP4.5 is now the most likely
emissions scenario, on current policy. Given that policy is likely to strengthen in the future,
the most likely scenario seems likely to be between RCP2.6 and RCP4.5.
It is much less clear what the risk of much higher emissions is, but the high emissions
SSP5-8.5 seems extremely unlikely (I think closer to 1 in 1,000 than 1 in 100) given the
15

Joeri Rogelj et al., ‘Paris Agreement Climate Proposals Need a Boost to Keep Warming Well below
2 °C’, Nature 534, no. 7609 (30 June 2016): 631–39, https://doi.org/10.1038/nature18307.

49required increase in coal use per person. One qualification to this might be that progress in
AI could drive an explosion in economic growth and in carbon emissions. I discuss thisbelow.

1.4. Will renewables and batteries solve climate change on
their own?
I have argued that we now have more cause for optimism on emissions than we have hadfor some time. However, some argue that even this view is too pessimistic because it
underestimates likely progress in renewables.
There are two versions of this argument. The more naive version argues that solar, wind andbatteries are now cheaper than fossil fuels and so will soon replace all fossil fuelinfrastructure, even without a strong change in climate policy. It might be natural to draw thisconclusion from this chart, for example.

50

There are several problems with this line of argument. Firstly, the levelised cost metric usedin the chart above is a misleading way to compare different energy technologies, especiallyintermittent and non-intermittent sources. Because solar and wind are intermittent,
increasing the penetration of renewables imposes costs on the system in terms of additionaltransmission, storage and backup generation. To understand whether solar and wind aregoing to take over the whole electricity system, we need to understand the costs of thewhole system once all of the apparatus has transitioned, not the costs to a businesspersonof building a marginal solar plant.

51

Secondly, electricity is only around 40% of emissions from fossil fuels and industry. A
substantial fraction of emissions come from other sectors like industry and transport. Someof these sectors can be electrified at reasonable cost, as with electric cars, but others, suchas aviation, shipping, steel and cement are much more difficult to electrify. The chart below
shows ‘difficult-to-eliminate’ emissions.

Source: Steven J. Davis et al., ‘Net-Zero Emissions Energy Systems’, Science 360, no. 6396 (29 June2018): fig. 2, https://doi.org/10.1126/science.aas9793.

So, even if renewables take over a substantial chunk of electricity, we would still have a long
way to go to get to net zero emissions.
The most sophisticated version of the ‘renewables takeover’ argument has recently beenpresented in a wonderful paper by Way et al (2021). They argue that if exponential progressin renewables, batteries and hydrogen (or other power-to-X technology) continues, we will
nearly completely decarbonise the economy by 2040 and save trillions of dollars, even
without accounting for the costs of climate change.
The main drawback of this paper is that it is not clear whether the cost declines in these low
carbon technologies will continue, and whether, even if they do, they will be scaled up
sufficiently. So far, the cost declines have been driven by Wright’s Law, which predicts thatcosts drop as a power law of cumulative production.16 This relationship is also called anexperience curve or learning curve. According to Wright’s Law, doubling cumulative capacity

16

Way et al, ‘Empirically grounded technology forecasts and the energy transition’, Oxford Martin
School, 2021, 4-5.

52

leads to a fixed percentage decline in cost. This means that to produce a given percentage
decline in cost, we need to add more and more capacity in absolute terms.
Unfortunately, there are social and political barriers to the dramatic scale-up of renewables.
In many countries, even as costs decline exponentially, renewables deployment is starting to
slow:

Source: Aleh Cherp et al., ‘National Growth Dynamics of Wind and Solar Power Compared to the
Growth Required for Global Climate Targets’, Nature Energy 6, no. 7 (July 2021): Fig. 3,
https://doi.org/10.1038/s41560-021-00863-0.

In Cherp et al’s sample of 27 countries, the growth of renewables is only accelerating in 5
countries, is stable in 11 and is stalling in 11.17 As Cherp et al (2021) note,“Declining costs of technologies have already led to a relatively high growth in theOECD, although currently this growth is becoming constrained on sociotechnical andpolitical rather than economic grounds”.18
One important barrier is land use concerns. Solar and wind require much greater land area
than fossil fuels or nuclear power to produce a given amount of energy. So, there is more
scope for local opposition. The chart below shows the land area that would be required toreplace South Korea’s oil consumption with hydrogen generated from solar and wind,compared to nuclear power (advanced heat sources).

17

Aleh Cherp et al., ‘National Growth Dynamics of Wind and Solar Power Compared to the GrowthRequired for Global Climate Targets’, Nature Energy 6, no. 7 (July 2021): Table 2,
https://doi.org/10.1038/s41560-021-00863-0.
18Aleh Cherp et al., ‘National Growth Dynamics of Wind and Solar Power Compared to the GrowthRequired for Global Climate Targets’, Nature Energy 6, no. 7 (July 2021): p. 751,
https://doi.org/10.1038/s41560-021-00863-0.

53Source: Lucid Catalyst and TerraPraxis, ‘Missing Link to a Livable Climate’, 2020, p. 34.

Note that this only accounts for oil and not for coal and gas consumption. Restrictive land
use policies are a well-known problem in rich countries.19 This seems like a significant barrier
to the scale-up of renewables.
Overall, while progress in renewables provides cause for optimism, it would be too hasty toconclude that renewables will soon completely solve climate change.

1.5. Worst-case emissions scenarios
A range of models now suggest that something around RCP4.5 is the most likely scenario,
on current policy. However, much less attention has been devoted to understanding theprobability of worst-case scenarios, which may be disproportionately important for climate
risk.

19

Edward L. Glaeser, Joseph Gyourko, and Raven Saks, ‘Why Is Manhattan So Expensive?
Regulation and the Rise in Housing Prices’, The Journal of Law and Economics 48, no. 2 (1 October2005): 331–69, https://doi.org/10.1086/429979.

54

1.5.1. How high could fossil fuel emissions be?
If climate policy went badly wrong, how much fossil fuel could we burn, and how muchcarbon could we release into the atmosphere? This question has received surprisingly little
attention in the literature.
Fossil fuel reserves and resources are defined as follows:
Reserves = A fossil fuel deposit that is economically exploitable at today’s prices andusing today’s technology.
Resources = A proven fossil fuel deposit that cannot currently be exploited or an
unproven but geologically possible fossil fuel deposit that may be exploitable in the
future.20
Reserves are dynamic: they change with technology and with market prices. If the price of
natural gas rises, then it may become economically viable to exploit previously non-viable
deposits.
Estimates of resources also change over time as our knowledge of particular fossil fueldeposits improves. There is much more uncertainty about total resources than there is about
reserves as there are much stronger market incentives to understand remaining reserves
and industrial actors naturally have much better knowledge of deposits that they are
currently exploiting than of potentially exploitable deposits.
Moreover, the definition of resources is not completely clear. What does it mean to say that adeposit ‘may be exploitable’ in the future? The most natural interpretation is that the deposit
would become economically exploitable given possible changes in technology and prices offossil fuels. But the claim that a change is ‘possible’ is extremely broad; such a change
would not be very action-relevant if it is extremely unlikely. It is difficult to know which
technological and price changes would be needed if we were to try to exploit a substantial
fraction of the remaining fossil fuel resources. For example, one researcher told me that coalseams under the North Sea are classed as part of Britain’s coal resource, but it is extremely
unlikely that these resources will ever be extracted, though we could if we really wanted to.
The IPCC uses the following estimates of fossil fuel reserves and resources from theGerman organisation BGR.21
Billion tonnes of carbon
Fossil fuel reserves

900

Fossil fuel resources

12,36020

BGR, ‘Energy Study: Data and Developments Concerning German and Global Energy Supplies’(Hannover, 2019), 192.
21
BGR, ‘Energy Study: Data and Developments Concerning German and Global Energy Supplies’(Hannover, 2019), Fig. 5.12.

55

I have created a sheet which summarises some data on fossil fuel reserves and resources
here.
For reference, we have released 460 billion tonnes of carbon since the Industrial Revolution.
Coal accounts for 63% of remaining fossil reserves and 93% of remaining resources (fossil
fuel sheet).
However, a crucial point that is often not made clear in the literature is that not all resources
are recoverable. An underground coal seam may be classed as a resource even if we might
only expect to extract a fraction of the coal from this deposit. Ritchie and Dowlatabadi claimthat recovery rates for surface coal resources are around 80%, but for underground coalseams, they would typically be 50% and for some seams as low as 20%.22 The IPCC’s Fifth
Assessment Report, which produced a similar estimate of remaining fossil fuel resources to
the Sixth Assessment, relied on Rogner et al (2012), which says “Resources are shown as insitu amounts; the eventually extractable quantities will be significantly lower”. Rogner et al(2012) contend that a lot of coal resources are very hard to extract: they are in narrow
seams more than a kilometre below the surface. Extraction rates in geologically difficult
deposits are typically below 40%, and with current technology would only be around 20%.23
This point is often misunderstood in the literature, which is understandable given the lack ofclarity about the meaning of ‘resource’ in the IPCC reports. For example, Winkelmann et al(2015) model the effects of releasing up to 12 trillion tonnes of carbon into the atmosphereand says “We hereby cover the full range of available carbon resources”.24
When we are evaluating a worst-case scenario, we actually need to estimate not total
resources, but rather what is known as the ‘Ultimately Recoverable Resource’.
(Note that, confusingly, only a fraction of coal reserves are recoverable, while close to 100%of oil and gas reserves are recoverable.)25
22“Common recovery factors are 80 percent for strip mining and 50 percent for underground reserves.
However, Zimmerman (1983) studies the US coal industry and suggests that 50 percent is areasonable estimate for reserves recoverable from any deposit. Reserve recovery in some regions
such as India can be as low as 20% (Bauer et al. 2016).” Justin Ritchie and Hadi Dowlatabadi, ‘The
1000 GtC Coal Question: Are Cases of Vastly Expanded Future Coal Combustion Still Plausible?’,Energy Economics 65 (1 June 2017): 9 n2, https://doi.org/10.1016/j.eneco.2017.04.015.
23“While coal resources are 20 times higher than known extractable coal reserves, there is
uncertainty about the minable portion of these in situ quantities. Information on the geomining
conditions of coal resources is insufficient for a reliable production assessment. For example, most of
the better delineated coal resources are situated at greater depths and thus belong to geomining
categories CIII and CIV. Extraction ratios in geologically difficult coal deposits can be below 40%(Kundel, 1985 ; Daul, 1995 ; USGS, 2009 ). Since many of the ‘in situ’ hard-coal resource deposits
are in narrow seams at depths of more than 1000 m, an overall recovery rate of 20% may well beachievable practically. For example, 60% of coal resources in China are found at depths deeper thanof 1000 m (Pan, 2005 ; Minchener, 2007 ). Without new extraction methods, a 20% recovery rate puts
the portion of coal resources that eventually could become available as reserves to 87,154 EJ.” H-H.
Rogner et al., ‘Energy Resources and Potentials’, in Global Energy Assessment - Toward a
Sustainable Future (Cambridge University Press, 2012), 464.
24
Ricarda Winkelmann et al., ‘Combustion of Available Fossil Fuel Resources Sufficient to Eliminate
the Antarctic Ice Sheet’, Science Advances 1, no. 8 (1 September 2015): e1500589,
https://doi.org/10.1126/sciadv.1500589.
25
Conversation with Sandro Schmidt, 21 October 2021.

56

The same conceptual problems about the definition of resources infect the definition of
Ultimately Recoverable Resources. Because the definition of a resource is unclear, it is hardto understand the technology assumptions that determine ultimately recoverable resource
estimates. Rather than use these sorts of imprecise definitions, it would be preferable either
to (1) provide a probability distribution across amounts of possible future coal extraction, or(2) outline the exact conditions in which a resource is likely to be extracted.

Will we try and extract all of the coal?
Ritchie and Dowlatabadi (2017) argue that much of the remaining coal resources are unlikely
to ever be extracted. A large fraction of the remaining coal resources are undergrounddeposits that could only feasibly be extracted using a technology called ‘underground coalgasification’. This involves drilling injection wells into a coal seam, and igniting the seam sothat temperatures reach 500 to 900ºC. This converts the coal to a mixture of CO2, CO, CH4
and H2, which is removed using extraction wells drilled into the seam.26 The CH4 can then beburned to produce energy.
Ritchie and Dowlatabadi (2017) are pessimistic about the prospects for underground coalgasification. Underground coal gasification was first proposed in the 19th century and has
failed the test of commercialisation for more than a century, with trials only having been run
for a few days or weeks. They argue that challenges around siting and environmental costs
make the future prospects look dim:“Experience from UCG test projects have indicated significant constraints on site
selection. For example, a 1997 pilot in Spain at a depth of 600 meters highlighted theimportance of avoiding aquifer systems because of the potential for explosions. In
this case, geological subsidence shifted the underground structure, leading tocollapse and a subsequent explosion (Walker 2007). UCG pilots in many locations
have caused severe groundwater contamination that persists for years after
gasification ceased, with high concentrations of phenols and PAHs readily detected
in aquifers extending dozens of kilometers from the gasification site (Campbell et al.
1979; Friedmann et al. 2009; Klimenko 2009; Liu et al. 2007). Given the documented
public response to large-scale coal synfuel and syngas projects (Yanarella andGreen 1987), it reasonable to expect that any social license for operation of UCG
facilities will face significant opposition, even if many of its environmental challenges
are successfully addressed…
Despite more than a century of experimentation, recent meta-assessments conclude
that UCG still needs decades of foundational research to establish any reasonableestimate of its commercial potential (Couch 2009). In this context UCG would need tocontend with rapid progress in commercial-scale renewable energy, unconventional
oil and gas and more energy efficient technologies (IEA 2016). This is a challenging
environment to justify a new wave of sustained public or private funding for researchand development of UCG. Thus, any plausible future reference case for global coal
recovery should not include estimates of total resources which are implicitly or

26

Ritchie and Dowlatabadi, ‘The 1000 GtC Coal Question’, p. 18.

57

explicitly consistent with theoretical potentials of UCG or other similar hypotheticaltechnologies.”27
Other coal resource experts I have spoken to also thought that underground coal gasification
would play at most a marginal role in the future.
The vast majority of remaining coal resources are located in five countries: the US, China,
Russia, Australia, and India. So, the amount of coal extracted will depend in part on the likely
environmental restrictions in these countries. Environmental regulations in China and the US
are increasing. Coal extraction remains an extremely important political issue in Australia,
and it is widely thought that the Labour Party lost the last election for being insufficiently
pro-coal. Australian coal is high quality and in high demand in neighbouring growing Asian
countries.
Assumptions about technological development in fossil fuel extraction technology need to beconsistent with assumptions about technological development in other domains. Worlds inwhich we develop advanced coal extraction technology are also likely to be worlds in which
technological progress in other domains, including low carbon technology, will be strong. As
Ritchie and Dowlatabadi (2017) note:“if it is appropriate to consider the implications and recovery rates of coal consistent
with UCG deployment in reference global energy scenarios, it would be equally
appropriate to consider the role of experimental technologies such as nuclear
fusion.”28
One natural concern with these arguments is that we may not have a good sense of thefossil fuel extraction technologies that we will develop in the future.

Ultimately recoverable resources
There are surprisingly few estimates of ultimately recoverable resources in the literature, andthe data on coal seems to be of much worse quality than the data for oil and gas. As Ritchieand Dowlatabadi (2017) note
“However, efforts to determine the potentially recoverable portion of world coalresources have been fragmented, compromising time-series analyses withnotoriously inconsistent and poor data.”29
Estimates of ultimately recoverable fossil fuel resources are show in the table below:
Study

Billion tonnes of carbon

Ritchie and Dowlatabadi (2017) for coal +author calcs using BGR data

1,200

Welsby et al

2,860

27

Ritchie and Dowlatabadi, ‘The 1000 GtC Coal Question’, pp. 19-20.
Ritchie and Dowlatabadi, ‘The 1000 GtC Coal Question’, p. 20.
29
Ritchie and Dowlatabadi, ‘The 1000 GtC Coal Question’, p. 3.
28

58

Mohr et al low estimate

1,040

Mohr et al best guess

1,580

Mohr et al high estimate

2,500

These are summarised in the fossil fuel data sheet.
I don’t have a great deal of confidence in these numbers. I spoke to Steve Mohr about how
they produced their estimates on coal resources and he suggested that a lot of judgement
calls are involved. As I understand it, a lot of the knowledge required is highly mine-specific,
requiring in-depth knowledge from in-country geologists, which is not publicly shared ortransparent.
In any case, all of these estimates are lower than the emissions implied in some emissions
scenarios considered in the literature. On SSP5-8.5, we would burn an additional 2,200
billion tonnes of carbon by 2100, which exceeds some of the estimates above.30 Climate
scientists have also outlined an extended version of SSP5-8.5, on which emissions continue
to 2500 and we burn 5 trillion tonnes of carbon, which exceeds all of the estimates shown
above.31
I will assume that ultimately recoverable resources in a worst-case scenario total 3,000
billion tonnes of carbon, which is higher than all of the estimates.
For comparison:●●●

Fossil fuel and cement emissions since 1750 = 464 billion tonnes of carbon.
Fossil fuel and cement emissions in 2018 = 10 billion tonnes.
On RCP4.5, emissions 2019 to 2100 = 848 billion tonnes.32

If emissions remained at current levels, it would take 300 years to burn through all of theultimately recoverable fossil fuel resources.
How plausible is it that we will burn through all of the fossil fuel resources? I think there are
two possible scenarios on which this could happen.

30

IPCC, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change (UNFCCC, 2021),
SPM.7.
31
Katarzyna B. Tokarska et al., ‘The Climate Response to Five Trillion Tonnes of Carbon’, Nature
Climate Change 6, no. 9 (September 2016): 851–55, https://doi.org/10.1038/nclimate3036; Malte
Meinshausen et al., ‘The RCP Greenhouse Gas Concentrations and Their Extensions from 1765 to
2300’, Climatic Change 109, no. 1–2 (1 November 2011): 213,
https://doi.org/10.1007/s10584-011-0156-z.
32
IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, SPM.7.

59

1.5.2. Explosive economic growth
Economic growth is an important driver of emissions growth. The SSPs make a range ofassumptions about economic growth. On the high growth future, SSP5-8.5, global GDP
increases by upwards of 2% per year.
Is it possible that growth could be much higher than this? If we had asked an economist in
1800 how fast growth would have been in the next 200 years, none would have predictedthat it would have risen to 1% or 2%. This was completely unprecedented, but neverthelessoccurred. Prior to that, for thousands of years, living standards barely improved and thegrowth rate was probably only 0.1%. The rate of economic growth rate is itself growing overtime. If we think that this long-term trend will continue in the future, then we should expect
the growth rate itself to increase again in the next few hundred years.
A second related reason to think that there might be explosive growth at some point in thenext 100 years is progress in Artificial Intelligence. According to endogenous growththeories, growth is caused by innovation: the production of new ideas and new technologies.
On this theory, population growth is the primary driver of economic growth: the more people
there are, the more ideas there are and the more innovation there will be.
Many people expect that at some point this century, AI systems will become so advanced
that we will be able to automate innovation. Quite when AI systems might reach the humanlevel at innovation remains unclear. The most sophisticated attempt to forecast
transformative AI is by Ajeya Cotra, a researcher at the Open Philanthropy Project and hermodel now suggests that it is most likely to be developed in 2040. If so, we would be able toautomate the production of ideas, and indeed the production of AI systems themselves. This
would allow more investment in AI, which would further increase growth, and so on. Indeed,
when you plug the assumption that AI systems can substitute for all human labour into
standard growth models, they predict explosive economic growth.33
For these reasons, the prospect of explosive (>10% per year) economic growth at somepoint in the next 100 years is not outlandish.
It is difficult to say what the implications would be for the planet. On the one hand, highgrowth has been a major driver of emissions growth so far. On the other hand, the enormous
technological progress would allow us to develop new technologies that could enable us tosolve climate change. What exactly would happen depends on the goals of the AI systems. Ifthey optimise for protecting the environment, then climate change would be quickly solved.
But if AI systems aren’t optimised to protect the environment, then emissions could increase
dramatically and we might burn through all of the fossil fuels, if doing so is the cheapest way
to achieve the goals of the AI system.
Thus, it would be very important to ensure that advanced AI systems optimise for sociallyvaluable goals, or in short, that AI systems are aligned. Indeed, if one is worried about thepotential climatic impacts of AI-driven explosive growth, the best way to reduce this risk
33

Philippe Aghion, Benjamin F. Jones, and Charles I. Jones, 9. Artificial Intelligence and Economic
Growth (University of Chicago Press, 2019); Philip Trammell and Anton Korinek, ‘Economic Growthunder Transformative AI’ (GPI Working Paper, 2020).

60

would be to work on AI alignment, rather than on more traditional climate activism or
advocacy. If the goals of AI systems are the main determinant of the risk of extreme
emissions, then we should work to ensure that those AI systems have good goals; changing
climate policy in the world more broadly will have a much smaller effect on these extreme
risks. This would also help to control the many other ways, aside from climate change, thattransformative AI could have extreme effects on society.
It is hard to reason about extreme and unprecedented scenarios like an AI-driven growth
explosion, but I think that in that world, climate change would be orders of magnitude lower
than other risks to humanity, and that the associated climate risks would be very low. Noneof the people who argue that AI risk is a serious problem have argued that the main risk it
poses to humanity and to the future is climate change, which is some evidence for this point.
AI researchers have highlighted several different potentially concerning AI scenarios.
One scenario is advanced AI systems deliberately trying to kill humans off. But there seemto be much more efficient ways to do this than climate change, such as lethal autonomous
weapons or engineered viruses.
Another scenario is that the government that had control of advanced AI systems would gaina decisive geopolitical advantage. But it seems extremely unlikely that causing extreme
climate change would be the chosen way to cement international dominance because
climate change would also affect the dominant country, and there seem to be much betterways to cement dominance over the international order than climate change.
The final scenario is a world with decentralised AI systems that dramatically speed up
scientific progress without killing their human controllers or giving dominance to a single
government. In this world, it would have to be the case that our technological capability was
far higher, but governments did not have greatly increased willingness to spend on climatechange, and international coordination was not strong enough to compel countries to reduce
emissions. In my view, this world seems quite unlikely to materialise. We are already seeing
countries at the current technological frontier reduce emissions despite income growth anddespite weak international coordination incentives. If there is dramatic technologicalprogress, we are much more likely to be in a world in which we have a breakthroughtechnology that makes solving climate change much less expensive relative to today.
Even if, as I believe, there is a decent chance of an AI-driven growth explosion this century,
the chance that it will lead to civilisation-destroying climate change seems slim.

1.5.3. Indefinitely stalled decarbonisation
The second scenario on which emissions might be very high is indefinitely stalled
decarbonisation.
It now seems likely that due to progress on renewables and batteries, we will decarbonise asubstantial chunk of the economy in the next few decades. But some fraction of emissions
are difficult to eliminate, as shown in the chart below:

61

Source: Steven J. Davis et al., ‘Net-Zero Emissions Energy Systems’, Science 360, no. 6396 (29 June2018), https://doi.org/10.1126/science.aas9793, Fig 2.

In brief, the reason these emissions are difficult to eliminate is that steel, cement and heavy
duty transport are difficult to electrify. Steel and cement rely on high-temperature heat, whichis expensive to produce with electricity. Arguably the most promising way to decarbonise
these sectors is with green hydrogen, which I discuss below. Batteries are not ideal forheavy-duty transport because they are heavy and take up a lot of space.
It is hard to know what share of electricity will be provided by renewables in the future.
Energy systems models suggest that costs will start to rise once the share of variable
renewables in electricity passes 80%, but social and political barriers might kick in wellbefore then, as suggested by the Cherp et al data above.
In other sectors, there is lots of scope for electrification to reduce emissions. For example, it
now seems like short-distance transportation will be almost completely electrified in the next
few decades, and electric heating could reduce a lot of residential and commercial
emissions.
However, it remains unclear how much of the rest of the economy will be decarbonised,especially the sectors highlighted above, where decarbonisation is particularly expensive.
Energy storage
As solar and wind start to play an increasing role in energy systems, the role of energy
storage is also likely to increase. The challenge of the electricity system is to ensure thatdemand matches supply perfectly at all times. To balance out the effects of high shares of62solar and wind, energy systems models use either controllable dispatchable sources ofenergy, such as gas, bioenergy, nuclear, or long-duration (multi-week or longer) energy
storage.34
At present, the leading form of long duration storage is pumped hydro, but this isgeographically constrained. The ten largest pumped hydro storage facilities in the U.S. arecollectively capable of storing a total of 43 minutes worth of U.S. energy consumption.35
Batteries are not suited to providing long duration multi-week seasonal storage.36 The maincontenders in long-duration storage at the moment are thermal energy storage, production ofhydrogen from electrolysis and storage in underground salt caverns or pressurised tanks.37
There is uncertainty about how far these technologies will be scaled up, but some of themshow promise.
Hydrogen
Liquid fuels such as hydrogen and ammonia (which can be made from hydrogen) are likely
to be hugely important for decarbonisation. Hydrogen can provide the high-temperature heatneeded to make steel and cement; ammonia can be used as a drop-in fuel in existing
planes, ships and trucks; and hydrogen can be used for long-term energy storage.38
Zero carbon hydrogen and ammonia are both more expensive than fossil fuel alternatives atpresent, but with technological progress, ammonia and hydrogen could be competitive withgasoline in the future.39 To make cheap zero carbon hydrogen, we need cheap electricity andcheap catalysers. Cheap electricity could come from super-cheap renewables, but as I
argued above, it is an open question whether cost declines and capacity scale-up willcontinue for those technologies. Another intriguing option would be to build nuclear fissionhydrogen gigafactories that run 24/7 and so don’t have to follow load or adjust to variable
renewables, which would reduce costs.40
Another key step would be to reduce the cost of catalysers, which can produce hydrogenthrough electrolysis. The costs of electrolysers are following Wright’s law: they are declining

34“Other studies partially or fully replace firm generation with one or more energy storage media
capable of sustained output over weeks or longer and suited to low annual utilization rates”. Jesse D.
Jenkins, Max Luke, and Samuel Thernstrom, ‘Getting to Zero Carbon Emissions in the Electric Power
Sector’, Joule 2, no. 12 (2018): 2498–2510.
35
Jesse Jenkins and Samuel Thernstrom, “Deep Decarbonization of the Electric Power Sector:
Insights from Recent Literature” (Energy Innovation Reform Project, March 2017), 6.
36
Jenkins and Thernstrom, 5–6; Jenkins, Luke, and Thernstrom, “Getting to Zero Carbon Emissions
in the Electric Power Sector,” 10.
37“Scenarios that eschew firm generation therefore must rely upon one or more long-term energy
storage technologies with an order-of-magnitude lower cost per kWh, including thermal energy
storage, production of hydrogen from electrolysis and storage in underground salt caverns orpressurized tanks, or conversion of electrolytic hydrogen to methane. Considerable uncertainty remains about the real-world cost, timing, and scalability of these storage options” Jenkins, Luke, andThernstrom, “Getting to Zero Carbon Emissions in the Electric Power Sector,” 2508.
38
Clean Air Task Force, “Fuels Without Carbon: Prospects and the Pathway Forward for Zero-Carbon
Hydrogen and Ammonia Fuels,” December 2018, https://www.catf.us/resource/fuels-without-carbon/.
39
Clean Air Task Force, 9.
40Lucid Catalyst and TerraPraxis, ‘Missing Link to a Livable Climate’, 2020.

63

exponentially with additional cumulative capacity. If the trend in scale-up continues, Way etal (2021) project that costs will decline by a factor of 10 by 2050.41
Nuclear fission
Nuclear power has been behind all of the most rapid electricity decarbonisation efforts inhistory.42 If the world were to copy the per-person rate at which Sweden deployed nuclear in
the 1970s and 1980s, we could decarbonise the global electricity system in less than ten
years.43
However, there is significant public opposition to nuclear fission in many countries due toconcerns about safety, waste and weapons proliferation. These problems are greatly
exaggerated. First, consider safety. Nuclear fission is actually one of the safest forms ofenergy production, and is about as safe as solar and wind per unit of energy produced.
Secondly, the volume of hazardous waste produced by nuclear power is small compared toother forms of waste we routinely manage.
“Whereas the ash from ten coal-fired power stations would have a mass of four
million tons per year (having a volume of roughly 40 litres per person per year [inBritain]), the nuclear waste from Britain’s ten nuclear power stations has a volume ofjust 0.84 litres per person per year – think of that as a bottle of wine per person per
year.”44
Even in a nuclear power-reliant country, the typical per person per year volume of nuclear
waste would be three orders of magnitude lower than the per person per year volume ofother non-nuclear hazardous waste.45
I discussed the link between nuclear power and nuclear weapons in Appendix 6 of the 2018
Founders Pledge Climate Change Report. I argued that civilian nuclear power hashistorically been at most a weak driver of weapons proliferation.
In spite of this, due to its unpopularity, nuclear fission is heavily regulated, which has brought
deployment to a standstill almost everywhere outside China.46 For its 2060 decarbonisation
plan, China plans to quintuple its domestic nuclear capacity. There is some chance that41

Way et al, ‘Empirically grounded technology forecasts and the energy transition’, Oxford Martin
School, 2021, p. 6.
42
Junji Cao et al., ‘China-U.S. Cooperation to Advance Nuclear Power’, Science 353, no. 6299 (5August 2016): 547–48, https://doi.org/10.1126/science.aaf7131; Staffan A. Qvist and Barry W. Brook,‘Potential for Worldwide Displacement of Fossil-Fuel Electricity by Nuclear Energy in Three Decades
Based on Extrapolation of Regional Deployment Data’, PLOS ONE 10, no. 5 (13 May 2015):
e0124074, https://doi.org/10.1371/journal.pone.0124074.
43“Analysis of these historical deployments show that if the world built nuclear power at no more thanthe per capita rate of these exemplar nations during their national expansion, then coal- and gas-fired
electricity could be replaced worldwide in less than a decade.” Staffan A. Qvist and Barry W. Brook,‘Potential for Worldwide Displacement of Fossil-Fuel Electricity by Nuclear Energy in Three Decades
Based on Extrapolation of Regional Deployment Data’, PLOS ONE 10, no. 5 (13 May 2015):
e0124074, https://doi.org/10.1371/journal.pone.0124074.
44David MacKay, Sustainable Energy - without the Hot Air, 169.
45
David MacKay, 170.
46
World Nuclear Association, World Nuclear Performance Report 2020, sec. 1.4.

64nuclear fission could scale up elsewhere in the future as there is a good chance that many
countries will struggle to meet their climate targets without it.
Nuclear fusion
In nuclear fusion, light atoms are transformed into heavier atoms to release energy, thesame process that occurs within the plasma core of the Sun. Fusion produces no waste andfusion plants cannot melt down.
It is commonly joked that commercial fusion is thirty years away. Decades of public funding
had seen steadily improving fusion performance, which slowed down in the 1990s.47
However, $2 billion of private money has recently been invested into private fusioncompanies, and some companies claim they will have commercially viable plants before
2030 or 2040. I have no idea whether these claims are plausible or not, but there is more
optimism in the field than there has been for a while.
Fossil fuels with carbon capture and storage
Carbon capture and storage involves capturing CO2 at point sources, such as industrial
chimneys, piping the CO2 away and then injecting it underground in natural rock formations.
Carbon capture and storage can be used to decarbonise:●●●Electricity sector emissions from coal and gas
Emissions from the production of high temperature heat in industrial processes
Direct process emissions from industrial processes, such as cement.

In all of these sectors, carbon capture and storage is pure cost addition compared to nottrying to capture CO2 at all.
According to theoretical estimates, the cost to avert a tonne of CO2 with CCS for any
purpose is typically at the very least $20 per tonne, ranging up to beyond $100 per tonne.48
However, these are not real world estimates, so the real world cost of CCS may be higher.
Some environmentally-motivated countries have been willing to pay implicit carbon prices of
upwards of $100 per tonne, so CCS might be scaled up in some countries in the future.
In my view, the most important drawback of CCS centres on political economy. Policies thatencourage CCS will probably involve giving subsidies to the fossil industry to store andsequester carbon they produce. This would keep the fossil fuel industry alive as a lobbying
body with strong incentives to game the system and to oppose restrictions on carbon
pollution. The real world promise of CCS very much remains to be seen.
Enhanced geothermal
Geothermal energy today provides only a tiny fraction of global energy. Conventional
geothermal removes heat from shallow, water saturated rock near Earth’s surface.
Consequently, commercial geothermal is today confined to a few parts of the world withhigh-temperature heat close to the ground, such as Iceland and Japan.
47

Anthony J. Webster, ‘Fusion: Power for the Future’, Physics Education 38, no. 2 (March 2003):
135–42, https://doi.org/10.1088/0031-9120/38/2/305.
48
Global CCS Institute, “Global Cost of Carbon Capture and Storage,” June 2017, 4,https://www.globalccsinstitute.com/archive/hub/publications/201688/global-ccs-cost-updatev4.pdf.

65However, another proposed form of geothermal, known as “Super Hot Rock geothermal” hasvast potential to produce low-carbon energy in much of the world. Super Hot Rockgeothermal involves drilling much deeper into hot, dry crystalline rocks and then injecting
water or CO2 into these formations where high temperatures and pressure creates“supercritical” fluid that is returned to the surface for energy production. Super Hot Rockgeothermal is a potentially geographically ubiquitous and cheap source of dispatchable
power. Super Hot Rock geothermal remains unproven and technical barriers remain, butsome experts argue that, with R&D support, the technology could become commercially
viable in the next few decades. David Roberts, a climate journalist, has argued thatenhanced geothermal is “an engineering problem that, when solved, solves energy”.
NET Power
The company NET Power is developing a gas power plant that captures carbon at zero
additional cost, using a process called the Allam Cycle. If this works, it would begame-changing, but I am not well-placed to judge whether it will succeed.
Bioenergy
Burning sustainable bioenergy, such as sustainably managed wood or corn ethanol, iscarbon-neutral in the following sense. If you burn the bioenergy, then carbon is released into
the atmosphere. As the trees from the area you previously harvested grow back, they
sequester the carbon again, making the process carbon neutral. Thus, although the process
is not carbon neutral at the point of combustion, it becomes carbon neutral once the trees
grow back.
The main downside of bioenergy is that it has large land use requirements. Current global
energy demand is about 154,000 TWh. To supply a third of this with bioenergy, as is
projected in some Integrated Assessment Models - would require 390 million hectares ofland to be used solely for the purpose of bioenergy49 - a third of the total arable land on theplanet50 or around 40% of the land area of the United States. This land would compete withcommercial, domestic and agricultural uses. This is likely a significant barrier to theadequate scale-up of bioenergy.
Summary of technology prospects
Overall, there are a range of low carbon technologies that seem to show significant promise.
However, there is some chance that technological progress in these sectors will get stuck.
There are several potential reasons for this.

Excessive regulation
One possibility is that excessive regulation will kill off emerging technologies. The history of
nuclear fission sets a chastening precedent. Several countries rapidly nearly completely
decarbonised their electricity systems at low cost using 1970s nuclear technology. It would
have been possible for the whole world, or at least the major emitters, to decarbonise
49

Alexandre Strapasson et al., “On the Global Limits of Bioenergy and Land Use for Climate Change
Mitigation,” GCB Bioenergy 9, no. 12 (2017): fig. 3, https://doi.org/10.1111/gcbb.12456.
50
Hannah Ritchie and Max Roser, “Land Use,” Our World in Data, November 13, 2013,https://ourworldindata.org/land-use.

66

electricity in the coming decades by following these countries’ lead. Decarbonising the restof the economy would then have been much more straightforward.
In reality, this is not what happened. Due to some highly salient nuclear accidents, public
opposition to nuclear power became intense. Nuclear regulation increased, leading todramatically rising costs and slow construction times, particularly in Europe and the US.51
This is despite the fact that nuclear power, notwithstanding the Chernobyl disaster, is about
as safe as solar and wind per unit of energy produced. The extent of nuclear regulations are
discussed at length in a recent book by Jack Devanney,52 which is summarised in thisLessWrong post. It is clearly possible to build nuclear plants quickly and cheaply: in recent
years, most new nuclear power plants have been built in China at low cost and typically inaround 6 years.53
Nuclear fission is unusually likely to be over-regulated. Nuclear accidents kill very few people
on average, but they are newsworthy and highly salient to the general public. 15,000
Japanese people died in the Tohoku earthquake but most of the media attention focused on
the meltdown at the Fukushima nuclear plant, which, at the time of writing, has caused 1
death from radiation and 573 from the evacuation.
Other emerging technologies are probably less prone to over-regulation than nuclear power.
Nuclear fusion cannot meltdown and does not produce waste. As I have argued above,
nuclear meltdowns and waste are trivial public health problems, but they are very importantfor public acceptability. It is difficult to see how energy storage could arouse much public
opposition. Enhanced geothermal is renewable, cannot melt down and does not produce
waste. But there might be some fracking-type opposition to this technology. NET Powermight also be opposed by the environmental movement because it would allow us tocontinue burning fossil fuels. Solar and wind are generally popular, but also face many public
acceptability barriers: the main barriers seem likely to be regulatory rather than economic ortechnological.

Low hanging fruit
Technological progress might be disappointing for other reasons. One concern is that ideas
are getting harder to find. Exponential growth in researcher-time has been accompanied bymerely constant growth in income per head. A good example is Moore's Law. The number of
researchers required today to achieve the famous doubling of computer chip density is morethan 18 times larger than the number required in the early 1970s. Bloom et al (2020)
consider a range of sectors and find a similar picture of declining per person research
productivity.54

51

Jessica R. Lovering, Arthur Yip, and Ted Nordhaus, “Historical Construction Costs of Global Nuclear
Power Reactors,” Energy Policy 91 (April 1, 2016): 379, https://doi.org/10.1016/j.enpol.2016.01.011.
52
Jack Devanney, Why Nuclear Power Has Been a Flop: At Solving the Gordian Knot of Electricity
Poverty and Global Warming (BookBaby, 2020).
53
World Nuclear Association, World Nuclear Performance Report 2020, sec. 1.4.
54
Nicholas Bloom et al., ‘Are Ideas Getting Harder to Find?’, American Economic Review 110, no. 4(April 2020): 1104–44, https://doi.org/10.1257/aer.20180338.

67

Another related issue is declining population.55 On endogenous growth models, economic
growth and technological progress is driven by ideas. Fertility rates in most places outside of
Africa are now below replacement, which means that natural population growth (notincluding immigration and emigration) will soon be negative in most places. China’s
population may already be declining. Since ideas come out of people’s heads, declining
fertility rates across the world could lead to long-term stagnation, especially if it proves to beharder than expected to create AI systems that can substitute for human workers.
However, in my view, it is plausible that we will have the technologies required to solve
climate change in the next 30 years, long before technological progress completely stops. If
we do not develop such technologies, long-run stagnation would start to become a concern
and could lumber us with centuries of emissions.
Countries might also respond by implementing policies to increase the fertility rate. Rich
countries could also increase immigration, which would be one way to massively increase
global research productivity.

Breakdown in international coordination
It is possible that there could be a breakdown in international coordination, which causes
countries to give up on their climate plans and instead to use whatever energy sources arecheapest. The most likely way this could happen is if there is a conflict between the great
powers. A range of sources suggest that the chance of a Great Power conflict before 2100 isaround 1 in 3.
Whether this would lead to indefinitely stalled decarbonisation depends on when the conflict
would occur. If global climate ambition continues on its current trajectory, it seems likely thata substantial fraction of the problem will be solved by around 2050 anyway. Even if there is a
Great Power conflict, if there are cheaper low carbon alternatives, there is little reason that
we would return to fossil fuels.

Overall assessment
The best guess model used in Pielke Jnr et al (2022) finds that we would decarbonise by
2090.56 Since this relies on IEA projections of the costs of renewables and batteries, I think
this is likely pessimistic. Their models are also likely to neglect other potential breakthroughtechnologies, such as solar perovskites, enhanced geothermal or nuclear fusion. My best
guess is that we will decarbonise completely between 2050 and 2080.
If decarbonisation does stall indefinitely, it seems unlikely that we would burn through all of
the fossil fuels. As I discussed in section 1.5.1, to burn through all of the remaining fossil
fuels, including the hardest to reach coal, we would have to extensive use of advanced coal
extraction technology, mainly underground coal gasification, which has failed the test ofcommercialisation for a century and is not currently part of the energy discussion. In the55

Jones, C. I. (2020). The end of economic growth? Unintended consequences of a declining
population (No. w26651). National Bureau of Economic Research.
56
Roger Pielke Jr, Matthew G. Burgess, and Justin Ritchie, ‘Plausible 2005–2050 Emissions
Scenarios Project between 2°C and 3°C of Warming by 2100’, Environmental Research Letters 17,
no. 2 (February 2022): Fig. 4, https://doi.org/10.1088/1748-9326/ac4ebf.

68

world in which decarbonisation stalls, in order to extract all of the recoverable fossil fuels,
there would have to be dramatic progress in coal extraction technology, but not in low carbon
technologies which are already getting significant policy and industry attention. This is notplausible.
One possibility is that we make enough technological progress in order to develop many low
carbon technologies as well as underground coal gasification this century, but progress on
low carbon technology stalls just before we are able to decarbonise: perhaps decarbonisingindustry and heavy duty transport turns out to be too hard.
Even in this world, I find it hard to see how we could extract all of the fossil fuels. Fossil fuelextraction technology usually progresses over time, but costs are stable over time as we
extract the easiest-to-reach deposits. If technological progress did truly stagnate, then wewould probably stop extracting fossil fuels well before we had exhausted all recoverable
resources.
One way to look at this is that so far, economic growth has been a major driver of emissions
growth. Countries that burn large amounts of coal are invariably experiencing high economic
growth. If economic growth stops, then we should also expect emissions growth to slow
dramatically.
All of these arguments are conditioned on the assumption that the technology required to
extract all of the fossil fuels would actually be difficult to create, which is what the current
evidence suggests but may not turn out to be true.
In light of this argument and the arguments about explosive economic growth, it is difficult tocome up with scenarios in which we would burn literally all of the recoverable fossil fuels.
If we do not use advanced coal extraction technologies, then recoverable fossil fuels arearound 1,200 GtC.57 This is below the RCP7 emissions scenario, which according to Liu andRaftery (2021) has around a 1-5% chance by 2100.
In this simple model, I estimate that the chance of indefinitely stalled decarbonisation leading
us to burn all of the fossil fuels is about 1 in 500,000. Perhaps the most disputable
assumption in the model is the input that there is a 1 in 1,000 chance of burning all the fossil
fuels conditional on technological stagnation, which one reviewer thought was too low andthought 1 in 100 was more plausible.
I also roughly calculate the time taken to burn through fossil fuels. I assume that by 2100,
emissions will fall to half their current levels. If so, it would take around 400 years to burnthrough all of the fossil fuels.

57

This is using Ritchie and Dowlatabadi’s estimates. For calculations, see the sheet on ultimately
recoverable fossil fuels.

69

1.6. Can we change course?
One counter to the argument that climate change poses serious global catastrophic risks isthat we can learn as we go. If climate sensitivity turns out to be higher than expected, or
impacts turn out to be worse than expected, then we can change course and so avoid thevery worst outcomes. How easy would it be to change course?

1.6.1. The physics
It is important to first clarify some physics. Reducing temperatures does not just merely
involve reducing emissions rates. Temperatures increase linearly with cumulative CO2
emissions, so as long as emissions are positive, temperatures will continue to increase. In
order to reduce temperatures, we need to reduce emissions to zero and remove CO2 fromthe atmosphere.
The speed at which we can remove CO2 from the atmosphere is limited by the fact that when
we remove carbon from the air, natural sinks of carbon (the oceans and the land) releasecarbon back into the atmosphere. So, sucking out and storing one tonne of carbon from theatmosphere does not cause there to be one tonne less of carbon in the atmosphere, oncethe system adjusts. Over a period of several decades, this would replace up to half of theCO2 that had been removed by negative emissions.58 If we ever want to return CO2
concentrations back to their pre-industrial level, we will eventually have to suck out all of the
carbon we have ever emitted.
Emitting CO2 is like adding concrete blocks to a tower.59 Each year we emit, concentrations
get higher, and the tower gets taller. Even if we stop adding blocks altogether, we still haveto figure out what to do with the tower. Unless we dismantle the tower ourselves, it will only
be eroded away after tens of thousands of years. Even after 100,000 years, remnants of thetower will remain.

1.6.2. How hard is changing course?
Emissions reduction should be the priority
The realities of climate physics mean that if we want to return CO2 concentrations back tosafe levels, we not only have to stop emitting, but also to start sucking CO2 out of theatmosphere ourselves. In my view, it would be rational to first focus on reducing emissions
before doing negative emissions.
Consideration of the basic physics illustrates why this is so. Although CO2 concentrations arehigh in terms of recent history, CO2 is still highly diffuse - atmospheric concentrations at the
moment are around 415 parts per million, or 0.04%. In contrast, the chimneys of fossil fuelplants often contain gas that is 10% CO2:60 concentrations are 250x higher than in the58

National Academy of Sciences, Climate Intervention: Carbon Dioxide Removal and Reliable
Sequestration (Washington, D.C.: National Academies Press, 2015), 29–32,
http://www.nap.edu/catalog/18805.
59
I owe this analogy to a tweet by Glen Peters.
60
Oliver Morton, The Planet Remade: How Geoengineering Could Change the World (London:
Granta, 2015), 245–46.

70

ambient air today.61 This suggests it will generally be easier to not emit CO2 in the first placethan to suck it back out of the air.
This reasoning is confirmed by cost estimates of negative emissions. The only highly
scalable negative emissions technology is direct air capture, but that costs hundreds ofdollars per tonne of CO2 sequestered, and one recent review suggests that the costs ofdirect air capture will never fall below $100 per tonne.62 In contrast, renewables and nuclear
can in principle substitute for fossil fuels at relatively low cost.
Even if one country reduces their emissions to zero, it would, if possible, make more sense
for them to pay other countries to stop burning fossil fuels than to start doing negativeemissions. However, historically, international offset programmes have been beset byproblems, and many argue that they do not bring real climate benefits.63 I have looked at thisin some depth and believe that most international offsets bring little environmental benefit.
So, it might make sense for states or groups of states to unilaterally deploy negativeemissions technology once they have reduced emissions to zero, even if other states are still
emitting.

1.6.3. Crash direct air capture
There are many different forms of negative emissions technology, and all of them havetechnological, economic and political limitations. The only form of negative emissions
technology that is well-suited to a drastic course correction is direct air capture and storage,
which captures carbon directly from the ambient air and stores it underground. This isbecause:64
●
●
●
●
●
●

●

Deployments are modular, scalable and highly controllable by the governments andfirms that invest
Carbon removals are verifiable
Deployment does not harm existing special interests or consumers
Though energy-intensive, direct air capture has no other biophysical limits
It does not require large land use change, which would compete with agricultural andother uses
Unlike strategies for controlling emissions from industry and the broader economy,
deploying direct air capture does not intrinsically require intrusive policy interventions
that might damage a country’s economic competitiveness.
It can be deployed unilaterally

Once we have decarbonised the economy, getting to net negative emissions would bestraightforward as we just could use spare low carbon energy for direct air capture.
61

Oliver Morton, The Planet Remade: How Geoengineering Could Change the World (London:
Granta, 2015), 245–46.
62
Sabine Fuss et al., ‘Negative Emissions—Part 2: Costs, Potentials and Side Effects’, Environmental
Research Letters 13, no. 6 (May 2018): Table 2, https://doi.org/10.1088/1748-9326/aabf9f.
63
Raphael Calel et al., ‘Do Carbon Offsets Offset Carbon?’, 2021; Kevin Anderson, ‘The Inconvenient
Truth of Carbon Offsets’, Nature 484, no. 7392 (2012): 7–7.
64
Ryan Hanna et al., ‘Emergency Deployment of Direct Air Capture as a Response to the Climate
Crisis’, Nature Communications 12, no. 1 (14 January 2021): 2,
https://doi.org/10.1038/s41467-020-20437-0.

71

Hanna et al (2021) outlines what would be involved in an emergency mass deployment of
direct air capture. They assume:
●

●

●

●

Scenarios: Hanna et al consider three scenarios of crash direct air capture
deployment.
○ By the US only
○ By the OECD group of rich democracies○ By International Bank for Reconstruction and Development members, which
includes some low- and middle-income countries.
Spending: The actors spend $1-1.6 trillion per year, which then grows with GDP overtime.
○ The US unilaterally spends 5% of its GDP, comparable to average wartime
deployment.
○ OECD countries spend 2% of GDP.65○ International Bank for Reconstruction and Development members spend $1.6
trillion.66 I’m not sure what this is as a percentage of IBRD GDP.
Technology costs○ Costs of direct air capture are assumed to initially be $150 to $1000 per tonneof CO2, but they fall to $75 to $600 per tonne by 2075.67
Scale-up speed○ Industry scale-up is limited to 20% per year, which is comparable to therecent growth in solar photovoltaics, the Liberty ship building programme in
the US in World War 2, and the French nuclear programme in the 1970s.
○ The growth rate is 20% per year in 2025 to 2050, 5% 2050 to 2075, and 1%
in 2075 to 2100.68

The chart below shows the effect this has on emissions and temperatures

65

The states spend $1.4 trillion. OECD GDP is $64 trillion. Ryan Hanna et al., ‘Emergency
Deployment of Direct Air Capture as a Response to the Climate Crisis’, Nature Communications 12,
no. 1 (14 January 2021): SI p9, https://doi.org/10.1038/s41467-020-20437-0.
66
The states spend $1.6 trillion. IBRD GDP is Hanna et al., 10.
67
Hanna et al., ‘Emergency Deployment of Direct Air Capture as a Response to the Climate Crisis’,Fig. 5.
68
Hanna et al., ‘Emergency Deployment of Direct Air Capture as a Response to the Climate Crisis’,Fig. 3.

72

By the end of the century, the OECD Direct Air Capture programme is removing 7 billion
tonnes of carbon each year, and from 2025 to 2100, it will remove 0.2 trillion cumulative
tonnes of carbon.69 By 2150, temperatures are 0.7°C lower relative to no DACs, halving thewarming that would have happened without direct air capture.70
69

Hanna et al., ‘Emergency Deployment of Direct Air Capture as a Response to the Climate Crisis’,Fig. 3.
70“Nevertheless, the effect of DAC on the temperature trajectory is substantial—it arrests the growthin the warming curve, which peaks at 2.4–2.5 °C in the 2090s. For model runs that extend further into
the future, DAC reverses temperature rise to 1.9–2.2 °C in 2150, a reversal of 38–61% of the warmingthat occurs without DAC, which sees temperatures reach 2.7 °C in 2150 and rise even further
thereafter.” Hanna et al., ‘Emergency Deployment of Direct Air Capture as a Response to the Climate
Crisis’.

73This also allows us to work out the effects of greater levels of direct air capture on emissions
reduction and on temperatures.71 Below I assume that deployment stays at 2100 levels
indefinitely, which will be close to correct as Hanna et al (2021) assume that capacity growthslows substantially by 2100.
Cost as a % of the GDP of
OECD democracies

Cumulative emissions
reduced (trillion tonnes ofcarbon per century)

Warming reduced by (°C)

2%

0.3

0.5

6%

0.9

1.4

8%

1.2

1.8

20%

3

4.5

Calculations are in this guesstimate model.
This illustrates that we can reverse climate change if we are willing to make significant
investments. If OECD countries spent 6% of GDP, they would capture 900 billion tonnes ofcarbon, which is greater than total carbon emissions on RCP4.5.

1.6.4. How likely is crash direct air capture?
It is clear that crash direct air capture is technically feasible, but how likely is it? Deploymentof negative emissions technology today is negligible.72 At the moment, typical costs are
estimated to be more than $250 per tonne, though they might fall to around $100 per tonne
with enough support. Would countries ever be willing to pay this much to reduce emissions?

Observed willingness to pay to abate
One natural way to assess willingness to pay to abate carbon is to look at observed carbon
prices. As of 2019, the global carbon price was around $2 per tonne. The global price now islikely somewhat higher due to increases in the EU Emissions Trading Scheme price, but I
think it is still below $10 per tonne. Indeed, once we take account of fossil fuel subsidies, thenet global carbon price is probably negative. The Information Technology and Innovation
71“The cooling (or avoided warming) due to CDR would be proportional to the cumulative amount of
CO2 removed from the atmosphere by CDR (Tokarska and Zickfeld, 2015; Zickfeld et al., 2016), as
implied by the near-linear relationship between cumulative carbon emissions and GSAT change”
IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, Ch. 3 sec. 4.6.3.2.
72“In the United Kingdom, Drax has begun a pilot project to capture CO2 from its biomass-fuelledpower plant. If the project is successful, it could become the world’s first negative emissions power
plant” “Today, more than 10 direct air capture plants are operating in Europe, the United States andCanada. Most of these plants are small and sell the captured CO2 for use – for carbonating drinks, forexample. However, the first large-scale direct air capture plant is now being developed in the UnitedStates by a partnership between Carbon Engineering and Occidental Petroleum. The plant willcapture up to 1 million tonnes of CO2 each year for use in enhanced oil recovery and could become
operational as early as 2023. In Iceland, the CarbFix project is capturing CO2 from the atmosphere forinjection and storage in basalt rock formations.” IEA, Going carbon negative: what are the technology
options? Jan 202074

Foundation estimates that in 2019, the effective net global carbon price was minus $10 per
tonne.73
However, global carbon prices are not a good indicator of willingness to pay to reduce
emissions because carbon pricing faces especially severe political economy problems
relative to other climate policies: they have highly visible costs, and are imposed on sectors
with very different abatement costs and with very different levels of political organisation.
Because the political economy barriers to carbon pricing are so severe, the price in global
carbon pricing schemes is not a good measure of observed willingness to pay to reduce
emissions. Many policies imposed in many jurisdictions have implicit carbon prices in excess
of $100 per tonne, and even of $1,000 per tonne.
●●●

The decision to close still viable coal plants in Ontario had an implicit carbon price of$80-$100 per tonne.74
The implicit carbon price of Germany’s solar power incentives was upwards of $500
per tonne.75
The implicit carbon price of a Norwegian suite of regulations on petrol cars amounts
to an implicit carbon price of more than 1000 Euros per tonne.76

So, it is not implausible that some rich environmentally-motivated democracies would be
willing to pay more than $100 per tonne to remove carbon through direct air capture.

How might willingness to pay change in the future?
Willingness to pay might also increase in the future because (1) incomes will be muchhigher, and (2) the impacts of climate change will be worse. On the middle of the roadshared socioeconomic pathway 2, GDP per capita is $80,000 in 2100, compared to around$10,000 today. On the high growth SSP5-8.5, GDP per capita is $140,000 in 2100. Since
environmental protection is plausibly a luxury good, we should expect people to be willing tospend a greater share of their income on it as their income rises.
Secondly, crash direct air capture would be more likely to happen if climate change werestarting to have especially bad impacts on certain countries; Hanna et al (2021) explicitly
envision the crash direct air capture programme in the case of a ‘climate emergency’.
There are cases of individual countries spending huge fractions of GDP in times of crisis.
COVID-19 is one example. During World War II, military spending was upwards of 20% of73

See endnote 24 of the ITIF report Omission Innovation 2.0 (2019).
See this review by Jaccard et al.
75“Results show that for the period analyzed both the RE carbon surcharge and the implicit carbon
price of wind are on the order of tens of euro per tonne of CO2, while for solar are on the order ofhundreds of euro per tonne of CO2.” Claudio Marcantonini and A. Denny Ellerman, ‘The Implicit
Carbon Price of Renewable Energy Incentives in Germany’, The Energy Journal 36, no. 4 (1 October2015), https://doi.org/10.5547/01956574.36.4.cmar.
76“The price of carbon characterizing the trade-off between conventional and battery electric cars in
Norway as of 2019 exceeds €1370 per ton of CO2.” Lasse Fridstrøm, ‘The Norwegian Vehicle
Electrification Policy and Its Implicit Price of Carbon’, Sustainability 13, no. 3 (January 2021): 1346,
https://doi.org/10.3390/su13031346.
74

75

GDP for most of the major powers. In Japan and Germany, it surpassed 90% of GDP in1944.

A key question then is how severe climate change might be relative to World War II for
countries with the spending power to invest in crash direct air capture. These will most likely
include current OECD countries as well as many emerging economies in Asia.
I argue in the remainder of this report that warming of 4ºC would pose a much smaller threat
to most countries than world war. Small island nations could face an existential threat, andsome of these may have high per capita GDP, but they would lack the financial resources tocarry out a direct air capture programme at any scale. The literature on warming of >5ºC ismuch thinner, so there is more uncertainty about how bad impacts would be at this level ofwarming.
Since World War II, American defence spending during wartime (for wars in Korea, Vietnam,
the Persian Gulf, Iraq, and Afghanistan) has generally been 1-10% of GDP.77 It is perhaps
more plausible that spending on negative emissions could reach this level if warming passed
3ºC, or if a tipping point appeared to have been crossed. Spending 6% of the GDP of theOECD countries over a century would reduce temperatures by around 1.5°C.
It is difficult to say exactly when states would be motivated to engage in crash direct air
capture. My best guess is that efforts would start to increase once warming passed 3ºC to77

Stephen Daggett, ‘Costs of Major U.S. Wars’ (Congressional Research Service, 29 June 2010),
https://sgp.fas.org/crs/natsec/RS22926.pdf, Table 1.

764°C. This level of warming would create very high levels of heat stress for countries in thetropics and subtropics, including regions that are likely to have a lot of economic might in the
future such as the US, China, India and Europe. At 6°C of warming, New York, New Orleans
and Washington DC would face higher heat stress than modern day Bahrain. At 4°C ofwarming, huge population centres in India and China would face heat stress only today seen
occasionally in the Persian Gulf.78
Although these countries would be able to adapt with air conditioning, it would still be
unpleasant to be outside in the summer months, and would make outdoor exercise
impossible. I would guess that at >4°C, the chance of decarbonisation and a direct air
capture programme costing >2% of the GDP of certain major powers is upwards of 50%.

78

On this, see Chapter 6.

77

2. How hot will it get?
Since the Industrial Revolution, temperatures have increased by about one degree Celsius,with most of the increase coming after 1980.

2.1. Climate sensitivity metrics
There are many different metrics that quantify the effect of greenhouse gas emissions on the
climate. These can be defined as follows:79
Equilibrium climate sensitivity = The global average warming we get following adoubling of atmospheric CO2 once all climate processes except ice sheets reachequilibrium. This can be roughly thought of as the warming we get over several
centuries, assuming that CO2 concentrations remain at a certain level.
Earth system sensitivity = The warming we get following a doubling of atmospheric
CO2 once the system reaches equilibrium over several millennia. This accounts forice sheet feedbacks.
Transient climate response to cumulative emissions = The warming we getfollowing a given amount of cumulative emissions.
79

There are also other metrics such as effective climate sensitivity, and transient climate response. S.
Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence’,
Reviews of Geophysics, 2020, e2019RG000678.

78

In climate science, most of the attention has been focused on estimating equilibrium climate
sensitivity. The equilibrium climate sensitivity metric rests on the insight that there is alogarithmic relationship between atmospheric CO2 concentrations and warming. Eachdoubling of CO2 concentrations produces the same amount of warming. Doubling CO2
concentrations from 280ppm to 560ppm, produces approximately the same warming as
doubling them again to 1,120ppm (with some caveats discussed below). Thus, there are“diminishing returns' ' from concentrations to warming. Earth system sensitivity rests on asimilar insight but considers a longer timeframe for the earth system to reach equilibrium.
However, the Transient Climate Response to Cumulative Emissions suggests that there is alinear relationship between emissions and warming. How can both equilibrium climate
sensitivity and the transient climate response to cumulative emissions be correct? How canthere be a logarithmic relationship between concentrations and warming, but a linear
relationship between emissions and warming?
The answer is that the diminishing effect of higher CO2 concentrations is compensated by
the diminishing ability of the ocean to take up heat and carbon.80 The amount of CO2 werelease that stays in the atmosphere - the ‘airborne fraction’ - changes depending on pastemissions and on temperatures. At present, the airborne fraction is around 45% - the rest isabsorbed by the oceans and the land. However, as emissions increase, carbon sinks also
become less effective at removing CO2 from the atmosphere, which results in a higher
airborne fraction. This is shown in the chart below:

80“The TCRE emerges from the diminishing radiative forcing from CO2 per unit mass being
compensated for by the diminishing ability of the ocean to take up heat and carbon” Thomas L.
Frölicher and David J. Paynter, ‘Extending the Relationship between Global Warming and Cumulative
Carbon Emissions to Multi-Millennial Timescales’, Environmental Research Letters 10, no. 7 (2015):

79

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, SPM. 8.

In addition, the oceans take up less heat as they warm. So, in a nutshell, there are:●●

Increasing returns from emissions to concentrations
Decreasing returns from concentrations to warming

These effects cancel out the logarithmic effect of atmospheric carbon on warming, such thatwarming is found to be proportionate to cumulative emissions in Earth System Models asshown in the chart below:

80Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, Fig. SPM. 10.

2.1.1. Advantages and disadvantages of different metrics
Each of these metrics has advantages and disadvantages.

Equilibrium metrics
In spite of its prominence, equilibrium climate sensitivity is only indirectly practically useful. It
measures the warming we get on the assumption that CO2 concentrations reach a certain
level and then stay there indefinitely. But, once emissions stop, CO2 concentrations would
actually decline due to natural uptake of CO2, initially by the oceans. Atmospheric CO2 overtime would look more like this after a given level of emissions over one million years:

81Source: N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric
CO2 in an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): 2–17,
https://doi.org/10.1002/2014GB005074.

CO2 concentrations would only stay at a certain level indefinitely if there were a very precise
low level of CO2 emissions sustained over many centuries to precisely compensate forocean CO2 uptake.81 This is unlikely to happen in the real world. It is more plausible thatemissions would cease altogether, or that emissions would decline over a long period by aconstant percentage. So, for plausible real world emissions scenarios, warming is not related
to equilibrium metrics in a simple way.
Earth system sensitivity is a more complete metric than equilibrium climate sensitivity
because it includes ice sheet loss. It takes thousands of years for ice sheets to fully melt
following warming, and because water and the land uncovered by ice melt are darker thanice, planetary reflectivity is reduced, which traps more heat. For this reason, earth systemsensitivity is thought to be 1-2 times greater than equilibrium climate sensitivity.82 Like
equilibrium climate sensitivity, earth system sensitivity assumes that concentrations remain
at a precise point indefinitely, which is unlikely to happen in the real world.

Transient Climate Response to Cumulative Emissions
The advantage of the transient climate response to cumulative emissions metric is that it
includes changes in the carbon cycle, so it does not require emissions to be tweaked tomaintain a constant level of concentrations. Rather, knowing the transient climate response
81“Physically this can be understood by realizing that the ECS is a theoretical quantity representing
the warming that would occur only if atmospheric concentrations of greenhouse gases were held
constant indefinitely while the climate system was allowed to come into equilibrium. Such a ‘constantradiative forcing’ scenario would require a very precise low level of emission of CO2 sustained overmany centuries to precisely compensate for ocean CO2 uptake. This is clearly not a particularly
policy-relevant scenario.” Richard Millar et al., ‘The Cumulative Carbon Budget and Its Implications’,
Oxford Review of Economic Policy 32, no. 2 (2016): 323–42.
82“Since we do not have accurate estimates of the ice sheet and vegetation for-cings, we instead use
an uncertain parameter to represent the amount by which these (generally slower) responses inflate
the response that would be generated by CO2alone. Lunt et al. (2010) argue that this ratio ESS/S isaround 1.4 for the Pliocene based on simulations using HadCM3, while Haywood et al. (2013) find an
ensem-ble mean ratio of 1.5 with considerable variation between models but with a total range of 1 to
2 across the models in the PlioMIP1 ensembleWe represent these results with an ESS inflation factor
1 +fESSwherefESSis distributed asN(0.5, 0.25).” S. Sherwood et al., ‘An Assessment of Earth’s
Climate Sensitivity Using Multiple Lines of Evidence’, Reviews of Geophysics, 2020, p. 59.

82

to cumulative emissions, we can infer from cumulative emissions how much warming thereis going to be. Because models of the TCRE include the carbon cycle, they can account for
how CO2 is drawn down by natural processes over time, whereas equilibrium metrics
necessarily hold CO2 concentrations constant.
Once emissions stop, temperature would stay roughly constant for about 100 years83 before
slowly declining over hundreds of thousands of years.
One downside of the TCRE is that it is not clear whether the relationship holds for emissions
beyond 1 trillion tonnes of carbon.84 Another drawback is that estimates of transient climate
response to cumulative emissions do not usually include climate system feedbacks that are
relevant on multi-millennial timescales, such as ice sheets and vegetation.
Overall, TCRE is a more useful metric than equilibrium climate sensitivity. It is easier toestimate warming using the Transient Climate Response to Cumulative Emissions, and thereal world implications of equilibrium climate sensitivity are unclear.

2.2. Estimates of climate sensitivity
2.2.1. Equilibrium Climate Sensitivity
Of all climate sensitivity metrics, equilibrium climate sensitivity has been the main focus ofscientific attention so far. The Swedish scientist Svante Arrhenius was the first person toquantify equilibrium climate sensitivity. In 1896 he estimated that for each doubling of CO2
concentrations, the world would warm by between 5°C and 6°C. In 1906, he revised thisdown to 4°C, which is remarkably close to modern estimates of climate sensitivity.85

83“The Zero Emissions Commitment (ZEC) is the change in global mean temperature expected tooccur following the cessation of net CO2 emissions and as such is a critical parameter for calculating
the remaining carbon budget… Overall, the most likely value of ZEC on multi-decadal timescales isclose to zero, consistent with previous model experiments and simple theory.” Andrew H. MacDougall
et al., ‘Is There Warming in the Pipeline? A Multi-Model Analysis of the Zero Emissions Commitment
from CO2’, Biogeosciences 17, no. 11 (15 June 2020): Figure 3b.
https://doi.org/10.5194/bg-17-2987-2020.
84“Overall, there is high agreement between multiple lines of evidence (robust evidence) resulting inhigh confidence that TCRE remains constant for the domain of increasing cumulative CO2 emissions
until at least 1500 PgC, with medium confidence of it remaining constant up to 3000 PgC because ofless agreement across available lines of evidence.” IPCC, Climate Change 2021: The PhysicalScience Basis, Ch. 5, sec. 5.5.1.2.1.
85“The citations of Arrhenius' calculations are usually based on the work published in 1896
[Arrhenius, 1896]. In this paper Arrhenius reported that CO2 doubling should increase theEarth's mean temperature by 5-6°C. In the same year, he estimated that it would take about3000 years for mankind to double the atmospheric concentration through the burning of fossil
fuels [Arrhenius, 1896]. However, the 1896 paper was not his last publication on the problem ofglobal warming. In later works, Arrhenius revised the estimates mentioned above. It is not clear
exactly how he derived his values, but these later values are much closer to modern estimates
than most think. For example, in the 1906 book, Worlds in the Making: The Evolution
ofthe Universe [Arrhenius, 1906], Arrhenius wrote that "...any doubling of the percentage ofcarbon dioxide in the air would raise the temperature of the Earth's surface by 4°C..."” Andrei G.
Lapenis, ‘Arrhenius and the Intergovernmental Panel on Climate Change’, Eos, Transactions
American Geophysical Union 79, no. 23 (1998): 271–271.

83For many decades after the 1970s, estimates of equilibrium climate sensitivity barelychanged. In the IPCC’s 2013-14 Fifth Assessment Report, the 95th percentile of climate
sensitivity was around 6ºC.86
This was a major motivation for arguments by the late great economist Martin Weitzman thatclimate change is a serious global catastrophic risk. In their 2015 book Climate Shock,Wagner and Weitzman argued that, based on what was then thought to be current policy(RCP6), there is an 11% chance of more than 6°C of warming.87
However, the IPCC’s Sixth Assessment Report has since narrowed the 90% confidence
interval for climate sensitivity at the lower and higher end, as shown in the diagram below

Source: CarbonBrief, IPCC: How the AR6 WG1 summary for policymakers compares to its
predecessor

The main reason that uncertainty about climate sensitivity has narrowed is because it is now
calculated using multiple lines of evidence.88 This is done in a formal Bayesian way in86

Though the IPCC wasn’t completely clear about this.
Gernot Wagner and Martin L. Weitzman, Climate Shock: The Economic Consequences of a Hotter
Planet (Princeton: Princeton University Press, 2015), Table 3.1.
88
“In AR6, the assessments of ECS and TCR are made based on multiple lines of evidence, with
ESMs representing only one of several sources of information. The constraints on these climate
metrics are based on radiative forcing and climate feedbacks assessed from process understanding(Section 7.5.1), climate change and variability seen within the instrumental record (Section 7.5.2),
paleoclimate evidence (Section 7.5.3), emergent constraints (Section 7.5.4), and a synthesis of alllines of evidence (Section 7.5.5). In AR5, these lines of evidence were not explicitly combined in theassessment of climate sensitivity, but as demonstrated by Sherwood et al. (2020) their combination
87

84

Sherwood et al (2020) ‘An Assessment of Earth's Climate Sensitivity Using Multiple Lines of
Evidence’,89 which influenced the IPCC’s recent estimates, though the IPCC’s own process
was more qualitative.90
The heavy tailed distributions found in some of Weitzman’s earlier work are a product of thefact that they update from a uniform prior across climate sensitivity,91 and do not
systematically update on multiple lines of evidence.
I discuss this paper in Appendix 1.

2.2.3. Transient Climate Response to Cumulative Emissions
The IPCC’s uncertainty about the TCRE has also narrowed. In the 2013-14 IPCC report, the
66% confidence range for the transient climate response to cumulative emissions was 0.8°C
to 2.5°C per trillion tonnes of carbon.92 In the latest IPCC report, this has narrowed to 1.0°C
to 2.3°C per trillion tonnes of carbon.93 The IPCC does not give a 5% to 95% range for thetransient climate response to cumulative emissions.

narrows the uncertainty ranges of ECS compared to that assessed in AR5.” IPCC, Climate Change
2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec. 7.5.
89
S. Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence’,
Reviews of Geophysics, 2020.
90
Dan Lunt, personal correspondence 9 May 2020.
See also “The broader evidence base presented in this Report and the general agreement among
different lines of evidence means that they can be combined to yield a narrower range of ECS values.
This can be done formally using Bayesian statistics, though such a process is complex and involves
formulating likelihoods and priors (Annan and Hargreaves, 2006; Stevens et al., 2016; Sherwood etal., 2020). However, it can be understood that if two lines of independent evidence each give a low
probability of an outcome being true, for example, that ECS is less than 2.0°C, then the combined
probability that ECS is less than 2.0°C is lower than that of either line of evidence. On the contrary, if
one line of evidence is unable to rule out an outcome, but another is able to assign a low probability,
then there is a low probability that the outcome is true (Stevens et al., 2016). This general principle
applies even when there is some dependency between the lines of evidence (Sherwood et al., 2020),
for instance between historical energy budget constraints (Section 7.5.2.1) and those emergent
constraints that use the historically observed global warming (Section 7.5.4.1). Even in this case the
combined constraint will be closer to the narrowest range associated with the individual lines ofevidence.” IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), sec. 7.5.5.
91
On this, see also J. D. Annan and J. C. Hargreaves, ‘On the Generation and Interpretation of
Probabilistic Estimates of Climate Sensitivity’, Climatic Change 104, no. 3–4 (1 February 2011):
423–36, https://doi.org/10.1007/s10584-009-9715-y.
92“The transient climate response to cumulative carbon emission (TCRE) is likely between 0.8°C to
2.5°C per 1000 PgC (high confidence), for cumulative carbon emissions less than about 2000 PgC
until the time at which temperatures peak” IPCC, Climate Change 2013: The Physical Science Basis,
Fifth Assessment Report (Cambridge University Press, 2013), Technical Summary p. 81.
93“In the literature, units of °C per 1000 PgC are used, and the AR6 reports the TCRE likely range as
1.0°C to 2.3°C per 1000 PgC in the underlying report, with a best estimate of 1.65°C.” IPCC, Climate
Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), Summary for
Policymakers, note 41.

85

2.3. Warming on different emissions scenarios
2.3.1. Current policy
It is not all that easy to estimate how much warming we might get from a given amount of
cumulative CO2 emissions. We not only need to predict CO2 emissions from fossil fuel andindustry, but also from deforestation and other forms and land use change, and in addition
we need to account for non-CO2 greenhouse gases like methane. One thing we can do is tostart with projections of which representative emissions pathways we might follow and thenuse the IPCC’s estimate of how much warming we get on those pathways.
The table below shows the warming we will get on different emissions scenarios. In Chapter
1 I argued that RCP4.5 is now plausibly the most likely scenario on current policy, and thatthe most likely future emissions scenario given strengthening policy is now between RCP2.6
and RCP4.5.

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, Fig. SPM. 1.

(The ‘very likely range’ is the 5% to 95% range).
On current policy (i.e. RCP4.5), by 2100 we are most likely to end up with around 2.7°C ofwarming and there is a 1 in 20 chance of more than 3.5°C. The IPCC does not say what the
chance of more than 6°C of warming is on RCP4.5, but it seems likely well below 1%, giventhat the upper 95th percentile is 3.5°C. This is an order of magnitude lower than estimatedby Wagner and Weitzman in 2015. Since climate policy seems likely to strengthen in the
future, the most likely level of warming will plausibly be 2°C to 2.5°C and the upper 95th
percentile bound is probably around 3°C.
Thus, the tail risks of climate change are now much lower than they once were.

2.3.2. All fossil fuels without underground coal gasification
I argued in section 1.5 that it is difficult to come up with plausible scenarios on which weburn all of the recoverable fossil fuels. A more plausible worst-case is one in which we burn86

through all of the fossil fuels that we can access without underground coal gasification. On
this scenario, we would emit a further 1,200 GtC.
My calculations, using the IPCC’s estimate of the TCRE imply a median warming of 3.5ºC
and a 66th percentile of 4.5ºC. This is broadly consistent with the IPCC’s own estimates. For
comparison, on RCP7, total cumulative emissions are around 1,500 GtC. According to the
IPCC, this implies a most likely level of warming of 3.6ºC and a 95th percentile of 4.6ºC.

2.3.3. The worst-case scenario
If we burned all the fossil fuels, there would most likely be around 7°C of warming relative tothe pre-industrial period, and a 1 in 6 chance of warming of 9.6°C. Calculations are in thissheet.
It is important to consider how long it would take us to burn through all of the fossil fuels andproduce such extreme levels of warming. If there is an AI-driven explosion incarbon-intensive economic growth, then we could burn through all of the fossil fuels in the
21st Century. If there is indefinitely stalled decarbonisation, it would take several centuries toreach these extreme levels of warming.

2.4. Structural uncertainty
Until recently, it would have been reasonable to claim that there was deep structural
uncertainty about equilibrium climate sensitivity. In the IPCC’s 2013-14 Fifth Assessment
Report, the IPCC’s estimate of climate sensitivity was determined in an informal way using
evidence from a range of models and from paleoclimate data.94 The range of models usedwere not trying to sample the tails of the distribution. Rather, each model was trying to makea best estimate and then the distribution across these best estimates was used to estimate
the final probability distribution across climate sensitivity.95 This process is likely tounderestimate tail risk.

94“As a consequence, in this chapter, statements using the calibrated uncertainty language are a
result of the expert judgement of the authors, combining assessed literature results with an evaluationof models demonstrated ability (or lack thereof) in simulating the relevant processes (see Chapter 9)
and model consensus (or lack thereof) over future projections” IPCC, Climate Change 2013: The
Physical Science Basis, Fifth Assessment Report (Cambridge University Press, 2013), p. 1040.
95“Ensembles like CMIP5 do not represent a systematically sampled family of models but rely on
self-selection by the modelling groups. This opportunistic nature of MMEs has been discussed, forexample, in Tebaldi and Knutti (2007) and Knutti et al. (2010a). These ensembles are therefore notdesigned to explore uncertainty in a coordinated manner, and the range of their results cannot be
straightforwardly interpreted as an exhaustive range of plausible outcomes, even if some studies haveshown how they appear to behave as well calibrated probabilistic forecasts for some large-scalequantities (Annan and Hargreaves, 2010). Other studies have argued instead that the tail of
distributions is by construction undersampled (Räisänen, 2007). In general, the difficulty in producing
quantitative estimates of uncertainty based on multiple model output originates in their peculiarities asa statistical sample, neither random nor systematic, with possible dependencies among the members(Jun et al., 2008; Masson and Knutti, 2011; Pennell and Reichler, 2011; Knutti et al., 2013) and of
spurious nature, that is, often counting among their members models with different degrees ofcomplexities (different number of processes explicitly represented or parameterized) even within the
category of general circulation models.” IPCC, Climate Change 2013: The Physical Science Basis, p.
1040.

87

However, for the IPCC’s Sixth Assessment Report, concerns about model uncertainty havebeen greatly reduced. This is because the new estimate of climate sensitivity, builds on thework of Sherwood et al (2020) and incorporates multiple different lines of evidence, which
are in part independent, including emergent constraints, the historical observational record,
process-based estimates, and paleoclimate data.96 One of the great virtues of Sherwood etal (2020) is that it carries out a sensitivity analysis of their conclusions, which is discussed in
section 7 of their paper. The chart below shows the effect of:●●●Different priors
Different distributions across different lines of evidence.
Excluding entire lines of evidence, such as historical evidence and evidence from
paleoclimatic periods.

The implications of this sensitivity analysis are shown below:

Source: Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of
Evidence’.

Sherwood et al (2020) argue that these sensitivity tests either make little difference to theirconclusions, or are implausible.97 For example, if we exclude modern observations andmodels of feedback processes and assume a uniform prior across effective climate
sensitivity, then the 5% to 95% range extends from 2.3°C to 6.9°C. But these assumptions
are difficult to justify. For instance, for excluding the process evidence to be valid, “new96

S. Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence’,
Reviews of Geophysics, 2020, e2019RG000678.
97
Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence’,
sec. 7.

88

evidence would need to come to light that would justify complete dismissal of all of themultiple elements of the process evidence (and hence much of our physical understandingof the climate system).”98
As the IPCC says in the Sixth Assessment Report:“In the climate sciences, there are often good reasons to consider representing deep
uncertainty, or what is sometimes referred to as unknown unknowns. This is naturalin a field that considers a system that is both complex and at the same time
challenging to observe. For instance, since emergent constraints represent a
relatively new line of evidence, important feedback mechanisms may be biased inprocess-level understanding, pattern effects and aerosol cooling may be large andpaleo evidence inherently builds on indirect and incomplete evidence of past climate
states, there certainly can be valid reasons to add uncertainty to the ranges
assessed on individual lines of evidence. This has indeed been addressed
throughout Sections 7.5.1–7.5.4. Since it is neither probable that all lines of evidence
assessed here are collectively biased nor is the assessment sensitive to single lines
of evidence, deep uncertainty it is not considered as necessary to frame the
combined assessment of ECS”99
In short, it is unlikely that all of the lines of evidence are systematically biased in one
direction. As a result, according to the IPCC, structural uncertainty is now a small fraction of
total uncertainty.
However, there are a number of caveats to this. Firstly, Sherwood et al (2020) only considers
the possible effects of two doublings of CO2, which would take us to around 1,100ppm.100
But, on the worst-case emissions scenario, concentrations could reach 1,600ppm. The
further that we push concentrations out of sample, the greater is our model uncertainty.
Indeed, the IPCC says that it is confident that temperatures are proportionate to cumulativeemissions up to 1 trillion tonnes of carbon; but only has medium confidence that therelationship holds for 1.5 to 3 trillion tonnes of carbon.101
Secondly, although the IPCC represents the broad scientific consensus on climate change,
the scientific community is not unanimous in accepting that uncertainty about climate

98

Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence’, p.
69.
99
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), sec. 7.5.5.
100“In choosing the reference scenario to define sensitivity for this assessment, for practical reasons
we depart from the traditional Charney ECS definition (equilibrium response with ice sheets andvegetation assumed fixed) in favor of a comparable and widely used, so-called “effective climate
sensitivity” S derived from system behavior during the first 150 years following a (hypothetical) sudden
quadrupling of CO2.” Sherwood et al., ‘An Assessment of Earth’s Climate Sensitivity Using Multiple
Lines of Evidence’.
101“Overall, there is high agreement between multiple lines of evidence (robust evidence) resulting inhigh confidence that TCRE remains constant for the domain of increasing cumulative CO2 emissions
until at least 1500 PgC, with medium confidence of it remaining constant up to 3000 PgC because ofless agreement across available lines of evidence.” IPCC, Climate Change 2021: The PhysicalScience Basis, Ch. 5, sec. 5.5.1.2.1.

89

sensitivity has declined. Some contend that structural uncertainty remains.102 Indeed, thehistory of science suggests that we should probably expect scientists to be overconfident
rather than underconfident, especially about complex systems.
Thirdly, the foregoing argument only applies to the IPCC’s estimate of warming, it is not true
for all aspects of the climate system that we might care about. For example, as discussed
later in this report, there is substantial model uncertainty about future sea level rise andchanging precipitation patterns, each of which could have important humanitarianconsequences.
It is clear that our understanding of climate science is still imperfect, although hugely
advanced over the last 50 years. Some argue that this undermines the case for climate
action.103 It is worth pausing to understand where this goes wrong. While we may be most
likely to end up with 2.5°C of warming, because we are uncertain, warming could well bemuch higher or lower than this. But the import of this is asymmetric - lower warming mightleave us with an OK outcome that is about the same as the world today, but high warming
could leave us with a very bad outcome. Uncertainty makes the case for action evenstronger, not weaker.
Suppose you were told that the science of rocket safety is unsettled. There is a decent
chance that your rocket will burst into flames when you turn it on, but also a decent chancethat it will be fine. Would this make you more or less worried about getting into a rocket?

102

See for example Joel Katzav et al., ‘On the Appropriate and Inappropriate Uses of Probability
Distributions in Climate Projections and Some Alternatives’, Climatic Change 169, no. 1 (25November 2021): 15, https://doi.org/10.1007/s10584-021-03267-x.
103
Steven E. Koonin, ‘Climate Science Is Not Settled’, Wall Street Journal, 19 September 2014, sec.
Life and Style, https://online.wsj.com/articles/climate-science-is-not-settled-1411143565.

90

3. Our future in paleoclimatic context
The Earth’s climate has changed dramatically since the evolution of complex life at the
Cambrian Explosion 540 million years ago. Putting future anthropogenic changes in context
helps us to understand how bad the impact of climate change might be.

3.1. Our climate
Human influence on the climate precedes the Industrial Revolution. In the thousands ofyears prior to 1800 AD, much of the world was deforested for agriculture, releasing
greenhouse gases into the atmosphere. Pre-industrial deforestation, along withmethane-producing rice farming, released greenhouse gases into the atmosphere,104
warming the planet today by a few tenths of a degree Celsius.105 There is disagreement in
the literature about the size of pre-industrial emissions.
Nonetheless, most of our species’ influence on the climate has come from the burning offossil fuels since the Industrial Revolution. Immediately prior to the Industrial Revolution, CO2concentrations were 278ppm, and global average surface temperature was around 14°C.106
Today, CO2 concentrations are at 410ppm and the global average temperature is around 1
degree higher.

3.2. Hothouses and rapid warming
To understand the risks to sentient life from global warming, it is useful to compare theanthropogenic future to the distant past. The Earth’s climate has changed dramatically over

104

This includes methane and carbon dioxide. Estimates of the size of the emissions differ. IPCC,
Climate Change: The Physical Science Basis, sec 6.2.2; W. F. Ruddiman et al., ‘The Early
Anthropogenic Hypothesis: A Review’, Quaternary Science Reviews 240 (15 July 2020): 1,
https://doi.org/10.1016/j.quascirev.2020.106386.
105
Estimates of the size of the effect vary. On the higher estimates of carbon dioxide emissions,
pre-industrial land use change caused 0.76C of warming. See Feng He et al., ‘Simulating Global andLocal Surface Temperature Changes Due to Holocene Anthropogenic Land Cover Change’,
Geophysical Research Letters 41, no. 2 (2014): 623–31, https://doi.org/10.1002/2013GL058085. On
the lower estimates, increasing carbon dioxide concentrations by 10ppm would have caused warmingof 0.16C (assuming a climate sensitivity of 3C). The IPCC estimates that there is a 33% to 66%
chance that anthropogenic land use change is responsible for the increase in methane 5,000 years
ago. Ruddiman et al (2003) estimates that the methane release caused 0.25C of warming. Following
the IPCC, assuming that there is a 50% chance that this was anthropogenic, the expected warmingfrom anthropogenic methane release was 0.13C. Overall, this suggests that the combined effect of
pre-industrial anthropogenic carbon dioxide and methane was warming of around 0.3C. IPCC, Fifth
Assessment Report, Climate Change: The Physical Science Basis, sec 6.2.2.2; William F. Ruddiman,‘The Anthropogenic Greenhouse Era Began Thousands of Years Ago’, Climatic Change 61, no. 3(2003): 285.
106
“Quantitative comparison with fully coupled climate model simulations indicates that global averagetemperatures were about 29, 26, 23 and 19 degrees Celsius in the early, early middle, late middle andlate Eocene, respectively, compared to the preindustrial temperature of 14.4 degrees Celsius.” Margot
J. Cramwinckel et al., ‘Synchronous Tropical and Polar Temperature Evolution in the Eocene’, Nature
559, no. 7714 (July 2018): 382–86, https://doi.org/10.1038/s41586-018-0272-2.

91the last half a billion years, shifting from extremely cold to extremely hot. Better
understanding our climatic past is crucial for understanding two important issues:
1. Ecosystem trauma. How did ecosystems cope with much hotter climates and with
rapid warming? If past warm periods caused substantial loss of animal, plant andinsect life, then that might indicate that global warming will make life harder for
sentient life in the future.
2. Tipping points. What is the risk that we will set off tipping points if we pass certain
climatic thresholds?
I will discuss ecological trauma in this Chapter and tipping points in Chapter 8.
In this Chapter, my main focus is on the relevance of past climate change for humanflourishing in the face of future climate change. I discuss the potential impact of futureclimate change for ecosystems in Chapter 5.
The chart below from the 2021 IPCC Sixth Assessment report shows future potential
warming in the context of the last 60 million years. For reference, on current policy, global
temperatures are most likely to increase by 2.5°C relative to pre-industrial times. On
RCP8.5, temperature would increase by 5°C by 2100.

92

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, Cross-Chapter Box 2.1, Fig. 1.

The chart below from Scotese et al (2021) shows global average temperatures further backin time:93

Source: Christopher R. Scotese et al., ‘Phanerozoic Paleotemperatures: The Earth’s Changing
Climate during the Last 540 Million Years’, Earth-Science Reviews 215 (1 April 2021): fig. 1,
https://doi.org/10.1016/j.earscirev.2021.103503.

The chart below from Foster et al (2017) shows the evolution of CO2 concentrations (toppane) and the warming effect of CO2 (bottom pane) over the last half billion years.107

107

Gavin L. Foster, Dana L. Royer, and Daniel J. Lunt, ‘Future Climate Forcing Potentially without
Precedent in the Last 420 Million Years’, Nature Communications 8, no. 1 (4 April 2017): 14845,
https://doi.org/10.1038/ncomms14845.

94

Source: Gavin L. Foster, Dana L. Royer, and Daniel J. Lunt, ‘Future Climate Forcing Potentiallywithout Precedent in the Last 420 Million Years’, Nature Communications 8, no. 1 (4 April 2017):
14845, https://doi.org/10.1038/ncomms14845, Fig. 4.

The table below shows key facts about the different periods I will discuss in this Chapter.
PeriodWhen?

Temp vspre-industrial

Global warming
per century

Regional warming
per century

CO2 ppm

RCP8.5

2100

5°C

4°C

4-10°C

900ppmCurrent policy

2100

2.5°C

1.5°C

2.5-5°C

600ppmTransition from last
ice age to
Holocene

20k to 6k
years ago

Last interglacial

127k years
ago

1°C

280ppm

Mid-Pliocene
Warm Period

3m years ago

3°C

390ppmMiocene Climatic
Optimum

16 million
years ago

7°C

500ppm

Eocene Climatic
Optimum

50m years
ago

14°C

1,800ppm

Paleocene-Eocene
Thermal Maximum

56m years
ago

17°C

Mid-Cretaceous

90m years
ago

20°C

2-15°C

0.05°C2,300ppm
1,000ppm

95

Permian to early
Triassic

250m years
ago

17°C

3,000ppm(Blanks mean that no good data is available, or that the category is not applicable)
Transitions between geological periods are often caused by or associated with climaticchanges. For example, the transition from the Permian to the Triassic was associated withthe eruption of the Siberian Traps and the huge global warming and extinction that followed.
Transitions into and out of glacial periods are primarily determined by the Earth’s orbit inrelation to the Sun, and the tilt of the Earth on its axis.108
To estimate past CO2 concentrations and temperatures, we have to rely on imperfectproxies, which creates substantial uncertainty.

3.3. Were past greenhouses inhospitable?
The picture that emerges from the paleoclimate is as follows:●●

Before 180 million years ago, climate change was a probable cause of massive lossof biodiversity
From 180 million years ago until today, climate change has not been associated withelevated species loss.

In the remainder of this section, I will discuss episodes in these paleoclimatic periods in
depth, and try to get to the bottom of why the response to warming was so different in thesedifferent geological periods.

3.3.1. Pre-Cretaceous warming and mass extinctions
Before I discuss trends in hospitability, we should first get clear on some taxonomic terms
that I will use in what follows. Genus is a taxonomic rank that comes above species. For
example, homo sapiens are a species in the homo genus, which once included other homospecies, such as neanderthals (homo neanderthalensis). Lions and jaguars are two species
in the genus panthera.
This chart from Wikipedia using data from Rohde and Muller (2005) shows the trend in
biodiversity during our current eon, the Phanerozoic, from the Cambrian explosion, whenalmost all animal phyla appeared, until today.109

108

IPCC, Climate Change: The Physical Science Basis, 399.
Robert A. Rohde and Richard A. Muller, ‘Cycles in Fossil Diversity’, Nature 434, no. 7030 (March
2005): 208–10, https://doi.org/10.1038/nature03339. For a similar picture for marine biodiversity see
Jeremy B. C. Jackson and Kenneth G. Johnson, ‘Measuring Past Biodiversity’, Science 293, no. 5539
(28 September 2001): 2401–4, https://doi.org/10.1126/science.1063789.
109

96

145 million years ago, the Jurassic (J on the chart above) ended and the Cretaceous (K)began. Prior to the Cretaceous, biodiversity was low and major extinction events occurredwith striking regularity. Since then, with the exception of the extinction event that killed off thedinosaurs, it has been relatively plain sailing for Earth’s various species, until humans
started killing off other species themselves.
Mass extinctions are categorised as times when the Earth loses >75% of its species in thespace of 2 million years or less.110 In the last 540 million years, there have been five mass
extinctions.
1.
2.
3.
4.
5.

Ordovician–Silurian: 450–440 million years ago.
Late Devonian: 375–360 million years ago.
Permian–Triassic (End Permian): 252 million years ago.
End Triassic: 201.3 million years ago.
End Cretaceous: 66 million years ago.

Aside from these events, there have been many other major extinction events that do not
qualify as mass extinctions.
Scholars disagree about the causes of mass extinctions, but the most popular explanation of
the causes of these and other extinction events is volcanic eruptions, apart from theextinction of the dinosaurs, which is widely agreed to have been due to an asteroid impact.

110

Anthony D. Barnosky et al., ‘Has the Earth’s Sixth Mass Extinction Already Arrived?’, Nature 471 (2March 2011): 51.

97

Source: David P. G. Bond and Stephen E. Grasby, ‘On the Causes of Mass Extinctions’,
Palaeogeography, Palaeoclimatology, Palaeoecology, 478 (15 July 2017): 3–29,
https://doi.org/10.1016/j.palaeo.2016.11.005.

This figure shows the correlation between volcanism and major extinction events. It also
shows how extinctions were especially bad during the time of the Pangean supercontinent, apoint I return to at the end of this chapter.

98

Source: David P. G. Bond and Stephen E. Grasby, ‘On the Causes of Mass Extinctions’,
Palaeogeography, Palaeoclimatology, Palaeoecology, 478 (15 July 2017): 3–29,
https://doi.org/10.1016/j.palaeo.2016.11.005.

The lines down from the top show the volume of lava from different volcanic eruptions, while
the lines from the bottom show the percentage of genera killed. In ‘On the causes of mass
extinctions’, Bond and Grasby note
“The temporal link between large igneous province (LIP) eruptions and at least half ofthe major extinctions of the Phanerozoic implies that large scale volcanism is themain driver of mass extinction.”111
Other posited causes for mass extinctions include Near Earth Objects and gamma ray
bursts.112 A huge asteroid impact in present day Mexico is currently the most popular
explanation for the extinction of the dinosaurs 65 million years ago at the end of the
Creacteous, though the eruption of the Deccan Traps also coincided with that event, asshown in the chart above.113

111

Bond and Grasby, ‘On the Causes of Mass Extinctions’, p. 3. Large Igneous Provinces (LIPs) are
the igneous rock formations left over after large volcanic eruptions. The Phanerozoic is the current
geological eon, which started 540 million years ago.
112
Bond and Grasby, ‘On the Causes of Mass Extinctions’.
113
Alfio Alessandro Chiarenza et al., ‘Asteroid Impact, Not Volcanism, Caused the End-Cretaceous
Dinosaur Extinction’, Proceedings of the National Academy of Sciences 117, no. 29 (21 July 2020):
17084–93, https://doi.org/10.1073/pnas.2006087117.

99

The chart above also nicely illustrates trends in ecological stress. In the 80 million years fromthe first Permian extinction event, the Capitanian, to the early Jurassic extinction events, theaverage rate of global genus extinctions in extinction events is 15-20%, and 12 events
produced global genus extinction rates in excess of 15%. But in the 145 million years since
the end of the Jurassic, the average rate of global genus extinctions from extinction events
has been around 5% and never passed 15%, except for the death of the dinosaurs.
There is no better illustration of the damage that volcanoes caused than the greatest
ecological disaster of all time: the Permian-Triassic extinction.

Warming at the Permian-Triassic boundary
252 million years ago, the eruption of the Siberian Traps put 30 to 40 trillion tonnes of carbon
into the atmosphere,114 and CO2 concentrations rose to around 3,000ppm.115 Average ocean
temperatures in South China before the end Permian disaster were 20°C and peaked at
40°C in the early Triassic.116 At no point did ocean temperatures in Southern China drop
below 32°C in the 5 million years after the end Permian event. According to Wignall, thesetemperatures are the highest ever recorded.117 For comparison, modern equatorial sea
surface temperatures typically average around 28°C and never exceed 30C.118 Peak ocean
temperatures were the same as you would find in a bowl of very hot soup.
Bond and Grasby (2017) comment“With 90% marine species loss, widespread devastation on land including the only
recorded mass extinction of insects it is Earth's greatest ever biotic crisis.”119
Many volcanic kill mechanisms have been proposed for the Permian-Triassic extinction.
Many of these mechanisms would also have been in play in the other Pangean extinction
events.

Volcanic kill mechanisms
Volcanoes inject a wide array of gases into the atmosphere, with water vapour, CO2 andsulphur dioxide the most important volumetrically. Volcanoes also release halogens, which

114“Clarkson et al.[12] estimated that the two phases of P-Tr extinction were driven first by a small
addition of isotopically light C (e.g. methane) and then by a massive addition of isotopically heavy C(e.g. from decarbonation of limestones intruded by Siberian Traps dikes and sills), with a total
emission of from 30 000–40 000 Pg C (consistent with an independent estimate by Svensen et al.
[9]).” Lee R. Kump, ‘Prolonged Late Permian–Early Triassic Hyperthermal: Failure of Climate
Regulation?’, Philosophical Transactions of the Royal Society A: Mathematical, Physical andEngineering Sciences 376, no. 2130 (13 October 2018): 20170078,
https://doi.org/10.1098/rsta.2017.0078.
115
Ying Cui and Lee R. Kump, ‘Global Warming and the End-Permian Extinction Event: Proxy andModeling Perspectives’, Earth-Science Reviews, Global review of the Permian-Triassic mass
extinction and subsequent recovery: Part II, 149 (1 October 2015): 5–22,
https://doi.org/10.1016/j.earscirev.2014.04.007.
116
P. B. Wignall, The Worst of Times: How Life on Earth Survived Eighty Million Years of Extinctions(Princeton: Princeton University Press, 2015), p. 97-98.
117
Wignall, The Worst of Times, p. 97-98.
118
Wignall, The Worst of Times, p. 98.
119
Bond and Grasby, ‘On the Causes of Mass Extinctions’, 10.

100could be damaging insofar as they destroy the ozone layer, which protects plant and animallife from UV radiation.
The kill mechanisms for each of the gases are as follows:
●

●

●

●

Sulphur dioxide○ Causes volcanic darkness, cooling and photosynthetic shutdown○ Causes acid rain, which is bad for plants and contributes to ocean anoxia
Carbon dioxide○ Leads to warming which exposes organisms to thermal stress, and changes
ecosystems which creates adaptation challenges for some species.
○ Warming reduces the capacity of the ocean to absorb oxygen, which can leadto ocean anoxia, which is implicated in several marine extinction events.
○ CO2 dissolves in the ocean causing ocean acidification, which is generally
bad for shellfish and marine organisms with carbonate shells.
○ CO2 can build up in tissues, a process known as hypercapnia, with potentiallyfatal consequences.
Gases like nitrous oxide, chlorine and fluorine○ Damage the ozone layer, which protects life on Earth from harmful UV rays.
○ Causes acid rain.
Release of toxic metals, especially mercury○ These toxic metals can be dangerous to life, though their role in mass
extinctions has yet to be fully evaluated.

A single pulse of sulphur dioxide into the atmosphere can cause cooling for two to three
years, after which it is rained out. Thus, sulphur dioxide is probably only capable of driving
death-by-cooling if eruptions were frequent and of high volume and were sustained forseveral centuries at a time. Unfortunately, the geological record of Large Igneous Provinces
is not sufficiently resolved to permit an evaluation of whether this has actually happened
during a mass extinction interval.120
CO2 is especially damaging to marine life and can cause warming on geological timescales:
about a third of CO2 emissions remain in the atmosphere 10,000 years after it is injected.121
As we have seen, the volume of CO2 released from some volcanic eruptions in the past islarge relative to potential anthropogenic emissions.
Huge releases of CO2 can do great damage to marine ecosystems. But extinctions on land
were plausibly driven by other gases. Recent research suggests that the eruption of theSiberian Traps was so damaging because the eruption happened to punch through andevaporate salt deposits, releasing halogens causing ozone destruction, which was the major
driver of extinctions of land-based communities. That is plausible why end-Permian level
extinction events do not happen more often.122
120

Bond and Grasby, ‘On the Causes of Mass Extinctions’, 16.
N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric CO2 in
an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): Fig. 2,
https://doi.org/10.1002/2014GB005074.
122
Thanks to Matthew Huber for raising this point. “Volatile emissions to the atmosphere associatedwith the Siberian Traps eruptions at the Permian-Triassic boundary were sourced from the outgassing
121

101

CO2 is the main volcanic kill mechanism relevant to anthropogenic climate change.123 Whilelarge releases of CO2 place great strain on marine ecosystems, they were probably not
sufficient to cause land-based extinctions. Thus, although past volcanic eruptions did cause
land-based species extinctions, the mechanism was probably not CO2. There might be someother reason that future anthropogenic warming will cause land-based species extinctions,but the paleoclimate record should not update us towards that view.
Still, the evidence from this period suggests that the release of carbon and global warming
could do enormous damage.

of primary magmas and the sedimentary host rocks into which they were intruded. Halogens involcanic gases may have played an important role in environmental degradation and in stratosphericozone destruction. Here we investigate how halogens behave during the interaction between salts
and basalt magma emplaced as sills and erupted as lava. We present whole-rock, trace, and halogen
concentrations for a suite of samples from three locations in the Siberian Traps Large Igneous
Province, including basalt lavas erupted, and dolerites intruded into both organic-bearing shales andevaporites. Dolerites are enriched in Cl, Br, and I; their enrichment in Cl is similar to MORB and OIB
that have been inferred to have assimilated seawater. The dolerites exhibit halogen compositional
systematics, which extend towards both evaporites and crustal brines. Furthermore, all analyzed
samples show enrichment in Rb/Nb; with the dolerites also showing enrichment in Cl/K similar to
MORB and OIB that have been inferred to have assimilated seawater. We infer that samples from all
three locations have assimilated fluids derived from evaporites, which are components of crustal
sedimentary rocks. We show that up to 89% of the chlorine in the dolerites may have been
assimilated as a consequence of the contact metamorphism of evaporites. We show, by thermal
modeling, that halogen transfer may occur via assimilation of a brine phase derived from heating
evaporites. Halogen assimilation from subcropping evaporites may be pervasive in the Siberian Traps
Large Igneous Province and is expected to have enhanced emissions of Cl and Br into theatmosphere from both intrusive and extrusive magmatism.” Svetlana Sibik et al., ‘Halogen Enrichmentof Siberian Traps Magmas During Interaction With Evaporites’, Frontiers in Earth Science 9 (2021),
https://www.frontiersin.org/articles/10.3389/feart.2021.741447.
But see also “We still do not have all the answers to the questions posed by Pangean mass
extinctions. The devastation of land communities is especially hard to explain. The extinction of Late
Permian terrestrial communities is a truly awesome phenomenon, which might be related toatmospheric changes such as ozone destruction. Massive volcanic halogen emissions provide one
cause, but then why did this only happen during Pangea’s lifetime? More recent eruptions, such as
those of the NAIP, would be expected to also emit huge amounts of halogens. Alternatively, somegeologists have attempted to link changes in the oceans to events on land. Thus anoxic oceans may
have leaked hydrogen sulfide into the atmosphere, where it would interfere with the formation ofozone.
Unfortunately, this probably is not the answer. Hydrogen sulfide is immensely reactive with oxygenand is unlikely to ever reach the stratosphere and damage the ozone shield; instead, it will oxidize
rapidly close to the sea surface. Perhaps terrestrial warming was the stress factor on land, but itwould have been most harsh on forests adapted to living in cold conditions. The extinctions show nosuch temperature dependence—equatorial forests suffered as badly as those at high latitudes. And so
the puzzle remains.” Wignall, The Worst of Times, 175.
123
Sulphur dioxide emissions peaked in 1980 and have declined every decade since then up to 2010
(there are no data beyond then). Mercury emissions are mainly a product of low economic
development, and are now low in high-income countries. Emissions of ozone depleting substances
peaked in 1988 and have declined since then. Since around 2005, the ozone hole has slowly started
to shrink.

102

3.3.2. Biodiversity after Pangea
The connection between warming and ecological stress disappeared after the break-up of
the Pangea supercontinent 180 million years ago. In this section, I will discuss some periods
involving high temperatures and/or rapid warming. Throughout, I will note disanalogies
between these periods and future anthropogenic warming
An excellent summary of some of these periods is provided in Willis and MacDonald (2011)‘Long-Term Ecological Records and Their Relevance to Climate Change Predictions for a
Warmer World’

Cretaceous warming
PeriodWhen?

Temp vspre-industrial

Mid-Cretaceous(tierney)

90m years ago

20°C

Global warming
per century

Regional warming
per century

CO2 ppm
1,000ppm

In the mid-Cretaceous, around 90 million years ago, temperatures were around 20ºC
warmer than pre-industrial levels, while CO2 levels were between 500ppm and 1,000ppm.124(The mid-Cretaceous may have been so warm in part for non-greenhouse gas-related
reasons.)125 Despite these extreme temperatures, as the charts above show, the rate ofspecies extinction did not seem to increase. Unlike the Paleocene-Eocene Thermal
Maximum, discussed below, there was no absence of plankton in tropical regions.126
These episodes may not be relevant to our current geological era - the Cenozoic - whichstarted after the extinction of the dinosaurs. Extinction events prior to the Cenozoic killedmuch of life on Earth, and the ecosystems that survived are very different to those that124

Jessica E. Tierney et al., ‘Past Climates Inform Our Future’, Science 370, no. 6517 (6 November2020): fig. 1, https://doi.org/10.1126/science.aay3701.
125“Atmospheric CO2 concentrations generally follow these swings in global temperature (Fig. 1).
Geochemical modeling demonstrates that the balance of geological sources (degassing throughvolcanism) and sinks (weathering and sedimentation) explains the general features of CO2’s
trajectory(8) and establishes causality high CO2 leads to high temperatures. The apparent exceptions
to this rule, including the end-Cretaceous and early Paleocene (70 to 60 Ma) and the Miocene (23 to
5.3 Ma), are areas of active research. One explanation for the decoupling of CO2 and temperature isthat uncertainties associated with the proxies blur the relationship. Estimation of past CO2 ischallenging. Beyond the ice core record (9), CO2 information comes from geochemical data, such as
isotope ratios of boron and carbon, or paleobotanical indicators such as the density of leaf stomata.
All of these proxies require assumptions about the physical, chemical, and biological state of the pastthat are not completely understood, sometimes leading to misinterpretations of the signal (10). Proxy
methodologies and assumptions continue to be refined, and there is some indication that CO2 at theend of the Cretaceous may have been higher than that shown in Fig. 1 (11). It is also possible thatthese discrepancies have another explanation, such as a greater than expected role for non-CO2
forcings and feedbacks. If the paleoclimate record has taught us anything, it is that the more we
probe, the more we learn.” Jessica E. Tierney et al., ‘Past Climates Inform Our Future’, Science 370,
no. 6517 (6 November 2020), https://doi.org/10.1126/science.aay3701.
126“No similar temporal absence of eukaryotic plankton has been observed in tropical regions during
the extreme warmth of OAE2 (Oceanic Anoxic Event 2), across the Cenomanian-Turonian transition(~94 Ma), where comparable TEXH 86 temperature estimates were obtained (51).” Joost Frieling etal., ‘Extreme Warmth and Heat-Stressed Plankton in the Tropics during the Paleocene-Eocene
Thermal Maximum’, Science Advances 3, no. 3 (1 March 2017): e1600891,
https://doi.org/10.1126/sciadv.1600891.

103

preceded them.127 It is therefore instructive to consider warming events that happened during
the Cenozoic.

The Paleocene-Eocene Thermal Maximum and Eocene Climatic Optimum
PeriodWhen?

Temp vspre-industrial

Eocene Climatic
Optimum

50m years
ago

14°C

Paleocene-Eocene
Thermal Maximum

56m years
ago

17°C

Global warming
per century

CO2 ppm
1,800ppm

0.05C

2,300ppm

During the Paleocene-Eocene Thermal Maximum, temperatures increased by around 5°Cover the course of 3,000 to 20,000 years,128 This occurred on top of a very warmbackground: at the peak of the PETM, temperatures were around 17C warmer thanpre-industrial, with a 5% to 95% range 10°C–25°C. Equatorial sea surface temperatures
passed 36C,129 compared to around 28C today.130
There is disagreement about the size of the carbon release, but most sources agree it is wellin excess of 1.5 trillion tonnes of carbon in the form of CO2 and/or methane,131 with one
recent estimate putting emissions at around 10 trillion tonnes of carbon over 50,000 years.132

127

Thanks to Matthew Huber for raising this point.
“During the PETM (56 Ma) CO2 rapidly rose from about 900 ppm to about 2000 ppm (Anagnostou
et al., 2020; Gutjahr et al., 2017; Schubert & Jahren, 2013; Table 41 2.1) in 3–20 kyr (Gutjahr et al.,2017; Turner, 2018; Zeebe et al., 2016).” IPCC, Sixth Assessment Report: Working Group I The
Physical Science Basis, sec. 2.2.3.1. “A major new compilation of proxy temperature data (Hollis etal., 2019) analysed using multiple statistical approaches (Inglis et al., 2020) indicates that GMST was
10°C–25°C (90% range) warmer than 1850–1900, or about 5°C warmer relative to the pre-PETM
state. A related synthesis study also estimates that PETM warmed by 5°C (no uncertainty assigned;
Zhu et al., 2019). A recent benthic isotope compilation (Westerhold et al., 2020) transformed to GMST
based on the formulation by Hansen et al. (2013c) (Cross-Chapter Box 2.1, Figure 1), and adjusted to
49 1850-1900 by adding 0.36°C, shows an increase of GMST by about 10°C during the PETM.”
IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, sec. 2.3.1.1.1.
129“On the basis of planktonic foraminiferal Mg/Ca and oxygen isotope ratios and the molecular proxy
TEXH 86, latest Paleocene equatorial SSTs were ~33°C, and TEXH 86 indicates that SSTs rose to>36°C during the PETM.” Joost Frieling et al., ‘Extreme Warmth and Heat-Stressed Plankton in the
Tropics during the Paleocene-Eocene Thermal Maximum’, Science Advances 3, no. 3 (1 March2017): e1600891, https://doi.org/10.1126/sciadv.1600891.
130
Yadong Sun et al., ‘Lethally Hot Temperatures During the Early Triassic Greenhouse’, Science 338,
no. 6105 (19 October 2012): Fig. 2, https://doi.org/10.1126/science.1224126.
131“This pattern is best explained by massive (>>1500 Gt) carbon input from at least one but likely
multiple reservoirs in the shape of CO2 and/or CH4” Joost Frieling et al., ‘Tropical Atlantic Climate
and Ecosystem Regime Shifts during the Paleocene–Eocene Thermal Maximum’, Climate of the Past
14, no. 1 (15 January 2018): 39–55, https://doi.org/10.5194/cp-14-39-2018.
132“Using our preferred age model (R07sm; Extended Data Table 1a), we diagnose a cumulative
PETM carbon release that reaches about 10,200 Pg, with almost all emissions occurring in the first 50
kyr (Fig. 3d). Marcus Gutjahr et al., ‘Very Large Release of Mostly Volcanic Carbon during the
Palaeocene–Eocene Thermal Maximum’, Nature 548, no. 7669 (August 2017): 573–77,
https://doi.org/10.1038/nature23646.
128

104

The CO2 release was at least 4-5 times lower than current centennial rates of CO2 release.133
CO2 concentrations reached 2,300ppm, with a range of 1,400ppm to 3,150ppm.134
The Eocene Climatic Optimum was also very warm compared to today - global averagetemperatures were 14C warmer with a 5% to 95% range of 10ºC to 18ºC.135 Temperatures
remained at this level for about 6 million years. In this time, there were no substantial polar
ice sheets. CO2 concentrations were between 1,150ppm and 2,500ppm.136
Even though temperatures were so high during the early Eocene, and even though warming
was rapid on geological timescales, there was no mass extinction. In fact, the Eocene was
generally a time of ecological flourishing: the name “Eocene” comes from the Ancient Greek
ēṓs, ("dawn"), and kainós, ("new") and refers to the "dawn" of modern ('new') fauna thatappeared during the epoch.137
Willis and MacDonald (2011) note:“Despite evidence for large-scale biotic turnover, little evidence suggests large-scale
global plant extinction during this interval of enhanced warmth”138
There was also a sharp increase in ordinal diversity in this period.

133“Estimated multi-millennial rates of CO2 accumulation during this event range from 0.3–1.5 PgC
yr-1 42 (Gingerich, 2019); were at least 4-5 times 43 lower than current centennial rates (Section
5.3.1.1)” IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, sec. 2.2.3.1.
134
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Table 2.1.
135
“These include 7 estimates of 7°C–18°C (90% range; Inglis et al., 2020) and 12°C–18°C (95%range; Zhu et al., 2019) 8 warmer than 1850–1900, and 10°C–16°C warmer than 1995-2014 “recent
past” conditions (2 standard error 9 range; Caballero & Huber, 2013). Together, they indicate that
GMST was 10°C–18°C warmer during the 10 EECO compared with 1850–1900 (medium
confidence).” IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, sec.
2.3.1.1.1.
136“Based on boron and carbon isotope data, supported by other proxies (Hollis et al., 2019),
atmospheric CO2 during the EECO (50 Ma) was between 1150 and 2500 ppm (medium confidence),
and then gradually declined over the last 50 Myr at a long-term rate of about 16 ppm Myr-1 46 (Figure2.3).” IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, sec. 2.2.3.1.
137
Online Etymology Dictionary, ‘Eocene’
138
K. J. Willis and G. M. MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate
Change Predictions for a Warmer World’, Annual Review of Ecology, Evolution, and Systematics 42,
no. 1 (2011): 271, https://doi.org/10.1146/annurev-ecolsys-102209-144704.

105

Source: David E. Fastovsky and Peter M. Sheehan, ‘The Extinction of the Dinosaurs in North
America’, Gsa Today 15, no. 3 (2005): 4–10.

Although many new fauna appeared during the Eocene, many mammals evolved transientdwarfism in order to cope with the extreme heat. This was one instance of an observation
known as ‘Bergmann’s rule’, that smaller species tend to be found in warmer climates.139
There are a number of possible explanations for this. One is that smaller animals have larger
surface area per body weight in order to efficiently release heat.140 Another related factor isthat higher temperatures increase metabolic rate and oxygen use, and larger animals use
139

P. B. Wignall, The Worst of Times: How Life on Earth Survived Eighty Million Years of Extinctions(Princeton: Princeton University Press, 2015), 70.
140
Abigail R. D’Ambrosia et al., ‘Repetitive Mammalian Dwarfing during Ancient Greenhouse Warming
Events’, Science Advances 3, no. 3 (1 March 2017): e1601430,
https://doi.org/10.1126/sciadv.1601430.

106more oxygen.141 During the Paleocene-Eocene Thermal Maximum, many mammals,
including ancestors of modern horses, were around a third smaller in order to cope with theextreme heat.142
The greatest negative impact of the early Eocene occurred in the ocean due to ocean
acidification, thermal stress and ocean anoxia. Extinctions during the Paleocene-Eocene
Thermal Maximum were limited to the benthic foraminifera, single-celled organisms that liveon the seafloor.143 Around Nigeria, there was also a massive decline in the abundance anddiversity of dinoflagellates - a single-celled protist.144
Overall, in the PETM, temperatures were upwards of 17°C higher than pre-industrial levels
and the only species that went extinct that we know of was a single-celled marine organism,and on land it was a time of ecological flourishing, persistence and diversity. Some people
paint the PETM and early Eocene as a time of great ecological trauma, which in my view is
not supported by the evidence.145
As Wignall writes in The Worst of Times:“Part of the problem for PETM scientists who claim that there was a crisis is that they
are dealing with a spectacular climate event, in particular, a story of rapid warmingthat was probably driven by the release of greenhouse gases, and so an extinction is
anticipated (just as a pending mass extinction is predicted for modern greenhousewarming). And yet, the PETM crisis was only the faintest echo of the Pangean

141

P. B. Wignall, The Worst of Times: How Life on Earth Survived Eighty Million Years of Extinctions(Princeton: Princeton University Press, 2015), 70.
142
Abigail R. D’Ambrosia et al., ‘Repetitive Mammalian Dwarfing during Ancient Greenhouse Warming
Events’, Science Advances 3, no. 3 (1 March 2017): e1601430,
https://doi.org/10.1126/sciadv.1601430.
143“For years scientists considered the PETM to be the supreme example of the opposite extreme:
the fastest climate shift ever known, rivaling the gloomiest projections for the future. In that light, the
PETM’s outcomes did not seem so bad. Aside from the unlucky foraminifera in the deep sea, allanimals and plants apparently survived the heat wave—even if they had to make some serious
adaptations to do so. Some organisms shrank. In particular, mammals of the PETM are smaller thanboth their predecessors and descendants. They evolved this way presumably because smaller bodies
are better at dissipating heat than larger ones. Burrowing insects and worms, too, dwarfed.” Lee R.
Kump, ‘The Last Great Global Warming’, Scientific American 305, no. 1 (2011): 56–61.
144“We attribute a massive drop in dinoflagellate abundance and diversity at peak warmth to thermal
stress, showing that the base of tropical food webs is vulnerable to rapid warming.” Joost Frieling etal., ‘Extreme Warmth and Heat-Stressed Plankton in the Tropics during the Paleocene-Eocene
Thermal Maximum’, Science Advances 3, no. 3 (1 March 2017): e1600891,
https://doi.org/10.1126/sciadv.1600891.
145
See for example Mark Lynas, Our Final Warning: Six Degrees of Climate Emergency (London: 4th
Estate, 2020), 226ff. Steffen et al (2018) say"For example, the Paleocene-Eocene Thermal Maximum (PETM) at 56 Ma BP (before present), a
warming that reached 5-6°C and lasted about 100,000 years, accompanied by a rise in sea level andocean acidification, drove the extinction of 35-50% of the deep marine benthic foraminifera and led tocontinent-scale changes in the distributions of terrestrial plants and animals". Will Steffen et al.,
‘Trajectories of the Earth System in the Anthropocene’, Proceedings of the National Academy of
Sciences 115, no. 33 (14 August 2018): SI p. 2, https://doi.org/10.1073/pnas.1810141115.

107extinctions… What actually happened at this time is much more subtle andnuanced.”146
However, there are at least two key disanalogies between the early Eocene and potential
anthropogenic warming. Firstly, the rate of anthropogenic warming could be much fasterthan warming in the PETM. Warming of 5°C over 10,000 years is 0.05°C per century. This isvery fast in geological terms, but is still two orders of magnitude slower than what we would
be in for on RCP8.5, which is warming of around 4°C per century, or RCP4.5, which isaround 2°C per century.
The rate of warming is likely a key determinant of the impact on ecosystems because it canaffect migratory and evolutionary responses. When the climate warms, flora and fauna haveto migrate to remain in their ecological niche. If the niche moves too fast due to rapid
warming, then species will go extinct. Warming may also be too fast for species to adapt
evolutionarily to higher temperatures. It would therefore be useful to explore cases in whichpast warming was as fast as future warming. One example of this is warming during thetransition into the Holocene, which I will discuss below.
As Willis and MacDonald (2011) note, the second disanalogy is that
“The ecological niche apparent for many species during the Eocene may not be
representative of modern-day flora. Over the past 55 million years numerous lineage
splits have occurred, and these may have resulted in a loss of genetic resilience.
Recent genetic work indicates that many modern species have appeared since thebeginning of the Miocene [23 million years ago]”147
Thus, it would be useful to explore how ecosystems coped in more recent periods.

Miocene Climatic Optimum
PeriodWhen?

Temp vspre-industrial

Miocene Climatic
Optimum

16 million years
ago

7°C

Global warming
per century

CO2 ppm
500ppmThe Miocene epoch spanned from around 23 million to 5 million years ago. In the Miocene
Climatic Optimum around 16 million years ago, temperatures were 6°C-7°C above
pre-industrial levels, with the northern high-latitudes and northern temperate zones
potentially 14°C and 9°C warmer.148 CO2 concentrations were 400-600ppm,149 compared to146

P. B. Wignall, The Worst of Times: How Life on Earth Survived Eighty Million Years of Extinctions(Princeton: Princeton University Press, 2015), p. 163
147
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 274.
148
“Estimates for the MCO propose global mean annual temperatures 5°C–6°C warmer thanpresent-day with the northern high-latitudes and northern temperate zones potentially 14°C and 9°C
warmer” M. Steinthorsdottir et al., ‘The Miocene: The Future of the Past’, Paleoceanography andPaleoclimatology 36, no. 4 (2021): 26, https://doi.org/10.1029/2020PA004037.
149“With higher temperatures and moderately higher pCO2 (∼400–600 ppm), the MCO has beensuggested as a particularly appropriate analog for future climate scenarios, and for assessing the108

business as usual CO2 concentrations of around 550ppm. There were no ice sheets in thenorthern Hemisphere150 and some models suggest that the Antarctic came close to being
completely ice-free.151
Marine biota generally thrived throughout the Miocene. Steinthorsdottir et al (2021) note that:“There were no major mass extinctions or great reorganizations and diversity among
marine vertebrates appears to have been relatively high in the mid- to late-Miocene.
Calcifiers such as coccolithophores, foraminifera, mollusks, and echinoderms
generally thrived although a marked increase in the Mg/Ca and episodicallyincreasing pH associated with atmospheric pCO2 reduction may have graduallyfavored aragonite producers. The extended warm phase of the MCO coincided with ageographical expansion of “tropical” biota such as warm-adapted plankton andscleractinian reefs while closure of the Tethys Seaway resulted in shifts in global
biodiversity hotspots.”152
The fate of terrestrial fauna in the Miocene is particularly interesting because the epoch “hasbeen heralded as marking the origins of “modern” terrestrial biomes as well as many of theworld's biodiversity hotspots”.153 While there was significant ecological change during the
Miocene Climatic Optimum,154 there was no major extinction event and there is little
evidence of ecological trauma, as shown by the chart below:

predictive accuracy of numerical climate models—the same models that are used to simulate futureclimate.” Steinthorsdottir et al., ‘The Miocene’, 1.
150“Continental-sized ice sheets were only present on Antarctica, but not in the northern hemisphere.”
Steinthorsdottir et al., ‘The Miocene’, 1.
151“These model-based estimates suggest a larger change in past Antarctic ice volume, with aminimum in ice volume close to complete Antarctic deglaciation during the MCO.” Steinthorsdottir etal., ‘The Miocene’, 33.
152
Steinthorsdottir et al., 15.
153
Steinthorsdottir et al., 15.
154“In this section, we review the palaeobotanical record, ecosystem history, and terrestrial faunas.
These records show that Miocene floras underwent the most dramatic changes of the Cenozoic, in apattern dominated by contraction of forest biomes and replacement by grasslands, a transition that
may have started already by the late Oligocene (Strömberg, 2005, 2011; Figure 4). This occurred both
latitudinally and within continental interiors, reflecting the overall cooling and drying of the Earth.
Although temporarily reversed during the MCO, vegetation changes were paralleled by diversificationand functional evolution of mammals, resulting in our familiar flora and fauna.” Steinthorsdottir et al.,
15.

109

Source: M. Steinthorsdottir et al., ‘The Miocene: The Future of the Past’, Paleoceanography andPaleoclimatology 36, no. 4 (2021): Fig. 1, https://doi.org/10.1029/2020PA004037.

Mid-Pliocene Warm Period
PeriodWhen?

Temp vspre-industrial

Mid-Pliocene
Warm Period

3m years ago

3°C

Global warming
per century

Regional warming
per century

CO2 ppm
390ppm

In the Mid-Pliocene Warm Period around 3 million years ago, temperatures were
2.5°C–4.0°C warmer than pre-industrial levels.155 CO2 concentrations were around390ppm.156 Temperatures were much higher at the poles:“Similar to current and predicted future climate change, the greatest warming
appears to have occurred in the high latitudes, as mean annual temperatures weremore than 10°C higher than present. Summer temperatures in the Beardmore regionof Antarctica, for example, are estimated to have been up to 16ºC warmer thanpresent.”157

155“Therefore, GMST during the MPWP is 22 estimated to have been 2.5°C–4.0°C warmer than
1850–1900 (medium confidence)” IPCC, Sixth Assessment Report: Working Group I The PhysicalScience Basis, sec. 2.3.1.1.1.
156
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Table 2.1.
157
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 274.

110

The biotic response to these temperatures is similar to the response in the early Eocene.
The overwhelming response was one of global range shifts.158 Willis and MacDonald
comment:“In terms of overall diversity, during this warm interval no evidence is apparent for
local, regional, or global plant extinctions. Rather, where studied, evidence supports
an increase in diversity. For example, on the basis of pollen-type richness, anincrease in overall rainforest diversity is apparent in southeast Asia, west Africa (e.g.,Morley 2000, 2007), and several sites in South America (van der Hammen &Hooghiemstra 2000, 2003). In some regions, evidence from the pollen records
suggests diversity considerably higher than the present day.”159
This is one piece of evidence that ecosystems closer to the present day are robust totemperatures that are 3°C higher than pre-industrial levels.
Again, there are disanalogies to the future warming. Firstly, global warming during themid-Pliocene was much slower than potential future warming. Secondly, the spatial andtemporal resolution of the Pliocene records are unable to tell us whether there wereextinctions of endemic species. However, they can be addressed by examining the Last
Interglacial and our current interglacial, the Holocene.

The Last Interglacial
PeriodWhen?

Temp vspre-industrial

Last interglacial

127k years ago

1°C

Global warming
per century

Regional warming
per century

CO2 ppm
280ppm

In the warmest millennium of the last interglacial, known as the Eemian, temperatures were
0.5 to 1.5°C above pre-industrial levels.160 In high latitude regions including Greenland andAntarctica, temperatures were up to 5°C higher.161 CO2 concentrations were around
280ppm162 - similar to pre-industrial levels.
Willis and MacDonald (2011) comment:“The generally high biodiversity and remarkable resilience of all vegetation types
during the Eemian are important. As far as it is possible to ascertain from the plant

158

Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 274.
159
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’.
160“In summary, GMST during the warmest millennia of the 38 LIG (within the interval of around
129–125 ka) is estimated to have reached 0.5°C–1.5°C higher values than 39 the 1850–1990
reference period (medium confidence” IPCC, Sixth Assessment Report: Working Group I The
Physical Science Basis, sec. 2.3.1.1.1.
161
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 276.
162
IPCC, Sixth Assessment Report: Working Group I The Physical Science Basis, Table 2.1.

111

fossil record, nothing went extinct despite these significantly higher temperatures
and, for some species, greatly reduced distributional ranges.”163
For all this, again, a key disanalogy may be that the rate of warming is especially importantfor ecosystem damage. We can get to grips with that issue by exploring the rapid warmingthat occurred in the transition into the Holocene.

Transition into the Holocene from last glacial
PeriodWhen?

Transition fromlast ice age to
Holocene (willis
and macdonald,
steffensen, alley)

20,000 to 6,000
years ago

Temp vspre-industrial

Rate ofglobalwarming
per century

Rate ofregional
warming
percentury

CO2 ppm

2-15°C

Triggered by increasing summer insolation in the high latitudes of the Northern Hemisphere,
the last transition between glacial and interglacial conditions commenced following the Last
Glacial Maximum (20,000 years ago) of the Pleistocene and extended into the middle
Holocene up to 6,000 years ago. Owing to declining summer insolation in the Northern
Hemisphere in many but not all regions, a general cooling has occurred since around 5,000
years ago.164 CO2 concentrations rose from 180ppm to 280ppm.165
Willis and MacDonald argue that the transition into the Holocene is particularly relevant tofuture warming for several reasons.166
1. Similar to current warming, the glacial-interglacial transition represents a global
climatic change that had repercussions from the tropics to the high latitudes.
2. The difference in global average annual temperature between glacial and interglacialconditions of 3.5ºC to 5.2°C is of similar magnitude to anticipated twenty-first century
warming.
3. As is the case for anticipated future warming, climatic conditions developed during
the Holocene warming that had no analog during the last glacial maximum.
4. Most importantly, regional rates of warming in this period are comparable to projected
future regional rates of warming.

163

Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 276-277.
164
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 278.
165
“The amount of CO2 in the atmosphere increased from approximately 180 ppmv during the glacial
to 280 ppmv during the interglacial, and this would have resulted in changes in photosynthetic activity
and plant stomatal density that are expected to influence functioning, such as moisture use efficiency(Bennett & Willis 2000).” Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevanceto Climate Change Predictions for a Warmer World’, p. 279.
166
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 278-279.

112

a. In Greenland temperatures may have risen by 10°C in the space of a few
years, 14,700 years ago, though this may be an error in climate proxies.167
b. In the Swiss Alps and other parts of Europe, a warming of 2 to 5°C appears tohave occurred in 200 years or less.
c. In the Sierra Nevada of California, rates of warming in the late glacial may
have been 4 to 5°C every 500 years around 15,000 years ago.
d. At the higher latitudes of the Northern Hemisphere, there were increases of
5°C and more over a few decades (11,700 years ago).
e. Data from Greenland ice cores suggest that a >10◦C warming may haveoccurred over 20 to 60 years (13,000 to 11,000 years ago).
f. In California, warming at the close of the Younger Dryas (11,300 years ago)
may have been on the order of 3°C in less than 100 to 200 years.
g. For the entire Southwest US, a general warming of 4°C may have occurred inless than a century (13,000 to 11,000 years ago).
During this transition, there is little evidence of plant extinctions. The predominant response
was one of ecological turnover and range adjustment.168
The Quaternary Megafaunal Extinction
Although no plants were lost during this period, there was an incredible loss of large landmammals, as part of the Quaternary Megafauna Extinction. Megafauna are classed as an
animal weighing more than 44kg.169 In North America more than 30 genera of large
mammals including horses, camels, mammoths, and mastodons were lost. In South America
100% of mammals weighing >1,000 kg and 80% of mammals weighing >44 kg went extinct.
In Australia, only 2 of 16 megafauna species survived.170
Some of the North American megafauna - the woolly rhino, the hornless rhino, the giant
ground sloth and the bear dog - are shown below:

167

Dan Lunt, personal communication, 9th May 2022.
Willis and MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World’, p. 279.
169
“But the extinctions at the end of the Pleistocene are uniquely different because they unfolded
almost instantly on an evolutionary timescale and had a disproportionate bias for megafauna, a term
once applied to any animal larger than a rabbit and now meaning animals with average adult bodymass 44 or 45 kg (100 lbs).” G. Haynes, ‘The Evidence for Human Agency in the Late Pleistocene
Megafaunal Extinctions’, The Encyclopedia of the Anthropocene 1 (2018): 219–26.
170
See the Our World in Data page on megafauna for a review.
168

113

The cause of the Quaternary Megafauna Extinction is the subject of heated scholarly
disagreement, with one camp arguing that human hunting killed the megafauna (aka the‘overkill hypothesis’) and the other camp arguing that climate change was the culprit. I haveread a lot of the literature on this topic and spoken with experts in the field and my credence
in the overkill hypothesis is >90%.
There are several reasons to think that humans were the primary cause. Firstly, extinction
timings on different continents closely match human arrival. At around the time that humans
spread to different continents, megafauna went extinct.

114

Proponents of overkill argue that the ‘last appearance date’ of a megafauna species fossil do
not always overlap with the ‘first appearance date’ of a human fossil in an area. But this iswell-explained by the patchiness of the fossil record. The last fossil we find is almost
certainly not the last instance of a species and the first human fossil we find is almost
certainly not the first instance of a human in an area. This is known as the ‘Signor-Lipps
Effect’. One illustration of this is shown by how data presented by one leading proponent of
the climate change hypothesis, David Meltzer, have changed over time.
●●●

In 1993, Meltzer noted that only 7 of the 35 extinct genera lasted until the arrival of
the Clovis hunters in North America.
In 2003, Grayson and Meltzer made the same argument, this time noting that 15
genera lasted until Clovis arrival.171
In 2020, Meltzer noted that only 18 genera lasted until Clovis arrival.172

171“In 1993, Meltzer (1993, p. 306) noted that ‘‘in recent years studies of the radiocarbon chronology
have shown that of the 35 species [sic] that went extinct lasted up until Clovis times.’’ A decade later
Grayson and Meltzer (2003) acknowledged that 15 of 35 extinct genera lasted until Clovis times, morethan twice as many as noted before. As I write now in 2006, the current count is 17 (Stafford et al.,2005).” Gary Haynes, ‘A Review of Some Attacks on the Overkill Hypothesis, with Special Attention to
Misrepresentations and Doubletalk’, Quaternary International, World of Elephants 2, 169–170 (1 July2007): 89, https://doi.org/10.1016/j.quaint.2006.07.002.
172“That so few of the 38 genera appear to have been hunted may be because, so far at least, only 18of them are known to have even survived up to the time Clovis people arrived in the Americas (12).”David J. Meltzer, ‘Overkill, Glacial History, and the Extinction of North America’s Ice Age Megafauna’,115This illustrates that the more archeology and paleontology we do, the more fossils we willfind and the more that megafauna and human appearance dates will overlap.
Secondly, the Quaternary extinctions were extremely size-selective: the very largest animals,
like mammoths and giant sloths, were preferentially killed off. The extent of the skew
towards large animals is completely unprecedented in the Cenozoic.

Source: Felisa A. Smith et al., ‘Body Size Downgrading of Mammals over the Late Quaternary’,
Science 360, no. 6386 (20 April 2018): 310–13, https://doi.org/10.1126/science.aao5987.

This size selectivity is easily explained by the overkill hypothesis: larger animals offer greater
rewards to human hunters. In contrast, the size selectivity is difficult to explain on the climatechange hypothesis. As Wignall notes:“The selective loss of only large animals (and those with low reproductive rates) isalso not well-explained by climate change models. Under the normal ‘rules’ of
extinction, highest losses generally occur among species with a relatively limited
habitat range, but the Pleistocene extinctions were fundamentally different. Many ofthe megafaunal species inhabited a vast geographic extent: the woolly mammoth andProceedings of the National Academy of Sciences 117, no. 46 (17 November 2020): 28555–63,
https://doi.org/10.1073/pnas.2015032117.

116

woolly rhino ranged across the whole of Eurasia and North America. Climate-driven
extinction models invoke habitat change, such as the loss of tundra to advancing
forests, to explain mammoth extinctions. These arguments do not account for thecontinuous presence of extensive tracts of all Pleistocene habitats up to the presentday. In contrast, the extinctions can simply be ascribed to the observation thathumans tend to hunt easy to find big animals.”173
Proponents of the climate change hypothesis respond to this by arguing that some smaller
animals also went extinct in the Pleistocene-Holocene transition.174 I don’t find this acompelling response to the size selectivity argument. The size selectivity argument does not
require that no small animals went extinct, only that extinction rates among larger animals
were hugely disproportionate.
Thirdly, the vast majority of the megafauna evolved more than a million years ago and sowould have had to live through more than a dozen glacial-interglacial transitions without
going extinct.175 Here is one reconstruction of temperature over the last 5 million years fromHansen et al (2013):

173

Paul B. Wignall, ‘6. What Happened to the Ice Age Megafauna?’, in Extinction: A Very ShortIntroduction, by Paul B. Wignall (Oxford University Press, 2019), 107-108,
https://doi.org/10.1093/actrade/9780198807285.003.0006.
174“Yet, it is important to see this episode in its broader context, for large mammal losses were notthe only significant changes that took place on the Late Pleistocene landscape. Some 20 generaof birds, multiple genera of reptiles, and even a spruce tree, Picea critchfieldii, also went extinct at
the end of the Pleistocene (Faith 2014a, Grayson 2007, Jackson & Weng 1999). Nor did the ninelarge mammal survivors emerge unscathed: Several, such as bighorn sheep and elk, decreased insize through the Late Pleistocene; a new species of bison arose; and there was substantial loss ofgenetic diversity, much of which began well before the first appearance of humans and testifies
to strong selective pressures in the environment (Boulanger & Lyman 2014, Hofreiter & Barnes
2010, Orlando & Cooper 2014).” David J. Meltzer, ‘Pleistocene Overkill and North American
Mammalian Extinctions’, Annual Review of Anthropology 44 (2015): 33–53.
175“Regarding the former (Table 2, rows), of the 38 genera that went extinct at the end of the
Pleistocene, half of the genera (n = 19) were present throughout the entire Pleistocene, and survived
multiple, previous glacial− interglacial cycles. As for the other 19 genera, six only appeared after the
onset of the Rancholabrean. This puts them in North America during the MIS 7 interglacial or (using
the younger age for bison arrival) during the MIS 6 glacial period. Either way, these taxa experienced
just one significant glacial−interglacial cycle prior to the terminal Pleistocene. The remaining 13
genera would have had to survive at least a dozen glacial−interglacial cycles, depending on when
they were first on the Irvingtonian landscape. Thus, all 38 genera experienced at least one
glacial−interglacial cycle, and all survived the higher-amplitude cycles of the last 800,000 y.” Meltzer,‘Overkill, Glacial History, and the Extinction of North America’s Ice Age Megafauna’.

117Source: James Hansen et al., ‘Climate Sensitivity, Sea Level and Atmospheric Carbon Dioxide’, Phil.
Trans. R. Soc. A 371, no. 2001 (28 October 2013): 20120294, https://doi.org/10.1098/rsta.2012.0294.

As this shows, there are many comparably dramatic climatic transitions that did not cause
huge species extinctions. It is only once humans made it to different continents, in thetransition to the Holocene, that the megafauna started dying off. The difference between the
Pleistocene-Holocene transition and the other climate transitions just does not seem large
enough to cause such a discontinuous change in species extinctions.
Moreover, on the climate change hypothesis, very different kinds of climate change caused
megafaunal extinctions on different continents in very different ecosystems: in some cases
warming, in some cooling, in some cases aridification, in some increasing wetness. As
Haynes notes:“The species that died out in parts of [North America] that became climatically drier
were the same as in the parts that became wetter; how can such opposite effects ofclimate changes kill every member of the same species?”176
176

Haynes, ‘A Review of Some Attacks on the Overkill Hypothesis, with Special Attention to
Misrepresentations and Doubletalk’, 84. See also “No climatic event in North America at the end of
the Pleistocene could have had such a rapid and almost simultaneous effect on so many genera withextremely variable diets (omnivory, carnivory, grazing, browsing, and mixed feeding) and living in somany different ecozones (e.g., cold northern landscapes, warmer southern regions, open grasslands,
temperate forests, and semiarid environments). In fact, Late Pleistocene changes in vegetation andhydrology actually may have improved some habitats for certain now extinct megafaunal genera, such
as Mammuthus columbi (Columbian mammoth) in temperate regions of North America, where
grasslands spread as forests retreated. Late Pleistocene changes in plant communities provided
adequate food for other extinct genera, such as the mixed feeder Nothrotheriops (Shasta groundsloth) in western North America, which successfully shifted its diet in response to different plant
availability under warming temperatures, yet became extinct around the time of the first humanpresence. Other extinct generalist feeders in North America such as Camelops also should have been118

It is difficult to see why these very different climatic changes happening in very different
ecosystems across the world would have preferentially killed off megafauna.
One way that I think proponents of the climate change hypothesis go wrong is by focusing
on extinctions on particular continents one at a time. For example, they would try to answer
the question ‘what caused the extinction of the megafauna in North America?’ and effectively
exclude evidence from other continents as irrelevant to this question.177 But the evidence
from other continents is relevant: we need to assess the conjunction of extinctions on
multiple different continents around the time of human arrival, not each continental extinction
taken individually. It is much more likely to be true that extinctions happened on multiple
continents around the time of human arrival, conditional on the overkill hypothesis beingtrue. So, taking each continent individually creates unfair bias against the overkill hypothesis.
Consider this analogy. The British serial killer Doctor Harold Shipman killed 250 of his elderly
patients, mainly old women. When assessing the cause of death of each of these people,
one approach would be to investigate the cause of each death in isolation and so assume
that the other 249 deaths were irrelevant. If we were to do this, it would probably be rational
to conclude that each of Shipman’s victims died of old age: this is by far the most likely
cause of death of old age pensioners as a whole and serial killers are extremely rare.
However, the deaths of Shipman’s other patients is relevant to a causal explanation of eachdeath: the sheer number of deaths is much more likely conditional on the hypothesis that
Shipman killed the elderly people. Ignoring the other 249 deaths ignores obviously relevant
evidence. In the same way, ignoring the extinctions in South America, Europe, Asia andAustralia ignores relevant evidence to the cause of the extinctions in North America. On the
hypothesis that humanity was a global serial killer, we are much more likely to see
extinctions happen on multiple continents at around the time of human arrival.
Fourthly, megafauna emerged relatively unscathed in Africa compared to other continents.

able to shift diets to accommodate available vegetation. The genus Loxodonta in Africa survived
dramatic dietary shifts without becoming extinct, changing from predominantly grazing in the earlier
Pleistocene to predominantly browsing in the Late Pleistocene and Holocene when grasslands werereduced by climate changes, demonstrating that very large animals do not necessarily become extinct
when forced to shift their diets.” G. Haynes, ‘The Evidence for Human Agency in the Late Pleistocene
Megafaunal Extinctions’, The Encyclopedia of the Anthropocene 1 (2018): 219–26.
177
See for example “Although overkill has been globally applied, my focus is on Pleistocene North
America south of the ice sheets” David J. Meltzer, ‘Pleistocene Overkill and North American
Mammalian Extinctions’, Annual Review of Anthropology 44 (2015): 33–53.

119

This is much easier to explain on the overkill hypothesis than the climate change hypothesis.
Hominids evolved with megafauna in Africa, giving megafauna time to adapt to hominid
hunting pressure. In other continents, this was not true and megafauna were easy prey.
It is difficult to see what the explanation for this would be on the climate change hypothesis.
Furthermore, African megafauna would also have had to live through dramatic climatechanges in the end-Pleistocene and yet stood a much better chance of survival.
The main response of proponents of the climate change hypothesis is to argue that there isinsufficient evidence for overkill: there are too few kill sites with clear evidence of humanhunting to justify the overkill hypothesis. For example, as of 2008, there were ‘only’ 14
documented Clovis megafauna kill sites in North America. This seems like a small number
given that the Clovis would have had to have killed hundreds of millions of individuals overthe course of a few thousand years.178
I don’t find this argument persuasive. The question we need to ask is: given the patchinessof the fossil record, how many kill sites should we expect to find? For example, one recent
study of North American megafauna found only around 500 megafauna fossils out of a
possible population in the hundreds of millions. It is, therefore, not surprising that there are
only a handful of fossils with clear evidence of spear wounds and butchery. Surovell andWaguespack (2008) argue that the number of discovered kill sites is actually much higher
than we should expect:
178“It is estimated that when Clovis hunters arrived there were hundreds of millions of these large
mammals on the landscape (1). Even so, there are only 16 occurrences in which humans killed or
scavenged one of these animals” Meltzer, ‘Overkill, Glacial History, and the Extinction of North
America’s Ice Age Megafauna’.

120

“The United States contains more megamammal killsites than there are elephantkillsites in all of Africa—a land mass that is much larger than the United States. Not
only is Africa much larger, but its hominin presence extends back at least 100 times
the chonostratigraphic span of the human presence in North America. Yet there arefewer than a dozen probable killsites, spanning a time range from Plio-Pleistocene to
mid-Holocene”“Obviously, given our arguments above, we should not be arguing that there are ‘‘sofew’’ or ‘‘only about twelve’’ mammoth kills, but instead asking why there are so
many? In over 1 million years of archeology spread over four continents, we haveattempted to demonstrate that there is likely nothing that has yet to be documented
archeologically that compares to Clovis in terms of the frequency of Proboscidean
exploitation, with the single possible exception of the Lower Paleolithic of Iberia.
Certainly, 14 sites do not seem like a very large number, but when viewed in acomparative context, it is in fact a very large number. Furthermore, the number ofelephant kill sites in Clovis is truly remarkable when we consider the total number ofelephant kills documented from four continents. Of the 21 sites we have identified in
the Old World, only two or three have weaponry associated with carcasses. The twobest cases are Lehringen and Lugovskoye, and the third case, Grobern, isquestionable. Therefore, in the entire archeological records of Africa, Europe, Asia,
and North America, there are a total of 16 strong cases for hunting of elephants, and
14 of these are found in Clovis. Furthermore, between of 28% and 46.7% of
excavated Clovis sites that have preserved fauna are mammoth or mastodon killsites. Why?”179
Proponents of the climate change hypothesis follow one particular approach to science
which puts a lot of weight on direct evidence of killing, and puts little weight on what might bedeemed ‘circumstantial evidence’, which I have discussed above. Critics of the overkill
hypothesis treat the question of how the megafauna died as something like a criminal trial: if
you are going to claim that someone killed something, you need to actually find the fossils
with spear marks. But this is not how we should form beliefs. Due to the patchiness of thefossil record, we should expect not to find much direct evidence of killing. Furthermore the‘circumstantial evidence’ is overwhelming. From a Bayesian point of view, there is no hard
distinction between the direct evidence of killing and the ‘circumstantial evidence’ I havediscussed above. Circumstantial evidence can sometimes be very strong.
Another counter-argument presented by proponents of the climate change hypothesis is thathumans could not possibly have killed off enough megafauna to cause extinctions. In fact,
modelling evidence shows that for large, slow-to-reproduce animals, it is only necessary tokill a small percentage of the population for a species to decline rapidly.180
179

Todd A. Surovell and Nicole M. Waguespack, ‘How Many Elephant Kills Are 14?: Clovis Mammoth
and Mastodon Kills in Context’, Quaternary International 191, no. 1 (2008): 82–97.
180“Quantitative predator-prey models have proven useful in studying the extinction of particularspecies, such as Eurasian mammoths, moas in New Zealand, or megafauna in northern Australia(Supplemental Table 4). The most comprehensive model coupled human and prey population
dynamics to simulate predation on 41 large species and an undifferentiated secondary resource(plants, small game) in North America (Alroy 2001b). Hunting efficiency, the geography of invasion,and competitive interactions were varied, and all simulations assumed that hunters nonselectively121

In my view, the evidence that humans were the primary driver of the Quaternary Megafaunal
Extinction is very strong. Nevertheless, scholars on the topic seem to be roughly evenly
divided and there are still many proponents of the climate change hypothesis. If so, one
might argue that the epistemically modest thing to do would be to be agnostic and to split
one’s credence 50/50 between the competing hypotheses. This is an interesting test case of
epistemic modesty and, I think, illustrates where the theory goes wrong. In advance ofinvestigating the arguments on a topic, the rational thing to do might be to pick a set ofexperts on it and then defer to them, and one sensible set of experts to choose might be‘academics at top universities who have spent >3 years working on the question’.
However, this may not be the right way to decide what to believe after assessing theobject-level arguments because the object-level arguments can be a good reason to give theviews of some groups of experts more weight than others. While this seems epistemicallyimmodest, as far as I know, no-one denies that it is appropriate in some cases in somecases to put less weight on the view of someone you previously believed to be an expert
based on the quality of their arguments, for instance if they appeal to something widely
known to be false.
One response to this is: what makes me better placed to judge than the proponents of theclimate change hypothesis? These experts have published academic papers and books on
this topic, whereas I have spent around two working weeks on it and discussed it witharound ten experts. The answer to this is twofold. The first part appeals to the object-level
arguments that I have outlined above. We at least need some strong object-level reasons if
we are going to go against academic experts on a topic. I think that in this case, we do havesome strong object-level reasons. But then, a natural response is: ‘surely these experts areaware of the evidence outlined above, isn’t it more likely that you have misunderstoodsomething than that they are not aware of these arguments?’.
This brings me to the second part of the answer: we have an error theory for why the critics
of the overkill hypothesis go wrong. The error theory is that they do not update on‘circumstantial’ evidence in a Bayesian way. Scholars do not have to take a Bayesian
approach to get published in academic journals, and many scholars are not Bayesian.
Although I am not an expert in megafaunal extinctions, I do have a background in
epistemology, and believe I have some justification in thinking that a Bayesian approach isthe correct one. If so, there is a good explanation of why many experts on the topic ofmegafaunal extinctions will systematically err.
took prey as encountered. Overkill occurred for a range of parameter values, although an error in theparameterization of prey r values makes it difficult to assess these results (Slaughter & Skulan 2001).
In a recalculation of the best-fit trial with appropriate r values, the model correctly predicted the fate of
34 out of 41 species, with a median time to extinction of 895 years (Alroy 2001a). In that trial huntersobtained 30% of their calories from large mammals and occurred at densities of 28 people per 100
km2, both within the range of values for modern hunter-gatherers. As in more generalized
optimal-foraging models, the key to overkill was a relatively high human population density subsidized
by smaller, faster-breeding prey. Hunting ability matters too, with greater hunting success leading togreater extinction rates, but overkill occurred even when success rates were fairly low.” Anthony D.
Barnosky and Emily L. Lindsey, ‘Timing of Quaternary Megafaunal Extinction in South America in
Relation to Human Arrival and Climate Change’, Quaternary International, Faunal Dynamics andExtinction in the Quaternary: Studies in Honor of Ernest L. Lundelius, Jr., 217, no. 1 (15 April 2010):
10–29, https://doi.org/10.1016/j.quaint.2009.11.017.

122

The neglect of circumstantial evidence is especially important in this case because thecircumstantial evidence in favour of the overkill hypothesis is extremely strong. To recap:●●●

Around the time that humans arrived on multiple different continents, megafauna
went extinct. This is far more likely on the overkill hypothesis than the climate change
hypothesis.
The extinctions were extremely size selective. This is far more likely on the overkill
hypothesis than the climate change hypothesis: big animals are more tempting
targets for hunters, but the size selectivity is nearly impossible to explain on the
climate change theory.
The climate change experienced at the end of the Pleistocene just were not different
enough to preceding episodes of climate change to explain why there were suchcatastrophic losses of megafauna in the end-Pleistocene compared to earlier
periods. This is far more likely on the overkill hypothesis than the climate change
hypothesis.

Returning to our main topic, if natural climate change did not kill off the ice age megafauna,then this is further evidence that future anthropogenic climate change would not make theworld radically inhospitable to humans.

3.4. Hominid flourishing and climate change
Technically, adult humans are themselves megafauna. But humans survived and flourishedduring the end-Pleistocene. Indeed, homo sapiens and our hominid ancestors have had tolive through several periods of dramatic climate change. Consider again the chart fromHansen et al (2013) showing the climate since the Pliocene (5.3 million years ago).

Homo sapiens evolved 300,000 years ago, and have survived through dramatic swings fromglacial periods to interglacials. We survived when temperatures were 5°C lower and when123

temperatures were 1°C higher. We would have had to live through the rapid regional
warming in the transition from the Pleistocene into the Holocene.
Hominids evolved 6 million years ago and would have survived when temperatures were
2.5°C to 4°C warmer than pre-industrial. Indeed, at this time, the hominids would all havebeen in Africa, so would have had to survive temperatures that were probably 15°C warmer
than modern day Britain.181
In many ways, our hominid ancestors were in a much more fragile situation than our own.
They were much less numerous, much less technologically advanced and much lesscapable of a rational response to problems than people today. Thus, the survival ofpre-modern hominids should provide some comfort that we will make it through 4°C ofwarming.
One key disanalogy between modern humans and our hominid ancestors is that we arereliant on agriculture, whereas they were hunter gatherers. I discuss the risks that climatechange poses to agriculture in Chapter 5.

3.5. Why did things change after Pangea?
There is, then, discrepancy in the correlation between warming and species extinctions.
Before the break-up of the Pangea supercontinent, there was a correlation, but after the
break-up of Pangea, there was not. Why was this? All of the volcanic kill mechanisms I
discussed above would have been in play. Why were these periods different and what does
this mean for the future habitability of the planet? The answer is unclear, but one explanation
points to the geology and ecology of Pangea, which made it poor at removing CO2 from theatmosphere.

3.5.1. Pangea
299 million years ago, our planet had an unfamiliar geography. Nearly all of the world’s
landmasses were united into a single giant continent known as ‘Pangea’ that stretched frompole to pole. Pangea was surrounded by a vast ocean, even larger than the present Pacific,
called Panthalassa. Pangea started to break apart 175 million years ago. Paul Wignall, aProfessor of Paleoenvironments at Leeds, has argued that Pangea was especiallyinhospitable to life, hence the title of his book ‘The Worst of Times’. Wignall argues that, as arule:
Massive volcanism + Pangea = major extinction182
But once Pangea is taken out of the equation, massive volcanism barely registers on thefossil record. For instance, 135 million years ago, an eruption went to form one of the largest
large igneous provinces - the Paraná-Etendeka Province. And yet, the eruption caused
neither catastrophic environmental change nor mass extinction.183 The massive eruption of181

Hominids first migrated out of Africa 1.8 million years ago.
Wignall, The Worst of Times, xvi
183
Wignall, The Worst of Times, 153ff.
182

124

the North Atlantic Igneous Province is one possible cause of the Paleocene-Eocene Thermal
Maximum,184 but as we saw above the PETM had mild ecological effects.
Wignall argues that Pangea was so inhospitable because various carbon cycle feedbacks
were not in play due to the unique geography and geology of the supercontinent. By theearly Cretaceous, the world was much more efficient at removing CO2, with the result that
atmospheric CO2 concentrations were a tenth of the Pangean level.185 There are several
reasons for this:186
●

●

●

●

Rainfall weathering - In a supercontinent, huge areas are too far away from the sea
to receive much rain, which reduces the scope for removal of CO2 by rainfall andweathering.
Limestone deposition - In Pangea, limestone deposition - which sequesters CO2 in
the oceans - was at a minimum because the shelf fringe of the supercontinent ismuch smaller than the shelf fringe of a collection of much smaller continents.
The evolution of coccolithophorids - Coccolithophorids appeared in the late
Triassic. They help to sequester carbon because they use CO2 in shell formation andthen sink to the bottom of the ocean when dead, which also helps to counteract
ocean acidification.
Terrestrial plants - The end Permian led to a mass extinction of terrestrial plants.
Without plants, the weathering feedback still occurs, but plants make it happen much
more rapidly. A world without plants is therefore much more prone to rapid climaticfluctuations.

Wignall provides the following diagram of the various processes of carbon sequestration:

184

Stephen M. Jones et al., ‘Large Igneous Province Thermogenic Greenhouse Gas Flux Could Have
Initiated Paleocene-Eocene Thermal Maximum Climate Change’, Nature Communications 10, no. 1 (5December 2019): 5547, https://doi.org/10.1038/s41467-019-12957-1.
185
Wignall, The Worst of Times, 168.
186
Wignall, The Worst of Times, Ch. 7.

125Source: Wignall, The Worst of Times

All of this suggests that the threat the ecosystem faced In the Pangean era was peculiar to
the geography and ecology of Pangea and has receded since the fracturing of thesupercontinent. Mercifully, our planet may now be more resilient than it once was.
Nonetheless, our understanding of past extinction events is highly imperfect, so it may wellbe that we do not yet properly understand some kill mechanisms that might be relevant tofuture warming.

1263.5.2. What about prior to Pangea?
The rate of huge extinction events prior to Pangea was also much higher from the Cambrian
up until the end of the Devonian. The Carboniferous, the period between the Devonian and
the Permian, was relatively peaceful. Wignall briefly notes that this may have been becauseof the lack of terrestrial vegetation: the first forests did not appear until toward the end of the
Devonian.187 I have not looked into this question in much depth.

3.6. Summarising lessons from the paleoclimate
There are no perfect paleoclimate analogues for potential future anthropogenic warming. As
far as we know, there has never been a time when temperatures increased by upwards of
4°C per century on top of a baseline similar to today with something close to our current
ecosystem.
But overall, I think the paleoclimate evidence provides some evidence that climate change
alone will not make the planet radically inhospitable to humans. There have been cases inwhich our modern ecosystems and continental configuration were broadly in place and:●●

Temperatures were much higher, though the rate of warming was much slower.
Warming was comparably fast on a regional basis, albeit from a lower baseline.

In these situations, climate change was not correlated with higher rates of extinctions, nor
did it lead to the extinction of hominids. Moreover, many of the previous major extinction
events were driven by volcanic eruptions which not only released greenhouse gases but also
toxic metals, sulphur dioxide and halogens. These other gases are the main posited causeof damage to terrestrial ecosystems but are not relevant to future anthropogenic climatechange.
Our much less advanced hominid ancestors survived when temperatures were 3°C warmer
than pre-industrial levels. In the Paleocene-Eocene Thermal Maximum, temperatures were
17°C warmer, but the ecological effects were surprisingly mild. There is little indication fromthe paleoclimate record that humanity would be killed off even by warming of this magnitude.
Another optimistic conclusion we can draw from all of this evidence is that, unless somethingimportant has changed over the course of the Holocene, future rapid warming seems
unlikely to cause major species extinctions. After reviewing some of the case of warming
discussed above, Willis and MacDonald (2011) conclude:“We argue that although the mechanisms responsible for these past changes inclimate were different (i.e., natural processes rather than anthropogenic), the rate
and magnitude of climate change were often similar to those predicted for the next
century and therefore highly relevant to understanding future biotic responses. In all
intervals we examine the fossil evidence for the three most commonly predicted
future biotic scenarios, namely, extirpation, migration (in the form of a permanent
range shift), or adaptation. Focusing predominantly on the terrestrial plant fossil
187

Wignall, The Worst of Times, p. 167.

127

record, we find little evidence for extirpation during warmer intervals; rather, range
shifts, community turnover, adaptation, and sometimes an increase in diversity areobserved.”188
Still, there are other reasons for caution.
1. It is not yet completely clear why CO2 release and warming were so disastrous prior
to the breakup of Pangea, so there is a small chance that some unknown factor inplay then might also be in play today.
2. Since there is no perfect analogue to future warming, something unexpected might
happen that would affect human civilisation.
3. The world today is different in important ways to the past. Most importantly, we arereliant on agriculture for food.
4. The paleoclimate is only one line of evidence relevant to the impact of future
warming. Other lines of evidence from observations and from models might paint a
different picture.

188

K. J. Willis and G. M. MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate
Change Predictions for a Warmer World’, Annual Review of Ecology, Evolution, and Systematics 42,
no. 1 (2011): 267–87, https://doi.org/10.1146/annurev-ecolsys-102209-144704.

128

4. Economic and demographic trends
Before I discuss the effects that climate change might have on the world, it is useful to firstunderstand the economic and demographic trends.

4.1. Economic trends
4.1.1. Global growth
I outlined the long-term economic history of the world in Chapter 1. This chart summarises
the last two millennia.

Rapid growth of 1-3% has only occurred since the Industrial Revolution. Prior to that,
average living standards had barely improved for thousands of years, arguably since thetimes of pre-agricultural hunter-gatherers.
Over the course of the 20th century, global GDP increased by 1,745%, while GDP perperson increased by 350%.189
It is very difficult to predict how much economic growth there will be in the future. Expert
surveys suggest that income per head might increase by between 200% and 2,000% by
2100 (5% to 95% range). The shared socioeconomic pathways give a range of possibilities

189

Data from Our World in Data.

129

for incomes in 2100, ranging from the stagnant SSP3, to the ‘middle of the road SSP2, to thehigh development SSP5.

Source: Keywan Riahi et al., ‘The Shared Socioeconomic Pathways and Their Energy, Land Use, andGreenhouse Gas Emissions Implications: An Overview’, Global Environmental Change 42 (1 January2017): 153–68, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

In Chapter 1, I discussed some reasons to think that this range of possibilities might beoverly conservative. If advanced AI systems can allow us to automate innovation, then we
could enter a new growth mode in which growth exceeds 10% per year. I would guess thatthe probability of this exceeds 10%.
I also mentioned that prolonged stagnation is also a real possibility. Again, I think theprobability that the world economy experiences zero growth from 2100 onwards is in excess
of 10%. It is also plausible that civilisation will collapse at some point in the future, so someof our descendents may be much worse off than we are.

130

4.1.2. Regional growth
The Industrial Revolution led to massively increased average incomes. Most of this progress
was driven by growth after 1950. On all objective measures of welfare, the post-1950 era
has brought more progress than all prior human history combined.
However, this progress has been highly unevenly distributed. Many countries, especially in
Africa, have essentially never experienced sustained increases in living standards. The chart
below shows 29 countries, all with populations in the millions, that are still close to
subsistence today and have experienced little growth over the last 70 years (though there
does seem to have been some improvement since 2000).

The table below shows where different regions are headed if economic growth continues on
the trend it has been on for the last 60 years.

131Source: Our World in Data, ‘Annual growth of GDP per capita, 1961 to 2020’

Growth rates in high-income countries have started to slow in recent years, so it seems
unlikely that incomes will indeed rise to such heights this century. The historical trends
suggest that many countries in Africa and Asia will remain poor. However, growth rates haveincreased in some Sub-Saharan African countries recently.
The Shared Socioeconomic Pathways make the following assumptions about regional
growth.

132

Source: Håvard Hegre et al., ‘Forecasting Civil Conflict along the Shared Socioeconomic Pathways’,
Environmental Research Letters 11, no. 5 (April 2016):
https://doi.org/10.1088/1748-9326/11/5/054002.

Overall, the historical trends suggest that the most plausible SSP is SSP4.

Regional growth and discounting
Economists often argue that because “we” will be richer in the future, we ought to discountthe future costs of climate change because people in the future will be better able to adapt toclimate change than we are. In this way, paying for mitigation now can be like redistributing
money from the poor (people today) to the rich (future generations).
The empirical facts I have outlined suggest that this argument is problematic.190 While theaverage person seems likely to be much richer in the future, the trends suggest that asignificant fraction of the world population, mainly in Sub-Saharan Africa, will not be muchbetter off. Consequently, it is inappropriate to discount future costs using global average
income growth. Rather, we need to discount future costs on the basis of likely future trends
in growth in different regions, which will be highly variable.

4.1.3. Growth, impacts, adaptation and course correction
One mistake it is easy to make when assessing the future impact of climate change is to failto take account of future trends in incomes, technology and adaptive capacity when
considering the impact of climate change. For example, the only Shared Socioeconomic
190

Thanks to Will MacAskill for discussion of this point.

133

Pathway that is consistent with RCP8.5 is SSP5. RCP8.5 promises to create some very bad
climate impacts by the end of the century, but they will be borne by people who are far
richer: global income per person will exceed $100,000 in all regions. This high growth futurewould be as different to today as today is to 1900.
SSP5 is just a scenario, but this does illustrate an important rule of thumb: high emissions
scenarios are most plausible on scenarios of high economic growth. The magnitude ofclimate change will likely be greatest where our adaptive capacity and ability to change
course is also greatest. One exception to this is a ‘boom and bust’ scenario, in which there isa subsequent societal collapse
Average incomes mask significant regional variation. And, as we will see in the chapters that
follow, many of the countries that are set to be hit hardest by climate change are also thosewith the worst prospects for growth.
The climate impacts literature typically explores the impacts of different levels of warming by
exploring impacts on the most likely level of warming on a particular Representative
Concentration Pathway or Shared Socioeconomic Pathway-Representative Concentration
Pathway combination. For instance, many papers explore the impacts on heat stress of themost likely level of warming on RCP8.5 by 2100, which is 4.4ºC. However, some papers also
combine these assessments with pessimistic SSPs, such as SSP3.191 Technically, thiscombination of SSP3 and RCP8.5 is not possible, so this combination of scenarios seems
inadmissible.
However, it can be useful to explore such combinations of scenarios because we shouldcare not just about the most likely level of warming on a given RCP, but also on the risk ofmuch higher levels of warming. On the SSP3-RCP7 baseline, there is a 5% chance of morethan 4.6ºC of warming.192 So, a climate impacts study which explores the impacts of SSP3
and RCP8.5, while technically impossible, is really exploring SSP3 and 4.4ºC, which verymuch is possible.

Subsequent collapse
Although average living standards are most likely to be higher in the future, there is somechance of civilisational collapse due to nuclear war or engineered pandemics. In fact, I think
the chance of a disaster killing more than 10% of the world population before 2100 is
upwards of 10%. The SSPs do not account for possibilities such as this.
This is important because it could mean that the majority of people who have to deal with
future climate change are worse-off than people today. Moreover, given how long CO2 stays
in the atmosphere, survivors of subsequent collapse will have to deal with a less hospitableclimate for millennia. This is one reason that climate change could make recovery fromcollapse harder.

191

See for instance the World Bank study discussed in Chapter 11, and the Bressler et al (2021)
discussed in Chapter 6.
192
Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, Fig. SPM. 1.

134

4.2. Demographic trends
Almost everywhere outside Africa, fertility has declined close to two births per woman or
below

This is a product of the demographic transition: as people get richer they have fewer
children. Due to rising incomes, the population growth rate peaked in 1968 and has halved
since then.

135

Because many African economies have been stagnant, they have not yet gone through thedemographic transition. This has far-reaching implications for future global demography. The
population in Africa is set to grow dramatically up to 2100 due to high fertility, and lower childmortality thanks to improved public health and medicine.

136

By 2100, the UN projects that 36% of the world population will be in Africa.
From the point of view of climate change, a key factor is that a lot of population growth is set
to happen in the tropics and subtropics, which will be hardest hit by climate change. For
example, here is the future population distribution on SSP3 (the stagnant future):137

Source: Chi Xu et al., ‘Future of the Human Climate Niche’, Proceedings of the National Academy of
Sciences 117, no. 21 (26 May 2020): 11350–55, https://doi.org/10.1073/pnas.1910114117.

138

5. Agriculture
In Chapter 3, we saw that early hominids survived and thrived in periods of dramatic climatechange, including periods 2.5°C to 4°C warmer than pre-industrial. One disanalogy between
those earlier warmer periods and potential future anthropogenic warming is that modern
society is reliant on industrial agriculture, whereas our hominid ancestors would have been
hunter-gatherers.
The effects of climate change on agriculture are prima facie some of the most potentiallyconcerning because agriculture is heavily dependent on key climatic variables like
temperature, precipitation, soil moisture, and the level of CO2. Indeed, agriculture only
started to thrive during our current warmer interglacial, the Holocene. Prior to that, homosapiens subsisted by hunting and gathering. Richersen et al (2001) argue that agriculture
was impossible during the cold, low CO2 and climatically variable Pleistocene, but mandatory
during the warm, high CO2 and climatically stable Holocene.193 As we start to move outsideof our Holocene climate envelope, it makes sense to explore how agriculture might fare.

5.1. Context and trends in agriculture
5.1.1. Food production
Food production depends on:
1. Food yield - the amount of food produced per acre
2. Land area - the amount of land area used to grow food3. Food loss - food lost between harvest and the point at which it is available toconsumers.
4. Food waste - food lost at the household level.
Rice, wheat, maize and soybean produce two thirds of agricultural calories,194 so I will mainly
focus on them here.
Since the Industrial Revolution, the yield of the major food crops has increased enormously.

193

Peter J. Richerson, Robert Boyd, and Robert L. Bettinger, ‘Was Agriculture Impossible during the
Pleistocene but Mandatory during the Holocene? A Climate Change Hypothesis’, American Antiquity
66, no. 3 (2001): 387–411.
194“Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we
track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly
two-thirds of global agricultural calories” Deepak K. Ray et al., ‘Yield Trends Are Insufficient to Double
Global Crop Production by 2050’, PLOS ONE 8, no. 6 (19 June 2013): e66428,
https://doi.org/10.1371/journal.pone.0066428.

139

Over the last 60 years, yields and production of cereals (which includes wheat, rice, maize,
barley, oats, rye, millet, sorghum, buckwheat, and mixed grains) have increased by 200% ormore and have outpaced population growth.

140

There is a similar picture for almost all other foods, as shown here. Massive increases inyield, rather than turning over land to food production, are responsible for almost all of theincrease in food production. The total land area used for farmland may have already peaked.
Yields have improved in all world regions, though progress has been markedly lesspronounced in Africa.

141

Global data for soybean yields going back to 1960 is not available, but global yields haveincreased by around 39% since 1990.

142

Much of the global improvement has been driven by the Green Revolution, which involved
the increased use of synthetic fertilisers and pesticides, irrigation, and higher yield cropvarieties.
However, there is evidence that yields for some food crops are starting to plateau

143

144

Source: Patricio Grassini, Kent M. Eskridge, and Kenneth G. Cassman, ‘Distinguishing between Yield
Advances and Yield Plateaus in Historical Crop Production Trends’, Nature Communications 4, no. 1(17 December 2013): 2918, https://doi.org/10.1038/ncomms3918.

Grassini et al (2013) comment that “Results from our analysis suggest that projections ofcrop yield trajectories based on extension of historical trends of the past five decades should
be viewed with caution because these past trends were driven by rapid adoption of green
revolution technologies that were largely one-time innovations.”
Food production is spread fairly evenly across the globe, though Africa produces markedly
less than other regions.

Calories per person have increased by 20% to 50% in almost all regions since 1961.

145

There has been a marked decline in famines over time.

146

There have been no famines in Asia since 1980, with the exception of North Korea. Famines
now only occur in extremely poor African countries
The global death rate from famines has declined by a factor of 100 since the 1960s.

147Countries with income per head of more than $5,000 tend not to suffer famines.

148

These improvements in food production have occurred in the context of around 0.8°C ofwarming since 1980. So far at least, global increases in yield and agricultural production
have swamped the effects of climate change.

5.1.2. Agriculture and the economy
In most advanced economies, less than 10% of the labour force is employed in agriculture.
In poor countries in Africa and the Middle East, more than half of the labour force works inagriculture.

Once average incomes pass $20,000 per person, typically much less than 10% of theworkforce is employed in agriculture

149

The share of the global labour force employed in agriculture has fallen from 43% in 1991 to
26% in 2017. Agriculture, forestry and fishing as a share of GDP has fallen from 11% in 1968
to 4.3% in 2021.

5.1.3. Historical trends in water use and irrigation
Water use
The chart below shows water consumption in different regions over the long-run.

150

Water use in rich OECD democracies has been flat since the 1980s despite economic
growth, but has increased outside the OECD since 1970.
Per capita water withdrawal rates vary quite substantially across the world151

Approximately 70% of freshwater withdrawals are used for agriculture, though this varies
across income groups.

152

Note that water withdrawals are not necessarily environmentally harmful because water canbe withdrawn but to water resources.195 So, water loss is more environmentally important,though it is difficult to get global data on water loss.

Irrigation
Irrigation — the deliberate provision or controlled flooding of agricultural land with water —has been an important input factor in the observed increase of crop yields across many
countries in recent decades. It has also been a strong driver in the quantity of water used for
agriculture.
Rates of irrigation vary substantially across countries.

195

Thanks to Linus Blomqvist for raising this point.

153

As this shows, even in low and middle-income countries the use of irrigation is high. In
Pakistan and Bangladesh, more than half of agricultural land is irrigated, while in Indiaaround 38% is.
According to Our World in Data, low crop yields in Africa have been attributed in part to low
uptake of irrigation.

5.2. By what mechanism does climate change affect foodproduction?
Climate change can detrimentally affect the food supply in three main ways:196
1. By exposing crops and livestock to increased thermal stress.197
196

Climate change will also influence the distribution of pests and weeds, which could also affectagricultural output. However, there is limited data on the aggregate effect of climate change on pests
and weeds to date. IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth
Assessment Report, 2022, sec. 5.4.1.3.
197“Increased frequency of unusually hot nights since 1961 are also attributable to human activity inmost regions (WGI AR5 Chapter 10). These events are damaging to most crops, an effect that has
been observed most commonly for rice yields (Peng et al., 2004; Wassmann et al., 2009; Welch et al.,2010) as well as rice quality (Okada et al., 2011). Extremely high daytime temperatures are also
damaging and occasionally lethal to crops (Porter and Gawith, 1999; Schlenker and Roberts, 2009),
and trends at the global scale in annual maximum daytime temperature since 1961 have been
attributed to GHG emissions (Zwiers et al., 2011). At regional and local scales, however, trends indaytime maximum are harder to attribute to GHG emissions because of the prominent role of soil154

2. By increasing the risk of drought in certain regions. This effect will not be universal: ingeneral, models suggest that climate change will make dry areas like the subtropics
drier and wet areas at higher latitudes wetter.198
3. By exposing agricultural workers to increased heat stress and flooding.
However, CO2 emissions also have some beneficial effects on crops.
1. Higher temperatures extend the growing season, which is especially beneficial tocountries at higher latitudes.199
2. Elevated CO2 levels speed up photosynthesis and water use efficiency due to theCO2 fertilisation effect. The meta-analysis used in the 2013-14 IPCC reports foundthat each ppm increase in CO2 increases yield by 0.06%.200 On RCP6,
concentrations would increase by 300ppm compared to today, so yields would
increase by 18%, other things equal.

5.3. What is the projected effect of climate change on
agricultural production?
5.3.1. Crop yields
In this section, I will outline the findings of major reviews on the effects of climate change oncrop yield. It is important to note that these studies measure impacts relative to a
counterfactual without climate change, not relative to today. While the world warms there arelikely to be countervailing improvements in agricultural productivity, I point I return to below.
IPCC Fifth Assessment Report
The chart below shows the results of a meta-analysis used in the IPCC’s Fifth Assessment
Report, on the effect of warming on food yield in different regions. The adaptations exploredmoisture and clouds in driving these trends (Christidis et al., 2005; Zwiers et al., 2011).” IPCC,
Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment Report, 2022, Ch. 7,
pp. 492-493.
198“Climate change is projected to reduce renewable surface water and groundwater resources
significantly in most dry subtropical regions (robust evidence, high agreement). {3.4, 3.5} This will
intensify competition for water among agriculture, ecosystems, settlements, industry, and energy
production, affecting regional water, energy, and food security (limited evidence, medium to highagreement). {3.5.1, 3.5.2, Box CC-WE} In contrast, water resources are projected to increase at highlatitudes” IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment
Report, 2022, Ch. 3 p. 232.
199
“In high latitudes (such as Russia, northern Europe, Canada, South America) global warming may
increase yields and expand the growing season and acreage of agricultural crops, although yields
may be low due to poor soil fertility and water shortages in some regions.” IPCC, Climate Change
2014: Impacts, Adaptation, and Vulnerability, Fifth Assessment Report (Cambridge University Press,
2014), Ch 7, p. 512.
200
“The model also inferred significant positive effects of precipitation (t =3.0; P =0.0031) and CO2 (t=3.1; P =0.0022) with average yield increases of 0.53% (per % 1P), 0.06% (per ppm 1CO2),
respectively (Table 1).” A. J. Challinor et al., ‘A Meta-Analysis of Crop Yield under Climate Change
and Adaptation’, Nature Climate Change 4, no. 4 (April 2014): 288,
https://doi.org/10.1038/nclimate2153.

155

in the meta-analysis are “changes in varieties, planting times, irrigation and residue
management”.201

Source: IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment
Report, 2022, p. 498

When considering these effects, it is important to note that local warming can be different toglobal average warming, as shown in the figure below:

201

A. J. Challinor et al., ‘A Meta-Analysis of Crop Yield under Climate Change and Adaptation’, Nature
Climate Change 4, no. 4 (April 2014): 287, https://doi.org/10.1038/nclimate2153

156

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, SPM.5.

At 4°C of warming, the Arctic is 7°C warmer. Warming is also greater on land than at sea, so
land warming will typically be higher than average global surface warming, on average. For
4°C of average global warming, many highly populated regions will see local temperatures
increase by 5-6°C.
Zhao et al (2017)
In a multi-model study of the effects of climate change on yields of the major crops, Zhao etal (2017) find that “Without CO2 fertilization, effective adaptation, and genetic improvement,each degree-Celsius increase in global mean temperature would, on average, reduce global
yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%.”202 So, on
these pessimistic assumptions, after 5ºC of warming, yields would decrease by 15% to 35%.
Once we relax these assumptions, the effects of climate change would be smaller.
Jägermeyr et al (2021)
Jägermeyr et al uses latest-generation crop and climate models to project future crop yields
under different climate change scenarios. One major advantage the study has is that it does
not just model the effects of temperature, but also precipitation changes,
temperature-moisture feedbacks and CO2 fertilisation.203 However, their models do notaccount for adaptation, or for other productivity improvements, which they leave to future
work.204
202

Chuang Zhao et al., ‘Temperature Increase Reduces Global Yields of Major Crops in Four
Independent Estimates’, Proceedings of the National Academy of Sciences 114, no. 35 (29 August2017): 9326–31, https://doi.org/10.1073/pnas.1701762114.
203
Recent literature has focused on capturing the temperature sensitivity of crops in isolation17–19.
To quantify climate change impacts more comprehensively, additional factors including precipitation
changes, temperature–moisture feedbacks and [CO2] need to be considered. The projections
presented here dynamically respond to these climate drivers and shed new light on the effects ofelevated [CO2], which are among the largest sources of uncertainty in long-term crop yield estimates”
Jägermeyr et al., ‘Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate
and Crop Models’, 2.
204“Cropping system adaptation can substantially reduce and even outweigh adverse climate change
impacts, for example, by switching to other crops51 or better-adapted varieties52. Integrated into
ISIMIP’s wider cross-sector activities, GGCMI will systematically evaluate farming system adaptationand changes in yield variability and extreme event impacts in subsequent efforts53,54.” Jonas
Jägermeyr et al., ‘Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate

157

Their results are shown below:

The overall picture is more pessimistic than found in earlier models around the time of the
IPCC’s Fifth Assessment Report, though it is unclear how adaptation might affect theirfindings. On RCP8.5, on which there is 4.4ºC warming by 2100, results for wheat are better
than before, but soy, rice and maize results are worse.
IPCC Sixth Assessment Report
In the 2022 Sixth Assessment Report, the IPCC again notes that climate change is likely tohave positive effects in temperate regions but negative effects in warmer regions.205 The
IPCC mainly relies on studies by Hasegawa et al on crop yields. The table below shows the
Hasegawa et al projection of the effect on climate change in different regions and on
different emissions scenarios, Recall that RCP4.5 implies about 2.7°C, while RCP8.5 implies
about 5°C of warming:

and Crop Models’, Nature Food 2, no. 11 (November 2021): 9,https://doi.org/10.1038/s43016-021-00400-y.
205“The projected effects of climate change are positive where current annual mean temperatures(Tave) are below 10 °C, but they become negative with Tave above around 15°C. At Tave>20°C, evena small degree of warming could result in adverse effects.” IPCC, Climate Change 2022: Impacts,
Ch. 5 sec. 5.4.3.2.

158

Source: Toshihiro Hasegawa et al., ‘A Global Dataset for the Projected Impacts of Climate Change on
Four Major Crops’, Scientific Data 9, no. 1 (16 February 2022)
https://doi.org/10.1038/s41597-022-01150-7.

This shows that there is scope for adaptation to significantly dampen the effects of climatechange, especially on maize yields.
By adaptation Hasegawa et al mean any changes from current methods in terms of“fertiliser, irrigation, cultivar, soil organic matter management, planting time, tillage, andothers”.206

Correlated yield declines
As well as projections of overall likely trends in average yields, it is useful to assess the riskof synchronised declines in yields in multiple crops, due to increasing yield variability and to
extreme weather events. Crop losses in a single, main crop producing region can be offset
through trade with other crop-producing regions. If several breadbaskets suffer fromnegative climate impacts at the same time, however, the effects could be more substantial.
Tigchelaar et al. (2018)

206

Hasegawa et al., ‘A Global Dataset for the Projected Impacts of Climate Change on Four Major
Crops’.

159

Tigchelaar et al. (2018) explore the risk that the currently top four maize producing countries
experience synchronised production declines of more than 10% relative to the average
today, warming scenarios. Their findings are as follows, assuming that there are no
improvements in the heat tolerance of crops.207●●

Today, the risk of such a synchronised decline in any given year is virtually zero
For 4ºC, the risk increases to 86% in any given year.

The following chart shows the findings of the effect of Tigchelaar et al (2018) on theprobability of different yield outcomes in different countries. The red line shows theprobability distribution across yields for 4ºC of warming, while the black line shows theprobability distribution across yields for the present day.

207

“We find that as the global mean temperature increases, absent changes in temperature variabilityor breeding gains in heat tolerance, the coefficient of variation (CV) of maize yields increases almost
everywhere to values much larger than present-day values.” Michelle Tigchelaar et al., ‘Future
Warming Increases Probability of Globally Synchronized Maize Production Shocks’, Proceedings of
the National Academy of Sciences 115, no. 26 (26 June 2018): 6644–49,
https://doi.org/10.1073/pnas.1718031115.

160

As this shows, Tigchelaar et al (2018) assume that for 4ºC of warming, average yield will belower than today. However, this assumption is not realistic because there will becountervailing improvements in agricultural productivity in the time it takes to reach 4ºC. I
discuss this point in more detail in section 5.4. Over the last 60 years, crop yields haveincreased by upwards of 200-300%, and it is reasonable to think that they will increase by afurther 100% in the next 80 years. So, overall yields would still be much higher than today,even if there were synchronised declines by 20% in major food producing regions.
Gaupp et al (2019)Gaupp et al (2019) assesses the risk of synchronised large declines in yield for multiple
crops. Specifically, they assess the probabilities of events when the climatic conditions are at161least as bad as occurred when historical yields were in the 25th percentile of yields in the
study period.208
They found that the return rate of synchronised low yields in all five breadbaskets increases
at 2ºC:209●●●

Wheat: Historical return rate is 43 years vs. 15 years under 2ºC.
Maize: Historical return rate is 16 years vs. <2 years under 2ºC.
Soybean: Historical return rate is 20 years vs. 9 years under 2ºC.

Production losses increase at 2ºC relative to 1.5ºC. If multiple breadbasket failures occur,
the losses would be:●●●

Wheat: 8.6 million tonnes (or 1% of 2018 production)210
Maize: 19.8 million tonnes (or 2% of 2018 production)211
Soybean: 9.9 million tonnes (or 3% of 2018 production)212

For comparison:“Historical examples of global crop production shocks include 7.2 million tons
soybean losses in 1988/99 and 55.9 million tons maize losses in 1988 which were
mostly caused by low rainfall and high temperatures during summer growing season
in the US.”213
Adjusting for the probabilities of these losses under 2ºC, expected the losses are:●●●

Wheat: 161,000 tonnes (or 0.02% of 2018 production)
Maize: 2,753,000 tonnes (or 0.3% of 2018 production)
Soybean: 265,000 tonnes (or 0.08% of 2018 production)

208“We identify a ‘breadbasket failure’ event as being when the climatic conditions are at least as
severe as those conditions associated with the 25 percentile of the logistically detrended yields (withdetrended yields as residuals of the non-linear logistic regression with a residual mean equal to zero).
The crop production loss for an event of this severity is the 25 percentile of the logistically detrended
yield multiplied with the 2012 harvested area” Franziska Gaupp et al., ‘Increasing Risks of Multiple
Breadbasket Failure under 1.5 and 2 C Global Warming’, Agricultural Systems 175 (2019): 40.
209“For wheat, which shows the smallest simultaneous climate risks, the return period for all fivebreadbaskets exceeding their climate thresholds decreases from 43 years (or 0.023 annual probability
under historical conditions to 21 years (0.047) in a 1.5 °C scenario and further down to around 15
years (0.066) under 2 °C. Soybean has a return period of simultaneous climate risks in all
breadbaskets of around 20 years (0.049 today which decreases to 9 (0.116) and 7 years (0.143 in a
1.5 and 2 °C warmer world respectively. Maize risks are highest in our study with an initial return
period of 16 years (0.061), decreasing to < 3 (0.39) and < 2 years (0.538) under future global
warming.” Gaupp et al., ‘Increasing Risks of Multiple Breadbasket Failure under 1.5 and 2 C Global
Warming’.
210
Wheat production in 2018 was 735 million tonnes (Our World in Data).
211
Maize production in 2018 was 1.1 billion tonnes (Our World in Data).
212
Soy production in 2018 was 348 million tonnes (Our World in Data).
213
Gaupp et al., ‘Increasing Risks of Multiple Breadbasket Failure under 1.5 and 2 C Global Warming’,
42.

162

As I discuss below, these expected effects are small relative to total production and relative
to likely improvements in agricultural production.

5.3.2. Crop migration
The literature discussed above assumes that crop migration is not available as an adaptive
response, which likely leads it to overestimate the effect on global agricultural production.
“Many studies have estimated the adverse effects of climate change on crop yields,
however, this literature almost universally assumes a constant geographic distributionof crops in the future. Movement of growing areas to limit exposure to adverse
climate conditions has been discussed as a theoretical adaptive response but has
not previously been quantified or demonstrated at a global scale”214
Sloat et al (2020) argue that crop migration has mitigated the effects of warming on yield
since 1973. Blomqvist et al (2020) argue that crop migration accounted for around one sixthof the global increase in crop production since 1960.215
According to Zabel et al (2014), with no adaptation and on A1B (a high emissions scenario
similar to RCP8.5), the land area suitable for agriculture is higher by 2100 mainly due togains in China, the US, Russia and Canada, though the tropics and subtropics lose out.

Source: Florian Zabel, Birgitta Putzenlechner, and Wolfram Mauser, ‘Global Agricultural Land
Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate
Change Conditions’, PLOS ONE 9, no. 9 (17 September 2014): e107522,
https://doi.org/10.1371/journal.pone.0107522.

214

Lindsey L. Sloat et al., ‘Climate Adaptation by Crop Migration’, Nature Communications 11, no. 1 (6March 2020): 1243, https://doi.org/10.1038/s41467-020-15076-4.
215‘Country share’ describes the geographic distribution of cropland. A shift in cropland fromlower-yielding to higher-yielding countries, for instance, would boost aggregate yield without any one
country improving its yields. Linus Blomqvist, Luke Yates, and Barry W. Brook, ‘Drivers of Increasing
Global Crop Production: A Decomposition Analysis’, Environmental Research Letters 15, no. 9(September 2020): fig. 2, https://doi.org/10.1088/1748-9326/ab9e9c.

163This suggests that using the meta-analyses of the effect of climate change on yield that only
account for in-situ adaptation will tend to overestimate the damage of climate change toglobal agriculture.

5.3.3. Water stress and agriculture
The effect of climate change on precipitation
My discussion here relies heavily on the overview of climate change on precipitation by
Carbonbrief here.
The figure below shows projected percentage change in precipitation at different levels ofwarming:

Source: IPCC, Climate Change 2021: The Physical Science Basis, Summary for Policymakers,
SPM.5.

Substantial parts of Latin America, the southern USA, southern Africa, the Mediterranean,and Australia are projected to dry out in the future. The Mediterranean region is expected tohave around 20% less precipitation by 2100 in an RCP8.5 world, with similar reductions also
found in southern Africa. Western Australia, Chile, and Central America/Mexico may all
become around 10% drier. These changes tend to increase with warming.
However, there is considerable disagreement among models about the effect of climatechange on precipitation. The figure below shows the driest projection and wettest projections
for each different part of the world across all the CMIP5 models, represented by the 10th and90th percentile of all the models (i.e. the 10% of models that show the most reduction inprecipitation and the 10% that show the most increase in precipitation for any region of theworld).

164

Source: Carbonbrief

Climate models are poor at reproducing observed changes in precipitation. The left-handpanes of the figure below show the systematic error of three of the leading models used in
the last IPCC report for precipitation in June, July and August for 1986 to 2005. The
right-hand pane shows the projected effect of RCP8.5 on precipitation by 2100 on the samemodels. There are widespread regions where the systematic error exceeds the climatechange signal (in some regions by more than a factor of 20)

Source: Tim Palmer and Bjorn Stevens, ‘The Scientific Challenge of Understanding and Estimating
Climate Change’, Proceedings of the National Academy of Sciences 116, no. 49 (3 December 2019):
24390–95, https://doi.org/10.1073/pnas.1906691116.

There is some agreement across models on projected effects on precipitation. The figure
below shows the same annual average change in precipitation between today and the end of
the century, but adds dots to indicate areas where at least nine out of 10 models agree on
the direction of change.

165

Source: Carbonbrief

There is widespread agreement among the models that both the tropical Pacific andhigh-latitude areas will have more precipitation in the future. India, Bangladesh andMyanmar will all become wetter, as will much of northern China.
The models largely agree that the Mediterranean region and southern Africa will have lessprecipitation in the future. They also agree on reduced precipitation in southwest Australia
around Perth, in southern Chile, the west coast of Mexico and over much of the tropical andsubtropical Atlantic ocean.
Given the problems climate models have at reproducing observed precipitation, it is difficult
to put much weight on regional projections even where there is agreement among 90% ofmodels. For example, the models agree that precipitation will increase across much of India
on RCP8.5, but then we saw above that three leading climate models made huge errors in
reproducing observed precipitation in India, and the sign of the error was different across thethree models. Therefore, even if 90% of the models agree on the sign of a change, there
seems to be a reasonable chance that they are all wrong.
As I mentioned in Chapter 2, this uncertainty means we should be more worried aboutclimate change, not less.

166

The effect of climate change on drought
The effect of climate change on drought is determined not only by changes in precipitation,
but also by higher temperatures, which increase the rate of evaporation and soil moistureloss.
The maps below show the effects on soil moisture at different levels of warming, according
to climate models.

Source: IPCC, Climate Change 2021: The Physical Science Basis, Summary for Policymakers,
SPM.5.

What effect will climate-induced water stress have on agriculture and the economy?
Changes in precipitation and drought create challenges for agriculture. Increased flooding
might affect crop production, but the greatest risk comes from increased aridity. As we haveseen, it is difficult to have much confidence in projections of which regions will dry out. Still
we know that climate change will cause some areas to dry out.
Irrigation is an effective adaptation measure
In the meta-analysis used in the IPCC’s Fifth Assessment report, shown above, increased
irrigation was found to be an effective adaptation to declining water availability.216 The reportcites three studies which project zero or modest (~15%) global increases in demand for
irrigation by 2100, with some regions, such as Europe, the USA and parts of Asia,
experiencing large increases (>40%), on some models.217
216“Supplementary Table 4 200 shows that of the four categories (for this analysis fertiliser was
included with “other”) only irrigation and “other”, on average, increase yields from baselines values. Of
the four categories, irrigation is the one that is most likely to systematically increase yields, since
planting date and cultivar changes can reduce yields.” A. J. Challinor et al., ‘A Meta-Analysis of Crop
Yield under Climate Change and Adaptation’, Nature Climate Change 4, no. 4 (April 2014): SI p. 5,
https://doi.org/10.1038/nclimate2153.
217“Using projections from 19 CMIP3 GCMs forced by SRES A2 emissions to drive a global
vegetation and hydrology model, climate change by the 2080s would hardly alter the global irrigationwater demand of major crops in areas currently equipped for irrigation (Konzmann et al., 2013).
However, there is high confidence that irrigation demand will increase significantly in many areas (bymore than 40% across Europe, USA, and parts of Asia). Other regions—including major irrigatedareas in India, Pakistan, and southeastern China—might experience a slight decrease in irrigation
demand, due for example to higher precipitation, but only under some climate change scenarios (alsosee Biemans et al., 2013). Using seven global hydrological models but a limited set of CMIP5

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The economic barriers to increasing irrigation to adapt to climate change seem low. 35% ofagricultural land in India is irrigated, but income per head at purchasing power parity is only
$6,400, compared to the global average of $17,000. In Bangladesh, 60% of agricultural land
is irrigated. Farmers in the Fertile Crescent at the dawn of agriculture made extensive use of
irrigation.218 In the future, when most countries will be richer and have access to better
technology, they are also likely to make use of irrigation if it is beneficial.
For low growth agrarian economies, the effects on water stress could have bad humanitarianconsequences. The people who have contributed the least to climate change would be
hardest hit. We have strong reasons to reduce emissions for the sake of these people.
Water management is poor
Water is very poorly managed throughout the world. Cheap or free water is one ofinnumerable implicit and explicit subsidies that farmers receive at the expense of society andthe environment. Damania (2020) notes that
“Most water is allocated to agriculture and much is made available at no cost to theuser. As a result, overuse of water is a common problem, especially in the
agricultural sector. The marginal value of water in different uses varies a great deal
because the prices paid by industry, agriculture, and residential users often have no
relation to the opportunity costs of supplying water to them. As an example,
Olmstead (2010) notes that in the desert state of Arizona in the United States, water
prices vary from $27/acre-foot for agriculture to $3,200/acre-foot for urban uses.
While some of the variation can be explained by the difference in the quality of the
product being delivered, most of it is a function of market and institutional failures that
do not allocate water based on its economic value. This suggests that the benefits ofreallocating water from farms to cities would be large.”
The potential benefits of market pricing of water are large, on the order of a 6% boost to
global GDP, on some estimates.219

projections, Wada et al. (2013) suggested a global increase in irrigation demand by the 2080s(ensemble average 7 to 21% depending on emissions scenario), with a pronounced regional pattern,a large inter-model spread, and possible seasonal shifts in crop water demand and consumption. By
contrast, based on projections from two GCMs and two emissions scenarios, a slight global decreasein crop water deficits was suggested in both irrigated and rainfed areas by the 2080s, which can be
explained partly by a smaller difference between daily maximum and minimum temperatures (Zhang
and Cai, 2013). As in other studies, region-to-region variations were very heterogeneous.” IPCC,
Climate Change 2022: Impacts, Ch. 7.
218“Throughout the region, irrigation is necessary for the best agricultural results and, indeed, is often
essential to any farming at all. Radiocarbon dating has shown that incipient agriculture and village
agglomerations in the Fertile Crescent there must be dated back to about 8000 BCE, if not earlier, andthat the use of irrigation followed rapidly.” Britannica
219“In a global context Roson and Damania (2017) find losses of about 6 per cent of GDP. Theirsimulations suggest that even if only a part of water use is allocated based on its economic price thatbrings supply and demand into balance, much of the distortionary impact vanishes” Richard Damania,‘The Economics of Water Scarcity and Variability’, Oxford Review of Economic Policy 36, no. 1 (6January 2020): 24–44, https://doi.org/10.1093/oxrep/grz027.

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Desalination
Another more expensive adaptive response to drought and declining freshwater availability
is desalination. There is a good review of trends in desalination use and cost in this review
by the Advisian Worley Consulting Group.
From 1960 to 2010, desalination costs fell by a factor of 10 from $10/m3 of water to less than
$1/m3. By 2017, costs had declined by a further 20%.
The costs to fill any shortfalls in water consumption through desalination seem manageable
for wealthy countries. Consider this illustrative example. Total Spanish per capita water
withdrawals per year, including agricultural, industrial and municipal uses, are around
1,000m3. This is above the European average.

To supply the entirety of Spanish per capita water consumption through desalination would
cost less than $1,000, compared to current GDP per capita of $30,000. Moreover, it isunrealistic to imagine that all Spanish water consumption would need to be supplied bydesalination - much of the supply would still come from freshwater sources.
If the trend in cost declines of desalination continues, then desalination costs would fall by afactor of 10 by 2070. Thus, the annual costs to supply each Spaniard’s annual water use
would fall to $100. However, two experts have told me that we are already close to
fundamental physical limits on desalination.

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Some water-stressed countries already deploy desalination at a large scale. For example,
Israel started a desalination programme in 2000 to deal with water stress. It now desalinates
750 million m3 of water per year, which is 83m3 per person, or around 30% of total perperson water use.
There would be strong incentives to use desalination if the supply of freshwater ever
becomes a problem. As before, poor agrarian economies would likely be unable to afford
desalination, so for them adaptation would be much harder.
What are the economic costs of water stress?
In recent years, innovative studies using panel data have been used to estimate the effect ofweather on GDP. The basic idea is that an average year in a particular country is the ‘control’
and a dry year is ‘the treatment group’. The same studies have been used to estimate thefuture effects of climate change.
One problem that these studies have is that interannual weather variation is different tolong-term climate change, a point I expand in Chapter 10. It is not clear in which direction
this biases the estimates.
The highest profile papers by Burke et al (2015a) and Dell et al (2012) conclude that there is
no robust evidence that rainfall variation has a consistent and negative effect on GDP
growth, though they find that temperature variation does have detrimental effects. I discussthese papers in more detail in Chapter 10. Other papers produce mixed results.

Source: Richard Damania, ‘The Economics of Water Scarcity and Variability’, Oxford Review of
Economic Policy 36, no. 1 (6 January 2020): 24–44, https://doi.org/10.1093/oxrep/grz027.

A study of the effect of future drought on Europe by Naumann et al (2021) found that, withadaptation, drought conditions at 4ºC of warming above pre-industrial levels would reduce
GDP in the EU and UK by 0.07% per year.

170Source: Gustavo Naumann et al., ‘Increased Economic Drought Impacts in Europe with
Anthropogenic Warming’, Nature Climate Change 11, no. 6 (June 2021): 485–91,
https://doi.org/10.1038/s41558-021-01044-3.

We saw above that the Mediterranean would be one of the regions affected the worst byincreasing droughts. According to Naumann et al (2021), there, the damage would rise to
0.14% of GDP per year by 2100, with adaptation. Low growth agrarian economies in thetropics and subtropics would be hit harder due to their limited ability to adapt. This suggeststhat the effects of climate change on water availability would be bad, but still very far fromglobal agricultural collapse.
Given all these lines of evidence, water stress seems to be a weak lever on the risk of a
global agricultural catastrophe. Indeed, over the 20th century, deaths from drought declined
enormously.

171Since 1980, global temperatures have increased by around 0.8ºC, but drought deaths haveremained very low. This is likely in large part because of economic development.

5.3.4. Heat stress for agricultural workers
Another important impact of climate change is on heat stress for agricultural workers. I
discuss this in more detail in Chapter 6.
In short, rising heat stress looks set to reduce labour capacity especially in the tropics, which
will be a site of rising population. The effect is summarised in this chart from Buzan and
Huber (2020), which assumes that people do not migrate and do not make additional use of
air conditioning.

Source: Jonathan R. Buzan and Matthew Huber, ‘Moist Heat Stress on a Hotter Earth’, Annual
Review of Earth and Planetary Sciences 48, no. 1 (2020): 623–55,
https://doi.org/10.1146/annurev-earth-053018-060100.

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There is some scope for countries to adapt to this by using mechanisation, though this
response would not be available to very poor countries. Livestock would also be exposed tothese rising levels of heat stress, which could be very damaging for animal welfare.
Lima et al (2021) estimate the effect that heat stress will have on agricultural production andprices:

Source: Cicero Z. de Lima et al., ‘Heat Stress on Agricultural Workers Exacerbates Crop Impacts of
Climate Change’, Environmental Research Letters 16, no. 4 (March 2021): 044020,
https://doi.org/10.1088/1748-9326/abeb9f.

5.4. Future agricultural progress
In the last 60 years, crop yields and food production have increased by upwards of 200%.
However, as discussed in this overview by the Breakthrough Institute, there are reasons tothink that this progress might slow down in the future. Historical trends and models suggest

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that global agricultural yields will increase by 25-150% up to 2050 due to technologicalprogress.220
Unless there is a huge trend break in agricultural progress, improvements in technology andefficiency look set to outpace the negative effects of climate change. Consequently, we
should expect food yields and food production to increase in the future, despite climatechange. However, climate change will damage food production, which would be damaging
given rising population and rising food demand.
FAO report
A good illustration of this is shown in research from the Food and Agriculture Organisation,
which finds that up to 2050 technological change will outpace the effect of climate change up
to 2050 in almost all scenarios, in all agricultural systems, and in all regions.

220

Keith Wiebe et al., ‘Climate Change Impacts on Agriculture in 2050 under a Range of Plausible
Socioeconomic and Emissions Scenarios’, Environmental Research Letters 10, no. 8 (August 2015):
fig. 3, https://doi.org/10.1088/1748-9326/10/8/085010. “We find that yields in these top four crops are
increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which isless than the 2.4% per year rate required to double global production by 2050. At these rates global
production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far
below what is needed to meet projected demands in 2050.” Deepak K. Ray et al., ‘Yield Trends Are
Insufficient to Double Global Crop Production by 2050’, PLOS ONE 8, no. 6 (19 June 2013): e66428,
https://doi.org/10.1371/journal.pone.0066428.

174

175

Source: Food and Agriculture Organization, ‘The Future of Food and Agriculture. Alternative
Pathways to 2050’, 2018,
http://www.fao.org/policy-support/tools-and-publications/resources-details/en/c/1259562/.

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Wiebe et al (2015)
Wiebe et al (2015) project that yields will increase by more than 50% and production will
increase by 25% to 125% relative to today, on a range of SSPs up to 2050.221

Compared to these improvements, the effect of trend changes in temperature andprecipitation are relatively small, though the study does not account for climatic variability.
The chart below from Wiebe et al (2015) shows the effect of warming relative to a world
without warming in 2050.

221

Keith Wiebe et al., ‘Climate Change Impacts on Agriculture in 2050 under a Range of Plausible
Socioeconomic and Emissions Scenarios’, Environmental Research Letters 10, no. 8 (August 2015):
085010, https://doi.org/10.1088/1748-9326/10/8/085010.

177

On RCP8.5, there would be nearly 2.4°C of warming by 2050. As the chart above shows,food production would fall by at most 10% on this scenario relative to a world without climatechange in 2050. But relative to today, production will be higher.
Van Dijk et al (2021)
Van Dijk et al (2021) explores future pathways in food consumption and risk of hunger on
different SSPs and RCPs. This chart projects average calories consumed per person. The

178

panel on the right compares the world without climate change and with climate change, on
RCP8.5 by 2050, which implies about 2.4C of warming:

Source: Michiel van Dijk et al., ‘A Meta-Analysis of Projected Global Food Demand and Population at
Risk of Hunger for the Period 2010–2050’, Nature Food 2, no. 7 (July 2021): 494–501,
https://doi.org/10.1038/s43016-021-00322-9.

As this shows, per capita calorie consumption looks set to increase with 2.4°C of warming.
On most SSPs, the fraction of the population at risk of hunger will decrease in the future,even with 3°C of warming, as shown in the right pane below.

179

This illustrates that socioeconomic development and economic growth will be the mostimportant determinant of the number of people at risk of hunger in the future. The difference
between the high growth future - SSP5 - and the low growth futures - SSP3 and SSP4 - aremuch larger than the projected effect of climate change.

5.5. Overall judgement on the effects of climate change on
agriculture
Climate change will damage agricultural output via a variety of mechanisms. The effects will
be worst for people in the tropics who have contributed the least to climate change and areleast able to adapt. These will also be the sites of the greatest future population growth. This
gives us a strong reason to reduce emissions and to encourage socioeconomic developmentin the affected regions.
However, the existing evidence suggests that, with warming of 4°C, total global foodproduction will very probably be higher than today. Even though food demand will rise thiscentury, food consumption per person will also very probably be higher than today. There issome evidence that synchronised food production declines for some major food crops will
increase, but these shocks will occur against a baseline in which food yields and production
are much higher.
General equilibrium effects via the price system will also attenuate some of the humanitariancosts of higher food prices. The world’s food producing reasons are geographically spreadout and climatically diverse, and there are enormous internalised market incentives to
produce food and to respond to the changing climate through crop switching, crop migration,
genetic modification, changes in cultivation practices, and so on. The overall effects of foodproduction on human welfare can be captured by economic models, which I discuss in
Chapter 10.

180

The effects of warming of 5°C and above on food production are not well-studied. The
limited modelling evidence that does exist suggests that yield losses would be around 20%
to 50% for 6°C of warming. Given how long it would take to reach 6°C, overall yields would
probably still be higher, assuming that society has not collapsed or stagnated for some otherreason. The effects of heat stress on agricultural workers is small relative to direct climaticeffects on crops.
I will now discuss the risks of extreme warming to agriculture in more depth.

5.6. Extreme warming and agriculture
What level of warming would threaten the global viability of agriculture? I have been unable
to find any studies that explore this question systematically, so will attempt my own answer.

5.6.1. Lethal limits
The majority of plant species on Earth use C3 photosynthesis, in which the first carbon
compound produced by photosynthesis contains three carbon atoms (hence the name). In
C4 photosynthesis, plants produce a compound containing four carbon atoms. 85% of plant
species are C3 plants including important sources of calories such as wheat, rice, barley,
oats, cowpeas, cassava, soybeans.
Once temperatures pass 35ºC, for C3 plants photorespiration starts to predominate overphotosynthesis.

181

Source: John R Porter and Mikhail A Semenov, ‘Crop Responses to Climatic Variation’, PhilosophicalTransactions of the Royal Society B: Biological Sciences 360, no. 1463 (29 November 2005):
2021–35, https://doi.org/10.1098/rstb.2005.1752.

Few C3 plants can survive temperatures persistently above 40ºC.222 The figure below showsa range of thresholds for wheat, rice and maize:

222“For C3 plants, photorespiration predominates over photosynthesis at temperatures in excess of
35°C (13), and few plants can survive temperatures persistently above 40°C (14).” Yadong Sun et al.,‘Lethally Hot Temperatures During the Early Triassic Greenhouse’, Science 338, no. 6105 (19 October2012): 366–70, https://doi.org/10.1126/science.1224126.

182

Source: David King et al., ‘Climate Change–a Risk Assessment’ (Centre for Science Policy, Universityof Cambridge, 2015), p. 68. www.csap.cam.ac.uk/projects/climate-change-risk-assessment223‘Maximum temperature’ here means that the relevant process will be put on hold rather thanpermanently stopped.224 For instance, while temperatures are beyond 28ºC, leaf initiation will
not occur in wheat, but once temperatures drop below 28ºC, leaf initiation can occur.
Soybean is subject to similar lethal limits.225 Lethal limits would have to be passed for around
1-5 days to kill the plant.226
The lethal limit for each of these crops is 42-47ºC. I will assume in what follows that 40ºC is
the lethal limit.

At what level of warming would plants pass lethal limits?
How high would global warming have to get for temperatures to be above 40ºC for asustained period in key food producing regions, with effects potentially sufficient to kill morethan 50% of the global population?
I am not aware of any studies that try to answer this question. So, I will attempt a roughanswer myself. I will try to determine when temperatures would pass lethal limits for major
food crops in food production in five key areas: North America, Europe, China, India andRussia. I include Russia because a lot of its frozen land would be freed up for agriculture, on
223

See also Porter and Semenov, ‘Crop Responses to Climatic Variation’, Table 2.
Mikhail Semenov, personal correspondence, 12 Aug 2021.
225
Wolfram Schlenker and Michael J. Roberts, ‘Nonlinear Temperature Effects Indicate Severe
Damages to U.S. Crop Yields under Climate Change’, Proceedings of the National Academy of
Sciences 106, no. 37 (15 September 2009): fig. 1, https://doi.org/10.1073/pnas.0906865106.
226“Figure 5 shows the influence of lethal temperatures on simulated yield for thresholds of 40, 45,
and 50 ◦C and their exceedence for 1, 3, and 5 consecutive days in order to lead to plant death.”
Ann-Kristin Koehler et al., ‘Influences of Increasing Temperature on Indian Wheat: Quantifying Limits
to Predictability’, Environmental Research Letters 8, no. 3 (August 2013): 034016,
https://doi.org/10.1088/1748-9326/8/3/034016.
224

183

extreme warming scenarios. Together, these regions today account for 70% of global cereal
production. North America, China and Europe alone account for 57% of global cereal
production.
I will consider warm and cold locations in each of these regions in order to represent a
reasonable spread of climatic conditions in the relevant regions.

184My sole focus in this section is on when different crops start to pass lethal limits. So, it isimportant to stress that there are many things my approach will leave out:●●●

Heat stress for agricultural workers
Changes in soil moisture and drought
Changes in pest distributions

I also do not consider how appropriate the other determinants of agricultural production,such as soil quality and type, might be in the regions considered. However, I will try tocompare specific regions where agriculture is currently carried out at large scale. The
exception to this is cold regions in Russia, which I investigate because they are relevant to
extreme future warming. In general, my answer will be quite rough and back of the envelope
but, I hope, better than nothing.
The map below shows annual average temperatures across the world.

185

Global warming will not be uniform across the world. Warming is expected to be higher athigh latitudes, and is generally larger over land than the oceans.

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, SPM.5.

However, annual average temperature masks significant seasonal and diurnal variation,
which has an important bearing on the prospects for agriculture.
The key risk to food crops from warming is that at some point in the period from planting toharvest, temperatures pass lethal limits. It is therefore important to consider the planting
seasons for the major food crops●

Wheat (source)

186

○

●

●
●

Winter wheat: planted in the autumn, 7-8 months to reach full maturity,
harvested in spring or early summer.
○ Spring wheat: planted in spring, 4 months to maturity, harvest in summer or
early autumn.
Rice (source)○ Plant in late winter/early summer, 4 months to reach maturity, harvest insummer to early winter.
Maize (source)○ Plant in spring/summer, 4 months to reach maturity, harvest in autumn.
Soy (source)○ Late spring/early summer, 2 months to reach maturity, harvest insummer/autumn.

EuropeTo capture a reasonable range across weather in Europe, I will compare Málaga in southern
Spain (a hot region) to Edinburgh (a cold region).
The charts below shows changes in monthly temperatures in Málaga

The temperature highs shown are mean daily highs in a given month. So, the highs would
mask some within-month variation. Data for August 2021 suggests that temperatures mightvary by around 6ºC either side in a given month. So, we can assume that the true
temperature high in the hottest months is 6ºC higher.
If there was local warming of 6ºC, then summer temperatures would approach lethal limits inthree of the summer months. However, the growing season for the major food crops is 8
months or less, so agriculture would still be viable even if temperatures were this high.
To destroy the growing season for most major food crops, lethal limits would have to bepassed for more than 8 months in a year. This would happen once local warming reached
15ºC. It is unclear what level of global average surface warming would be required to187

produce 15ºC of warming in Málaga, but, based on the IPCC map of future regionaltemperatures, ~10ºC of global average surface warming is a reasonable bet.

●●

Lethal limits in summer: 17ºC local warming227
Wipe out 8 months of the growing season: 24ºC.
○ Equates to ~20ºC of global average warming.

North America
For North America, I will compare Houston, Texas (a hot region) with Calgary, Alberta (a cold
region).

●●227

Lethal limits in summer: 5ºC local warming
Wipe out 8 months of the growing season: 15ºC.
○ Equates to ~10ºC of global average warming.

This is using the same method used for Málaga, described above.

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●
●

Lethal limits in summer: 14ºC local warming
Wipe out 8 months of the growing season: 31ºC.
○ Equates to ~15ºC of global average warming because warming is so muchhigher at high latitudes.

China
For China, I will compare Guangzhou (a hot region) with Shenyang (a colder region).

●●

Lethal limits in summer: 3ºC local warming
Wipe out 8 months of the growing season: 11ºC.
○ Equates to ~11ºC of global average warming

189

●
●

Lethal limits in summer: 10ºC local warming
Wipe out 8 months of the growing season: 31ºC
○ Equates to ~31ºC of global average warming

RussiaFor Russia, I will compare Moscow (a fairly climatically typical city) to Yakutsk (the coldest
city on Earth)●●

Lethal limits in summer: 13ºC local warming
Wipe out 8 months of the growing season: 34ºC.
○ Equates to ~20ºC of global average warming

190

●
●

Lethal limits in summer: 13ºC local warming
Wipe out 8 months of the growing season: 59ºC.
○ Equates to ~30ºC of global average warming

India
There is less variation in temperatures across India than in the regions I have considered sofar. To illustrate the variation, I choose Kochi, a city in the south of India, and New Delhi inthe north.

●

Wipe out 8 months of the growing season: 9ºC.
○ Equates to ~9ºC of global average warming.

191

●

Wipe out 8 months of the growing season: 10ºC.
○ Equates to ~10ºC of global average warming.

These findings are summarised in the table below
RegionEuropeNorth America

China

Russia

India

City

Global average warming todestroy 8 months of growing
seasonMálaga10ºC

Edinburgh

20ºC

Houston

10ºC

Calgary

15ºC

Guangzhou

11ºC

Shenyang

31ºC

Moscow

20ºC

Yakutsk

30ºC

Kochi

9ºC

New Delhi

10ºC

To repeat, this exercise only assesses thermal limits to plants, and not other determinants offood production.
This analysis suggests that more than 10ºC of global average surface warming would
destroy agriculture in India and regions with similar climates, as well as warm regions in
China, Europe and North America. Warming of 15ºC would destroy agriculture in North
America, while warming of around 30ºC would destroy agriculture in China and Russia.

192

These estimates are uncertain, but I wouldn’t expect them to be wrong by more than 6ºC
either side.

5.6.2. Extreme heat stress and agricultural labour
In the previous section I discussed the findings of de Lima et al (2021) which found that for
5ºC of warming, agricultural production would decline by 1-3% due to heat stress. I am notaware of any studies quantifying the effect of more than 5ºC of warming.
The findings of Buzan and Huber (2020) on labour supply are shown below

Source: Buzan and Huber, ‘Moist Heat Stress on a Hotter Earth’.

Buzan and Huber (2020) find that, assuming people do not move and do not take adaptation
measures, agricultural labour would be all-but impossible in the tropics for 8ºC of warming,and at the global level, population-weighted labour capacity would decline to 50% of its
potential. Given the pessimistic assumptions about response and adaptation, this estimate isplausibly on the high side, but the scope for adaptation in agriculture is also limited.

5.6.3. Summary of extreme warming and agriculture
I argued in Chapter 2 that on a worst-case scenario in which we burn all the fossil fuels,
warming would most likely reach 7ºC, and there is a 1 in 6 chance of more than 9.5ºC ofwarming. The evidence we have suggests that even this level of warming would not comeclose to destroying global agriculture, though it would have disastrous effects. Agriculturalland would be freed up in Russia and Canada, which would offset some of the costs in othercountries.
In general, the persistence of agriculture relies on:●●●●

Sunlight
Enough rain and water resources in enough regions of the world
Sufficient CO2 levels
Temperatures not falling below freezing in enough regions of the world, which
destroys the growing season

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●

Temperatures not rising above lethal limits for major food crops in enough regions of
the world

It is easy to see how an event like nuclear winter could threaten the global viability ofagriculture. A nuclear winter would block out the Sun, and would cause temperatures to drop
below freezing in many regions, which would destroy the growing season. In contrast, it isdifficult to see how plausible levels of global warming could do comparable damage.
●●●

There would still be sunlight and enough CO2 to allow photosynthesis.
Lethal limits for the major food crops are a very long way away.
No climate models project that rain will stop completely due to climate change. In
fact, global average precipitation will increase.228 Some regions would get wetter andsome would get drier. There would still be enough water resources in some regions
to maintain agriculture.

Although climate change will be damaging to agriculture, it is difficult to come up withrealistic scenarios in which food production would decline by more than half due to climatechange.
One important possible caveat to this is the risk of tipping points, which I discuss in Chapter
8.

5.7. Ecosystem collapse and threats to agriculture
One possibility is that climate change could lead to ecosystem collapse which would
undermine global agriculture. The causal chain would look something like this:Global warming + habitat loss + human predation + pollution => global species loss=> global ecosystem collapse => global agricultural catastrophe
For example, Steffen et al (2015) propose that reducing biodiversity too far below
pre-industrial levels would consist in crossing a ‘planetary boundary’. According to Steffen etal (2015), crossing these boundaries would greatly increase the risk of driving “the Earthsystem to a much less hospitable state”.229
This argument focuses on the instrumental benefits of ecosystems. Ecosystems may also
have intrinsic value, but my focus here is the benefits they provide to human civilisation.

5.7.1. Trends in biodiversity
Our World in Data recently released several entries about biodiversity, which this section
relies on.

228

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Summary for Policymakers, p. 19.
229
Will Steffen et al., ‘Planetary Boundaries: Guiding Human Development on a Changing Planet’,
Science 347, no. 6223 (13 February 2015): p. 737, https://doi.org/10.1126/science.1259855.

194

Species extinctions
Since 1500, 900 species have been recorded as extinct.

However, the share of species that have been evaluated for extinction varies acrosstaxonomic groups

195This means that the estimate of the number of extinct and threatened species is anunderestimate.
Threatened species are the sum of the following three categories●●●

Critically endangered species have a probability of extinction higher than 50% in
ten years or three generations.
Endangered species have a greater than 20% probability in 20 years or fivegenerations.
Vulnerable species have a probability greater than 10% over a century.

However, this is all based on ‘business as usual’ assumptions about how we will treat these
species in the future. This assumption may not hold because classifying a species as‘endangered’ or ‘vulnerable’ can be a call to action for conservation groups. For instance, arecent study of 48 bird and mammal populations found that many species classed as‘endangered’ or ‘vulnerable’ had increasing populations, though many are still decreasing.

196

The following chart shows among the taxonomic groups for which at least 80% of species
have been assessed, the share of species threatened with extinction.

197

Due to elevated extinction rates, many scholars argue that we are in the middle of a Sixth
Mass Extinction.
The rate of extinction is measured in extinctions per million-species years (E/MSY). The
natural rate of extinction is 0.1 to 1 extinctions per million-species years. The rate of species
extinctions is much faster than the natural background rate.

Since 1500, 1% of species have gone extinct, so we seem to be quite far from the 75%
threshold. But we also need to consider the number of species threatened with extinction.
Recall that vulnerable species have a probability of extinction greater than 10% over a
century. If you assume that all vulnerable species will go extinct, then we are much closer tothe 75% threshold.

198

It is unclear how we should estimate the time it would take for there to be a sixth mass
extinction. One option would be to base the estimate solely on the number of species
recorded as extinct. Since 1500, we have lost 1% of species. If you project this average rate
forward, then it would take 37,500 years to reach the 75% threshold. This is fast ingeological terms but we would have lots of time to adapt and respond to this. If the
post-1980 rate continues into the future, we would get there faster - in 18,000 years.
But this estimate would change if we make different assumptions about which species will go
extinct:

199

The dark blue scenarios are pessimistic. Threatened species include ‘vulnerable’ species
which have a 10% chance of going extinct within 100 years, so it would be a surprise if
100% went extinct over 100 years. The light red scenarios are optimistic. ‘Criticallyendangered’ species are defined as those that have a probability greater than 50% of going
extinct in the next 50 years, so it would be a surprise if it took 500 years for 100% of them togo extinct, rather than 100 years.
So, the light blue and dark red scenario seem more plausible: they suggest that it would takeroughly 1,000 to 3,000 years for there to be a mass extinction, if current trends continue.

Population losses
Another argument that we are in a sixth mass extinction points to population losses.230 The
Living Planet Report assesses trends in more than 20,000 wildlife populations, including
4,000 species across the world, which is only a small sample of the 2 million identified
230

Gerardo Ceballos, Paul R. Ehrlich, and Rodolfo Dirzo, ‘Biological Annihilation via the Ongoing Sixth
Mass Extinction Signaled by Vertebrate Population Losses and Declines’, Proceedings of the National
Academy of Sciences 114, no. 30 (2017): E6089–96.

200species. The report is global in scope. The chart below shows which populations are
increasing and which are decreasing.

The average decline across wildlife populations since 1970 is 68%. This means that theabsolute magnitude of the decline is much greater in the declining populations than in theincreasing populations. This doesn’t mean that 68% of studied populations are declining.
Our World in Data discusses the meaning of the average figure here.
The import of this for the Sixth Mass Extinction is unclear. If species are evenly spread
across the increasing and decreasing populations, then even if some populations go extinct,
other populations would increase in abundance and so the whole species would not go
extinct. But it might be that all of the species are concentrated on one side of the chart:maybe all rhino populations are declining, whereas all seagulls populations are increasing.
The chart above does not tell us what is happening at the species level.

Speciation and hybridisation
Overall biodiversity is determined by the balance between extinction and speciation.
Although extinctions have been increasing, speciation has also been increasing. Thomas(2015) explains why speciation has increased:“The human-assisted movement of plants, animals, and microbes around the world
has increased hugely over recent centuries, breaking down geographic barriers
between species that exhibit incomplete genetic barriers to reproduction and, hence,setting the scene for a massive increase in levels of hybridisation [8–12]. Subsequent201

genetic changes, including duplication of the entire genome (polyploidy) andchromosomal rearrangements, have, in one or a few generations, converted small
numbers of these hybrid individuals or their offspring into sexually reproducing
species that have limited compatibility with the parental species [9,12–16]. The new
hybrids can be at least as genetically distinct (by virtue of genomes derived from twoparental species) as congeneric species that have arisen through geographic
separation over longer periods of time.”231
Direct genetic modification of plants is another way that humans can accelerate speciation.
Thomas (2015) estimates that current speciation could be 100 - 10,000 times faster than thenatural background rate.232 Data on both speciation and extinction is very poor, but according
to Thomas (2019), as a rule, due to anthropogenic influence, local diversity stays about thesame, regional diversity increases and global diversity declines.233 More new plant species
have come into existence in Europe over the past three centuries than have been
documented as becoming extinct over the same period, even though most new hybrid-origin
species are likely to remain undetected.234 But at the global level species diversity has
declined.
As I discuss below, ecosystem services are largely determined by the relationships within
local ecosystems.

5.7.2. Drivers of species extinctions
The main posited drivers of species extinctions so far have been:235●●●

Land use change and habitat loss
Direct exploitation or predation
Sea-use change

Many scholars believe that climate change will have an increasingly large impact in the
future.

231

Chris D. Thomas, ‘Rapid Acceleration of Plant Speciation during the Anthropocene’, Trends in
Ecology & Evolution 30, no. 8 (2015): 1. See also Bull, J. W., & Maron, M. (2016). How humans drive
speciation as well as extinction. Proceedings of the Royal Society B: Biological Sciences, 283(1833),
20160600.
232“Considering these together, the Anthropocene plant speciation rate could be two to four orders ofmagnitude greater than the background rate.” Thomas, ‘Rapid Acceleration of Plant Speciation during
the Anthropocene’, 6.
233
Chris D. Thomas, ‘The Development of Anthropocene Biotas’, Philosophical Transactions of theRoyal Society B: Biological Sciences 375, no. 1794 (16 March 2020): sec. 4,https://doi.org/10.1098/rstb.2019.0113.
234“Considering these together, the Anthropocene plant speciation rate could be two to four orders ofmagnitude greater than the background rate.” Thomas, ‘Rapid Acceleration of Plant Speciation during
the Anthropocene’, 1.
235
Sandra Diaz et al., ‘The Global Assessment Report on Biodiversity and Ecosystem Services’, The
United Nations’ Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services,
2019, 28.

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The Species-Area Relationship
Biodiversity loss is often estimated using the Species-Area Relationship, which is describedas “ecology’s oldest law”.236 The Species-Area Relationship describes a widespread
relationship between the area investigated and the number of species present. Bigger areas
have more species, but the relationship between area and species number is curved: it
typically follows a power function of the form S = cAz , where S is the number of species, A is
area, and c and z are constants. Thus, on log-transformed axes the relationship between S
and A is linear and the slope of the best fitting regression line gives the exponent of thepower function, z.
The charts below the species area relationship for a species of butterfly (note the log scale)

Source: Owen T Lewis, ‘Climate Change, Species–Area Curves and the Extinction Crisis’,
Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1465 (29 January2006): 163–71, https://doi.org/10.1098/rstb.2005.1712

236

Owen T Lewis, ‘Climate Change, Species–Area Curves and the Extinction Crisis’, PhilosophicalTransactions of the Royal Society B: Biological Sciences 361, no. 1465 (29 January 2006): 163–71,
https://doi.org/10.1098/rstb.2005.1712

203

Since bigger areas have more species, destroying habitat leading to some species going
extinct or being ‘committed to extinction’, which means that species may survive for many
generations.

Climate change as a driver
The Species Area Relationship has been applied to assess the potential impact of climatechange. Since climate change alters the area and location of habitat available to a species,
climate change will also cause extinctions, according to the Species Area Relationship.
One prominent pessimistic paper on the effect of climate change on ecosystems is byThomas et al (2004). They assume that the ‘climate envelope’ available to species will movefaster than species are able to disperse. How many species will go extinct as a result
depends on species’ ability to track shifting climates. Thomas et al (2004) estimate that due
to climate change, 15-37% of all species will become ‘committed to extinction’ by
mid-century.237 Since species are ‘committed to’ extinction and not actually extinct, there may
be a lag of a few decades between a species being committed to extinction and actually
going extinct.238
Most studies are less pessimistic than Thomas et al (2004). For example, theIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services states:“For instance, a synthesis of many studies estimates that the fraction of species atrisk of extinction due to climate change is 5 per cent at 2°C warming, rising to 16 percent at 4.3°C warming.”239
The percentages here are lower and ‘risk of extinction’ is a lower bar than ‘committed toextinction’, as the latter suggests that the extinction is guaranteed.
The IPCC provides the following diagram which illustrates how, according to some models,
warming will be so fast on RCP8.5 that many species will not be able to move fast enough tostay in their ecological niche.

237

Chris D. Thomas et al., ‘Extinction Risk from Climate Change’, Nature 427, no. 6970 (January2004): 145–48, https://doi.org/10.1038/nature02121.
238“decades might elapse between area reduction (from habitat loss) and extinction” Chris D. Thomas
et al., ‘Extinction Risk from Climate Change’, Nature 427, no. 6970 (January 2004): 145–48,
https://doi.org/10.1038/nature02121.
239“Globally, land-use change is the direct driver with the largest relative impact on terrestrial andfreshwater ecosystems, while direct exploitation of fish and seafood has the largest relative impact inthe oceans (well established) (Figure SPM.2) {2.2.6.2}. Climate change, pollution and invasive alien
species have had a lower relative impact to date but are accelerating” Sandra Diaz et al., ‘The Global
Assessment Report on Biodiversity and Ecosystem Services’, The United Nations’ Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services, 2019, 16.

204

Source: IPCC, Climate Change 2014: Impacts, Adaptation, and Vulnerability (Cambridge University
Press, 2014), Fig. 4-5.

5.7.3. Could ecosystem collapse destroy agriculture?
The causal chain for the ecosystem collapse argument is as follows:205

Global warming + habitat loss + human predation + pollution => global species loss=> global ecosystem collapse => global agricultural catastrophe
I will now examine the plausibility of each part of this causal chain.

From human intervention to the destruction of agricultural ecosystem services
I will start by examining the first part of the causal chain
Global warming + habitat loss + human predation + pollution => global species loss
Before I discuss this part of the causal chain, it should be noted that the instrumental valueof ecosystems is almost entirely a product of the relationships within local ecosystems.
Global average loss of biodiversity matters only insofar as it is indicative of local biodiversity
loss. Following the discovery of the Hawaiian Islands by the Polynesians 1500 years ago,
they eliminated so many species that even the decadal global extinction rate would havebeen exceptional.240 This has next to no bearing on the risk of ecological collapse outside of
Hawaii. (It also did not cause catastrophic ecosystem collapse in Hawaii).
Thus, damage to biodiversity only threatens global civilisation if local biodiversity is declining
in all regions. I will now discuss several reasons that this appears not to be the case.
Extinctions have mainly occurred on islands
Of extinctions registered up to 2012, 95% occurred on islands or Australia and not on
continents.241 Since 1500, only six continental birds and three continental mammals havegone extinct. The rate of continental extinctions per million species years is therefore 1.61,
which is below one estimate of the ‘natural’ or background rate of 2 extinctions per million
species years (though this is likely an overestimate of the background rate).242
This is despite enormous deforestation. According to Loehle and Eschenbach (2012),“human predation (e.g. unregulated hunting and gathering of eggs) was a major factor inmost confirmed extinctions, particularly on islands”.
“However, it is worth noting that to date, no continental mammal or bird in our
databases has been documented to have gone extinct solely because of habitat
reduction. Early prehistoric waves of extinction (America around 12,000BP andAustralia over 50,000 year ago) also were not because of habitat alteration (exceptfire) but largely because of hunting and other exploitation (e.g. egg gathering)(Trueman et al., 2005; Sodhi et al., 2009; Prideaux et al., 2010; Ripple & Van
Valkenburgh, 2010). All extinctions of marine mammals are strictly because of
240

José M. Montoya, Ian Donohue, and Stuart L. Pimm, ‘Planetary Boundaries for Biodiversity:
Implausible Science, Pernicious Policies’, Trends in Ecology & Evolution 33, no. 2 (1 February 2018):
71–73, https://doi.org/10.1016/j.tree.2017.10.004.
241
Craig Loehle and Willis Eschenbach, ‘Historical Bird and Terrestrial Mammal Extinction Rates andCauses’, Diversity and Distributions 18, no. 1 (2012): 84–91,
https://doi.org/10.1111/j.1472-4642.2011.00856.x.
242
Gerardo Ceballos et al., ‘Accelerated Modern Human–Induced Species Losses: Entering the Sixth
Mass Extinction’, Science Advances 1, no. 5 (1 June 2015): e1400253,
https://doi.org/10.1126/sciadv.1400253.

206

hunting and are obviously not because of habitat alteration or introduced
predators.”243
This weakens one argument for the following causal links:
Habitat loss => global species loss
Climate change => global species loss
As we saw above, one argument maintains that because the rate of species extinctions is sohigh, we are facing a global mass extinction event if trends continue. But this does not follow
because these extinctions are heavily concentrated among endemic species on islands. The
rate of continental species extinctions is close to one estimate of the ‘natural’ backgroundrate. The extinction of the dodo, which was endemic to Mauritius, is quite representative of
extinctions in the last few centuries, but it is not relevant to ecosystem services outside of
Mauritius.
This also suggests that the species that are being killed off are not providing ecosystemservices that are necessary for global agriculture. Animal extinctions are concentrated
among endemic species on islands, rather than species that might be necessary to global
agriculture.
Each of these points undermine one argument for the ‘ecosystem collapse => global
agricultural collapse’ causal chain. However, other arguments for that causal chain might bemore plausible.
Forest cover is increasing in many regions
Deforestation is declining in temperate regions. Net forest loss peaked in the 1980s and isdeclining.

243

Craig Loehle and Willis Eschenbach, ‘Historical Bird and Terrestrial Mammal Extinction Rates andCauses’, Diversity and Distributions 18, no. 1 (2012): 84–91,
https://doi.org/10.1111/j.1472-4642.2011.00856.x.

207

Houghton and Nassikas (2017) estimate that forest area started increasing outside thetropics in 1950 not 1990, but that deforestation in the tropics peaked in 2000 rather than in
the 1980s.244 I’m not sure what explains these discrepant findings.
There is also research suggesting that conventional estimates of global net forest change
are overestimates because they neglect the countervailing effects of fire management,
plantations and replanting. Accounting for this, Mendelsohn and Sohngen (2019) argue thatnet forest cover has increased substantially since 1900.245 This is just one study however
and it is too soon to know whether opinion in the field will shift towards their position.
All of this is relevant to the following part of the causal chain:
Habitat loss => global species loss
Since forest habitat is increasing outside the tropics, habitat destruction is unlikely to be adriver of net species extinctions outside the tropics. If the extent of habitat loss is the main
driver of biodiversity loss, then one would expect the biodiversity situation to improve intemperate regions. Thus, habitat loss does not threaten global agricultural catastrophe due
to ecosystem collapse.

244

R. A. Houghton and Alexander A. Nassikas, ‘Global and Regional Fluxes of Carbon from Land Useand Land Cover Change 1850–2015’, Global Biogeochemical Cycles 31, no. 3 (2017): Fig. 1,
https://doi.org/10.1002/2016GB005546.
245
Robert Mendelsohn and Brent Sohngen, ‘The Net Carbon Emissions from Historic Land Use andLand Use Change’, Journal of Forest Economics 34, no. 3–4 (2019): 263–83.

208

Local species richness is stable
Although it is declining globally, biodiversity is increasing or stable in many regions. This isbecause losses are being offset by increasing numbers of introduced or ‘invasive’ species.
Various meta-analyses have shown that local species richness is fairly stable:“Regardless of habitat type, geographic region, or most of the dominant ecologicalimpacts discussed by the authors of individual studies (e.g., grazing, climate change,or pollution), the average change in species richness was not significantly different
from zero (Vellend et al., 2013; Figure 4.2a). In a meta-analysis of this nature, onemust always worry about biases in terms of where and when people have conductedempirical studies. For example, our sample of studies was distributed in a decidedly
non-random way around the globe, with a preponderance of studies in Europe andNorth America—as is the case for essentially any general topic in ecology. However,in the 28 studies from under-represented parts of the globe (South America, Africa,
Asia, and Australia), there was, if anything, a slight tendency for increases in species
richness over time. To us, our results constituted a strong contradiction of theassumption that local-scale plant biodiversity loss is widespread and of large
magnitude (e.g., >20%) in situations where biodiversity change can potentially impact
ecosystem services.”“Interestingly, our study was followed in quick succession by three othermeta-analyses using independent data from different taxa and ecosystems showing
essentially the same thing—no significant directional biodiversity change when
averaged across many studies (Dornelas et al., 2014; Supp and Ernest, 2014)—or
even an average increase in local-scale diversity in marine systems (Elahi et al.,2015; see Figure 4.2b,c). Taken together, to my eye, there is now clear evidence
countering the notion of biodiversity declines at the local scale as a general,
widespread rule.”246
The figure below shows there to be little change in species richness over time

246

Mark Vellend, ‘Are Local Losses of Biodiversity Causing Degraded Ecosystem Function?’, in
Effective Conservation Science, by Peter Kareiva, Michelle Marvier, and Brian Silliman (Oxford
University Press, 2017).

209

Source: Mark Vellend, Are Local Losses of Biodiversity Causing Degraded Ecosystem Function?

One potential counter-argument to this is that although local biodiversity is stable, (1) the
new local ecosystems have lower ecosystem services than the original ecosystems, and (2)the failure of these ecosystems would be more correlated, which poses a greater risk toglobal agriculture.
On (1), one thing we can say is that if lower ecosystem services have had an effect, it has
not much effect so far given the massive increase in food yields over the last century. But it
might be that there is some unknown nonlinear tipping point after which ecosystem services
collapse. I discuss this possibility below. Independent of that, it is difficult to see why (1)would be true. A meta-analysis of the effect of ‘invasive’ species by Vilà et al (2011) foundthat “the magnitude and direction of the impact varied both within and between different
types of impact… On average, abundance and diversity of the resident species decreased in
invaded sites, whereas primary production and several ecosystem processes wereenhanced.”247
Track record of predictions based on species area relationships
Many claims that habitat loss and climate change will cause biodiversity loss depend on thespecies area relationship. Some, though not all, of these predictions have a poor trackrecord. The table below lists some of these predictions.

247

Montserrat Vilà et al., ‘Ecological Impacts of Invasive Alien Plants: A Meta-Analysis of Their Effects
on Species, Communities and Ecosystems’, Ecology Letters 14, no. 7 (2011): 702–8.

210

Source: Nigel E. Stork, ‘Re-Assessing Current Extinction Rates’, Biodiversity and Conservation 19,
no. 2 (1 February 2010): 357–71, https://doi.org/10.1007/s10531-009-9761-9.

With the benefit of hindsight, all of the estimates from the 1980s are substantial
overestimates. If these estimates were correct, we would expect 25% to 100% of species tobe committed to extinction today, which is far higher than estimates of ‘critically endangered’species today.

211

The table also shows the estimate from Thomas et al (2004), which entails an implied
species loss of 3.2% to 7.8% per decade. Since Thomas et al (2004) was published 17
years ago, his prediction would suggest that due to climate change, 7.5% of species are
already committed to extinction due to climate change. This is at odds with documented
extinctions so far. As I have mentioned, of the confirmed extinctions, 95% have occurred on
islands due to direct predation, not due to global climate change.
Predictions of biodiversity loss from the species-area relationship have been the subject of
criticism. Botkin et al (2007) outline six limitations of the species-area relationship as a tool
to model biodiversity loss:“First, it assumes an equilibrium (or very slowly changing) relationship between
species number and area.
Second, the future climate probably will not be an exact analog of the current one, so“moving” a bioclimatic zone for an ecological type may not be accurate (Malcolm etal. 2006).
Third, topographic variation, which affects the species–area curve shape, may begreater or less in the future zone.
Fourth, factors relating to the shape of areas and the amount of their fragmentationsuggest that an alternative “endemics–area curve” may enable more accurate
predictions (Harte et al. 2004).
Fifth, the correct z value must be chosen: It must apply to the entire area underconsideration, and it must also consider the type of area and timescale applicable(Rosenzweig 1995).
Sixth, many species are not confined to a particular vegetation zone or type. For the
species–area relationship to predict species extinctions, the area must be for closed
communities. Thomas and colleagues (2004) used individual species distributions as
the basis for their analysis. They examined changes in realized niches without taking
into account the likelihood of changed interactions and adaptation, and thus the newareas that they predicted were probably too small. How these area changes relate tochanges in area of closed communities is unclear.”248
He and Hubbell (2011) argue that species–area relationships always overestimate extinction
rates from habitat loss:

248

Daniel B. Botkin et al., ‘Forecasting the Effects of Global Warming on Biodiversity’, BioScience 57,
no. 3 (1 March 2007): 227–36, https://doi.org/10.1641/B570306. See also Carsten F. Dormann,‘Promising the Future? Global Change Projections of Species Distributions’, Basic and Applied
Ecology 8, no. 5 (3 September 2007): 387–97, https://doi.org/10.1016/j.baae.2006.11.001; Owen T
Lewis, ‘Climate Change, Species–Area Curves and the Extinction Crisis’, Philosophical Transactionsof the Royal Society B: Biological Sciences 361, no. 1465 (29 January 2006): 163–71,
https://doi.org/10.1098/rstb.2005.1712.

212

“The key mathematical result is that the area required to remove the last individual ofa species (extinction) is larger, almost always much larger, than the sample area
needed to encounter the first individual of a species, irrespective of species
distribution and spatial scale. We illustrate these results with data from a global
network of large, mapped forest plots and ranges of passerine bird species in thecontinental USA; and we show that overestimation can be greater than 160%”249
Models vs the paleoclimate
In section 3, I discussed at length how well ecosystems fared during periods in which
temperatures were much higher than today, and warming was comparable fast on a regional
basis, compared to today. Since the breakup of Pangea, climate change has not beencorrelated with elevated rates of species extinctions.
In contrast to this, the recent IPBES report predicts substantial species extinctions: 5% of
species at risk of extinction at 2ºC, rising to 16% at 4ºC.250 As we saw above, this was on thebasis of the Species-Area Relationship, which predicts that species’ ecological niche will
move faster than their ability to disperse. This same model therefore predicts that there
would have been extensive species loss during the transition from the Pleistocene to the
Holocene.
As we saw in Chapter 3, that is not what happened. Despite regional warming of 2ºC to 15ºC
per century, there is little evidence of local extinction (aka ‘extirpation’).251 This strongly
suggests that the models are wrong.
Willis and MacDonald (2011) list several reasons that might explain where the models go
wrong.
1. CO2 fertilisation - Higher levels of CO2 increase photosynthesis and carbon uptake,“which may account for increased growth rates in the African and American
rainforests in the last 30 years.” “Recent modeling has demonstrated that increased
primary productivity has a linear positive relationship with diversity capacity such thata given ecosystem can support a greater number of species during intervals of higher
CO2 (Woodward & Kelly 2008). Interestingly, when results from this model are249

Fangliang He and Stephen P. Hubbell, ‘Species–Area Relationships Always Overestimate
Extinction Rates from Habitat Loss’, Nature 473, no. 7347 (2011): 368–71.
250“Globally, land-use change is the direct driver with the largest relative impact on terrestrial andfreshwater ecosystems, while direct exploitation of fish and seafood has the largest relative impact inthe oceans (well established) (Figure SPM.2) {2.2.6.2}. Climate change, pollution and invasive alien
species have had a lower relative impact to date but are accelerating” Sandra Diaz et al., ‘The Global
Assessment Report on Biodiversity and Ecosystem Services’, The United Nations’ Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services, 2019, 16.
251
Terence P. Dawson et al., “Beyond Predictions: Biodiversity Conservation in a Changing Climate,”
Science 332, no. 6025 (April 1, 2011): 53–58, https://doi.org/10.1126/science.1200303; Christian Hofet al., “Rethinking Species’ Ability to Cope with Rapid Climate Change,” Global Change Biology 17,
no. 9 (September 1, 2011): 2987–90, https://doi.org/10.1111/j.1365-2486.2011.02418.x; K. J. Willis
and G. M. MacDonald, “Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World,” Annual Review of Ecology, Evolution, and Systematics 42, no. 1(2011): 267–87, https://doi.org/10.1146/annurev-ecolsys-102209-144704; Daniel B. Botkin et al.,“Forecasting the Effects of Global Warming on Biodiversity,” BioScience 57, no. 3 (March 1, 2007):
227–36, https://doi.org/10.1641/B570306.

213

applied to future climatic scenarios, the output predicts enhanced plant growth, anincrease in ecosystem productivity, and higher diversity (Woodward 2010). What is
also apparent from this model, however, is that “weed” species for which migration isan insignificant barrier are likely to fill future diversity capacities”.
2. Higher levels of ecological tolerance - Many species had a much wider ecologicaltolerance than is apparent from their present day distributions, and thus they containgene variations that enable tolerance of much higher temperatures and water stress.
3. Plants survived in refugia - “those plants unable to adapt became restricted tosmall, microenvironmentally favorable refugia where they were able to persist”.
“Another factor leading to persistence appears to have been the survival of
populations in small, environmentally favorable refugial localities, as seen for many
European alpine species during the mid-Holocene climatic optimum”
A common response to this is to argue that species will today have to adapt in the context offragmented habitat, which gives less scope for dispersal. For instance, the IPCC says:“Finally, evidence from the paleontological record indicating very low extinction rates
over the last several hundred thousand years of substantial natural fluctuations inclimate—with a few notable exceptions such as large land animal extinctions during
the Holocene—has led to concern that forecasts of very high extinction rates due
entirely to climate change may be overestimated (Botkin et al., 2007; Dawson et al.,2011; Hof et al., 2011a; Willis and MacDonald, 2011; Moritz and Agudo, 2013).
However, as indicated in Section 4.2.3, no past climate changes are precise analogs
of future climate change in terms of speed, magnitude, and spatial scale; nor did theyoccur alongside the habitat modification, overexploitation, pollution, and invasive
species that are characteristic of the 21st century. Therefore the paleontologicalrecord cannot easily be used to assess future extinction risk due to climatechange”252
It may be true that climate change is happening in the context of habitat modification,overexploitation, pollution and invasive species, but the models predicting substantial
species loss due to climate do not factor this in. Therefore, the paleoclimatic evidence does
indeed show that the models predicting substantial species loss are not reliable.
This does not necessarily mean that there will not be substantial species loss due to climatechange. It is just to point out that the current models are mistaken. Indeed, it is plausible that
because it will occur in the context of habitat modification, climate change will cause
biodiversity loss.

Global species loss and agriculture
So far, I have focused on the first part of the causal chain
Global warming + habitat loss + human predation + pollution => global species loss
I am now going to focus on the later parts of the causal chain

252

IPCC, Climate Change 2014: Impacts, 301.

214

Global species loss => global ecosystem collapse => global agricultural catastrophe

Clarifying the argument
It is worth clarifying exactly what is posited to happen in this part of the causal chain. The
idea is that there would be a loss of species which are crucial to the production of all major
food crops in all major food producing regions. This would be a very dramatic event, and I
have not seen a detailed description of what exactly is meant to happen.
One example would be if pollinators like bees and butterflies went extinct due to climatechange. This is discussed by Our World in Data here. A third of crop production depends onpollinators; staple cereals that account for the majority of food production do not depend on
pollinators. Moreover, few crops are entirely dependent on pollinators. According to Our
World in Data, “studies suggest crop production would decline by around 5% in higher
income countries, and 8% at low-to-middle incomes if pollinator insects vanished”.
Outside of crops dependent on pollinators, it is difficult to see how species loss could
threaten destruction of all major crops in all major food producing regions in very diverse
ecosystems and climates.

Closeness to pre-modern ecology and living standards are negatively correlated
Many scholars writing on biodiversity loss seem to believe that any departures from thepre-agricultural or pre-industrial ecosystem are bad. The Biodiversity Intactness Index
measures such departures and is “defined as the average abundance of a taxonomically andecologically broad set of species in an area, relative to their abundances in an intact
reference ecosystem”, where the reference is usually the pre-industrial ecosystem.253
However, closeness to pre-industrial ecology appears to be negatively correlated with levelsof consumption now and into the long-term.
Steffen et al (2015) argued that the planetary boundary for the Biodiversity Intactness Index
is 90%.254 What exactly the implications of passing this alleged planetary boundary aremeant to be is somewhat unclear, but one natural interpretation is that we risk passing athreshold which would diminish consumption substantially or threaten human civilisation.
Indeed, the IPBES interprets the threshold in this way:“That framework suggests that large regions whose biotic integrity – i.e., the fractionof originally-present biodiversity that remains – falls below 90% risk large-scale
failure of ecosystem resilience that would cause critical reductions in the flows ofnature’s contributions to people (Steffen et al. 2015b) though there is a great deal of
uncertainty about precisely where any boundary should be placed”255

253

Samantha L. L. Hill et al., ‘Worldwide Impacts of Past and Projected Future Land-Use Change on
Local Species Richness and the Biodiversity Intactness Index’, BioRxiv, 1 May 2018, 311787,
https://doi.org/10.1101/311787.
254
Will Steffen et al., ‘Planetary Boundaries: Guiding Human Development on a Changing Planet’,
Science 347, no. 6223 (13 February 2015): 1259855, https://doi.org/10.1126/science.1259855.
255
Sandra Diaz et al., ‘The Global Assessment Report on Biodiversity and Ecosystem Services’, The
United Nations’ Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services,
2019, chap. 2.2.

215

The 90% boundary figure seems arbitrary. Steffen et al give no reason that it should be 90%,
rather than 1% or 99% or that there is no meaningful boundary.
“Due to a lack of evidence on the relationship between BII and Earth-system
responses, we propose a preliminary boundary at 90% of the BII but with a very large
uncertainty range (90 to 30%) that reflects the large gaps in our knowledge about the
BII–Earth-system functioning relationship.”I think proposing that there is a planetary boundary of 90% with no argument or evidence isunjustifiable. Brook et al (2013) argue that there is reason to think that there is no global
level planetary boundary for biodiversity.
“By evaluating potential mechanisms and drivers, we conclude that spatial
heterogeneity in drivers and responses, and lack of strong continental
interconnectivity, probably induce relatively smooth changes at the global scale,without an expectation of marked tipping patterns. This implies that identifying criticalpoints along global continua of drivers might be unfeasible and that characterizing
global biotic change with single aggregates is inapt.”256
For further criticism of the idea of planetary boundaries see Nordhaus et al (2012).257
Many regions today have a Biodiversity Intactness Index of less than 90%.

Source: Sandra Diaz et al., ‘The Global Assessment Report on Biodiversity and Ecosystem Services’,
The United Nations’ Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
Services, 2019.

It is notable that many of the regions with the lightest colours are some of the richest in theworld, including the US and many European countries. Many of the poorest countries in the
256

Barry W. Brook et al., ‘Does the Terrestrial Biosphere Have Planetary Tipping Points?’, Trends in
Ecology & Evolution 28, no. 7 (1 July 2013): 396–401, https://doi.org/10.1016/j.tree.2013.01.016.
257
Ted Nordhaus, Michael Shellenberger, and Linus Blomqvist, ‘The Planetary Boundaries
Hypothesis: A Review of the Evidence’ (Breakthrough Institute, June 2012),
https://static1.squarespace.com/static/5c9e9320348cd97cfd4c123c/t/5e1a7a7a3a904b6e1b424be0/1
578793595305/Planetary_Boundaries.pdf.

216

world score well on the Biodiversity Intactness Index, including for example DRC, Angola,
and the Central African Republic.
Clearly then, the claim cannot be that declines in Biodiversity Intactness instantaneously
bring about declines in welfare. Rather, the claim must be that the decline in Biodiversity
Intactness brings about a decline in welfare at some point in the future that overwhelms any
gains in welfare prior to that, perhaps due to passing some unknown tipping point.
But this seems to be the opposite of the truth for many regions. If one were to bet which
regions would have the highest levels of consumption in 300 years, one would probably say
the US, Europe and China, all areas where the biodiversity intactness index is well below
90%. Indeed, extremely rich countries such as England, France and Denmark seem to haveIndex scores below even the Steffen et al 30% lower planetary boundary uncertainty range.
The Biodiversity Intactness Index just does not seem to be a good measure of the long-term
consumption prospects of different regions.
There is also strong evidence from history that complete destruction of ecosystems is very
unlikely to trigger tipping points that do severe damage to society. Significant deforestation
started across Eurasia thousands of years ago. According to one estimate, in England in
1,000 BC, 90% of land potentially suited to agriculture was covered with forest. By 1400 AD,
this had plummeted to 17%. By 1850 AD, forest cover had fallen further to only 2%.258
At no point throughout this period did England experience nonlinear ecological collapse.
Agricultural production in England increased enormously despite the complete destruction ofpre-industrial ecosystems.

258

Jed O. Kaplan, Kristen M. Krumhardt, and Niklaus Zimmermann, ‘The Prehistoric and Preindustrial
Deforestation of Europe’, Quaternary Science Reviews 28, no. 27–28 (2009): Table 3.

217

There is a similar picture across the rest of Europe.259 While pre-modern and pre-industrial
ecosystems have been destroyed, living standards have increased enormously.
Raudseppe-Hearne et al (2010) call this the ‘environmentalist’s paradox: human well-being
has increased despite large global declines in most ecosystem services.260

Overall judgement on ecosystem collapse and agriculture
The causal chain for the ecosystem collapse argument is as follows:Global warming + habitat loss + human predation + pollution => global species loss=> global ecosystem collapse => global agricultural catastrophe
Each part of this causal chain seems to be flawed in several important ways. In my view, therisks of ecosystem collapse to global agricultural production are minimal.261 This does not259

Jed O. Kaplan, Kristen M. Krumhardt, and Niklaus Zimmermann, ‘The Prehistoric and Preindustrial
Deforestation of Europe’, Table 3.
260
Ciara Raudsepp-Hearne et al., ‘Untangling the Environmentalist’s Paradox: Why Is Human
Well-Being Increasing as Ecosystem Services Degrade?’, BioScience 60, no. 8 (1 September 2010):
576–89, https://doi.org/10.1525/bio.2010.60.8.4.
261
Kareiva and Carranza reach a similar conclusion: “The interesting question is whether any of theplanetary thresholds other than CO2 could also portend existential risks. Here the answer is not clear.
One boundary often mentioned as a concern for the fate of global civilization is biodiversity (Ehrlich &218mean that global ecosystem damage does not matter. My focus here has been on the
instrumental value of ecosystems to human society.

Ehrlich, 2012), with the proposed safety threshold being a loss of greater than .001% per year
(Rockström et al., 2009). There is little evidence that this particular .001% annual loss is a
threshold—and it is hard to imagine any data that would allow one to identify where the threshold was(Brook et al., 2013; Lenton & Williams, 2013). A better question is whether one can imagine any
scenario by which the loss of too many species leads to the collapse of societies and environmental
disasters, even though one cannot know the absolute number of extinctions that would be required tocreate this dystopia. While there are data that relate local reductions in species richness to alteredecosystem function, these results do not point to substantial existential risks. The data are small-scale
experiments in which plant productivity, or nutrient retention is reduced as species number declines
locally (Vellend, 2017), or are local observations of increased variability in fisheries yield when stock
diversity is lost (Schindler et al., 2010). Those are not existential risks. To make the link even moretenuous, there is little evidence that biodiversity is even declining at local scales (Vellend et al 2017;
Vellend et al., 2013). Total planetary biodiversity may be in decline, but local and regional biodiversity
is often staying the same because species from elsewhere replace local losses, albeit homogenizing
the world in the process. Although the majority of conservation scientists are likely to flinch at thisconclusion, there is growing skepticism regarding the strength of evidence linking trends inbiodiversity loss to an existential risk for humans (Maier, 2012; Vellend, 2014). Obviously if all
biodiversity disappeared civilization would end—but no one is forecasting the loss of all species. It
seems plausible that the loss of 90% of the world’s species could also be apocalyptic, but not one ispredicting that degree of biodiversity loss either. Tragic, but plausible is the possibility our planet
suffering a loss of as many as half of its species. If global biodiversity were halved, but at the sametime locally the number of species stayed relatively stable, what would be the mechanism for an
end-of-civilization or even end of human prosperity scenario? Extinctions and biodiversity loss are
ethical and spiritual losses, but perhaps not an existential risk.” Peter Kareiva and Valerie Carranza,‘Existential Risk Due to Ecosystem Collapse: Nature Strikes Back’, Futures, 5 January 2018,https://doi.org/10.1016/j.futures.2018.01.001.

219

6. Heat stress
Heat stress is dependent on temperature, humidity, wind speed and metabolic heatgeneration. Even though the hottest temperatures occur in subtropical deserts, relative
humidity there is so low that maximal annual heat stress is no higher than in the deep
tropics.262 In more humid climates, sweating is less effective at reducing our bodytemperature.
There are two main ways that increasing heat stress could affect human society: by reducing
labour capacity and increasing heat-related morbidity and mortality.

6.1. Metrics
There are numerous heat stress metrics used at the moment, all of which combine
temperature and humidity. The most common in climate change research are Wet Bulb
Temperature and Wet Bulb Globe Temperature.
Wet Bulb Temperature = The temperature that an air parcel would reach throughevaporative cooling once fully saturated.263 Or, in layman’s terms, the reading from athermometer when covered in a wet cloth and swung in the air.
Wet Bulb Globe Temperature = A function of Wet Bulb Temperature, dry bulb
temperature (what you would see on an ordinary thermometer), and a black globe
thermometer (that measures the effect of solar radiation). It is (0.7*Wet Bulb +0.1*Dry Bulb + 0.2*Black Globe Temperature).
Wet Bulb Temperature is usually used to measure extreme survivability limits, whereas Wet
Bulb Globe Temperature is usually used to measure safe limits on activities for exposed
people. (I am going to put ‘globe’ in bold to avoid confusion).
The table below shows Wet Bulb Globe Temperature limits on activities for acclimatised
people.264 The limit is about 2°C lower for non-acclimatised people.265
Level of activity

WBGT limit for acclimatised people

Resting

34

Walking/hammering

28

262

Steven C. Sherwood and Matthew Huber, ‘An Adaptability Limit to Climate Change Due to HeatStress’, Proceedings of the National Academy of Sciences 107, no. 21 (25 May 2010): 9552–55,
https://doi.org/10.1073/pnas.0913352107.
263
Ethan D. Coffel, Radley M. Horton, and Alex de Sherbinin, ‘Temperature and Humidity Based
Projections of a Rapid Rise in Global Heat Stress Exposure during the 21st Century’, Environmental
Research Letters 13, no. 1 (December 2017): 014001, https://doi.org/10.1088/1748-9326/aaa00e.
264
David Newth and Don Gunasekera, ‘Projected Changes in Wet-Bulb Globe Temperature under
Alternative Climate Scenarios’, Atmosphere 9, no. 5 (May 2018): fig. 5,
https://doi.org/10.3390/atmos9050187.
265
Newth and Gunasekera, fig. 5.

220

Intense - running, digging, sport

26

Once you get above 26, the risk starts to be high. At a Wet Bulb Globe Temperature of 32,
the army suspends all training.266 At a Wet Bulb Globe Temperature of >39, one cannot
survive in the shade for more than a few hours.267
Physical labour becomes difficult to impossible when Wet Bulb Temperature exceeds
31°C.268 At a Wet Bulb Temperature of more than 35, humans cannot survive for more than afew hours, even if they are in the shade, doused in water and have a fan pointed at them.
The lethality of heatwaves depends on the preparedness of the subject population so isn’t
that reliable a guide to the objective heat stress that people face. For example, the heatwave in Europe in 2003 killed thousands, but the one in 2005 didn’t even thoughtemperatures were similar.269

6.2. Heat stress today
The tropics and subtropics and coastal areas are most at risk from heat stress. This isaverage peak daily Wet Bulb Globe Temperature in the hottest month of the year, for
1981-2010:

266

Katharine M. Willett and Steven Sherwood, ‘Exceedance of Heat Index Thresholds for 15 Regions
under a Warming Climate Using the Wet-Bulb Globe Temperature’, International Journal of
Climatology 32, no. 2 (2012): 161–77, https://doi.org/10.1002/joc.2257.
267“For day-time heat we set the threshold for survivability according to the WBGT that causes core
body temperature to rise to 42°C, for an average individual at rest,ii in the shade, for four hours. We
estimate this occurs when the daily maximum WBGT is ≥ 40°C.” David King et al., ‘Climate Change–a
Risk Assessment’ (Centre for Science Policy, University of Cambridge, 2015), 57,
www.csap.cam.ac.uk/projects/climate-change-risk-assessment/.
268
Jonathan R. Buzan and Matthew Huber, ‘Moist Heat Stress on a Hotter Earth’, Annual Review ofEarth and Planetary Sciences 48, no. 1 (2020): 623–55,
https://doi.org/10.1146/annurev-earth-053018-060100.
269“Echoes of this can be seen in the fact that the European heat wave of 2005 killed very few
compared to 2003, despite being just as hot.” Jonathan R. Buzan and Matthew Huber, ‘Moist HeatStress on a Hotter Earth’, Annual Review of Earth and Planetary Sciences 48, no. 1 (2020): 623–55,
https://doi.org/10.1146/annurev-earth-053018-060100.

221

Source: Tord Kjellstrom et al., ‘Estimating Population Heat Exposure and Impacts on Working People
in Conjunction with Climate Change’, International Journal of Biometeorology 62, no. 3 (1 March2018): 291–306, https://doi.org/10.1007/s00484-017-1407-0.

These levels of Wet Bulb Globe Temperature would probably obtain for around four hours.270
The chart below shows a map of annual maxima of Wet Bulb Temperature. Recall that a Wet
Bulb Temperature of 35 is a hard survival limit.

Source: Steven C. Sherwood and Matthew Huber, ‘An Adaptability Limit to Climate Change Due toHeat Stress’, Proceedings of the National Academy of Sciences 107, no. 21 (25 May 2010): fig. 1,
https://doi.org/10.1073/pnas.0913352107.

270

Tord Kjellstrom, personal correspondence, 28 Jan 2021.

222

Adjacent nighttime minima of Wet Bulb Temperature are typically within 2–3 °C of the
daytime, and adjacent daily maxima are typically within 1°C.271 So, within-day and day-to-day
changes don’t reduce heat stress that much.
Raymond et al (2020) recently showed that weather stations in the UAE and Pakistan haverecorded Wet Bulb Temperature above 35 several times,272 and multiple places in South Asiaand the Middle East above 30 Wet Bulb Temperature. Numerous other places also suffervery high levels of heat stress.273
These events do not seem to have been associated with morbidity and mortality,274 which
illustrates that thus far people have taken adaptive measures. In the UAE, this would likely
include air conditioning, but I am less sure how far people in Pakistan could have taken thisstep. According to Orlov et al (2020) in South Asia fewer than 10% of households have air
conditioning.275 For reference, the average cost of air conditioning for households in thesoutheastern US is about $525 per year.
For the Global Burden of Disease study, Zhao et al (2021) find that between 2000 and 2019,
on average there were around 500,000 heat-related deaths and around 4.5 million
cold-related deaths each year.276 The literature suggests that the benefits of climate change
in reducing cold-related deaths in temperate regions will be outweighed by the increase inheat-related deaths in warmer regions.277
271

Sherwood and Huber, ‘An Adaptability Limit to Climate Change Due to Heat Stress’, 9554.
“In presenting boxplots of all TW recordings by month, Fig. S20 makes a similar argument for thetwo stations with the most TW=35ºC readings (Ras Al-Khaimah, UAE, and Jacobabad, Pakistan).”Colin Raymond, Tom Matthews, and Radley M. Horton, ‘The Emergence of Heat and Humidity Too
Severe for Human Tolerance’, Science Advances 6, no. 19 (1 May 2020): SI p3,
https://doi.org/10.1126/sciadv.aaw1838.
273“Furthermore, some regions are already experiencing heat stress conditions approaching the upper
limits of labour productivity and human survivability (high confidence). These include the Persian Gulf
and adjacent land areas, parts of the Indus River Valley, eastern coastal India, Pakistan,north-western India, the shores of the Red Sea, the Gulf of California, the southern Gulf of Mexico,and coastal Venezuela and 27 Guyana (Krakauer et al., 2020);(Li et al., 2020);(Raymond et al.,2020);(Saeed et al., 2021);(Xu et al., 2020).” IPCC, Climate Change 2022: Impacts, Adaptation and
Vulnerability, Sixth Assessment Report, 2022, chap. 7, sec. 7.2.4.1.
274“Such efforts may also help resolve the reasons for the paucity of reported mortality and morbidity
impacts associated with observed near 35°C conditions.” Raymond, Matthews, and Horton, ‘The
Emergence of Heat and Humidity Too Severe for Human Tolerance’.
275
Anton Orlov et al., ‘Economic Costs of Heat-Induced Reductions in Worker Productivity Due to
Global Warming’, Global Environmental Change 63 (1 July 2020): Fig. 4,
https://doi.org/10.1016/j.gloenvcha.2020.102087.
276
Qi Zhao et al., ‘Global, Regional, and National Burden of Mortality Associated with Non-Optimal
Ambient Temperatures from 2000 to 2019: A Three-Stage Modelling Study’, The Lancet Planetary
Health 5, no. 7 (1 July 2021): Table 1, https://doi.org/10.1016/S2542-5196(21)00081-4.
277“In temperate areas such as northern Europe, east Asia, and Australia, the less intense warming
and large decrease in cold-related excess would induce a null or marginally negative net effect, with
the net change in 2090–99 compared with 2010–19 ranging from −1·2% (empirical 95% CI −3·6 to1·4) in Australia to −0·1% (−2·1 to 1·6) in east Asia under the highest emission scenario, although thedecreasing trends would reverse during the course of the century. Conversely, warmer regions, suchas the central and southern parts of America or Europe, and especially southeast Asia, would
experience a sharp surge in heat-related impacts and extremely large net increases, with the netchange at the end of the century ranging from 3·0% (−3·0 to 9·3) in Central America to 12·7% (−4·7
to 28·1) in southeast Asia under the highest emission scenario.” Antonio Gasparrini et al., ‘Projectionsof Temperature-Related Excess Mortality under Climate Change Scenarios’, The Lancet Planetary
Health 1, no. 9 (1 December 2017): e360, https://doi.org/10.1016/S2542-5196(17)30156-0.
272

223

According to the IPCC, there is evidence from high income countries that the health costs ofheat stress are declining over time, due to heat warning systems, increased awareness, andimproved quality of life.278 Air conditioning explains up to 20% of this decline.279

6.3. What are high levels of heat stress actually like?
Dhahran in Saudi Arabia is one of the most heat stressed inhabited places in the world andregularly has Wet Bulb Temperature above 30 each year since 1970.280 This is a description
sent to the Washington Post from someone who lived there:“When the winds come off the Persian Gulf you just can’t imagine how awful it gets.
On the hottest and most humid days, you’d walk outside and it felt immediately likesomeone pressed a hot wet towel, like you sometimes get on airplanes, over yourentire head. I wear glasses, and they’d immediately fog up. You sweat instantly.
People just avoid being outside in any way they can. In the summers, my friends andI would become nocturnal as a way to beat the heat. Crime is basically non-existent,
so my parents didn’t worry about us being out all night. I’d usually have breakfast
with my dad and then sleep through the heat of the day, waking up when he got
home from work. At night it was still stifling, but the edge was off.
Air conditioning is everywhere. You can trace the population explosion in the country
directly to the advent of air conditioning – it allowed people to settle down and stop
living the nomadic life that was common into the middle of the 20th century. We lived
on a compound for employees of the Saudi national oil company, and they treated air
conditioning repair like ambulances or fire trucks – they had crews on 24-hour call,and you could have them dispatched at a moment’s notice by calling the special air
conditioning emergency hotline. In the summer, the air-conditioned school buses
would stop outside every individual kid’s house, so they didn’t have to wait at a stopand could stay in the AC. Off the compound, air conditioning is still common, even forthe poorest migrant workers there. Shopping was done in huge air-conditioned malls.
The great open-air souks operate in the winter or very early in the morning onsummer weekends”
Friends in Dubai have told me that in summer, people move between different air
conditioned environments in their car/office/home.

278“Several lines of evidence point to a possible decrease in population sensitivity to heat, albeit
mainly for high57 income countries (high confidence), arising from the implementation of heat warning
systems, increased awareness, and improved quality of life” IPCC, Climate Change 2022: Impacts,
Ch. 7, sec. 7.2.4.1.
279“Although there is a paucity of global 46 level studies of the effectiveness of air conditioning for
reducing heat-related mortality, a recent assessment 47 indicates increases in air conditioning
explains only part of the observed reduction in heat-related excess 48 deaths, amounting to 16.7% in
Canada, 20.0% in Japan, 14.3% in Spain and 16.7% in the US (Sera et al., 49 2020).” IPCC, Climate
Change 2022: Impacts, Ch. 7, sec. 7.2.4.1.
280
Jeremy S. Pal and Elfatih A. B. Eltahir, ‘Future Temperature in Southwest Asia Projected to Exceeda Threshold for Human Adaptability’, Nature Climate Change 6, no. 2 (February 2016): fig. 2,
https://doi.org/10.1038/nclimate2833.

224

6.4. Impacts
Rising heat stress would increase heat-related deaths and reduce outdoor labour capacity
for regions that cannot afford to adapt. It is important to stress that most future population
growth will be in the tropics, which will be hardest hit by rising heat stress.
The map below shows average daily peak Wet Bulb Globe Temperature during the hottest
month of the year for today, for 4.4°C and 7.7°C of warming.

Source: King et al., ‘Climate Change–a Risk Assessment’, 62.

For reference, orange is above the safe limit for walking for acclimatised people, according
to international standards. In the summer months, outdoor activity would be very difficult in225the tropics. At 6°C of warming, New York City would become more heat stressed thanBahrain is today.281
This could have very bad effects on labour capacity for countries with limited adaptive
capacity. Using standard safety guidelines, Buzan and Huber (2020) calculate the reductionsin population-weighted labour capacity at different levels of warming assuming no air
conditioning or other adaptation, and that people do not migrate:

Source: Buzan and Huber, ‘Moist Heat Stress on a Hotter Earth’.

As this shows, while the high latitudes emerge relatively unscathed, the tropics areespecially badly affected by rising heat stress, with labour capacity falling to 40% of its
capacity at 5°C and 20% of its capacity at 8°C.
This estimate is a pessimistic upper bound on the effect because it assumes no adaptationand no migration. I discuss the prospects for adaptation below.
Turning to hard survivability limits, the chart below shows how heat stress will change in the
Middle East and Persian Gulf, one of the world’s most heat stressed regions. This shows
annual maximum Wet Bulb Temperature on 2.7°C of warming (green line) and 4.4°C (redline).

281

John P. Dunne, Ronald J. Stouffer, and Jasmin G. John, ‘Reductions in Labour Capacity from HeatStress under Climate Warming’, Nature Climate Change 3, no. 6 (June 2013): 563–66,
https://doi.org/10.1038/nclimate1827, fig. 1.

226

Source: Jeremy S. Pal and Elfatih A. B. Eltahir, ‘Future Temperature in Southwest Asia Projected to
Exceed a Threshold for Human Adaptability’, Nature Climate Change 6, no. 2 (February 2016): fig. 2,
https://doi.org/10.1038/nclimate2833.

The map below shows the number of days that people are exposed to Wet Bulb
Temperature above different levels for 2.7°C of warming (RCP4.5) and 4.4°C of warming(RCP8.5).

227

Source: Coffel, Horton, and Sherbinin, ‘Temperature and Humidity Based Projections of a Rapid Risein Global Heat Stress Exposure during the 21st Century’, fig. S5.

Recall that outdoor labour is difficult or impossible once Wet Bulb Temperature passes 31°C.
As this shows, for 2.7°C of warming, regions in the tropics and subtropics experience Wet
Bulb Temperature above 32°C for around 1-10 days each year. This is close to the most heatstressed places in the Persian Gulf today. For 4.4°C of warming, a much larger number ofpeople would be exposed to 1-10 days of Wet Bulb Temperature above 32 each year.
These temperature extremes will persist across much of the summer in the places affected,
as temperatures don’t drop much either side of the hottest month. For example, May is thehottest month in Bihar in India at 38°C (dry bulb i.e. measured by a normal thermometer)and April and June are around 37°C.282 So, we would be looking at regularly exceeding a
Wet Bulb Temperature of 30 in the summer months in these places.
A good summary of the recent literature on heat stress associated with 4°C of warming is in
Coffel et al (2017),283 and there are various recent papers by Eltahir and others on heatstress in specific regions.284
282

Data here is from Accuweather
Coffel, Horton, and Sherbinin, ‘Temperature and Humidity Based Projections of a Rapid Rise in
Global Heat Stress Exposure during the 21st Century’.
284
Pal and Eltahir, ‘Future Temperature in Southwest Asia Projected to Exceed a Threshold for
Human Adaptability’; Eun-Soon Im, Jeremy S. Pal, and Elfatih A. B. Eltahir, ‘Deadly Heat Waves
Projected in the Densely Populated Agricultural Regions of South Asia’, Science Advances 3, no. 8 (1August 2017): e1603322, https://doi.org/10.1126/sciadv.1603322; Suchul Kang and Elfatih A. B.
283

228

The map below from the classic Sherwood and Huber (2010) shows annual maximum Wet
Bulb Temperature for 11°C of warming above pre-industrial:

Source: Sherwood and Huber, ‘An Adaptability Limit to Climate Change Due to Heat Stress’, fig. 1.

In this extreme worst-case scenario. Heat stress levels would pass lethal limits at some pointin the year for the majority of the global population, as people are currently distributed.
People will still be able to survive at higher latitudes and altitudes, and people in the tropics
could survive in air conditioned environments. Humanity would be much diminished, though
we would survive.

6.4.1. The scope for adaptation
Despite rising levels of heat stress, in rich countries at least, heat-related deaths are
declining. As countries get richer, we should expect the effects of heat stress to become lessbad, other things being equal.
One important factor is that in growing regions, a smaller fraction of the global population will
work outdoors in agriculture. Growing economies will have income per head in excess of$20,000 per person by 2100, which suggests that agricultural employees will constitute lessthan 10% of the workforce, compared to a global average today of around 25%.

Eltahir, ‘North China Plain Threatened by Deadly Heatwaves Due to Climate Change and Irrigation’,
Nature Communications 9, no. 1 (31 July 2018): 2894, https://doi.org/10.1038/s41467-018-05252-y.

229

Orlov et al (2020) argue that there is scope for outdoor workers in construction andagriculture to adapt by using mechanisation: it is easier to farm with a tractor and machinery
than by hand. This could slightly reduce the energy requirements of outdoor labour.

Source: Orlov et al., ‘Economic Costs of Heat-Induced Reductions in Worker Productivity Due to
Global Warming’.

Another reason that richer regions can better adapt to heat stress is that they can afford air
conditioning and other adaptive measures. Orlov et al (2020) project air conditioning trends
in different regions on different Shared Socioeconomic Pathways.

230

Source: Orlov et al., ‘Economic Costs of Heat-Induced Reductions in Worker Productivity Due to
Global Warming’.

There is a large range in the penetration of air conditioning in the poorest regions - South
Asia and Sub-Saharan Africa - depending on the SSP. On the high growth SSP5,
penetration is above 60%, whereas on the unequal SSP4, it is close to zero.
Although there is a fair bit of scope for adaptation in rich and/or growing regions, to say theleast, the >4°C world is not a compelling vision of the future. Billions of people in the tropics
and subtropics would be unable to do much outside during the summer months, including
things like jogging or even walking for a few hours. People would have to move between
different air conditioned pods, as people do in Dubai in summer. For countries that are still
reliant on agriculture and outdoor labour in 2100, this would be very damaging.

6.4.2. Death estimates
Bressler et al (2021) quantifies the effect that climate change will have on global all-cause
mortality with and without adaptation

231Source: R. Daniel Bressler et al., ‘Estimates of Country Level Temperature-Related Mortality Damage
Functions’, Scientific Reports 11, no. 1 (13 October 2021): 20282,
https://doi.org/10.1038/s41598-021-99156-5.

With income-related adaptation, on RCP4.5, which implies 2.7°C of warming, the global
mortality rate would increase by around 1%. On the UNs median projections, deaths would
increase by around 1 million per year by 2100. On RCP8.5, which implies around 4.4°C ofwarming, the global mortality rate would increase by 4.2%, which translates to 5 million extra
deaths per year, on net. (Calculations are here).
There are two reasons to think that this estimate is overly pessimistic. Firstly, it excludes
some adaptive measures.
“It is possible that other forms of adaptation and technological changes, other than
through income-based adjustments, might modify the temperature-mortality
relationship over time. For example, provision of public heat alert systems, improved
preparation of the medical system for heat-related diseases, or people learning to
avoid activity during the hottest parts of the day might all reduce the adverse effects
of extreme heat over time. Several studies reviewed by Arbuthnott et al. show
evidence of decreasing sensitivity of heat-related mortality over time. These effects
are not included in the estimates given here.”232

The main income-based adaptation that Bressler et al (2021) mention is air conditioning.285
According to the IPCC, air conditioning only explains 15-20% of the decline in heat-related
deaths seen in high-income countries. This suggests that by focusing only on income-based
adaptation, Bressler et al (2021) may underestimate the total effects of adaptation by up to afactor of five. For 4.4°C of warming, the adaptation measures considered by Bressler et al
reduce the increase in the death rate from 6.2% to 4.2%. If this 2 percentage point reductionin the death rate is indeed underestimated by a factor of 5, then the heat-related effects ofwarming would be eliminated.
Secondly, Bressler et al (2021) considers the impacts of heat stress conditional on
pessimistic assumptions about socioeconomic development. They consider the impacts of
RCP4.5 and RCP8.5 on Shared Socioeconomic Pathway 3. SSP3 is the lowest growth SSP,
with particularly sluggish growth for Africa and parts of Asia. The predicted growth for Asia
seems overly pessimistic, which is important for future heat stress projections.
As discussed in Chapter 1, RCP8.5 is only possible on SSP5. The ‘current policy’ baseline
on SSP3 is RCP7. Moreover, as I said in Chapter 1, this baseline scenario seems far too
pessimistic about likely emissions on SSP3 and on current policy. This being said, Bressler
et al (2021) only measures the most likely level of warming conditional on a given emissions
pathway: for RCP8.5, this is around 4.4°C. But warming might be higher than we expect. On
RCP7, the chance of 4.5°C is around 1 in 20.286
Overall, the 5 million deaths estimate should be taken as a probably pessimistic estimate of
the most likely level of deaths due to heat stress at 4.4°C of warming.

6.5. The future human climate niche‘The Future of the Human Climate Niche’ is an interesting 2020 paper by Xu et al which
explores the climatic environments that people live in today and how they will change with
future warming.287 Xu et al only explore the potential impact of temperature, and exclude
other effects of climate change, such as changes in water stress. Xu et al discuss the effects
of temperature change on heat stress, economic productivity and general livability.
Xu et al note that “human populations have resided in the same narrow part of the climaticenvelope available on the globe, characterized by a major mode around ∼11 °C to 15 °C
285

See for example “Finally, Model 4 adds an additional interaction term with per-capita income,reflecting the ability of individuals and groups to make investments that mitigate the negative mortality
effect of heat, such as installing air conditioning”; “We also see a negative interaction with log(GDPPCc) (i.e. a negative β4 coefficient) indicating that richer countries can ameliorate some of thedamages associated with higher temperatures. This could well be associated with air conditioning
penetration, which several studies have shown to be strongly associated with higher incomes,
particularly in warmer, middle-income countries3”; “When the reduced sensitivity to heat associatedwith rising incomes, such as greater ability to invest in air conditioning, is accounted for, the expected
end-of-century increase in the global mortality rate is 1.1% [95% CI 0.4–1.9%] in RCP 4.5 and 4.2%
[95% CI 1.8–6.7%] in RCP 8.5.” R. Daniel Bressler et al., ‘Estimates of Country Level
Temperature-Related Mortality Damage Functions’, Scientific Reports 11, no. 1 (13 October 2021):
20282, https://doi.org/10.1038/s41598-021-99156-5.
286
See Chapter 1.
287
Chi Xu et al., ‘Future of the Human Climate Niche’, Proceedings of the National Academy of
Sciences 117, no. 21 (26 May 2020): 11350–55, https://doi.org/10.1073/pnas.1910114117.

233

mean annual temperature”. On the most pessimistic scenario to 2070, which assumes SSP3
and RCP8.5, and that people do not migrate, by 2070, due to climate change and population
growth in hotter regions, one third of the global population will experience an average annual
temperature of more than 29°C, a situation found in the present climate only in 0.8% of the
global land surface, mostly concentrated in the Sahara, but in 2070 projected to cover 19%of the global land.
The chart below shows the future human climate niche on different socioeconomic andemissions scenarios:

234

Source: Xu et al., ‘Future of the Human Climate Niche’, SI Fig. S7.

For reference, on RCP2.6, warming at 2070 would be around 1.7°C. On RCP4.5, it would bearound 2.3°C, and on RCP8.5, it would be around 3.4°C.

235

As Xu et al note, their model also assumes no migration, but this is a likely adaptive
response to increased heat stress.
One can examine the effect that population growth in tropical regions has on theseprojections by comparing the ‘zero [population] growth’ scenario to the SSPs. The SSP withthe highest population growth is SSP3. On RCP8.5, the increase in the number of people
experiencing a mean annual temperature above 25°C is several times smaller in the world
with no population growth, compared to the world with SSP3-levels of population growth.
Global warming and population growth in hot places work in tandem to increase thepopulation exposed to higher temperatures.
It is instructive to explore how mean annual temperature experienced by humans will change
in the future, but we can get an even richer picture by exploring more of the temperature
range experienced by humans over the course of a year. People do not live the whole year
at an average temperature. Rather they live through large diurnal and seasonal temperaturechanges. One way to guide intuitions about the effects of climate change is to think of it as
shifting up the temperature distribution in different locations across the world. It must again
be stressed that the shift will not be uniform across the world.
For example, the annual average temperature for people in India is around 25°C. But
residents of Delhi live through large temperature changes over the course of a year:

Source: Google weather

236

In January, Delhi residents live through diurnal temperature variation of more than 10°C from monthly lows of 8°C to highs of 21°C. In the transition from winter to summer, people
will live through a temperature change of 22°C: from the winter low of 8°C to the summer
high of 40°C.
Climate change shifts these distributions up on the y-axis. For instance, here are high andlow temperatures in Delhi assuming uniform 4°C of local warming across the year:

As this shows, warming exposes Delhi residents to a new higher peak summer temperature.
However, for the vast majority of the year, people live within the same climate envelope thatthey did before. Except in May and June, the temperatures experienced by Delhi residents
are all within the pre-warming temperature envelope. Even in May and June, for the majorityof the time, people will be within the pre-warming envelope.
Global warming is not uniform. For Delhi residents, global average surface warming will infact be fairly close to local Delhi warming. For others, such as people in the Middle East, incentral Africa and northern Europe, the local temperature will be higher, according to climate
models. For instance, in Bamako, the capital of Mali, local warming will be far higher thanglobal average surface warming; 4ºC of global warming would translate into closer to 6ºC of
local warming. Thus, the temperature envelope would change as follows:237

In Bamako, people would still spend the majority of their year within the current temperatureenvelope, but peak mean daily temperatures would be higher than current peak mean daily
temperatures for around 7 months of the year.
As I have said, this does not provide the complete picture because it excludes many
avenues of climate impact, such as droughts, fires, rising sea level and so on. But I do think
this is a better way to guide intuition about climate impacts than focusing on mean annual
temperatures.
Annual average temperatures vary substantially across heavily populated regions:238

With 4°C of warming, London would have a temperature similar to Madrid and Madrid would
have a temperature similar to Shenzhen. Although this would be very bad, it is hard to see
how this could completely destroy modern industrial civilisation: advanced economies
already thrive at these annual average temperatures.

239

7. Sea level rise
Due to global warming, sea levels have risen by about 20cm on average since 1900.288 The
effect of CO2 emissions on sea level over the next few millennia brings home the importanceof taking a longterm perspective: sea level rise would be Earth-changing over the coming
millennia, even on relatively modest emissions scenarios.
Sea level rise threatens the world’s coasts through a range of impacts including289 i)permanent submergence of land by mean sea levels or mean high tides; ii) more frequent or
intense coastal flooding; iii) enhanced coastal erosion; loss, degradation, and change ofcoastal ecosystems; iv) salinization of soils and of ground and surface water; and v)
impeded drainage of natural (e.g. rivers) and artificial (e.g. sewage) water systems. Thesebiophysical impacts will in turn have socioeconomic impacts on coastal residents and theirlivelihoods, such as flood damage to buildings, disruption and relocation of economic
activities, migration, and degraded coastal agriculture. Here, I focus mainly on permanent
submergence and temporary flooding as these two impacts are the ones best studied.

7.1. Past trends in relative sea level rise and flooding
It is important to note that relative sea level rise is what matters for impacts, and thisdepends on both global warming-induced sea level rise as well as on local vertical land
movement in terms of either land uplift or subsidence.

7.1.2. Vertical land movement
Subsidence is a major problem for many coastal cities around the world, and is mainly
man-made.290 Due to subsidence, many cities have experienced very large (>1m) relative
sea level rise over the 20th century. The problem is especially bad in Asia.291
This chart shows subsidence since 1900 in various cities.

288

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), p. 5.
289
IPCC, ‘Special Report on the Ocean and Cryosphere in a Changing Climate’.
290“Natural subsidence, mainly due to the compaction of young sediments in deltas, is widespreadand noteworthy. However, the most rapid rates of subsidence are human-induced. These are causedby accelerated compaction primarily due to withdrawal of underground fluids including groundwater,
oil and gas, as well as drainage of organic soils… these processes are marked in many of the world’s
deltas and are often compounded by both local flood defences within the delta and upstream dams,
which collectively reduce the sediment supply that maintains these sedimentary landforms. Sand
extraction and mining can exacerbate this loss of sediment supply” Robert J. Nicholls et al., ‘A Global
Analysis of Subsidence, Relative Sea-Level Change and Coastal Flood Exposure’, Nature Climate
Change, 8 March 2021, 1–5, https://doi.org/10.1038/s41558-021-00993-z.
291“Cumulatively, human effects on subsidence are at their largest in some coastal cities located ondeltas and alluvial plains: a net subsidence of more than 4m has occurred during the twentieth century
in parts of Tokyo, and 2 to 3m in Shanghai, Bangkok, Jakarta and New Orleans. Many deltas andsubsiding cities are in Asia, and the World Bank has recognized that subsidence could be as
influential as climate-induced SLR in parts of coastal Asia over the twenty-first century” Nicholls et al.

240

Source: Robert J. Nicholls, ‘Adapting to Sea-Level Rise’, in Resilience: The Science of Adaptation to
Climate Change, ed. Zinta Zommers and Keith Alverson (Elsevier, 2018).

As this shows, some cities such as Tokyo have managed to adapt to sea level rise of up to
4m (40mm per year). Subsidence rates are extremely high in some places. In Jakarta,
subsidence is at 100 millimetres per year, or 10 metres per century.292
Conversely, some places such as Helsinki have seen negative relative sea level rise due toglacial uplift.293
While the average coastal area experiences relative sea level rise of less than 3mm peryear, the average coastal resident experiences a rise of around 9mm per year, due tosubsidence. This is because coastal residents are concentrated in areas experiencing faster
relative sea level rise.

292

Hasanuddin Z. Abidin et al., ‘Land Subsidence of Jakarta (Indonesia) and Its Relation with Urban
Development’, Natural Hazards 59, no. 3 (11 June 2011): 1753,
https://doi.org/10.1007/s11069-011-9866-9.
293
Nicholls, ‘Adapting to Sea-Level Rise’, fig. 2.2.

241

Source: Robert J. Nicholls et al., ‘A Global Analysis of Subsidence, Relative Sea-Level Change andCoastal Flood Exposure’, Nature Climate Change, 8 March 2021, 1–5,
https://doi.org/10.1038/s41558-021-00993-z.

7.1.3. Floods
One of the major impacts of relative sea-level rise are more frequent and intense coastal
floods, because higher mean sea-levels raise extreme sea-levels (tides, surges, waves),
which then propagate incland causing losses and damages. While deaths from coastal
floods declined dramatically over the course of the 20th century due to improved disasterrisk reduction,294 economic losses continue to rise, mainly due to increased exposures and,
locally, due to subsidence.295

294

Bouwer, L.M., Jonkman, S.N., 2018. Global mortality from storm surges is decreasing.
Environmental Research Letters 13, 014008. https://doi.org/10.1088/1748-9326/aa98a3.
295
Kron, W., 2013. Coasts: the high-risk areas of the world. Nat Hazards 66, 1363–1382.
https://doi.org/10.1007/s11069-012-0215-4.

242

7.2. How much will sea level rise due to global warming?
The potential impact of future sea level rise illustrates two important points: (1) we need to
pay attention to low probability but high-impact risks, and (2) taking the long-term
perspective can fundamentally change how we should assess global risks.
The figure below shows the 17-83% confidence range of sea level rise on different
emissions scenarios to 2100 and to 2300:

243

Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021). SPM.8.

The IPCC does not say how likely the suggested 15 metre sea level rise might be on
RCP8.5, but only that it “cannot be ruled out”. For this very high emissions scenario, deep
uncertainty is a real concern and the IPCC has low confidence that the models capture all of
the processes at play.296 Various studies have explored the implications of the rapid collapse296“By contrast, for SSP5-8.5, the SEJ and MICI projections exhibit 17th -83rd percentile ranges of0.02-0.56 m and 0.19-0.53 m by 2100, consistent with one another but considerably broader than thelikely contribution for medium confidence processes of 0.03 to 0.34 m. This lower level of agreement
for higher emissions scenarios reflects the deep uncertainty in the AIS contribution to GMSL change
under higher emissions scenarios (Box 9.4). This deep uncertainty grows after 2100: by 2150, under244

of the West Antarctic Ice Sheet, causing 5m of sea level rise over 100 years (50mm peryear).297
Over longer timescales, the effects on sea level rise will be even more pronounced. Sea
levels will continue to rise in the millennia after emissions stop, as the ice sheets melt. The
table below compares observed peak rates and magnitudes of relative sea level rise frompast observations and for future projections under different scenarios:
Location

PeriodConsidered
drivers of rel
sea-level rise

ScenarioPeak rate ofrel sea level
rise

Magnitudeof rel sea
level rise

Past observations
New
Orleans

20th
Century

Subsidence

N/A

25mm/year

2.5m298

Tokyo

20th
Century

Subsidence

N/A

40mm/year

4m299

Jakarta

1982-2010

Subsidence

N/A

100mm/year

2.8m300

Globalcoastalpopulation

1993-2015

Subsidence +climate change

N/A

9mm/year

2m301

Future projections
Globalmean

To 2100

2.7°C

8mm/year

0.7m302

Globalmean

To 12,000
AD

2.7°C

10mm/year

10-20m303

SSP 5-8.5, medium confidence processes likely lead to a -0.1 to 0.7 m AIS contribution, while SEJ
and MICI-based projections indicate 0.0-1.1 m and 1.4-3.7 m, respectively” IPCC, Climate Change
2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), ch. 9, sec. 9.6.3.2.
297
For a review, see Robert J. Nicholls, Richard S. J. Tol, and Athanasios T. Vafeidis, ‘Global
Estimates of the Impact of a Collapse of the West Antarctic Ice Sheet: An Application of FUND’,
Climatic Change 91, no. 1 (25 June 2008): 171, https://doi.org/10.1007/s10584-008-9424-y.
298
Robert J. Nicholls, ‘Adapting to Sea-Level Rise’, in Resilience: The Science of Adaptation to
Climate Change, ed. Zinta Zommers and Keith Alverson (Elsevier, 2018).
299
Nicholls, ‘Adapting to Sea-Level Rise’.
300
Nicholls, ‘Adapting to Sea-Level Rise’.
301
Robert J. Nicholls et al., ‘A Global Analysis of Subsidence, Relative Sea-Level Change and Coastal
Flood Exposure’, Nature Climate Change, 8 March 2021, 1–5,
https://doi.org/10.1038/s41558-021-00993-z.
302
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), SPM.8.
303
Clark et al. (2016, fig. 4a) project that on a medium-low ‘emissions scenario’ close to RCP4.5, sealevel would rise by 20 metres, while Van Breedam et al. (2020, Tab. 1) find that it would rise by 10
metres. Peter U. Clark et al., ‘Consequences of Twenty-First-Century Policy for Multi-Millennial
Climate and Sea-Level Change’, Nature Climate Change (8 February 2016),
https://doi.org/10.1038/nclimate2923; Jonas Van Breedam, Heiko Goelzer, and Philippe Huybrechts,‘Semi-Equilibrated Global Sea-Level Change Projections for the next 10,000 Years’, Earth System
Dynamics 11, no. 4 (6 November 2020): 953–76, https://doi.org/10.5194/esd-11-953-2020.

245

Globalmean

To 2100

4.4°C

15mm/year

0.9m304

Globalmean

To 2120

Collapse of West
Antarctic Ice Sheet

50mm/year

5m305

Globalmean

To 12,000
AD

All fossil fuels

10mm/year

20-30m306

This shows how much of a difference taking a longtermist perspective can make: onbusiness as usual - roughly, RCP4.5 - it is the difference between a 0.75 metre rise over 100
years and a >10 metre rise over 10,000 years.

7.3. What is the scope to adapt to relative sea level rise?
7.3.1. Adaptation options
The main adaptive responses are:307●●●●

Protection - blocking the inland propagation of mean and extreme sea-levels by, e.g.
building sea walls or dikes.
Advance/attack - creation of new land by building into the sea.
Accommodation - adapting settlements to higher water levels e.g. insurance,
flood-proofing houses or putting them on stilts.
Retreat - abandoning current settlements.

304

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021). SPM.8.
305
Robert J. Nicholls, Richard S. J. Tol, and Athanasios T. Vafeidis, ‘Global Estimates of the Impact ofa Collapse of the West Antarctic Ice Sheet: An Application of FUND’, Climatic Change 91, no. 1 (25June 2008): 171, https://doi.org/10.1007/s10584-008-9424-y.
306Peter U. Clark et al., ‘Consequences of Twenty-First-Century Policy for Multi-Millennial Climate and
Sea-Level Change’, Nature Climate Change (8 February 2016): fig. 2b,
https://doi.org/10.1038/nclimate2923.
307
IPCC, ‘Special Report on the Ocean and Cryosphere in a Changing Climate’, sec. 4.1.4.

246

Source: Nicholls, ‘Adapting to Sea-Level Rise’.

7.3.2. Future adaptation prospects
The scope to adapt to future relative sea level rise depends on the rate and magnitude ofsea level rise.
On a population basis, future sea level rise mainly threatens Asian countries and island
nations.

247

Source: Robert J. Nicholls, ‘Adapting to Sea-Level Rise’, in Resilience: The Science of Adaptation to
Climate Change, ed. Zinta Zommers and Keith Alverson (Elsevier, 2018).

I will now discuss the prospects for adaptation in different scenarios.

Up to 2 metres over 80 years
The picture that emerges from the literature on adaptation to relative sea level rise is that
there are few technical limits to sea level rise of up to 2 metres by 2100 (25mm per year on
average).

Source: Jochen Hinkel et al., ‘The Ability of Societies to Adapt to Twenty-First-Century Sea-Level
Rise’, Nature Climate Change 8, no. 7 (July 2018): 570–78,
https://doi.org/10.1038/s41558-018-0176-z.

Generally, rich countries will have the incentives and the resources to invest in sea level
adaptation, and in particular coastal protection. The IPCC notes that:“If governments undertook adaptation investments in all coasts (e.g., building
protective dikes), then the study suggests... a population of less than half a million
displaced under the 2.0 m sea level rise scenario.”308
Such protection measures are likely to be implemented given the long history of coastal
protection (not least due to subsiding cities as discussed above), high benefit-cost ratios ofcoastal protection and the high cost of not protecting urban centres.309

308

IPCC, Climate Change 2014: Impacts, Adaptation, and Vulnerability: Summary for Policymakers(Cambridge University Press, 2014), chap. 12.
309
Lincke, D., Hinkel, J., 2018. Economically robust protection against 21st century sea-level rise.
Global Environmental Change 51, 67–73. https://doi.org/10.1016/j.gloenvcha.2018.05.003

248

However, for poorer and very exposed countries, including island nations and somecountries in Africa and Asia, adaptation could in some cases be too costly, so abandonment
might be the chosen path.310“Effective protection requires investments on the order of tens to several hundreds ofbillions of USD yr–1 globally (high confidence). While investments are generally cost
efficient for densely populated and urban areas (high confidence), rural and poorer
areas will be challenged to afford such investments with relative annual costs forsome small island states amounting to several percent of GDP (high confidence).
Even with well-designed hard protection, the risk of possibly disastrous
consequences in the event of failure of defences remains.”311
As I argued in Chapter 4, it is inappropriate to discount future costs on the basis that future
people will be better off because there is a good chance that many people in Asia and Africa
will not actually be much better off.
Moreover, as shown in the table above, there are social and political barriers which could
lead to suboptimal adaptation even in rich countries.

Relative sea level rise of 5 metres over 100 years
If the Western Antarctic Ice Sheet collapses, there would be 5 metres of sea level rise overthe course of 100 years, which is around 50mm per year. Would we be able to adapt to suchan extreme event?
Past experience with subsidence and living below sea level
Some cities and regions have adapted to multimetre relative sea level rise caused bysubsidence, which provides some insights on what could happen in the future under 5 metreof sea-level rise. For example, Tokyo adapted to 4m of relative sea level rise over the 20th
century (40mm per year).
So far, retreat has been an unpopular option and has mainly been reserved for small
communities or carried out to create new wetland habitat.312 Rather, successful protection
has been the dominant response in almost all populated places suffering from subsidence,
which usually also enjoy high economic and population growth.313 One exception to this is310“Vulnerability to sea-level rise is not uniform and small islands, Africa and south, southeast andeast Asia are recognized as the most vulnerable regions [11]. This reflects their high and growing
exposure and low adaptive capacity. These regions are the areas where protection is most likely to
not occur or fail, and they collectively contain a significant proportion of potential environmental
refugees, especially the Asian regions (figure 3). Many of the people in Asia live in deltas, which areextensive and often subsiding coastal lowlands, amplifying global changes and making them morechallenging environments for adaptation [47,48,82]. Small islands have relatively small population andgiven that implementing protection could also present significant problems, forced abandonment
seems a feasible outcome for small changes in sea level” Robert J. Nicholls et al., ‘Sea-Level Riseand Its Possible Impacts given a “beyond 4°C World” in the Twenty-First Century’, PhilosophicalTransactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1934
(13 January 2011): 161–81, https://doi.org/10.1098/rsta.2010.0291.
311
IPCC, ‘Special Report on the Ocean and Cryosphere in a Changing Climate’, 2019, 56,
https://www.ipcc.ch/srocc/.
312
IPCC, ‘Special Report on the Ocean and Cryosphere in a Changing Climate’, 55.
313
Nicholls, ‘Adapting to Sea-Level Rise’.

249New Orleans, which experienced relative sea level rise of 2-3m over the 20th century due tosubsidence (20-30mm per year).314 Despite having the technical and economic capacity toprotect New Orleans, the defences were breached during Katrina and the population levels
have not returned to pre-Katrina levels.315
Currently, “at least 20 million people accept the risk of living up to several metres below
normal high tides in countries such as Belgium, Canada, China, Germany, Italy, Japan, theNetherlands, Poland, Thailand, the United Kingdom and the United States”.316

Future projections of adaptation
The Atlantis Project explored the scope to adapt to 5-6 metres of sea level rise over thecourse of 100 years (50mm per year). This could be caused by the collapse of the West
Antarctic Ice Sheet. Given certain assumptions about the costs of adaptation, “the length of
the world’s coast that is protected declines from about 85% to about 50% of the exposed
and populated coastline, reflecting that protection becomes too expensive in many areas”.317
However, because population is unevenly distributed along the coasts, this would still protect>95% of the coastal population.318

Source: Nicholls, Tol, and Vafeidis, ‘Global Estimates of the Impact of a Collapse of the West Antarctic
Ice Sheet’.

At a rate of only 5mm per year, on this model, it is still true that much of the world’s coastline
would be abandoned (this is shown by the green line).
Regional studies suggest that the response would be a mix of protection, accommodation
and retreat even in rich countries.

314

Robert J. Nicholls et al., ‘A Global Analysis of Subsidence, Relative Sea-Level Change and Coastal
Flood Exposure’, Nature Climate Change, 8 March 2021, 1–5,
https://doi.org/10.1038/s41558-021-00993-z.
315
The population was 390,000 in 2019 compared to 500,000 before Katrina. Nicholls, ‘Adapting to
Sea-Level Rise’, 19.
316
Hinkel et al., ‘The Ability of Societies to Adapt to Twenty-First-Century Sea-Level Rise’, 575.
317
Robert J. Nicholls, Richard S. J. Tol, and Athanasios T. Vafeidis, ‘Global Estimates of the Impact ofa Collapse of the West Antarctic Ice Sheet: An Application of FUND’, Climatic Change 91, no. 1 (25June 2008): 171, https://doi.org/10.1007/s10584-008-9424-y.
318
Nicholls, Tol, and Vafeidis, 187.

250

Source: Nicholls, Tol, and Vafeidis.

This is driven not by economic cost or technical feasibility but rather by political and social
barriers which preclude the implementation of optimal policy.319
One study suggests that London could mitigate the risk of 8m of sea level rise by moving theThames Barrier to Canvey.320 There is research showing that the Netherlands could adapt to
up to 5 metres of sea level rise using current engineering technology.321

7.3.3. Effects on 21st Century sea level rise on land loss and migration
One important way that sea level rise might drive domestic and international political tension
is by causing migration from coastal areas. Lincke and Hinkel (2021) explore cumulative
land loss and cumulative displacement over the 21st Century on different warming scenariosand on different Shared Socioeconomic Pathways. They consider five sea level rise
scenarios ranging from 33–170 cm in 2100 (RCP2.6 low ice melt, RCP2.6 high ice melt,
RCP8.5 low ice melt, RCP8.5 high ice melt and high-end).

319“Although the Netherlands and the UK have the technological and economic wherewithal to adapt
to extreme sea-level rise, the case studies suggest that this necessary condition is not a sufficient
one, as assumed by the model.” Nicholls, Tol, and Vafeidis.
320
Jim W. Hall, Hamish Harvey, and Lucy J. Manning, ‘Adaptation Thresholds and Pathways for Tidal
Flood Risk Management in London’, Climate Risk Management 24 (1 January 2019): 42–58,
https://doi.org/10.1016/j.crm.2019.04.001.
321“Research also showed that with an investment of around €80 billion, it may be possible topreserve territorial integrity of the Netherlands even under 5 m of SLR, using current engineering
technology” Hinkel et al., ‘The Ability of Societies to Adapt to Twenty-First-Century Sea-Level Rise’,
574.

251

Source: Daniel Lincke and Jochen Hinkel, ‘Coastal Migration Due to 21st Century Sea-Level Rise’,
Earth’s Future 9, no. 5 (2021): e2020EF001965, https://doi.org/10.1029/2020EF001965.

For around 1 metre of sea level rise (RCP8.5 high scenario),322 30-45 million people will
migrate cumulatively over the 21st Century, depending on the socioeconomic scenario. Thisis the cumulative effect over the whole 21st Century. So, this works out at around 375,000 to
600,000 people per year. If sea level rise is limited to 30cm (RCP2.6 low), then around 20
million cumulatively would be displaced. So, 1 metre of sea level rise compared to 30cm
increases the cumulative number of displaced people by up to 25 million, or around an extra
310,000 per year.
Migration will mainly be concentrated in Asia:

322

Lincke and Hinkel, ‘Coastal Migration Due to 21st Century Sea-Level Rise’, Table S2.

252

Source: Daniel Lincke and Jochen Hinkel, ‘Coastal Migration Due to 21st Century Sea-Level Rise’,
Earth’s Future 9, no. 5 (2021): e2020EF001965, https://doi.org/10.1029/2020EF001965.

As an upper bound on the effects of sea level rise, we can consider what would happen if allof the ice caps were to melt.323 The US Geological Survey estimates that this would cause
sea levels to rise by 80 metres. The effects this would have are shown on the map below:

323

I owe this point to Benjamin Hilton’s review of climate change for 80,000 Hours.

253

As the Atlas for the End of the World says, in this scenario “vast new coastlines and inlandseas will be created and 50 of the world's major cities would become architectural reefs”. But
still, the vast majority of the world would be above water.

7.3.4. Economic effects of sea level rise
I discuss estimates of the economic costs of sea level rise in Chapter 10.

7.4. Overall verdict on sea level rise
Sea level rise illustrates two important insights that have been stressed by longtermist andeffective altruist researchers. Firstly, we need to pay attention to low probability, high-impact
events because these may account for most of the expected costs of sea level rise. While on
RCP8.5, the most likely level of sea level rise in 2300 is around 4 metres, we cannot rule out
a rise of 15 metres.
Secondly, if we ignore all impacts beyond 2100 we would in effect ignore some truly hugechanges that future generations will have to deal with. On business as usual, by 2100, there
would be around 75 cm of sea level rise. But in 10,000 years’ time, sea level would be morethan 10 metres higher. Millennia into the future, the world will look very different.
Historical experience and modelling studies suggest that most coastal regions will
successfully adapt to up to 2 metres of sea level rise by 2100. The technical barriers to
successful adaptation seem low. Rich and densely populated areas are very likely to invest
in the requisite adaptation measures given the large net benefits, though political and social
factors might stand in the way of adaptation in some cases. Poorer, particularly exposed (i.e.
small island states) and rural areas, however, will likely not be able to afford such protection
and will thus be confronted with the need to migrate away from the coast.

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Studies also suggest that adaptation to extreme scenarios, such as 5 metres of sea level
rise due to the collapse of the West Antarctic Ice Sheet, is technically and economicallyfeasible, though successful adaptation would be much harder.
In any case, two things must be emphasised.324 First, even if societies by and large manage
to adapt to several metres of sea-level rise during the 21st century, such a scenario is likely
to go along with massive human suffering among those who lose their homes andlivelihoods due to retreat, or those being affected by coastal disasters in the case thatcoastal protection measures fail. Second, it must be emphasised that if sea-levels havealready risen by 2 or more metres at the end of the century, sea-level rise will progressbeyond 2100 at very high rates and will eventually also threaten those places that had
managed to protect against 21st century sea-level rise.

324

Thanks to Jochen Hinkel for raising this point.

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8. Tipping points
For those concerned about the long-term future and global catastrophic risk, potential tipping
points or non-linearities in the climate system are especially important.
Non-linearities can be gradual or sudden. For instance, climate sensitivity might increasesuper-linearly in a relatively modest and gradual way, or there might be a dramatic stepchange at higher levels of warming or emissions. Sudden and dramatic non-linearities posethe most severe global catastrophic risks. Tipping points are abrupt changes in a system thatare irreversible over a short timescale, such as a few decades. The impacts of tipping points
need not be abrupt. For example, it might be that passing a certain level of warming willcause the Greenland ice sheet to break up over thousands of years, and that this effectcannot be reversed, except on millennial timescales. But the impact of this change might still
be felt only over many thousands of years as the ice sheets slowly melt.
The tipping points with the greatest human impact will tend to be those that have most of
their effects over the course of years to decades because we would have less time to adapt.
For instance, rising sea levels over thousands of years would be bad, but give us lots of time
to adapt.
This nice diagram from CarbonBrief outlines some of the most important tipping points.

8.1. Permafrost carbon release
Permafrost is ground that has been frozen for at least two consecutive years. Its thickness
ranges from less than one metre to more than a kilometre. Typically, it sits beneath an“active layer” that thaws and refreezes every year. When temperatures rise, the permafrost
may start to thaw. Permafrost thaw is one of the most frequently discussed potential tipping
points.

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About 1 trillion tonnes of carbon is stored in permafrost.325 The IPCC’s Sixth Assessment
Report estimates that for each 1ºC of warming, permafrost emissions will increase by 18
GtC, with a 5% to 95% range of 3 to 42 GtC.326 For reference;●●●

Cumulative emissions from fossil fuel and industry since the Industrial Revolution =464 GtC.
Global emissions from fossil fuel and industry in 2019 = 10 GtC.
Cumulative emissions on RCP4.5 (2019-2100) = 850 GtC.327

On RCP4.5, temperatures would increase by a further 1.5ºC relative to today. This would
increase permafrost emissions by 5 GtC to 60 GtC (5% to 95% range), which is importantbut small relative to cumulative anthropogenic emissions. An additional 60 GtC would add
around 0.1ºC to global average temperatures.328
The IPCC estimates that up to half of the permafrost carbon could be released abruptly, andthe rest gradually.329
Beyond 2100, permafrost emissions would increase further. Different models produce
different estimates of permafrost emissions on different anthropogenic emissions scenarios.
Some of these are shown below:330●●●

RCP2.6 = 20-40 GtC.
RCP4.5 = 17 GtC (range: minus 14 GtC to 54 GtC)
RCP8.5 = 314 GtC (81 to 642 GtC)

So, on RCP4.5, permafrost emissions are small relative to anthropogenic emissions. On
RCP8.5, at the upper end of the model range for 2100-2300, more than 600 GtC would be
released, enough to warm the planet by about a degree. Still, on the extension of RCP8.5 to
325“The new northern permafrost zone carbon inventory reports the surface permafrost carbon pool
(0–3 m) to be 1,035 ±150 Pg carbon (mean ±95% confidence interval, CI).” E. a. G. Schuur et al.,‘Climate Change and the Permafrost Carbon Feedback’, Nature 520, no. 7546 (April 2015): 171–79,
https://doi.org/10.1038/nature14338.
326
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Box 5.1.
327
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Summary for Policymakers, SPM. 8.
328This is using the IPCC’s TCRE estimate of 1.65C of warming per 1,000GtC.
329“Abrupt thaw processes can contribute up to half of the total net greenhouse gas release frompermafrost loss, the rest attributed to gradual thaw (Schneider von Deimling et al., 2015; Turetsky etal., 2020).” IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Box 5.1.
330
“Beyond 2100, models suggest that the magnitude of the permafrost carbon feedback strengthensconsiderably over the period 2100–2300 under a high-emissions scenario (Schneider von Deimling etal., 2015; McGuire et al., 2018). Schneider von Deimling et al., (2015) estimated that thawing
permafrost could release 20–40 PgC of CO2 in the period from 2100 to 2300 under a RCP2.6
scenario, and 115–172 PgC of CO2 under a RCP8.5 scenario. The multi-model ensemble in (McGuireet al., 2018) project a much wider range of permafrost soil carbon losses of 81–642 PgC (mean 314
PgC) for an RCP8.5 scenario from 2100 to 2300, and of a gain of 14 PgC to a loss of 54 PgC (meanloss of 17 PgC) for an RCP4.5 scenario over the same period” IPCC, Climate Change 2021: The
Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), Box 5.1.

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2300, anthropogenic CO2 emissions would be 5,000 GtC, so fossil fuel emissions remainmuch larger than potential permafrost emissions. It is important to bear in mind that, as
discussed in Chapter 2, higher end recoverable fossil fuel estimates are 3,000 GtC, so this
scenario may not be feasible.
The evidence from the paleoclimate also suggests that warming of around 4ºC is unlikely totrigger the abrupt release of huge amounts of carbon from permafrost. During the Pliocene,
higher latitudes were upwards of 10ºC warmer than today,331 but there is no sign of a hugecarbon release over the course of years to decades.
In summary, the thawing of permafrost looks set to release additional carbon into theatmosphere, which will have important effects, but the effect is small relative to fossil fuelemissions.

8.2. Methane clathrates
Methane clathrates or hydrates are forms of ice that contain large amounts of methane.
They form at low temperatures and high pressures in continental margin marine sediments
or within and beneath permafrost. The total global clathrate reservoir is estimated to contain
1500–2000 GtC,332 which, as we saw in Chapter 1, is close to many estimates of the carbon
contained in recoverable fossil fuels. The release of vast amounts of methane fromclathrates is often brought up in discussion of climate disaster scenarios.333 For instance,
Whiteman et al (2013) stated:“As the amount of Arctic sea ice declines at an unprecedented rate, the thawing ofoffshore permafrost releases methane. A 50-gigatonne (Gt) reservoir of methane,stored in the form of hydrates, exists on the East Siberian Arctic Shelf. It is likely tobe emitted as the seabed warms, either steadily over 50 years or suddenly.”334
331

See Chapter 3.
“The total global clathrate reservoir is estimated to contain 1500–2000 PgC” IPCC, Climate
Change 2021: The Physical Science Basis, sec. 5.4.9.1.3.
333
See for example “A more remote (but even more vivid) possibility, which in principle should also be
included, is heat-induced releases of the even vaster offshore deposits of methane trapped in theform of clathrates.6 There is a very small and unknown (but decidedly nonzero) probability over thelong run of having destabilized methane from these offshore clathrate deposits seep into theatmosphere if the temperature of the waters bathing the continental shelves increases just slightly.
The amount of methane involved is huge, although it is not precisely known. Most estimates place thecarbon-equivalent content of methane hydrate deposits at about the same order of magnitude as allother fossil fuels combined. Over the long run, a methane outgassing–amplifier process could
potentially precipitate a disastrous strong positive feedback warming. If it occurred at all, such an
event would likely take centuries to materialize because the presumed initiator would be theslow-acting gradual warming of ocean waters at the depths of the continental shelves. Thus, while it isa low-probability event that might only transpire centuries from now (if at all), the possibility of aclimate meltdown is not just the outcome of a mathematical theory but has a real physical basis.”.
Martin L. Weitzman, “Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change,”
Review of Environmental Economics and Policy 5, no. 2 (July 1, 2011): 275–92,
https://doi.org/10.1093/reep/rer006.
334
Gail Whiteman, Chris Hope, and Peter Wadhams, “Climate Science: Vast Costs of Arctic Change,”
Nature, July 24, 2013, https://doi.org/10.1038/499401a.
332

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Methane stays in the atmosphere for around 10 years, eventually oxidising to carbon
dioxide. This University of Chicago climate model allows you to test the effect of a given
amount of methane on warming. It suggests that adding 50 billion tonnes of methane in asingle slug will lead to warming of around 2°C over ten years, which would be very
damaging.
The evidence and expert opinion suggests that an abrupt and massive release of methane isvery unlikely.

8.2.1. Methane emissions so far
There is mixed evidence of increasing overall methane emissions from the permafrost regionso far. Atmospheric measurements show no detectable trends in methane emissions fromthe permafrost regions over the past 30 years, though the IPCC has high confidence thatobservations understate methane emissions.335

8.2.2. Most methane would not reach the atmosphere
If methane hydrate does melt, most of it would not reach the atmosphere. As Carolyn
Ruppel, chief scientist for the US Geological Survey’s Gas Hydrates Project,“If the methane released during gas hydrate degradation reaches the ocean, it would
mostly be consumed by bacteria in the water column and not reach the atmosphere.
In permafrost areas, degrading gas hydrate is usually deeply buried, so permafrost
thaw is the more important contributor to greenhouse gas emissions.”336

8.2.3. The paleoclimate record
One way to test how much methane might be released from clathrates in the future is bylooking at past episodes of warming in which there was Arctic warming or the Arctic wasmuch warmer than today.
335“Atmospheric measurements and inversions performed at the global and regional scales do notshow any detectable trends in annual mean CH4 emissions from the permafrost region over the past
30 years (Jackson et al., 2020; Saunois et al., 2020; Bruhwiler et al., 2021), consistent withatmospheric measurements in Alaska that showed no significant annual trends, despite significant
increase in air temperature (Sweeney et al., 2016). Atmospheric inversions and biospheric models do
not show any clear trends in CH4 emissions for wetland regions of the high latitudes during the period
2000–2016 (Patra et al., 2016; Poulter et al., 2017; Jackson et al., 2020; Saunois et al., 2020). Large
uncertainties on wetland extent and limited data constraints place low confidence in these modelling
approaches.
The SROCC also assessed with high confidence that CH4 fluxes have been under-observed due totheir high variability at multiple scales in both space and time, and that there is a persistent mismatch
between top-down and bottom-up methane budgets, with emissions calculated by upscaling groundobservations typically higher than emissions inferred from large-scale atmospheric observations(Thornton et al., 2016a; Saunois et al., 2020).” IPCC, Climate Change 2021: The Physical Science
Basis, Sixth Assessment Report (UNFCCC, 2021), Box 5.1.
336
Ruppel Carolyn D. and Kessler John D., “The Interaction of Climate Change and Methane
Hydrates,” Reviews of Geophysics 55, no. 1 (February 8, 2017): 126–68,
https://doi.org/10.1002/2016RG000534.

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One relevant analogue is the warming from the Pleistocene into the Holocene, whentemperatures increased by around 4ºC. The IPCC concludes that
“In conclusion, several independent lines of evidence indicate that permafrost thaw
did not release vast quantities of fossil CH4 associated with the transient warming
events of the [Last Deglacial Transition]. This suggests that large emissions of CH4
from old carbon sources will not occur in response to future warming (medium
confidence).”337
If there were going to be a large methane input from melting clathrates, that would also likely
have happened during the Last Interglacial, when global average temperatures were 1ºC
above pre-industrial levels.338 In the Arctic, temperatures were around 1-2ºC higher than
today,339 and there is some evidence that the Arctic was perennially ice-free.340 Despite that,
there is no evidence of a massive release of methane from clathrates in this period.
There is disagreement about how much of a role, if any, the melting of methane hydrate
played in the Paleocene-Eocene Thermal Maximum.
“There is low to medium confidence in evaluations of the total amount of carbon
released during the PETM, as proxy data constrained estimates vary from around3000 to more than 7000 PgC, with methane hydrates, volcanic emissions, terrestrial
and/or marine organic carbon, or some combination thereof, as the probable sourcesof carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., 2017; Elling et al., 2019;
337

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Box 5.1.
338
“In summary, GMST during the warmest millennia of the 38 LIG (within the interval of around
129–125 ka) is estimated to have reached 0.5°C–1.5°C higher values than 39 the 1850–1990
reference period (medium confidence” IPCC, Sixth Assessment Report: Working Group I The
Physical Science Basis, sec. 2.3.1.1.1.
339
In the Last Interglacial, temperatures were around 4-5C above pre-industrial levels. Today,
temperatures are around 3C warmer in the Arctic than pre-industrial levels. “Stronger LIG
summertime insolation at high northern latitudes drove Arctic land summer temperatures 4–5°C
higher than in the pre-industrial era.” Maria-Vittoria Guarino et al., ‘Sea-Ice-Free Arctic during the Last
Interglacial Supports Fast Future Loss’, Nature Climate Change 10, no. 10 (October 2020): 928–32,
https://doi.org/10.1038/s41558-020-0865-2. On recent Arctic warming, see the Arctic Report Card.
340“While knowledge of past Arctic temperatures is robust, thanks to the available observations2,10,
the interpretation of Arctic sea-ice changes during the LIG has previously been afflicted byuncertainty8,10,12,13. Water-isotope measurements from ice cores have been interpreted to suggest
that, alongside the Arctic warming, there was a reduction in the mean annual sea-ice area8 .
Microfauna in LIG marine sediments recovered from boreholes on the Beaufort Sea Shelf have beeninterpreted as implying a lack of perennial Arctic sea-ice cover14, as have planktonic foraminifera
recovered from some Arctic marine cores15,16. Similarly, ostracodes on the Lomonosov and
Mendeleyev Ridges and Morris Jesup Rise have been interpreted as indicative of minimum sea-ice
coverage during peak LIG warmth17. However, measurements of the recently developed sea-ice
proxy IP25 (a carbon-25 highly branched isoprenoid lipid), when combined with terrestrial andopen-water phytoplankton biomarkers, have been interpreted as evidence of perennial LIG ice coverin the central part of the Arctic Ocean13. While aspects of this particular application of IP25 aredebated18, this result (see also Methods), along with the fact that no coupled climate models havesimulated an ice-free Arctic during the LIG (refs. 10,11,13,19), has meant that the researchcommunity has spent considerable time debating whether or not summer sea ice disappeared during
this important past warm period8,12,13,19.” Maria-Vittoria Guarino et al., ‘Sea-Ice-Free Arctic during
the Last Interglacial Supports Fast Future Loss’, Nature Climate Change 10, no. 10 (October 2020):
928–32, https://doi.org/10.1038/s41558-020-0865-2

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S.M. Jones et al., 2019; Haynes and Hönisch, 2020). Methane emissions related tohydrate/permafrost thawing and fossil carbon oxidation may have acted as positive
feedbacks (Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al.,2019), as the inferred increase in atmospheric CO2 can only account for
approximately half of the reported warming (Zeebe et al., 2009).”341
The PETM occurred against a background that was 12ºC warmer than today. If methane
clathrates did play a role, they would have been destabilised by the release of more than 1.5
trillion tonnes of carbon, mainly in the form of carbon dioxide, on top of a much warmer
baseline.342
Overall, there is little indication from the paleoclimate record that warming of 4ºC relative topre-industrial will cause an abrupt and massive release of methane from clathrates, as
posited by Whiteman et al. The paleoclimatic evidence suggests that methane clathrates
may become more of a concern if warming passes 10ºC above pre-industrial levels and/or
carbon emissions are huge.
The anti-climate sceptic website Skeptical Science provides an accessible overview of someof these arguments.

8.2.4. Expert estimates of emissions from methane hydrates
The view expressed by Whiteman et al (2013) is at odds with the consensus in the literatureand has been the subject of significant criticism. There have also been two responses in
Nature criticising the paper.343 In a review article, Schuur et al comment that
“A large pulse release of permafrost carbon on this timescale could cause climatechange that would incur catastrophic costs to society, but there is little evidence fromeither current observations or model projections to support such a large and rapid
pulse.”344
The IPCC’ Sixth Assessment Report says that there is a 1% to 10% chance that “CH4emissions from clathrates will substantially warm the climate system over the next few
centuries”.345 It is unclear how to interpret this because it is not clear what they mean by‘substantial’.
Sayedi et al (2020) carried out an expert elicitation study of 25 permafrost researchers on
the stocks and sensitivity of carbon in the subsea permafrost domain. The study’s findings
on cumulative emissions from the permafrost domain on different emissions scenarios are
341

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), sec. 5.1.2.1.
342
See section 3.3.2.
343
Frans-Jan W. Parmentier and Torben R. Christensen, “Arctic: Speed of Methane Release,”
Comments and Opinion, Nature, August 28, 2013, https://doi.org/10.1038/500529a; Dirk Notz, Victor
Brovkin, and Martin Heimann, “Arctic: Uncertainties in Methane Link,” Comments and Opinion,
Nature, August 28, 2013, https://doi.org/10.1038/500529b.
344
Schuur et al., “Climate Change and the Permafrost Carbon Feedback.”
345
IPCC, Climate Change 2021: The Physical Science Basis, sec. 5.4.9.1.3.

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shown below. The questions assume that concentrations reach a peak near to 2100 andthen remain at that level indefinitely.346 As discussed in Chapter 2, in practice it is more likelythat concentrations would slowly decline.

Source: Sayedeh Sara Sayedi et al., ‘Subsea Permafrost Carbon Stocks and Climate Change
Sensitivity Estimated by Expert Assessment’, Environmental Research Letters 15, no. 12 (December2020): 5, https://doi.org/10.1088/1748-9326/abcc29.

The implications for warming of emissions from the permafrost region are summarised in theguesstimate figure below (from this model).

346“The selected RCPs were RCP2.6, which has a peak concentration of ∼490 ppm CO2-equivalent(CO2e) reached before 2100, RCP4.5 with a peak of ∼650 ppm CO2e at 2100, and RCP8.5 with apeak of ∼1400 ppm CO2e at 2100 (Moss et al 2010, Koenigk et al 2013).” Sayedeh Sara Sayedi etal., ‘Subsea Permafrost Carbon Stocks and Climate Change Sensitivity Estimated by Expert
Assessment’, Environmental Research Letters 15, no. 12 (December 2020): 5,
https://doi.org/10.1088/1748-9326/abcc29.

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As mentioned in Chapter 2, the IPCC does not give a 90% confidence interval for theTransient Climate Response to Cumulative Emissions, but does give a 66% confidence
interval of 1.0°C to 2.3°C per trillion tonnes of carbon. In the model, I have roughly guessed
at a 90% confidence interval of 0.8 to 2.5ºC per trillion tonnes of carbon
One concern with these numbers is that, because they rely on the CO2-equivalent metric,
they overweight the warming effect of methane.347 Thus, the estimates in the table above arelikely biased high to some extent.
Nonetheless, even if the CO2-equivalent metric is correct, this does illustrate that the claimsof Whiteman et al (2013) about the likelihood of a sudden release of methane fromclathrates causing 2ºC of warming is well outside the scientific mainstream: on business as
usual (RCP4.5), the 95th percentile of warming due to both permafrost carbon and methane
clathrates is around 0.2ºC by 2100.

8.3. Amazon forest dieback
Climate change has competing effects on forests. On the one hand, higher temperaturesand increased drying is damaging to forests, but on the other hand higher CO2 levels aregood because of the CO2 fertilisation effect, which encourages photosynthesis and theefficient use of water. Some early climate models found that global warming of 3°C would
cause the Amazon rainforest to die off, which would in turn release further CO2 into theatmosphere.348 The models produced this result even without taking account of deforestationand fires.
The researchers who initially found the effect suggest that the risk is probably smaller thanfirst estimated.349

347

Myles Allen, ‘Short-Lived Promise: The Science and Policy of Cumulative and Short-Lived Climate
Pollutants’, Oxford Martin School, 2015.
348
P. M. Cox et al., ‘Amazonian Forest Dieback under Climate-Carbon Cycle Projections for the 21stCentury’, Theoretical and Applied Climatology 78, no. 1 (1 June 2004): 137–56,
https://doi.org/10.1007/s00704-004-0049-4.
349“The latest Earth system models show limited evidence of Amazon forest dieback in the absence of
direct human deforestation. A handful of models show reductions in Amazonian forest cover due toclimate change, but most models show increasing forest cover due to CO2 fertilisation. None of thesemodels include phosphorus limitations or forest fires, though, so the jury is still out. However,personally, I am less concerned about climate-change driven Amazon dieback than I was when we
published our study in 2000.” Guest post by Peter Cox on Carbon Brief.

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Most Earth System Models now suggest that the effect of CO2 fertilisation overwhelms theeffect of climate change. However, there are reasons to think that the models could be
biased in either direction.
●●

In the real world, plants might be less vulnerable than models suggest due to greater
plant trait diversity and possible acclimation to warming.
Plants might be more vulnerable than models suggest due to insect outbreaks, ormodel limitations in representing wildfires and droughts.350

The paleoclimate record provides some comfort on the risk of Amazon dieback.
Temperatures were more than 10°C higher during the Eocene, but tropical forests
flourished.351 Willis and MacDonald posit that models project more damaging effects
because they neglect CO2 fertilisation.
However, one important difference for future warming is that warming may occur in thecontext of rising deforestation.352 Deforestation has been on the rise since 2012 and has
accelerated since Bolsonaro’s election in 2019. Amazonian deforestation in 2021 was thehighest it has been since 2006.

350“Most ESMs project continuing carbon accumulation in tropical forests as a result of direct CO2
effects overwhelming the negative effects of climate change (Huntingford et al., 2013; Drijfhout et al.,2015; Boulton et al., 2017). In the real world, forests may be less vulnerable to climate changes thanthose modelled in ESMs because of the greater plant trait diversity which confers additional resilience(Reyer et al., 2015; Levine et al., 2016; Sakschewski et al., 2016), and also because of possible
acclimation of vegetation to warming (Good et al., 2011, 2013; Lloret et al., 2012; Mercado et al.,2018). Contrary, forests may be more vulnerable in the real world due to indirect climate change
effects such as insect outbreaks and diseases not considered here (Section 5.4.3.2) or model
limitations in representing the effects disturbances such as wildfire and droughts. In general, forests
are most vulnerable when climate change is combined with increased rates of direct deforestation(Nobre et al., 2016; Le Page et al., 2017).” IPCC, Climate Change 2021: The Physical Science Basis,
Sixth Assessment Report (UNFCCC, 2021), Ch. 5, sec. 5.4.9.1.1.
351
K. J. Willis and G. M. MacDonald, ‘Long-Term Ecological Records and Their Relevance to Climate
Change Predictions for a Warmer World’, Annual Review of Ecology, Evolution, and Systematics 42,
no. 1 (2011): sec. 2, https://doi.org/10.1146/annurev-ecolsys-102209-144704.
352
Thanks to James Ozden for pressing me on this point.

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Source: Mongabay

Boulton et al (2022) point out that due to climate change and land use change, threequarters of the Amazon has been losing resilience since 2000.353 Thus, once we adjust for
deforestation, Earth System Models likely understate the risks of dieback.
The IPCC Sixth Assessment Report estimates that on the pessimistic assumption (1-10%
chance) of no CO2 fertilisation, this would release 50GtC per 1ºC of warming. So for 4ºC,
Amazon dieback would add at most 200 billion tonnes of carbon to the atmosphere,354 which
would cause around 0.3°C of warming.
For more, see this CarbonBrief post.

353

Chris A. Boulton, Timothy M. Lenton, and Niklas Boers, ‘Pronounced Loss of Amazon Rainforest
Resilience since the Early 2000s’, Nature Climate Change 12, no. 3 (March 2022): 271–78,
https://doi.org/10.1038/s41558-022-01287-8.
354“In order to estimate an upper limit on the impact of Amazon forest dieback on atmospheric CO2,
we consider the very unlikely limiting case of negligible direct-CO2 effects (Section 5.4.1). Emergent
constraint approaches (Section 5.4.6) may be used to estimate an overall loss of tropical land carbon
due to climate change alone, of around 50 PgC per ºC of tropical warming (Cox et al., 2013b; Wenzel
et al., 2014). This implies an upper limit to the release of tropical land carbon of <200 PgC over the
21st century (assuming tropical warming of <4ºC, and no CO2-fertilisation), which translates to
dCO2/dt < 0.5 ppm yr-1” IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment
Report (UNFCCC, 2021), Ch. 5, sec. 5.4.9.1.1.

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8.4. Boreal forest dieback
Boreal forests are found in the cold climates of the northern hemisphere high latitudes.355
They lie just to the south of the Arctic tundra, where tree growth is restricted by year-roundfreezing or near-freezing temperatures and a lack of rain. Boreal forests are characterised by
species that can cope with the cold, such as pine, spruce and larch. They cover vast
stretches of North America and northern Europe and Asia. They are the largest biome
anywhere on Earth and account for 30% of the world’s forests, and hold around a third of theworld’s terrestrial carbon.
The boreal region is warming twice as fast as the global average. Some models suggest thatthe parts of boreal forests in the south could pass a tipping point, and transition to open
woodland and grassland, while the northern part of the forest would start to encroach into
the Arctic permafrost. Due to the competing effects in the north and south, the IPCC claims
that boreal forest is not expected to be a major source of CO2 emissions.
“Boreal forest dieback is not expected to change the atmospheric CO2 concentration
substantially because forest loss at the south is partly compensated by (i) temperate
forest invasion into the previous boreal area and (ii) boreal forest gain at the north(Friend et al., 2014; Kicklighter et al., 2014; Schaphoff et al., 2016) (medium
confidence). An upper estimate of this magnitude, based on statistical modelling ofclimate change alone, is of 27 Pg vegetation C loss in the southern boreal forest,
which is roughly balanced by gains in the northern zone (Koven, 2013). Carbon
release from vegetation and soil due to wildfires in boreal regions (Eliseev et al.,2014b; Turetsky et al., 2015; Walker et al., 2019a) is also not expected to change thisestimate substantially because of its small present-day value of about 0.2 PgC yr-1
(van der Werf et al., 2017), and because of likely increases in precipitation in boreal
regions.”356

8.5. Collapse of the Atlantic Meridional Overturning Circulation
The Atlantic Meridional Overturning Circulation, known as the AMOC, is an ocean systemthat plays a major role in regulating the climate. It is driven by a delicate balance of ocean
temperatures and salinity, which is at risk from being upset by a warming climate.
The potential collapse of the Atlantic Meridional Overturning Circulation is one of the moreworrying tipping points discussed in the literature. Carbonbrief’s review of AMOC collapse isavailable here.
The diagram below shows a schematic of the AMOC

355
356

All of this is from CarbonBrief.
IPCC, Climate Change 2021: The Physical Science Basis, sec. 5.4.9.1.1.

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Climate change affects the AMOC by diluting the water at higher latitudes (through ice melt
and rainfall) and heating it up. This reduces the density of the water and weakens the
AMOC. Weakening of the AMOC could happen gradually or suddenly. The AMOC would
only recover several centuries after emissions stop.
About 14,500 years ago, the Earth started to emerge from the Last Ice Age to the warmer
Holocene interglacial. Partway through this transition, temperatures in the Northern
Hemisphere suddenly returned to glacial conditions over the course of decades. This is
known as the Younger Dryas event and it is thought to have been caused by a severe
weakening of the AMOC due to an influx of fresh meltwater into the North Atlantic. The
Younger Dryas also caused widespread changes in precipitation: the African and Asianmonsoons weakened but those in the Southern Hemisphere strengthened.357

357“During the last deglacial transition, one such slowdown in AMOC—during the Younger Dryas
event (12,800–11,700 years ago)— caused worldwide changes in precipitation patterns. These
included a southward migration of the tropical ITCZ (Peterson et al., 2000; McGee et al., 2014;
Schneider et al., 2014; Mohtadi et al., 2016; Reimi and Marcantonio, 2016; Wang et al., 2017c) andsystematic weakening of the African and Asian monsoons (Tierney and deMenocal, 2013;
Otto-Bliesner et al., 2014; Cheng et al., 2016; Grandey et al., 2016; Wurtzel et al., 2018). Conversely,
the Southern Hemisphere monsoon systems intensified (Cruz et al., 2005; Ayliffe et al., 2013; Stríkis
et al., 2015, 2018a; Campos et al., 2019). Drying occurred in Mesoamerica (Lachniet et al., 2013)
while the North American monsoon system was largely unaffected (Bhattacharya et al., 2018). The
mid-latitude region in North America was wetter (Polyak et al., 2004; Grimm et al., 2006; Wagner etal., 2010; Voelker et al., 2015), while Europe was drier (Genty et al., 2006; Rach et al., 2017;
Naughton et al., 2019).” IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth
Assessment Report, 2022, sec. 8.6.1.

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8.5.1. Climatic effects of AMOC collapse
A collapse of the AMOC would have a range of climatic effects on sea levels and floods,358
on winter storms,359 and on temperature and precipitation. I will focus on temperature andprecipitation here.
The figure below illustrates the changes that result compared to the pre-collapsed stateaccording to one model. Shutdown of the AMOC results in a cooling (blue shading) of thewhole northern hemisphere, particularly the regions closest to the zone of North Atlantic heatloss (the “radiator” of the North Atlantic central heating system). In these regions the coolingexceeds the projected warming due to greenhouse gases, so a complete shutdown in the
21st century could result in a net cooling in regions such as western Europe.

For reference, the map below from the IPCC shows future regional warming at different
levels of global warming:

358

Christopher M. Little et al., ‘The Relationship Between U.S. East Coast Sea Level and the Atlantic
Meridional Overturning Circulation: A Review’, Journal of Geophysical Research: Oceans 124, no. 9(2019): 6435–58, https://doi.org/10.1029/2019JC015152.
359
T. Woollings et al., ‘Response of the North Atlantic Storm Track to Climate Change Shaped by
Ocean–Atmosphere Coupling’, Nature Geoscience 5, no. 5 (May 2012): 313–17,
https://doi.org/10.1038/ngeo1438.

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Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Summary for Policymakers, SPM. 5.

This suggests that areas around the North Atlantic might cool by 1-2°C on net.
The IPCC has low confidence in model projections of the timing and magnitude of AMOC
weakening in the 21st Century, and there is large disagreement across models about theregional effects.360
There would also be large effects on precipitation patterns. The pane on the left shows theeffect on precipitation during the Younger Dryas, and the right pane shows the effect on
precipitation due to modelled AMOC collapse.

360“Note that these ranges are based on ensemble means of individual models, largely smoothing out
internal variability. If single realizations are considered, the ranges become wider, especially bylowering the low end of the range (Section 4.3.2.3). In summary, it is very likely that AMOC will decline
in the 21st century, but there is low confidence in the model’s projected timing and magnitude.” IPCC,
Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec.
9.2.3.1.

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Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Chap 8, Fig. 8.27.

Since there is so much uncertainty across models, the true net effect may be significantly
different.
AMOC collapse would also affect global monsoons, with African and Asian monsoons
weakening and southern hemisphere monsoons strengthening.361

8.5.2. How would AMOC collapse affect human society?
The collapse of the AMOC would have a range of negative effects on society. In Northern
Europe, winters would be colder, which would likely increase temperature-related deaths.362
Winter storminess could also cause damage to infrastructure. Probably the most damaging
effects would occur due to changes to monsoons in Africa and Asia. This could have very
bad effects by creating droughts and damaging agriculture.
Cooling is generally worse for agriculture than warming because one day of frost destroys
the entire growing season: this is why nuclear winter is so bad. Reduced precipitation andsoil moisture is bad for obvious reasons.
As far as I am aware, there have been no studies on the effects that AMOC collapse might
have on global agriculture. Jackson et al (2015) explore the effect on net primary productivityof vegetation in Western Europe in spring (left pane) and summer (right pane):

Source: Jackson et al., ‘Global and European Climate Impacts of a Slowdown of the AMOC in a High
Resolution GCM’, Fig. 13.

Some regions suffer net primary productivity losses of 50%, though the effects are moremodest in many regions.
Ritchie et al (2020) explore the effects of AMOC collapse on farming in Britain. They
conclude that in the absence of irrigation:
361“A collapse of the Atlantic Meridional Overturning Circulation could weaken the African and Asianmonsoons but strengthen the Southern Hemisphere monsoons (high confidence).” IPCC, Climate
Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), Box TS. 13.
362
See Chapter 6.

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“The expected overall area of arable production is predicted to fall dramatically from32 to 7% of land area (Extended Data Figs. 2 and 3). This in turn generates a major
reduction in the value of agricultural output, with a decrease of £346 million perannum (Table 1), representing a reduction in total income from British farming of~10% (ref. 33). The key driver of the arable loss seen across Great Britain is climate
drying due to AMOC collapse, rather than cooling.”363
With irrigation:“land area under arable production still increases from 32 to 38% by 2080, with anaccompanying increase in output value of £79 million per annum”364
The irrigation would cost more than £800 million per year, and would be technically difficult.
UK GDP is currently around £2.2 trillion, so this would be 0.3% of UK GDP. We should
expect crop yields to have improved substantially in the next few decades due totechnological improvements. For reference, over the last 60 years, crop yields haveincreased by upwards of 200%. For the purposes of producing enough food to feed its own
people, the UK would be rich enough to adapt, though the economic costs would be large.
The cooling effects of AMOC collapse would not be sufficient to shorten the growing season
in other regions because any cooling effects would be cancelled out by higher backgroundtemperatures due to the strengthening greenhouse effect.
The effects of precipitation changes are also harder to predict. Irrigation would be aneffective response to any shortfall of rain, but there would be large transition costs for
affected countries. For countries that are poor and agrarian at the time of AMOC collapse,
the scope for adaptation would be much more limited. AMOC collapse is expected to
weaken the monsoons around West Africa, the Sahel and India, which supply much of theannual rainfall in those regions. As I discussed above, weakening of the West African
monsoon between the late 1960s and 1980s caused a drought that killed tens of people.
Overall, it is clear that global agriculture would still be viable, and that rich countries that arerich at the time of collapse would be able to adapt to AMOC collapse: people would not
starve, though they would suffer substantial economic costs. Some regions would lose out
while others would gain, though the net effect seems likely to be strongly negative given the
populations in losing regions (India, West Africa and the Sahel).

8.5.3. The probability of AMOC collapse
The AMOC is projected to weaken on all emissions scenarios up to 2100. The IPCC’s
Special Report on the Ocean and the Cryosphere in a Changing Climate claimed that AMOC

363

Paul DL Ritchie et al., ‘Shifts in National Land Use and Food Production in Great Britain after a
Climate Tipping Point’, Nature Food 1, no. 1 (2020): 77.
364
Paul DL Ritchie et al., ‘Shifts in National Land Use and Food Production in Great Britain after a
Climate Tipping Point’, Nature Food 1, no. 1 (2020): 79.

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collapse was about as likely as not by 2300.365 The IPCC provides the following chart whichshows the probability of AMOC collapse at different levels of warming.

Source: IPCC, ‘Special Report on the Ocean and Cryosphere in a Changing Climate’, 2019, Fig 6.9.

As this shows, at 4°C of warming, the risk of AMOC collapse is around 5%, rising to nearly
50% at 8°C. However, the IPCC cautions that existing models do not capture someprocesses that are relevant to the risk of AMOC collapse, so the above chart probably
understates the risk.366
The AMOC would only recover several centuries after the cessation of emissions.367
365“Both AR5 (Collins et al., 2013) and SROCC (Collins et al., 2019) assessed that an abrupt
collapse of AMOC before 2100 was very unlikely, but SROCC added that, by 2300, an AMOC
collapse was as likely as not for high-emissions scenarios.” IPCC, Climate Change 2021: The
Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec. 9.2.3.1.
366“The SROCC also assessed that model-bias may considerably affect the sensitivity of the modelled
AMOC to freshwater forcing. Tuning towards stability and model biases (Valdes, 2011; Liu et al., 2017;
Mecking et al., 2017; Weijer et al., 2019) provides CMIP models a tendency toward unrealistic stability(medium confidence). By correcting for existing salinity biases, Liu et al. (2017) demonstrated that
AMOC behaviour may change dramatically on centennial to millennial timescales and that theprobability of a collapsed state increases. None of the CMIP6 models features an abrupt AMOC
collapse in the 21st century, but they neglect meltwater release from the Greenland ice sheet and a
recent process study reveals that a collapse of the AMOC can be induced even by small-amplitudechanges in freshwater forcing (Lohmann and Ditlevsen, 2021). As a result, we change theassessment of an abrupt collapse before 2100 to medium confidence that it will not occur” IPCC,
Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec.
9.2.3.1.
367“Based on a large initial condition ensemble of simulations with a CMIP5 model (CanESM2) withemission scenarios leading to stabilization of global warming of 1.5°C, 2.0°C, or 3.0°C relative to1850–1900, AMOC continues to decline for 5–10 years after GSAT is effectively stabilized at thegiven GWL (Sigmond et al., 2020). This is followed by a recovery of AMOC strength for about the next
150 years to a level that is approximately independent of the considered stabilization scenario. These
results are replicated in simulations in a CMIP6 model (CanESM5) with emissions cessation after
diagnosed CO2 emissions reach 750 Gt, 1000 Gt, or 1500 Gt. These emissions levels lead to global272

8.6. Changes to the West African monsoon
The Carbonbrief review of the West African monsoon is here. My introduction here borrows
from the Carbonbrief review.
The term “monsoon” in its strictest sense refers to the seasonal reversal of winds and its
accompanying rainfall. Along with India, West Africa is one of the few places on Earth where
this happens.
The West African monsoon brings rainfall to West Africa and the Sahel – a band of semi-arid
grassland sandwiched between the Sahara desert to the north and tropical rainforests to thesouth. The Sahel stretches from the Atlantic coast of Mauritania and Senegal through to
Sudan, Eritrea and the Red Sea.

West Africa’s dry season, which runs from November through to May, sees prevailing dry
and dusty winds come from the desert. The monsoon brings rain to the region from around
June to September.
The West African monsoon is notoriously unreliable. Between the late 1960s and 1980s, alack of rain hit much of the Sahel, with average rainfall declining by more than 30% overmost of the region compared to the 1950s. This plunged the region into an extended
warming stabilization at 1.5°C, 2.0°C, or 3.0°C relative to 1850–1900. In summary, in these model
simulations the AMOC recovers over several centuries after the cessation of CO2 emissions.” IPCC,
Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec.
4.3.2.3.

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drought, contributing to a famine that killed tens of thousands of people and triggering aninternational aid effort.
Models and paleoclimate evidence suggests that greenhouse warming will strengthen
monsoon precipitation. However, anthropogenic aerosol emissions in the second half of the20th Century outweighed the effect of greenhouse gases, causing drying.368
Paleoclimate evidence suggests that there were past ‘Green Sahel’ states in which rainfall
was much higher in the Sahel.369 Models suggest that climate change will have mixed effects
on rainfall patterns in the Sahel, with monsoon precipitation increasing in the central Sahel,
but decreasing in the Western Sahel.370
All of the evidence suggests that the Sahel is very sensitive to climatic drivers and that there
could be abrupt shifts in precipitation.
As discussed above, the West African monsoon would also be affected by AMOC collapse.
Impacts on the monsoon are reversible within years to decades if greenhouse gas
concentrations are reduced.371

8.7. Indian monsoon shift
The Carbonbrief review of Indian monsoon shift is here. The introduction here borrows fromthe Carbonbrief review.
India receives around 70% of its annual rainfall during the monsoon season. For some areasof western and central India, it accounts for as much as 90%. The monsoon rains are crucial
for India’s farm sector, which makes up about a sixth of India’s economy and employs about
half of the country’s 1.3 billion population. The monsoon season starts around June andtypically ends at the end of August.

368“Northern Hemispheric anthropogenic aerosols weakened the regional monsoon circulations inSouth Asia, East Asia and West Africa during the second half of the 20th century, thereby offsetting
the expected strengthening of monsoon precipitation in response to GHG-induced warming (highconfidence).” IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Box TS.13.
369“Paleoclimate reconstructions provide evidence of past Green Sahara states (DeMenocal andTierney, 2012), under which rainfall rates increased by an order of magnitude (Tierney et al., 2017),
leading to a vegetated landscape (Jolly et al., 1998) with large lake basins (Gasse, 2000; Drake andBristow, 2006).” IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Ch. 8, sec. 8.6.2.2.
370“Over South and South East Asia, East Asia and the central Sahel, monsoon precipitation is
projected to increase, whereas over North America and the far western Sahel it is projected todecrease (medium confidence).” IPCC, Climate Change 2021: The Physical Science Basis, Sixth
Assessment Report (UNFCCC, 2021), Box TS.13.
371
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Chap 4, Table 4.10.

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The IPCC has medium confidence that climate change will increase the strength of the
Indian monsoon.372 Monsoons are inherently hard to model, and there is scientificdisagreement about how climate change will affect the Indian monsoon.373
Some studies suggest that there could be an abrupt transition from wet to dry monsoon overthe course of hundreds to thousands of years if there is high air pollution or low greenhousegas levels or both.374
As discussed above, the Indian monsoon would also be affected by AMOC collapse.
Impacts on the monsoon are reversible within years to decades if greenhouse gas
concentrations are reduced.375

8.8. Cloud feedbacks
In my view, the most worrying potential non-linearities in the climate system stem from cloud
feedbacks. Most of the uncertainty about climate sensitivity is driven by uncertainty aboutclouds.376 There are several reasons to think that clouds may contribute to non-linear
increases in warming as emissions and warming increase.
Firstly, there is evidence from the paleoclimate that cloud feedbacks may account for the factthat equilibrium climate sensitivity seems to rise with temperatures, or is ‘state-dependent’.377
The early Eocene period, 56 to 48 million years ago, was the hottest period in the Cenozoic.
During the Paleocene-Eocene Thermal Maximum, temperatures increased by 5°C following
an increase of atmospheric CO2 of 70% to 100%, which suggests an equilibrium climate
372“For the North American monsoon, projections indicate a decrease in precipitation, whereas
increased monsoon rainfall is projected over South and South East Asia and over East Asia (medium
confidence) (Box TS.13, Figure 1).” IPCC, Climate Change 2021: The Physical Science Basis, Sixth
Assessment Report (UNFCCC, 2021), Box TS.13.
373
See the discussion in the Carbonbrief overview.
374“Some papers have suggested the possibility for more abrupt changes in the Indian monsoon. A
2005 study, for example, used a simple model to identify the possibility of the monsoon having two
stable states: wet (as it is now) and second state characterised by low rainfall. Key to these two statesis the so-called “moisture advection feedback”. This, the paper explains, is where “the land to oceanpressure gradient, which drives the monsoon circulation, is reinforced by the moisture the monsoon
itself carries from the adjacent Indian Ocean”. In other words, a significant factor in maintaining themonsoon is the heat released when the water vapour it holds condenses to form rain. Another paper,
published in the Proceedings of the National Academy of Sciences (PNAS) in 2009, suggests thisfeedback acts as an “internal amplifier” for the monsoon. The implication is that this feedback
magnifies anything that affects the air pressure gradient generated by warm air rising over the Asian
landmass. Thus “relatively weak external perturbations” could lead to “abrupt changes” in the
monsoon, the PNAS paper says. The model simulations in the 2005 study suggest how a switch
between states could be triggered. This includes cooling of the land surface through large amounts of
air pollution, cooling through very low CO2 levels in the atmosphere, or a combination of the two.” See
the discussion in the Carbonbrief overview.
375
Source: IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report(UNFCCC, 2021), Chap 4, Table 4.10.
376
Bjorn Stevens and Sandrine Bony, ‘What Are Climate Models Missing?’, Science 340, no. 6136 (31May 2013): 1053–54, https://doi.org/10.1126/science.1237554.
377
Jonah Bloch-Johnson et al., ‘Climate Sensitivity Increases Under Higher CO2 Levels Due toFeedback Temperature Dependence’, Geophysical Research Letters 48, no. 4 (2021):
e2020GL089074, https://doi.org/10.1029/2020GL089074.

275

sensitivity of 5°C, well in excess of IPCC estimates.378 For this reason, climate models
without state-dependent climate sensitivity have struggled to reproduce the Eocene
climate.379
This is how climate sensitivity changed throughout the Eocene period from 56 million years
ago to 33 million years ago.

378

Jiang Zhu, Christopher J. Poulsen, and Jessica E. Tierney, ‘Simulation of Eocene Extreme Warmthand High Climate Sensitivity through Cloud Feedbacks’, Science Advances 5, no. 9 (1 September2019): eaax1874, https://doi.org/10.1126/sciadv.aax1874.
379“The Early Eocene, a period of elevated atmospheric CO2 (>1000 ppmv), is considered an analog
for future climate. Previous modeling attempts have been unable to reproduce major features of
Eocene climate indicated by proxy data without substantial modification to the model physics.” Jiang
Zhu, Christopher J. Poulsen, and Jessica E. Tierney, ‘Simulation of Eocene Extreme Warmth andHigh Climate Sensitivity through Cloud Feedbacks’, Science Advances 5, no. 9 (1 September 2019):
eaax1874, https://doi.org/10.1126/sciadv.aax1874.

276

277

Source: E. Anagnostou et al., ‘Proxy Evidence for State-Dependence of Climate Sensitivity in the
Eocene Greenhouse’, Nature Communications 11, no. 1 (7 September 2020): 4436,
https://doi.org/10.1038/s41467-020-17887-x.

Newer climate models that better represent possible nonlinear cloud feedbacks suggest thatequilibrium climate sensitivity is state-dependent. These models can in turn better represent
the climate of past warm periods like the Eocene.380
Goldblatt et al (2021) argue that cloud feedbacks may explain the ‘Faint Young Sun
Problem’, which is that the Sun was dimmer earlier in Earth’s history, but glaciation was rarein the Precambrian. According to their model, because the Sun was dimmer and greenhousegas levels were high in the precambrian, there was a substantial decrease in stratocumulus
decks and consequently a decrease in planetary reflectivity.381
While it is generally agreed that cloud feedbacks could have non-linear effects on
temperature, there is disagreement about the nature and magnitude of the effect. Somemodels suggest that the warming from cloud feedbacks will be fairly smooth and gradual,
whereas others project effects that are huge and sudden. Perhaps the most worrying climatechange paper in recent years is Schneider et al (2019), which suggest that once CO2
concentrations pass 1,200-1,300ppm, there would be 8°C of global warming over the courseof days, on top of the 5ºC warming we would already have lived through. In total, there
would be around 13°C of warming relative to pre-industrial levels.

380“Overall, the modelling evidence indicates that there is medium confidence that the net feedback
parameter, α, increases (i.e., becomes less negative) with increasing temperature (i.e., that sensitivity
to forcing increases with increasing temperature), under global surface background temperatures atleast up to 40° C (Meraner et al., 2013; Seeley and Jeevanjee, 2021), and medium confidence that
this temperature dependence primarily derives from increases in the water vapour and shortwave
cloud feedbacks. This assessment is further supported by recent analysis of CMIP6 model
simulations (Bloch-Johnson et al., 2020) in the framework of nonlinMIP (Good et al., 2016), whichshowed that out of ten CMIP6 models, seven of them showed an increase of the net feedback
parameter with temperature, primarily due to the water vapour feedback.” IPCC, Climate Change
2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), sec. 7.4.3.1.
381“We perform global climate model experiments, using two versions of the Community Atmosphere
Model, in which a reduced solar constant is offset by higher CO2. Model runs corresponding to past
climate show a substantial decrease in low clouds and hence planetary albedo compared withpresent, which contributes 40% of the required forcing to offset the faint Sun. Through time, theclimatically important stratocumulus decks have grown in response to a brightening Sun anddecreasing greenhouse effect, driven by stronger cloud-top radiative cooling (which drives low cloudformation) and a stronger inversion (which sustains clouds against dry air entrainment from above).
We find that systematic changes to low clouds have had a major role in stabilizing climate throughEarth’s history, which demonstrates the importance of physical feedbacks on long-term climate
stabilization, and a smaller role for geochemical feedbacks.” Colin Goldblatt, Victoria L. McDonald,and Kelly E. McCusker, ‘Earth’s Long-Term Climate Stabilized by Clouds’, Nature Geoscience 14, no.
3 (March 2021): 143–50, https://doi.org/10.1038/s41561-021-00691-7.

278

Source: Tapio Schneider, Colleen M. Kaul, and Kyle G. Pressel, ‘Possible Climate Transitions fromBreakup of Stratocumulus Decks under Greenhouse Warming’, Nature Geoscience 12, no. 3 (March2019): 163–67, https://doi.org/10.1038/s41561-019-0310-1, Fig. 3.

Although the model finds that warming would happen over the course of around a month, inreality the transition is likely to take years to decades.382 There is also uncertainty about
when the threshold would be crossed, and alternative assumptions imply that the threshold
could be between 1,400ppm to 2,200ppm.383
On RCP8.5, concentrations would reach around 900ppm in 2100. As I mentioned in Chapter
1, RCP8.5 is a very extreme scenario. On current policy (RCP4.5), concentrations would
reach around 550ppm. Nonetheless, the threshold is within reach in principle: if we burn allof the recoverable fossil fuels - 3 trillion tonnes of carbon - concentrations would rise toaround 1,600ppm.384

382“The breakup of the stratocumulus clouds is more rapid than it would be in nature because of theunrealistically small thermal inertia of the underlying slab ocean.” Schneider, Kaul, and Pressel,‘Possible Climate Transitions from Breakup of Stratocumulus Decks under Greenhouse Warming’, SI
p. 5; Tapio Schneider, personal communication, 20th August 2021.
383“The CO2 level at which the instability occurs depends on how largescale dynamics change withclimate, which is heuristically parameterized in our simulations and hence is uncertain. In particular,
the large-scale subsidence in the troposphere weakens under warming32, which lifts the cloud tops
and counteracts the instability15,19,24. Indeed, when we weaken the parameterized large-scale
subsidence by 1 or 3% per Kelvin of tropical SST increase (within the range of GCM responses towarming33), the stratocumulus instability occurs at higher CO2 levels: around 1,400ppm with 1%K–1
subsidence weakening, and around 2,200ppm with 3%K–1 (Fig. 4).” Schneider, Kaul, and Pressel,‘Possible Climate Transitions from Breakup of Stratocumulus Decks under Greenhouse Warming’384
N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric CO2 in
an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): fig. 2,
https://doi.org/10.1002/2014GB005074.

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8.8.1. How plausible are rapid cloud feedbacks?
This research is controversial, and scientists are divided on how plausible it is. In thediscussion by CarbonBrief, several scientists say that they think the result is plausible,
whereas a news article in Science interviewed several scientists who were more sceptical.
Unfortunately, it is difficult to know just what would happen at 1,300ppm because CO2
concentrations have not been that high for at least tens of millions of years. For the early
Eocene, the best temperature proxies only have ~4,000 year resolution.385 Thus, the speedof a ~100-year feedback would not show up in the proxy record. The IPCC notes that there
is little evidence of such extreme nonlinear warming in the paleoclimate record, possibly
short of the Paleocene-Eocene Thermal Maximum, which started from a baseline 12ºC
warmer than pre-industrial.386 Overall, cloud feedback in some form seems like a plausible
cause of state-dependent climate sensitivity that we see in the paleoclimate evidence, but it
is hard to know just how rapid and drastic the feedback might be. We cannot rule out the
feedback found in Schneider et al (2019).
Finally, it is interesting to explore the potential impact such a tipping point might have on
ecosystems. If the tipping point were real, then we would expect it to have kicked in during
the Paleocene-Eocene Thermal Maximum when temperatures were 17°C higher and CO2
concentrations were more than 2,000ppm. But if there were 8°C of warming over the courseof years, then according to many climate-biodiversity models, we should expect there tohave been a huge extinction event. The warming would be so rapid that it would outpace theability of species to shift their range to their new ecological niche. So, either: the tipping point
is not real, or the biodiversity models are wrong. I argued in Chapter 3 that the biodiversity
models are probably wrong: ecosystems seem quite resilient even to rapid warming.
Schneider et al (2019) should update us further towards that position.
Given progress on climate policy, it now seems unlikely that we could reach the 1,200ppm 2,200ppm threshold that might trigger the cloud feedback. Liu and Raftery show the
probability of different emissions scenarios:

385“Average resolution for the interval from 0 to 34 Ma is one sample every 2 ky; for the interval from34 to 67 Ma, it is one sample every 4.4 kyr.” Thomas Westerhold et al., ‘An Astronomically Dated
Record of Earth’s Climate and Its Predictability over the Last 66 Million Years’, Science, 11 September
2020, https://www.science.org/doi/abs/10.1126/science.aba6853.
386““History has seen a multitude of studies (e.g., Svensmark, 1998; Lindzen et al., 2001; Schwartz,2007) mostly implying lower ECS than the range assessed as very likely here. However, there arealso examples of the opposite such as very large ECS estimates based on the Pleistocene records(Snyder, 2016), which has been shown to be overestimated due to a lack of accounting for orbital
forcing and long term ice sheet feedbacks (Schmidt et al., 2017b), or suggestions that global climate
instabilities may occur in the future (Steffen et al., 2018; Schneider et al., 2019). There is, however, noevidence for unforced instabilities of such magnitude occurring in the paleo record temperatures of thepast 65 million years (Westerhold et al., 2020), possibly short of the PETM excursion (Chapter 5,
Section 5.3.1.1) that occurred at more than 10°C above present (Anagnostou et al., 2020). Looking
back, the resulting debates have led to a deeper understanding, strengthened the consensus, andhave been scientifically valuable.” IPCC, Climate Change 2021: The Physical Science Basis, Sixth
Assessment Report (UNFCCC, 2021), sec. 7.5.5.

280

Peiran R. Liu and Adrian E. Raftery, ‘Country-Based Rate of Emissions Reductions Should Increaseby 80% beyond Nationally Determined Contributions to Meet the 2 °C Target’, Nature
Communications Earth & Environment 2, no. 1 (9 February 2021): Figure 1.

CO2 concentrations on these different pathways are shown below

Thus, the probability of getting to 1,200ppm before 2100 now seems extremely small,probably below 0.1%. We could pass the threshold if we burned all the fossil fuels, on someestimates of remaining fossil fuels, but this would take several centuries, and seems
extremely unlikely. As I discuss in Chapter 1, I put the probability at 1 in 500,000.

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8.8.2. What effects would rapid cloud feedbacks have on humansociety?
It is very hard to say what effect the rapid cloud feedback would have on human society; thisquestion has not been studied and it is very far outside the sample of human experience.
Nevertheless, we have to try and form our best judgements with the information we have.
One way to guide thinking about this is to consider evidence from the paleoclimate. Much
less intelligent and numerous species made it through when temperatures were 17°C higher
during the Paleocene-Eocene Thermal Maximum, so this might make us optimistic about ourown prospects. However, there are many disanalogies between our world and the
Paleocene-Eocene Thermal Maximum, the most important being that we are reliant on
agriculture. So: what effect would the cloud tipping point have on agriculture?
The major food crops would pass lethal limits in major food producing regions in the tropics
and subtropics, making agriculture at least very difficult in regions that currently support
billions of people.387 However, there would perhaps be scope to mitigate some of theseeffects by switching to heat-tolerant crops. Crops would not pass lethal limits in all regions,and land would be freed up at higher latitudes in Canada and Russia. So, agriculture would
probably not stop entirely at the global level.
Droughts would likely become much worse across much of the world. This would have direhumanitarian consequences in certain regions. However, the general pattern is that climatechange makes wet areas wetter and dry areas dryer. Moreover, many regions would be able
to make use of irrigation to adapt to decreasing soil moisture. So, the effects on drought
seem very unlikely to make agriculture impossible.
The final important factor is heat stress. Warming of 12ºC would make the tropics andsubtropics essentially uninhabitable due to heat stress and would make agriculture
impossible (see Chapters 5 and 6). I am not aware of any studies of the effects of 13°C ofheat stress on labour productivity. However, if we extrapolate the chart below, this suggeststhat for 13°C of warming, outdoor labour would be all but impossible in the tropics, andwould be reduced by around 50% in temperate regions.

387

See Chapter 5.

282

Source: Buzan and Huber, ‘Moist Heat Stress on a Hotter Earth’, 2020.

One possibly important mitigating factor is that agriculture may improve over the next few
centuries in ways that we do not yet understand. I discuss the prospects for agriculture
under extreme warming scenarios in Chapter 5.
Overall, the rapid cloud feedback would have extremely bad humanitarian effects, especiallyon the tropics, and my best guess is that it would lead to the starvation of billions of millions
of people. Although this question has not been studied in depth and I am very uncertain, mybest guess is that agriculture would still be possible and civilisation would survive, albeit
greatly diminished.
Moreover, people would be stuck with the extreme greenhouse world for tens of thousandsof years. So, the risk of long-run stagnation would increase, which would extend theso-called ‘time of perils’: the period in which we have the technology to destroy ourselves,
but lack the political institutions necessary to manage that technology. This would also makerecovery from catastrophe (from e.g. nuclear war or engineered pandemic) much harder.
Moreover, with such extreme warming, the indirect risks, such as mass migration and conflict
would also be much higher. I discuss indirect risks in Chapters 11-13.

8.9. Hothouse Earth
Steffen et al’s ‘Trajectories of the Earth System in the Anthropocene’ (2018) has been citednearly 2,000 times at the time of writing. It argues that 2°C of warming is a planetary
boundary beyond which we could enter into a ‘Hothouse Earth’ driven chiefly by nonlinear
climate tipping points, rather than by fossil fuel burning.388

388

Will Steffen et al., ‘Trajectories of the Earth System in the Anthropocene’, Proceedings of the
National Academy of Sciences 115, no. 33 (14 August 2018): 8252–59,
https://doi.org/10.1073/pnas.1810141115.

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The central claim of the paper is unclear because most of its central claims are about thetipping points that ‘might’ or ‘could’ be triggered due to global warming,389 or about the fact
there might be a risk of such a tipping point.390 However, the claim “x could happen” isextremely broad, and the fact that we cannot exclude a risk doesn’t tell us anything about themagnitude of the risk.
It is also not clear what ‘Hothouse Earth’ means. They provide the following diagram whichshows the purported 2°C tipping point.

389“However, here we suggest that biogeophysical feedback processes within the Earth System
coupled with direct human degradation of the biosphere may play a more important role than normallyassumed, limiting the range of potential future trajectories and potentially eliminating the possibility of
the intermediate trajectories” 8253; “Beyond this threshold, intrinsic biogeophysical feedbacks in the
Earth System (Biogeophysical Feedbacks) could become the dominant processes controlling thesystem’s trajectory” Steffen et al., ‘Trajectories of the Earth System in the Anthropocene’, p. 8254.
390“This analysis implies that, even if the Paris Accord target of a 1.5 °C to 2.0 °C rise in temperatureis met, we cannot exclude the risk that a cascade of feedbacks could push the Earth System
irreversibly onto a “Hothouse Earth” pathway”, Steffen et al., ‘Trajectories of the Earth System in the
Anthropocene’, p. 8254.

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This diagram suggests that the Hothouse Earth is a world that is 6°C above pre-industrial,
and that this state would be reached over the course of millennia. They argue that Hothouse
Earth ultimately calls into question “the habitability of the planet for humans”.391 This is astrong claim and they do not argue for it.
Moreover, Steffen et al (2018) do not present any evidence or argument showing that there
is a non-negligible risk that at 2°C, tipping points alone could take us up to 6°C over thecourse of millennia. The table below shows the combined effect of the feedbacks theymention in the main text:

391

Steffen et al., ‘Trajectories of the Earth System in the Anthropocene’, p. 8256.

285

By their own calculations, they find that 2ºC of warming would cause an extra 0.47°C by
2100 from these feedbacks. They do not explain what might cause the remaining 3.5°C ofwarming over millennia implied by their Figure 1. Furthermore, some of the numbers in theirtable are not supported by the latest evidence:●●●

The weakening of land and ocean carbon sinks is a feedback that is accounted for in
IPCC estimates of the transient climate response to cumulative emissions. Althoughcarbon sinks weaken as temperature rises, CO2 concentrations have a diminishingeffect on warming as they increase. The evidence suggests that these effects cancelout such that temperatures increase in proportion to cumulative emissions, and whenemissions stop, the evidence suggests that temperatures will be roughly constant for
100 years before slowly declining over millennia.392 Thus, the consensus in theliterature is that warming past 2ºC would not cause additional warming, unless there
were further CO2 emissions.
As discussed above, the IPCC Sixth Assessment Report suggests that boreal forest
dieback will have roughly neutral net impact on emissions.
Regarding Amazon forest dieback, most models now suggest that the effect of CO2
fertilisation overwhelms the effect of warming and precipitation change with respect
to changes in the Amazon biome. On the very pessimistic assumption of no CO2
fertilisation, the IPCC finds that for 2ºC, Amazon forest dieback would increase
emissions by up to 100GtC, which would increase temperatures by 0.017ºC,393 which
is below outside the range suggested in Steffen et al’s Table 1.

Again, there is evidence from the paleoclimate record that once we pass 2°C, feedback
effects alone will not cause further warming of 4°C. During the Pliocene, temperatures were
3°C warmer than pre-industrial without tipping the climate system into a Hothouse Earthstate.

392“The Zero Emissions Commitment (ZEC) is the change in global mean temperature expected tooccur following the cessation of net CO2 emissions and as such is a critical parameter for calculating
the remaining carbon budget… Overall, the most likely value of ZEC on multi-decadal timescales isclose to zero, consistent with previous model experiments and simple theory.” Andrew H. MacDougall
et al., ‘Is There Warming in the Pipeline? A Multi-Model Analysis of the Zero Emissions Commitment
from CO2’, Biogeosciences 17, no. 11 (15 June 2020): Figure 3b.
https://doi.org/10.5194/bg-17-2987-2020.
393
This is using the IPCC’s estimate of the transient climate response to cumulative emissions, whichis 1.65ºC per 1,000 billion tonnes of carbon.

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If we look at the specific feedbacks considered by Steffen et al (2018), there is no evidence
for these dramatic tipping points in our climate past: the rainforest flourished during the
Eocene and there was no abrupt release of permafrost carbon during the Pliocene when thepoles were 10°C warmer than today.
The IPCC also suggests that there is little evidence in the paleoclimate of the extreme
climate sensitivity values implied by Steffen et al (2018).
“History has seen a multitude of studies (e.g., Svensmark, 1998; Lindzen et al., 2001;
Schwartz, 2007) mostly implying lower ECS than the range assessed as very likely
here. However, there are also examples of the opposite such as very large ECS
estimates based on the Pleistocene records (Snyder, 2016), which has been shown
to be overestimated due to a lack of accounting for orbital forcing and long term ice
sheet feedbacks (Schmidt et al., 2017b), or suggestions that global climate
instabilities may occur in the future (Steffen et al., 2018; Schneider et al., 2019).
There is, however, no evidence for unforced instabilities of such magnitude occurringin the paleo record temperatures of the past 65 million years (Westerhold et al.,2020), possibly short of the PETM excursion (Chapter 5, Section 5.3.1.1) that
occurred at more than 10°C above present (Anagnostou et al., 2020). Looking back,the resulting debates have led to a deeper understanding, strengthened theconsensus, and have been scientifically valuable.”394

394

IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), sec. 7.5.5.

287

9. Greenhouses and habitability
Some worry that climate change could directly bring about human extinction. The mainroutes to extinction that have been discussed in the literature are the ‘runaway greenhouseeffect’ and the risk that Earth transitions into a ‘moist greenhouse’.
In The Precipice, Toby Ord estimates that the probability that climate change could directly
cause extinction in the next 100 years is 1 in 1,000.395 This is largely on the basis of thepossibility that we could, by burning fossil fuels, cause a moist greenhouse or runaway
greenhouse. I will argue that the risk of human extinction due to the loss of water on Earth isseveral orders of magnitude lower than this. To be clear, my argument is not (only) about theprobability of extinction given a certain amount of emissions, but is rather about the
all-things-considered probability of extinction, taking into account the probability of different
emissions scenarios.
Planetary sterilisation by the loss of water to space is extremely unlikely to kill all humans.
Other mechanisms, such as the destruction of agriculture are more probable, but still very
unlikely.
Disclosure: I carried out some research for the climate change section of The Precipice.

9.1. The long-term habitability of Earth
The Sun pours tremendous amounts of energy on to the Earth. Around a third is reflectedback to space by the atmosphere, while the remainder hits the Earth surface and becomes
heat. Greenhouse gases are more transparent to the shortwave radiation coming from the
Sun than they are to the longwave radiation (heat) emitted by the surface of the earth.
Consequently, greenhouse gases like water vapour, CO2 and methane act like a quilt,
trapping heat and warming the planet. The greenhouse effect makes the Earth 33°C warmer
than it would otherwise be and so makes life on Earth possible. This same physical process
will eventually make the planet uninhabitable.
The Sun is increasing in brightness by 1% every 110 million years.396“In 7.6 billion years, the Sun will have grown so vast that it will balloon out beyond
Earth’s own orbit, either swallowing our planet or flinging it out much further. And
either way, in 8 billion years our Sun itself will die.”397
Well before then, the Earth will become uninhabitable.
Warming climates are thought to transition through two distinct phases: the moist
greenhouse and the runaway greenhouse. A ‘moist greenhouse’ refers to hot water-richatmospheres, in which all of the Earth’s water is eventually lost to space. The standard
395

Toby Ord, The Precipice: Existential Risk and the Future of Humanity (Bloomsbury Publishing,2020), p. 167.
396
E. T. Wolf and O. B. Toon, ‘Delayed Onset of Runaway and Moist Greenhouse Climates for Earth’,Geophysical Research Letters 41, no. 1 (2014): 167–72, https://doi.org/10.1002/2013GL058376.
397
Ord, The Precipice, Ch. 8.

288threshold for a moist greenhouse is a global mean surface temperature of 67ºC (compared
to 15°C today).398
A runaway greenhouse differs from a moist greenhouse. In a runaway greenhouse, all of the
Earth’s water is contained in the atmosphere as vapour and cloud. Due to the thickening
quilt of water vapour in the atmosphere, the Earth is unable to cool down through releasing
long-wave radiation to space. Thus, the planet warms uncontrollably until all the surface
water has evaporated and surface temperatures reach 1,300ºC.399
A runaway greenhouse probably happened on Venus. Venus’ atmosphere is almost pureCO2. Were it not for the greenhouse effect, the surface temperature of Venus would be-42°C. In reality, the surface of Venus is about 470ºC - hot enough to melt lead. This is evenhotter than Mercury (a relatively temperate 166ºC) - even though Mercury is closer to the
Sun than Venus.
A moist greenhouse is generally believed to precede a runaway greenhouse and so places amore stringent limit on planetary habitability. Simulations with modern models suggest that
we are safe from a moist greenhouse for at least a billion years, though more simple models
suggest that we could reach a moist greenhouse in hundreds of millions of years.400 Once
we reached a moist greenhouse threshold, it would take hundreds of millions of years to lose
the oceans to space.

9.2. Could humans cause a moist greenhouse or runaway
greenhouse?
I argued in Chapter 1 that ultimately recoverable fossil fuels are 1-3 trillion tonnes of carbon.
If we were to burn 3 trillion tonnes of carbon, CO2 concentrations would increase to around
1,600ppm.401 This is markedly lower than estimates of recoverable fossil fuels sometimes
used in climate science. For example, some studies model the effects of burning more than398

“One-dimensional radiative-convective calculations predict that stratospheric water vapor mixing
ratios of ~3 × 10−3 can occur if the global mean surface temperature reaches 340 K, under theassumptions of water vapor saturation and a 200 K isothermal stratosphere [Kasting, 1988]. This
description is often taken as the standard threshold for a moist greenhouse climate. A moist
greenhouse is generally believed to precede a runaway greenhouse in the evolutionary sequence ofwarming terrestrial atmospheres. While a moist greenhouse is climatologically stable, unlike a
runaway greenhouse, the potential for rapid water loss makes it the more proximal boundary to theinner edge of the habitable zone [Kasting et al., 1993; Kopparapu et al., 2013].” (wolf and toon 2015)
399
E. T. Wolf and O. B. Toon, ‘The Evolution of Habitable Climates under the Brightening Sun’, Journalof Geophysical Research: Atmospheres 120, no. 12 (2015): 5775–94,
https://doi.org/10.1002/2015JD023302.
400“Nonetheless, our results imply that Earth's climate may remain safe against both water loss andthermal runaway limits for at least another 1.5 billion years and probably for much longer.” “Cloud-free
1-D models with saturated atmospheres predict that Earth will reach moist greenhouse conditions(Ts = 340 K) when the solar constant increases by only 1.5% above its present level [Kopparapu et al.,2013]. Thus, our home planet may be subject to a moist greenhouse climate in a mere ~170 million
years [Gough, 1981].” Wolf and Toon, ‘Delayed Onset of Runaway and Moist Greenhouse Climatesfor Earth’.
401
N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric CO2 in
an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): 2–17,
https://doi.org/10.1002/2014GB005074.

289

10 trillion tonnes of carbon,402 which would increase CO2 concentrations past 5,000ppm.403
The lower estimate of recoverable fossil fuels has important implications for the likelihood ofmoist or runaway greenhouse on Earth.
The table below shows the findings of different models on the conditions required to trigger a
runaway greenhouse.
Study

Findings

Kasting and Ackerman(1986)404

Slightly over 10,000ppm would be required to cause a moist
greenhouse.

Goldblatt et al (2013)405

Anthropogenic emissions could “in theory” produce runaway
greenhouse, but are probably insufficient. 30,000ppm may trigger
a runaway.

Hansen et al (2013)406

No plausible human-made greenhouse gas forcing can cause arunaway greenhouse effect.
Warming of more than 16-24°C could produce a moist
greenhouse, but natural weathering would remove the excess
atmospheric CO2 on a time scale of 1,000 to 10,000 years, wellbefore the ocean is significantly depleted.

Ramirez et al (2014)407

On “the most alarmist assumptions possible”, the model nearlyruns away at 3,300ppm. But on more plausible assumptions,
runaway appears to be impossible from fossil fuel emissions.

Wolf and Toon (2014)408

A 15.5% increase in solar forcing doesn’t lead to moist greenhouseor runaway greenhouse. Since each 2% increase in solar forcing isequivalent to a doubling of CO2, this is equivalent to nearly 8
doublings, which is CO2 concentrations of 70,000ppm.

Wolf and Toon (2015)409

Significant water loss begins to happen if solar forcing increases
by 19%. This is equivalent to more than nine doublings ofpre-industrial CO2, or CO2 concentrations of 140,000ppm.

402

Ricarda Winkelmann et al., ‘Combustion of Available Fossil Fuel Resources Sufficient to Eliminate
the Antarctic Ice Sheet’, Science Advances 1, no. 8 (1 September 2015): e1500589,
https://doi.org/10.1126/sciadv.1500589.
403
N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric CO2 in
an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): 2–17,
https://doi.org/10.1002/2014GB005074.
404
J. F. Kasting and T. P. Ackerman, ‘Climatic Consequences of Very High Carbon Dioxide Levels in
the Earth’s Early Atmosphere’, Science 234, no. 4782 (12 December 1986): 1383–85,
https://doi.org/10.1126/science.11539665.
405
Colin Goldblatt et al., ‘Low Simulated Radiation Limit for Runaway Greenhouse Climates’, Nature
Geoscience 6, no. 8 (August 2013): 661–67, https://doi.org/10.1038/ngeo1892
406
James Hansen et al., ‘Climate Sensitivity, Sea Level and Atmospheric Carbon Dioxide’, Phil. Trans.
R. Soc. A 371, no. 2001 (28 October 2013): 20120294, https://doi.org/10.1098/rsta.2012.0294.
407
Ramses M. Ramirez et al., ‘Can Increased Atmospheric CO2 Levels Trigger a Runaway
Greenhouse?’, Astrobiology 14, no. 8 (1 August 2014): 714–31, https://doi.org/10.1089/ast.2014.1153
408
Wolf and Toon, ‘Delayed Onset of Runaway and Moist Greenhouse Climates for Earth’.
409
E. T. Wolf and O. B. Toon, ‘The Evolution of Habitable Climates under the Brightening Sun’, Journalof Geophysical Research: Atmospheres 120, no. 12 (2015): 5775–94,
https://doi.org/10.1002/2015JD023302.

290

Popp et al (2016)410

Increasing concentrations to 1,500ppm causes a transition to amoist greenhouse. However, this assumes that the world is madeentirely of ocean that is only 50 metres deep. Adjusting for this, theforcing is more like 4,400ppm. Moreover, Max Popp told me inpersonal correspondence that their paper is more relevant to thehabitability of exoplanets than current or near-future climatechange, and that their paper doesn’t suggest that CO2
concentrations of 4,000ppm would trigger a transition into a moist
greenhouse climate.411

In his book Six Degrees, Mark Lynas argues that six degrees of warming could possibly
result in human extinction due to the risk of runaway feedbacks leading to a moist
greenhouse or runaway greenhouse. So, his claim is not that the impacts of 6°C of warming
would themselves directly cause human extinction, but rather 6°C of warming might cause
further warming which could then cause human extinction. Specifically, he mentions thecloud feedback found in Schneider et al (2019), according to which once CO2 concentrations
pass 1,200ppm, there is warming of 8°C on top of the warming we have alreadyexperienced, which would be around 5°C. On top of this, other feedbacks from permafrost,
methane clathrates or forest dieback could kick in. All told, this could bring us perilously
close to a runaway greenhouse.
As we have seen, this is strongly at odds with the findings from models. For example, Lynas
reports the finding of the Ramirez et al (2014) study without also pointing out that they only
get a runaway at 3,300ppm on “the most alarmist assumptions possible”.412
Moreover, there is strong reason from the paleoclimate record to believe that, even if thefinding of the Schneider et al (2019) paper is correct, the risk of triggering a runaway bypushing warming to 6°C is minimal. If this feedback were real, then it would have kicked induring the Paleocene-Eocene Thermal Maximum when CO2 concentrations were above
2,000ppm and temperatures were 17°C above pre-industrial levels, or the early Eocene
when temperatures were 10°C than pre-industrial levels, or during the mid-Cretaceous whentemperatures were 20°C higher, or during the end-Permian when temperatures were
upwards of 17°C higher. The other feedbacks Lynas mentions would also have kicked in atthese times: since there were no ice caps in all of these periods, if huge amounts ofpermafrost carbon and methane clathrates are going to be released in the future, they would
have been released in these periods. In none of these cases did we trigger feedback
processes that caused a feedback that killed all life on Earth. Therefore, the paleoclimate
evidence agrees with the models that such a scenario is extremely unlikely.
One difference mentioned by Lynas is that the Sun is getting hotter by 1% every 110 million
years. So, since the Eocene, the Sun would be 0.5% hotter, which roughly translates to half

410

Max Popp, Hauke Schmidt, and Jochem Marotzke, ‘Transition to a Moist Greenhouse with CO 2
and Solar Forcing’, Nature Communications 7, no. 1 (9 February 2016): 10627,
https://doi.org/10.1038/ncomms10627.
411
Max Popp, personal correspondence, 11th and 12th April 2021.
412
Mark Lynas, Our Final Warning: Six Degrees of Climate Emergency (London: 4th Estate, 2020),
266–67.

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a degree of additional warming.413 This would only make a small difference to the likelihoodof a runaway or moist greenhouse.
There might also be other differences that mean that in previous super-greenhouse periods,
the planet was lucky to avoid a runaway.414 For example, the Earth’s orbit in relation to the
Sun or the Earth’s position on its axis might have changed, which places us at greater risk
today. It is difficult to completely eliminate residual uncertainty about a potential runaway.
Goldblatt and Watson (2012) provide a nice summary of the argument from residual
uncertainty:“Here, we review what is known about the runaway greenhouse to answer thisquestion, describing the various limits on outgoing radiation and how climate will
evolve between these. The good news is that almost all lines of evidence lead us tobelieve that is unlikely to be possible, even in principle, to trigger full a runaway
greenhouse by addition of non-condensible greenhouse gases such as carbon
dioxide to the atmosphere. However, our understanding of the dynamics,
thermodynamics, radiative transfer and cloud physics of hot and steamy
atmospheres is weak. We cannot therefore completely rule out the possibility thathuman actions might cause a transition, if not to full runaway, then at least to a much
warmer climate state than the present one”415
I am not sure how to quantify and bound this kind of extreme residual model uncertainty.
I spoke to one researcher about this, and they estimated that if we burn all the fossil fuels,the chance of a moist or runaway greenhouse is around 10%. This seems too high to me
given the evidence from the paleoclimate and the models, but it makes sense for me to deferto them on this. It would be useful to have expert elicitation studies on this question.

413

This is calculated as follows. A 2% increase in solar irradiance is equivalent to one doubling ofCO2. Hansen et al., ‘Climate Sensitivity, Sea Level and Atmospheric Carbon Dioxide’, p. 2.
A doubling of CO2 is equivalent to an increase in radiative forcing of around 3.7 watts per square
metre. A. Farnsworth et al., ‘Climate Sensitivity on Geological Timescales Controlled by Nonlinear
Feedbacks and Ocean Circulation’, Geophysical Research Letters 46, no. 16 (2019): 9880–89,
https://doi.org/10.1029/2019GL083574.
So, increasing solar irradiance by 0.5% would be equivalent to increasing radiative forcing by around
0.9 watts per square metre. There is around 0.7 degrees of warming for every additional watt per
square metre of radiative forcing. Jeffrey T. Kiehl and Christine A. Shields, ‘Sensitivity of the
Palaeocene–Eocene Thermal Maximum Climate to Cloud Properties’, Philosophical Transactions of
the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 2001 (28 October2013): 20130093, https://doi.org/10.1098/rsta.2013.0093.
So, the additional warming from the brightening Sun since the early Eocene would cause anadditional 0.6 degrees of warming.
414
Thanks to Andrew Watson for discussion of this point.
415
Colin Goldblatt and Andrew J. Watson, ‘The Runaway Greenhouse: Implications for Future Climate
Change, Geoengineering and Planetary Atmospheres’, Philosophical Transactions of the Royal
Society A: Mathematical, Physical and Engineering Sciences 370, no. 1974 (13 September 2012):
4197–4216, https://doi.org/10.1098/rsta.2012.0004.

292

One counter-argument to this is that this argument from extreme model uncertainty also
applies in the other direction. There is also some risk that in the absence of CO2 emissions,
the world will enter a glacial period. This could also threaten civilisation because it would
make agriculture very difficult. Our models and paleoclimate evidence suggest that given
how high CO2 concentrations now are, re-entering a glacial period is extremely unlikely.416
But again, there is some residual model uncertainty that is difficult to eliminate. It seems
plausible to me that these kinds of model uncertainty largely cancel out; it is not clear thatthe risk is greater in one direction rather than the other.

9.3. How might a moist or runaway greenhouse kill us?
If we did produce a moist or runaway greenhouse on Earth, how would it kill us? One
possibility is that the planet would be sterilised because the Earth’s water would be lost to
space. However, this kill mechanism seems unlikely to be the one that destroys civilisation.
Even if we did transition to a moist greenhouse, it would take hundreds of millions of years tolose the Earth’s oceans to space. However, once man-made emissions stop, naturalsequestration processes would reduce CO2 concentrations close to natural levels after tensof thousands of years. CO2 concentrations would look like this in the long-term:

Source: N. S. Lord et al., ‘An Impulse Response Function for the “Long Tail” of Excess Atmospheric
CO2 in an Earth System Model’, Global Biogeochemical Cycles 30, no. 1 (2016): Fig. 2,
https://doi.org/10.1002/2014GB005074.

The Earth’s water would be lost to space only if CO2 concentrations remain high for millionsof years, but in fact they will fall back close to natural levels much sooner.417 Even if we were416

“It is virtually certain that orbital forcing will be unable to trigger widespread glaciation during thenext 1000 years. Paleoclimate records indicate that, for orbital configurations close to the present one,glacial inceptions only occurred for atmospheric CO2 concentrations significantly lower thanpre-industrial levels. Climate models simulate no glacial inception during the next 50,000 years if CO2
concentrations remain above 300 ppm.” IPCC, Climate Change 2013: The Physical Science Basis,
Fifth Assessment Report, p. 399.
417
“For simulations +19%, +20%, and +21% S0, the entirety of Earth's oceans could evaporate in as
little as ~3.5 Gyr, ~672 Myr, and ~130 Myr given a static climate. However, more detailed calculationsof atmospheric water loss from moist greenhouse atmospheres are warranted.” E. T. Wolf and O. B.
Toon, ‘The Evolution of Habitable Climates under the Brightening Sun’, Journal of GeophysicalResearch: Atmospheres 120, no. 12 (2015): 5775–94, https://doi.org/10.1002/2015JD023302.

293

to burn through all of the fossil fuels, we would not be killed off by the oceans being lost to
space.
Even so, the moist or runaway greenhouse would make the planet uninhabitable long before
the Earth’s oceans are lost to space. Recall that the threshold for a moist greenhouse issurface temperatures of around 66ºC, compared to 15ºC today. If the world were this hot, it
is very difficult to see how human civilisation could survive - crops would pass lethal limits
almost everywhere and unlivable heat stress would envelop the world.

9.4. Quantifying the direct extinction risk of climate change
It is very difficult to estimate the overall probability that climate change would directly cause
human extinction. It is important to note that Toby Ord’s ‘1 in 1,000’ estimate is for the next
100 years. But if we are going to cause a moist or runaway greenhouse, we would have toburn huge amounts of carbon, which would most likely take several centuries. This is one
reason that the 1 in 1,000 figure is probably too high.
I will explore two ways of quantifying the direct extinction risk.
1. Runaway feedbacks: Assuming that moist or runaway greenhouse feedback effects
would almost certainly kill life on Earth, what is the probability that we trigger such
feedbacks?
2. Extinction-level warming thresholds: Define some warming thresholds past whichhumans would go extinct, and calculate the probability of passing such thresholds.
Runaway feedbacks
First consider the approach of calculating the risk of triggering runaway feedbacks. We saw
above that one expert thought if we burn all the fossil fuels (releasing 3,000 GtC and causing
concentrations to rise to 1,600ppm), then the risk of a runaway or moist greenhouse is 10%.
This seems too high to me given model findings and paleoclimate evidence. But assuming
this is true, what is the probability that we could trigger these extreme feedbacks?
I argued in section 1.5 that it is difficult to come up with plausible scenarios in which we burn3,000 GtC because it would require dramatic progress in advanced coal extraction“High stratospheric H2O would also result in increased rates of water loss by way of photodissociation
followed by hydrogen escape—a phenomenon sometimes termed a moist greenhouse (Kasting,1988). However, atmospheric CO2 concentrations would presumably be restored to more normal
values by silicate weathering within a few million years (Walker et al., 1981), before substantial water
loss could occur.” Ramses M. Ramirez et al., ‘Can Increased Atmospheric CO2 Levels Trigger a
Runaway Greenhouse?’, Astrobiology 14, no. 8 (1 August 2014): 714–31,
https://doi.org/10.1089/ast.2014.1153.
“A warming of 16–24◦C produces a moderately moist greenhouse, with water vapour increasing toabout 1% of the atmosphere’s mass, thus increasing the rate of hydrogen escape to space. However,if the forcing is by fossil fuel CO2, the weathering process would remove the excess atmospheric CO2
on a time scale of 104–105 years, well before the ocean is significantly depleted. Baked-crust
hothouse conditions on the Earth require a large long-term forcing that is unlikely to occur until thesun brightens by a few tens of per cent, which will take a few billion years [121].” James Hansen et al.,‘Climate Sensitivity, Sea Level and Atmospheric Carbon Dioxide’, Phil. Trans. R. Soc. A 371, no. 2001
(28 October 2013): 20120294, https://doi.org/10.1098/rsta.2012.0294, p.24

294

technology and societal willingness to deploy the technology, but little progress in low carbon
technologies. There are no estimates in the literature of such extreme emissions scenarios.
Liu and Raftery (2021) estimate a 90% confidence interval for cumulative emissions to 2100
of roughly 550 GtC to 1,200 GtC.418 Thus, it at least seems fair to say that the probability of
3,000GtC by 2100 is well below 1%.
My own rough back of the envelope model suggests that the chance over all time is 1 in
500,000. Combined with the probability of extinction conditional on burning all the fossil
fuels, the risk of direct extinction from climate change is 1 in 5 million.
Extinction-level warming thresholds
Martin Weitzman defends using 10ºC as an ‘illustrator threshold’ of a level of warming thatwould threaten human civilisation. Weitzman relies mainly on the paper on heat stressdiscussed by Sherwood and Huber (2010) discussed in Chapter 5, which found that at 12ºC,
the majority of the population would be exposed to lethal heat stress, as people are currently
distributed.
“Thus, a temperature change of 10ºC would appear to represent an extreme threat tohuman civilization and global ecology as we now know it, even if it might notnecessarily mean the end of Homo sapiens as a species.”419
I agree with Weitzman that it is difficult to see how 10ºC could cause human extinction. The
heat stress mechanism discussed by Sherwood and Huber (2010) would not itself cause
human extinction since higher latitudes and altitudes would be spared, and there would besome scope for adaptation with air conditioning and migration.
It also seems that, despite rising heat stress, human labour would still be viable at higher
latitudes, though it would likely be impossible in the tropics, as discussed in Chapter 8.
I discussed lethal limits to food production in Chapter 5. Research on the effects of morethan 4.4ºC of warming on agricultural production is limited. But crops would not pass lethal
limits in the major food producing regions even if there were 10ºC of warming. The level ofwarming required to completely destroy food production in the main food producing regionsis in excess of 20ºC.
If we pessimistically assume that the 10ºC threshold is the correct threshold for extinction, it
is difficult to quantify the risk of passing the threshold. The only published quantitative
estimates in the literature project out to 2100 and find a much lower range of potential
warming. The findings of Liu and Raftery (2021) are fairly representative of other studies in
the literature and they find that the 90% confidence interval for warming by 2100 is 2ºC to
3.9ºC. The risk of 10ºC of warming in the next 100 years seems extremely small.

418

Liu and Raftery (2021) do not give explicit numbers in their paper, but this is inferred from their
Figure 1. As I discussed in Chapter 1, I think this range is too pessimistic.
419
Martin L. Weitzman, ‘Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change’,
Review of Environmental Economics and Policy 5, no. 2 (7 January 2011): 275–92,
https://doi.org/10.1093/reep/rer006.

295

My back of the envelope model suggests that the chance of burning all the fossil fuels over
all time is 1 in 500,000. If we burn all of the recoverable fossil fuels (3,000 GtC), there is a 1
in 6 chance of more than 9.6ºC. This suggests that over all time, the direct risk of extinction
from climate change over all time is on the order of 1 in 3 million.
On the scenario in which we burn all of the fossil fuels, doing so takes around 400 years.
The risk of burning all of the fossil fuels over the next 100 years is far smaller, plausibly by atleast an order of magnitude. The 100-year direct extinction risk would be correspondingly
smaller.
A related way to approach this might be to estimate the probability that we trigger the tipping
point identified by Schneider et al (2019) that I discussed in Chapter 8. If Schneider et al(2019) is correct, then once concentrations pass 1,200ppm to 2,200ppm, a cloud feedback
would cause warming of 8ºC over decades. Suppose that this would cause extinction if it
occurred.
Given the ‘indefinitely stalled decarbonisation’ model, the chance of passing 1,200ppm isplausibly on the order of 1 in 100,000. As discussed in Chapter 8, scientists are divided on
how plausible the cloud feedback is. If we assume it has 50% probability, then the risk ofextinction is around 1 in 200,000.

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10. Economic costs
10.1. Context and trends
The context and trends for economic growth are discussed in Chapter 5. In brief:●●●

Over the course of the twentieth century, world GDP grew by 1,800%, and per capita
world GDP rose by 448%.
In one 2018 expert elicitation study, experts predicted that per capita GDP will rise by
between 200% and 2,000% by 2100 (95% confidence interval).420
However, some countries, predominantly in Sub-Saharan Africa, have essentiallynever experienced a sustained increase in living standards and billions of people
there are still close to subsistence.

The range of GDP and GDP per capita on different Shared Socioeconomic Pathways isshown below

420

P. Christensen, K. Gillingham, and W. Nordhaus, ‘Uncertainty in Forecasts of Long-Run Economic
Growth.’, Proceedings of the National Academy of Sciences of the United States of America 115, no.
21 (2018): fig. 1.

297

As discussed in Chapter 5, given historical trends, the most plausible SSP is SSP4, thoughno scenarios do not account for the possibility of an acceleration of growth driven byadvanced AI.

10.2. The social cost of carbon
The social cost of carbon is the net present value of future global climate change impacts
from one additional tonne of CO2 emitted to the atmosphere at a particular point in time. In
short, it is the marginal cost of an additional tonne of CO2. The social cost of carbon allows
us to prioritise marginal climate change policy using cost-benefit analysis.
The social cost of carbon is computed by comparing two scenarios:
1. Reference scenario: projecting a future global socioeconomic scenario for centuries,
and the resulting global greenhouse gas emissions, climate change, and net global
damages.

298

2. Pulsed scenario: projecting a future scenario the same as the reference scenario,
except with an additional pulse of CO2 from some specific year, e.g. 2020.
The social cost of carbon is the discounted net present value of the additional net damages
in the pulsed scenario compared to the reference scenario.421
Models that assess the social cost of carbon are comprised of multiple different modules,
including:
1. Socio-economic module: characterising the future evolution of the economy,
including future CO2 but without climate change.
2. Climate module: Quantifies how the earth system responds to emissions.
3. Damages module: Quantifies how the much damage changes in the Earth system
do to the economy.
4. Discounting module: Converts the sum of future damages into a net present value.
We may want to discount because people in the future are likely to be richer or
because of an evaluative judgement that future people’s welfare is worth less thanthat of present people.
This is illustrated in the following schematic

421“The SCC is conceptually the marginal cost to society of emitting carbon dioxide (CO2).
Computationally, the SCC is the net present value of future global climate change impacts from one
additional net global metric ton of CO2 emitted to the atmosphere at a particular point in time (Figure1). An SCC value is computed using two long-run scenarios – a reference scenario projecting a future
global socioeconomic condition for centuries, and the resulting global greenhouse gas emissions,climate change, and net global damages from that climate change; and, a pulsed scenario projecting
the incremental climate change and damages over time from the addition of a pulse of CO2 in an
individual year (e.g., 2020) to the reference scenario. An SCC in 2020, therefore, is the discounted
value of the additional net damages from the marginal emissions increase in 2020 relative to thereference condition.” Steven K. Rose, Delavane B. Diaz, and Geoffrey J. Blanford, ‘Understanding theSocial Cost of Carbon: A Model Diagnostic and Inter-Comparison Study’, Climate Change Economics
08, no. 02 (May 2017): 1750009, https://doi.org/10.1142/S2010007817500099.

299

Source: Resources for the Future, The Social Cost of Carbon: Advances in Long-Term
Probabilistic Projections of Population, GDP, Emissions, and Discount Rates, October 2021

My main focus here will be on the damage module, as it is more relevant to my mainquestion and I have discussed the other modules elsewhere in this report. The OpenPhilanthropy Project is working on a report which explores the social cost of carbon in moredetail.

10.3. Estimating climate damages
Quantifying the economic cost of climate change is very difficult. Modellers are faced withthe challenge of predicting the damage that a range of climate impacts - on sea level rise,
agriculture, heat effects on productivity, the effects on tropical diseases etc - will do over thecourse of several centuries.
There are two broad ways to calculate climate damages:422

422This framework is borrowed from Council of Economic Advisors, Office of Management andBudget, Climate-Related Macroeconomic Risks and Opportunities, 4 April 2022,
https://www.whitehouse.gov/wp-content/uploads/2022/04/CEA_OMB_Climate_Macro_WP_2022.pdf.
“Here we describe two strands of the literature most relevant to the task of incorporating physicaldamages into macroeconomic models: (1) “top-down” studies that attempt to estimate the effect ofclimate change on aggregate economic outcomes like GDP directly; and (2) “bottom-up” studies thatassess the effects of climate change on a specific economic sector or category of damages, which
can then be aggregated across categories to estimate total damages.”

300

Top down studies that attempt to estimate the effect of climate change on
aggregate economic outcomes, like GDP, directly.
Bottom up studies that assess the effects of climate change on a specific economic
sector or category of damages, which can then be aggregated across categories toestimate total damages.
Hybrids of these two approaches are also possible.
One important difference between top-down and bottom-up approaches is that bottom-up
approaches can in principle include non-market damages, i.e. damages that do not show up
in GDP statistics. These include the intrinsic value of illness, death and the intrinsic value of
ecosystems. A study that does not include non-market impacts might account for the
instrumental but not intrinsic costs of illness or death, such as costs to the health system orin terms of lost productivity. But illness and death are important independently of their impact
on GDP. It is better for people not to suffer or to die prematurely regardless of the effect thishas on economic output.
These intrinsically bad non-market climate damages can be monetised and included in adamage function (though there is significant disagreement about how to do this and whether
monetising these costs is appropriate rather than valuing them directly in units of welfare).423
The meaning of an estimate of ‘climate damages as a fraction of GDP’ will therefore bedifferent depending on whether studies include non-market damages.

423

I briefly discuss some of these issues in this blogpost.

301

The IPCC’s Sixth Assessment Report provides the following useful diagram of damage
estimates from different types of studies.

Source: IPCC, Impacts, Sixth Assessment Report, Ch 16, Figure Cross-Working Group Box
ECONOMIC.1

Most studies find that for 4ºC of warming, the monetised costs of climate change range from
5-10% of GDP. However, one set of top-down approaches which use statistical modelling todirectly estimate impacts on GDP find much higher costs. The most prominent such study,
Burke et al (2015), is shown in green in the chart above and finds that 4ºC reduces global
GDP by around 23%, and there is a 5% chance that it reduces global GDP by 60%. It istherefore very important to understand which of these studies are more plausible.
It is important to stress that these costs are relative to a counterfactual without climatechange, not to today’s level of income. On the low growth SSP3, global GDP per capita is
not much higher than today, while on the high growth SSP5, it is nearly 1,000% higher. The
other SSPs project that income per head will be several hundred percent higher. Thus, whena study says that, for instance, climate change of 4ºC reduces GDP per capita by 10% on
SSP2, it is saying that climate change causes average GDP per capita to be 270% higher,
when it could have been 300% higher.

302

10.4. Bottom-up studies
Bottom-up studies assess the effects of climate change on a specific economic sector or
category of damages, which can then be aggregated across categories to estimate total
damages. Bottom-up studies will include many of the impacts I have discussed so far,including the effect on agriculture, sea level rise and the direct effects of rising heat on
productivity.

10.4.1. Microeconomic evidence of how temperature affects productivity
Some microeconomic evidence suggests that rising temperatures might damage labour
supply and labour productivity.
In Chapter 6 on heat stress, I discussed how climate change could affect labour supply.
Several studies use industrial and military guidelines on safe working conditions to explore
how rising heat stress might affect labour supply in different regions. The effect issummarised in the chart below from Buzan and Huber (2020), which assumes that people
do not migrate and do not make use of extra air conditioning relative to today.

Source: Jonathan R. Buzan and Matthew Huber, ‘Moist Heat Stress on a Hotter Earth’, Annual
Review of Earth and Planetary Sciences 48, no. 1 (2020): 623–55,
https://doi.org/10.1146/annurev-earth-053018-060100.

Other studies also find that high temperatures reduce hours of labour supplied in industries
with high exposure to climate.424
There is also microeconomic research suggesting that heat stress damages labour
productivity per hour worked:●

Lab experiments suggest that there is a productivity loss in various cognitive tasks ofabout 2 percent per degree Celsius for temperatures over 25°C.425

424

Joshua Graff Zivin and Matthew Neidell, ‘Temperature and the Allocation of Time: Implications for
Climate Change’, Journal of Labor Economics 32, no. 1 (2014): 1–26.
425
Dell, Jones, and Olken, ‘What Do We Learn from the Weather?’, 23.

303

●

Observational and experimental studies also show a strong relationship between
temperature and the productivity of factory, call centre, and office workers, as well as
students. A meta-analysis of these studies concludes that increasing temperaturefrom 23ºC to 30ºC reduces productivity by about 9 percent.426

The chart below from Heal and Park (2013) shows levels of performance at different
temperatures. The optimal temperature for performance is around 22ºC.

Source: Geoffrey Heal and Jisung Park, ‘Feeling the Heat: Temperature, Physiology & the Wealth of
Nations’ (National Bureau of Economic Research, 2013).

Heal and Park (2013) find that air conditioning dampens the non-linear effects oftemperature on economic performance, after imposing some controls.427
426Dell, Jones, and Olken, 23.
“Consistent with the notion that higher levels of thermoregulatory capital dampen the impact ofthermal stress on productivity, the subset of countries with above median air conditioning penetration
feature a less concave relationship between temperature and income per capita. The
temperature-income gradient implied by the coefficients on temperature and temperature squared incolumns (26), (28), (30), and (32) – the subset countries with above-median air conditioning – isshallower than that implied by the coefficients in columns (25), (27), (29), and (31) – which represent
the subset of countries with below-median air conditioning. Moreover, it seems that this difference is
not being driven wholly by the correlation between air conditioning and other unobservables that are
correlated with income. While countries with better access to thermoregulatory capital tend to bericher on average, there are also relatively hot and poor countries with high air conditioning
penetration (for instance, Libya; see Table 7.6). It seems that the vulnerability to thermal stress as
implied by access to thermoregulatory capital is not simply a function of “poorness” per se. This is anadmittedly crude measure, but points us in the right direction for pressing policy-relevant research on
climate adaptation.” Heal and Park, ‘Feeling the Heat’.
427

304

Rising heat stress would have the worst effects on the productivity of people working
outdoors in industries like mining, construction and agriculture. This suggests that warming
could be especially damaging for people in poor countries reliant on agriculture.
Today, agriculture accounts for around 4% of global GDP, and around two thirds of the world
economy is in services. There is more scope for people in the service sector to adapt torising temperatures, both because they are indoors and because service-reliant economies
tend to be richer. Insofar as workers are able to use air conditioning, they will be able to limit
the damage to productivity from rising temperatures. The extent to which people will be able
to make use of air conditioning depends on their socioeconomic prospects. The implicationsof different shared socioeconomic pathways for air conditioning penetration, according to
one model, is shown below:

Source: Orlov et al., ‘Economic Costs of Heat-Induced Reductions in Worker Productivity Due to
Global Warming’.

I noted above that SSP4 is one of the more plausible SSPs. It suggests that therichest/highest growth regions - North America, Europe, Oceania and East Asia - will be able
to reduce the effects of rising temperatures on productivity, while the low growth regions South Asia and Africa - will have low take-up of air conditioning and so low ability to adapt torising heat stress. Modellers disagree about how to model future air conditioning penetration,and some much more optimistic levels of penetration, on the SSPs.428
All in all, rising heat stress for workers is one plausible way that climate change could
damage economic output.

428

Filippo Pavanello et al., ‘Air-Conditioning and the Adaptation Cooling Deficit in Emerging
Economies’, Nature Communications 12, no. 1 (9 November 2021): SI Fig. 5,
https://doi.org/10.1038/s41467-021-26592-2.

305

10.4.2. Included sectors and impacts
The table below summarises the assumptions and findings of the bottom-up studies includedin the above chart from the IPCC that summarises damage estimates in the literature.
Study

Sectors included

Non-market
impacts?

Post-AR5
literature?

Tipping
points?

Indirecteffects?

Cost of4ºC (%of GDP
in
2100)

Roson andvan der
Mensbrugge(2012)● Sea level rise
● Variations in crop
yields● Water availability● Human health● Tourism● Energy demand

No429

No

No

No

~5%

Bosello et al(2012)

●
●
●
●
●
●
●

Sea level rise
Energy demand
Agriculture
Tourism
Forests
FloodsReduced work
capacity due to heatstress

No

No

No

No

~4%

Dellink et al(2017)● Sea level rise
● Crop yields● Changes in fisheries
catches● Capital damages
from hurricanes● Temperature effects
on labour
productivity andhuman health● Tourism● Energy demand fromheating and cooling

No430

No

No

No

~5%

429

“Changes in morbidity and mortality are interpreted as changes in labor productivity and demandfor health care, and are used to shock exogenous parameters in a computable general equilibrium
model, including 16 regions.” Roberto Roson and Dominique Van der Mensbrugghe, ‘Climate Change
and Economic Growth: Impacts and Interactions’, International Journal of Sustainable Economy 4, no.
3 (2012): 270–85.
430“The health impacts of climate change have economic consequences that go beyond market costs.
These costs, such as the costs of premature deaths, cannot be accounted for in the ENVLinkages
model. However, they can be evaluated using WTP techniques and, for premature deaths, the Valueof a Statistical Life.” Rob Dellink, Elisa Lanzi, and Jean Chateau, ‘The Sectoral and Regional
Economic Consequences of Climate Change to 2060’, Environmental and Resource Economics 72,
no. 2 (1 February 2019): 309–63, https://doi.org/10.1007/s10640-017-0197-5

306

Kompas et al(2018)● Sea level rise
● Losses inagricultural
productivity● Temperature effects
on labour
productivity andhuman health● Energy demand● Tourism

No431

No

No

No

~2%

FUND

●
●
●
●
●
●

Sea level rise
Agriculture
Forestry
Water resources
Energy consumption
Human valuation of
ecosystems● Human health● Extreme weather

Yes432

No

No

No

~5%

Takakura etal (2019)

●
●
●
●
●

Yes433

Yes

No

No

~6%

●
●
●
●

Fluvial flooding
Coastal inundation
Agriculture
Undernourishment
Heat-related excess
mortality
Cooling/heating
demandOccupational-healthcosts
Hydroelectric
generation capacity
Thermal power
generation capacity

As this shows, there is a fair amount of modeller discretion in bottom-up studies: different
studies evaluate the effect on different sectors and use different literature to quantify theseeffects. As a rule, we should expect damage estimates to be biased low because they may
have missed important sectoral impacts of climate change. None of the studies include theimpact of tipping points or indirect effects. Only two of the models - the FUND integrated
assessment model and Takakura et al (2019) - account for non-market impacts, like deaths
and illness.

431“We aggregate and simulate labor productivity loss and human health damages via a negativelabor productivity loss.” Tom Kompas, Van Ha Pham, and Tuong Nhu Che, ‘The Effects of Climate
Change on GDP by Country and the Global Economic Gains from Complying with the Paris Climate
Accord’, Earth’s Future 6, no. 8 (2018): 1153–73.
432
FUND quantifies effects on health using the value of a statistical life, as discussed in the
documentation here.
433“For undernourishment, heat-related excess mortality and fluvial flooding, the non-market values of
lives lost represented by willingness-to-pay to avoid these risks were also incorporated.” Jun’ya
Takakura et al., ‘Dependence of Economic Impacts of Climate Change on Anthropogenically Directed
Pathways’, Nature Climate Change 9, no. 10 (October 2019): 737–41,
https://doi.org/10.1038/s41558-019-0578-6.

307Out of all of these models, Takakura et al (2019) seems like the most reliable because it
incorporates a wide range of impacts which are commonly held to be important, it uses
recent literature,434 and tries to account for non-market damages. The complete findings of
Takakura et al (2019) are shown in the chart below

Source: Jun’ya Takakura et al., ‘Dependence of Economic Impacts of Climate Change on
Anthropogenically Directed Pathways’, Nature Climate Change 9, no. 10 (October 2019): 737–41,
https://doi.org/10.1038/s41558-019-0578-6.

On current policy, the chance of 4ºC is around 5%, so the expected costs of the prospect of
4ºC (i.e. weighting by the probability of the outcome) are around 0.4% of GDP.
However, Takakura et al (2019) excludes some important climate impacts, such as tipping
points and indirect effects,435 and it also measures costs at aggregated regional scales
which masks how uneven impacts are.

434

Their supplementary information shows that much of the literature cited is up to date.
“Although we covered most of the major sectors that will probably be affected by climate change,
there also remain sectors such as confict and crime, and the causal relationship between these
sectors and climate change is controversial. The consequences of catastrophes were also notconsidered. Incorporation of these sectors is the next step in future work.”
435

308

10.4.3. Tipping points
Most bottom-up studies exclude the impact of potential tipping points - low-probability
high-impact events, such as the collapse of the West Antarctic Ice Sheet, the collapse of theAtlantic Meridional Overturning Circulation, or rapid warming from cloud feedbacks.436
Although these events are quite unlikely, their expected damage might be very high, so they
probably account for a disproportionate share of the costs of climate change.
Dietz et al (2021) note that the treatment of tipping points in the literature is far from optimal“A growing body of research has explored climate tipping points using economic
models. We reviewed this literature and identified 52 papers that model the economic
consequences of at least one climate tipping point (SI Appendix, Table S1). Many ofthese studies, however, represent climate tipping points in a highly stylized way.
Examples include an instantaneous jump in the model's equilibrium climate
sensitivity (11), an arbitrary reduction in global gross domestic product (GDP) (12),and a one-off permanent reduction in global utility (13).”437
Dietz et al (2021) estimate the economic cost of different tipping points by collecting together
damages estimates for different tipping points from different integrated assessment models.
They quantify the effect on GDP of the following tipping points:
●
●
●
●
●
●

Permafrost carbon
Methane hydrates
Feedbacks from melting of Arctic sea ice
Disintegration of the Greenland and West Antarctic Ice Sheets
Slowdown of the Atlantic Meridional Overturning Circulation
Variability of the Indian monsoon.

The chart below shows the effect that this on the estimate of the social cost of carbon

436“Another major problem with using IAMs to assess climate change policy is that the models ignore
the possibility of a catastrophic climate outcome” Robert S. Pindyck, ‘Climate Change Policy: What Do
the Models Tell Us?’, Journal of Economic Literature 51, no. 3 (September 2013): sec. 4,https://doi.org/10.1257/jel.51.3.860. See also Martin L. Weitzman, ‘GHG Targets as Insurance against
Catastrophic Climate Damages’, Journal of Public Economic Theory 14, no. 2 (2012): 221–44.
437
Simon Dietz et al., ‘Economic Impacts of Tipping Points in the Climate System’, Proceedings of the
National Academy of Sciences 118, no. 34 (2021).

309

Source: Simon Dietz et al., ‘Economic Impacts of Tipping Points in the Climate System’, Proceedingsof the National Academy of Sciences 118, no. 34 (2021).

Tipping points increase the social cost of carbon by around 25% relative to no tipping points.
They argue that accounting for non-market damages makes little difference to thisestimate.438 Dietz et al (2021) acknowledge that their estimates are preliminary and likely too
low.439
438“SI Appendix, Fig. S20 and Table S13 report the effect of including a leading estimate of global
nonmarket dam-ages from climate change using the nonmarket damage module from the MERGE(Model for Evaluating Regional and GlobalEffects of GHG reductions policies) IAM (26). The
resulting estimates of the SCC are more comprehensive but arguably more uncertain. The effectof all tipping points combined on the expected SCC increases marginally, to 26.9%” Simon Dietz etal., ‘Economic Impacts of Tipping Points in the Climate System’, Proceedings of the National
Academy of Sciences 118, no. 34 (2021).
439“Most of our numbers are probable underestimates, given that some tipping points, tipping pointinteractions, and impact channels have not been covered in the literature so far” Simon Dietz et al.,‘Economic Impacts of Tipping Points in the Climate System’, Proceedings of the National Academy of
Sciences 118, no. 34 (2021).

310

Their estimates have been criticised for relying on arbitrary damage functions relating
temperature to damages and for relying on unreliable literature.440 For instance, as the chart
above shows, the model suggests that AMOC collapse would have net economic benefits,
which doesn’t seem plausible from the discussion in Chapter 8, which highlighted majortransition costs from cooling and from precipitation changes, as well as weakening of themonsoons.
Given the lack of literature on the economic costs of tipping points, it may be useful todevelop ballpark estimates of the costs of these tipping points informed by the scientific
literature. In Chapters 7 and 8, I discussed the following tipping points:
1.
2.
3.
4.
5.
6.
7.
8.

Permafrost carbon
Methane clathrates
Amazon forest dieback
Boreal forest dieback
Collapse of the Atlantic Meridional Overturning Circulation
Changes in monsoons
Rapid cloud feedbacks
Collapse of the West Antarctic Ice Sheet

The key question is: what effect do these potential tipping points have on the estimate of thecosts of climate change outlined in the previous subsection? I put most weight on Takakura
et al (2019), which finds that for 4.4ºC, climate damages are around 8% of GDP in 2100.
The first four tipping points are carbon cycle feedbacks and so are not relevant to the costsof climate change at a given level of warming, though Amazon dieback would have large
costs for Latin America.

AMOC collapse
AMOC collapse would cause cooling and drying around the North Atlantic. Britain would beparticularly badly affected, and Ritchie found that AMOC collapse would reduce British GDP
by around 0.3% due to irrigation spending. AMOC collapse would also affect global
monsoons, with African and Asian monsoons weakening and southern hemispheremonsoons strengthening.441 This could have large negative humanitarian consequences bycausing drought and damaging agriculture. I am not sure how to quantify such costs.
We also need to weight these expected damages according to the probability of AMOC
collapse. On business as usual warming of around 2.7ºC, models suggest that the chance ofcollapse is well below 1%, while for 4ºC (which has around 5% chance on business as
usual), the chance is 1% to 5%, though models probably understate the risk. Thus, oncurrent policy, the risk of AMOC collapse is probably upwards of (5%*5%=) 1 in 400. Since
climate policy will probably strengthen in the future, the all-things-considered probability is440

Steve Keen et al., ‘Estimates of Economic and Environmental Damages from Tipping Points
Cannot Be Reconciled with the Scientific Literature’, Proceedings of the National Academy of
Sciences 119, no. 21 (24 May 2022): e2117308119, https://doi.org/10.1073/pnas.2117308119.
441“A collapse of the Atlantic Meridional Overturning Circulation could weaken the African and Asianmonsoons but strengthen the Southern Hemisphere monsoons (high confidence).” IPCC, Climate
Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC, 2021), Box. TS.13.

311

lower. This suggests that the expected costs of AMOC collapse are at least one hundred
times smaller than the actual cost of AMOC (because the badness of the outcome isweighted according to its probability).
Overall, if it were to occur, it seems plausible that AMOC collapse would have large costs,
especially in Africa and Asia. However, given progress in emissions, the risk now seems low
enough that it would not make a large difference to the bottom-up estimates of direct costs in
Takakura et al (2019).

West African Monsoons
The IPCC suggests that greenhouse gases will probably cause there to be more rainfall in
the central Sahel, but less in the Western Sahel. I am not aware of any models that try toquantify the effects this would have on social welfare. If the IPCC estimates are correct,
there would be some benefits from increased rainfall in some regions, which would be offset
by drying in other regions. The more concerning prospect seems to be weakening of themonsoons caused by AMOC collapse

Indian monsoon shift
The IPCC suggests that greenhouse gases will probably cause a strengthening of the Indian
monsoon. I am not sure what the net effects of this would be on social welfare, but the moreconcerning prospect seems to be weakening of the monsoons caused by AMOC collapse

Collapse of the West Antarctic Ice Sheet
The collapse of the West Antarctic Ice Sheet would cause sea level rise of 5 metre over thecourse of around 100 years. Nicholls et al (2008) estimate that the costs of coastal
protection for 5 metres of sea level rise quickly rise to $30 billion per year, assuming perfectadaptation, which is around 0.04% of current global GDP.442 Regional case studies of theRhone, the Thames and the Netherlands suggest that there is greater potential forabandonment than if there were perfect adaptation.443 I am not aware of any estimates of thecosts of coastal flooding assuming that adaptation measures are not taken.
Again, we need to weight the expected costs of these scenarios according to how probablethey are. Models have mainly considered the risk of collapse of the West Antarctic Ice Sheetfor 4.4ºC, and there is around a 5% chance of that on current policy. The probability ofcollapse of the ice sheet conditional on 4.4ºC is unclear, and the IPCC says that there is
deep uncertainty about this possibility for more than 3ºC of warming.444

442

Nicholls, Tol, and Vafeidis, ‘Global Estimates of the Impact of a Collapse of the West Antarctic Ice
Sheet’, Fig. 5.
443“While some observations of response to abrupt relative sea-level rise due to subsidence support
the global model results, detailed case studies of the WAIS collapse in the Netherlands, Thames
Estuary and the Rhone delta suggest a greater potential for abandonment than shown by the global
model.” Nicholls, Tol, and Vafeidis, ‘Global Estimates of the Impact of a Collapse of the West
Antarctic Ice Sheet’.
444
IPCC, Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (UNFCCC,2021), Table 4.10.

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Rapid cloud feedbacks
The effects of rapid cloud feedbacks outlined by Schneider et al (2019) would be much moredamaging: there would be 8ºC of warming over decades on top of around 5ºC of warming.
This would be extremely damaging, destroying agriculture in the tropics and subtropics andgenerally making those regions uninhabitable. The economic costs would be extremely large
and I am not sure how to quantify them in a sensible way.
Given recent progress on climate policy, the probability of reaching the CO2 levels which
would trigger the cloud feedback - of 1,200ppm to 2,200ppm - now seem extremely unlikely.
Burning all the fossil fuels would increase CO2 concentrations to around 1,600ppm. In
Chapter 1, I estimated that the chance of burning all of the fossil fuels was on the order of 1
in 500,000, and would take several centuries.

How do tipping points affect the economic costs of climate change?
Overall, the literature on the economic costs of tipping points is badly underdeveloped.
Some of the tipping points seem like they would have very large costs if triggered, especially
AMOC collapse, collapse of the West Antarctic Ice Sheet, and rapid cloud feedbacks.
However, given progress on emissions, it looks increasingly unlikely that we will trigger thesetipping points. If, as now seems likely, we limit warming to 2.7ºC or below, the risk of themost damaging tipping points seems low enough that they would not make a dramaticchange to the estimates from the bottom-up studies.

10.4.4. Indirect effects
According to Howard and Sterner (2017) most models do not include indirect effects such asconflict and crime.445 This biases damage estimates downward. I discuss these effects in
Chapters 11-13.

10.4.5. Regional or global aggregation
In the main IAMs, known as DICE, FUND and PAGE, “much of the poverty associated withhigh levels of vulnerability is masked by regional averaging of economic variables”.
“For instance, the models used by the US Environmental Protection Agency toestimate the social cost of carbon [DICE (12), FUND (13), and PAGE (14); see ref.
15)] do not disaggregate below the level of continental regions. In particular, theentire population of each region is taken to consume the regional average. (AndDICE does not disaggregate below the global level.)”446
Takakura et al (2019) aggregate the world into 17 regions.447
445“Most of the underlying estimates systematically omit key climate impacts that could significantly
increase climate damages, including socially-contingent climate impacts (migration, social andpolitical conflict, and violence), ocean acidification, etc. (Howard 2014; Revesz et al. 2014).” Howard
and Sterner, ‘Few and Not So Far Between’.
446
Francis Dennig et al., ‘Inequality, Climate Impacts on the Future Poor, and Carbon Prices’,
Proceedings of the National Academy of Sciences 112, no. 52 (29 December 2015): 15827–32,
https://doi.org/10.1073/pnas.1513967112.
447“The impacts were originally calculated for a 0.5°×0.5° grid for each year during the twenty-frst
century and the calculated impacts were aggregated to 17 regions unless otherwise noted (above,

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This has hugely important implications for the costs of climate change because there issignificant inequality between countries within regions. Dennig et al (2015) modify a leadingclimate-economy model, Regional Integrated model of Climate and the Economy (RICE), to
include what is known about economic inequality within regions and countries. Their findings
are striking:“When subregional differences are modeled in this way, several policy-relevant
aspects of the model can change dramatically even when other assumptions andparameters from RICE are held constant. As we show below, even when RICE
regional damage functions are used to establish the damage level of each region, thedistribution of damage within regions can cause some members of future generations
to be less affluent than their current counterparts. If the distribution of damage is lessskewed to high incomes than the distribution of consumption, then weak or no
climate policy will result in sufficiently large damages on the lower economic strata toeventually stop their welfare levels from improving, and instead cause them todecline. This paints a different picture from the standard narrative in leadingcost–benefit IAMs, where regional average consumptions continue to grow evenunder business-as-usual (BAU).”448
This is important on utilitarian, egalitarian and prioritarian grounds. Standard
climate-economy models suggest that everyone will be better-off despite climate change, but
this is not realistic. As I discussed in the introduction to this report, this is one reason that
discounting the future costs of climate change is inappropriate.
This is one area where presenting the overall costs of climate change as a fraction of GDP,rather than in terms of units of wellbeing lost, may cause confusion. A cost in terms of dollars
is worth more to someone in Congo than someone in Norway. By presenting costs in termsof GDP, studies on damages mask this fact, and so understate the welfare costs of climatechange. Once we correct for regional aggregation, even if we use Nordhaus’ damage modeland his assumptions about discounting, it is optimal to limit warming to 2ºC. In contrast,
without regional disaggregation, it is optimal to limit warming 3.3ºC. Regional disaggregationof impacts increases the optimal carbon price by a factor of 5 to 10.449 I’m not sure what
effect this would have on the Takakura et al (2019) damage estimates.

10.4.6. Growth or levels?
Whether climate change affects the level of GDP or the growth rate is a crucial determinantof the size of climate damages. If temperature shocks merely have a level effect, then after
damage in one bad year, the economy will bounce back in the next year. But if they have a
growth effect, we would expect to see longer lasting and larger effects of temperatureshocks.

results are further aggregated into seven regions).” Takakura et al., ‘Dependence of Economic
Impacts of Climate Change on Anthropogenically Directed Pathways’.
448
Dennig et al., ‘Inequality, Climate Impacts on the Future Poor, and Carbon Prices’.
449
Dennig et al., ‘Inequality, Climate Impacts on the Future Poor, and Carbon Prices’, Fig. 1.

314

Stern and Stiglitz (2022) comment that in most integrated assessment models, economic
growth is exogenous.450 Scholars have pointed to several mechanisms by which climatechange might affect economic growth:451●●●

Adaptation spending would divert spending away from R&D, which drives economic
growth.452
Morbidity, mortality and heat stress would damage technological progress andhuman capital.
Damaged capital stocks

It is not clear how big an effect global warming would have via these mechanisms. However,
my best guess is that the effects are small relative to the other determinants of growth. It istrue that spending on adaptation would divert spending away from R&D, but this is also trueof all government waste, and seems a weak and indirect lever on overall spending on R&D.
There is evidence that heat stress damages productivity, which could damage innovation,
but this effect can be removed with air conditioning, and the effects of plausible levels ofwarming seem small.
As I discuss below, some top-down studies try to quantify the effect on growth directly,
though I am suspicious about some of the econometrics used. I would favour a bottom-up
approach with a sophisticated causal model of the mechanisms by which climate change
might impact growth, which is informed by the surrounding empirical literature on thosemechanisms.

10.4.7. Overall judgement on bottom-up studies
Bottom-up approaches are, at first pass, a plausible way to calculate the costs of climatechange. They can in principle use the scientific literature and sophisticated economic models
to quantify and aggregate the most important climate impacts highlighted in the literature.
The main drawbacks of extant bottom up studies are as follows:
1. Modeller discretion means that certain important sectors may be excluded or thatscientific literature is not up-to-date.
2. No studies have well-developed treatment of tipping points.
3. Many studies quantify effects at high levels of regional aggregation without
accounting for significant intra-regional inequality.
4. Most studies do not attempt to model potential effects on economic growth

450“There is no damage to capital stocks in most IAMs, nor any reduction in the underlying growthrate, which is assumed to be exogenously determined.” Nicholas Stern and Joseph Stiglitz, ‘The
Economics of Immense Risk, Urgent Action and Radical Change: Towards New Approaches to the
Economics of Climate Change’, Journal of Economic Methodology, 2022, 1–36.
451
Jarmo S. Kikstra et al., ‘The Social Cost of Carbon Dioxide under Climate-Economy Feedbacks
and Temperature Variability’, Environmental Research Letters 16, no. 9 (September 2021): 094037,
https://doi.org/10.1088/1748-9326/ac1d0b.
452“Moreover, the high levels of expenditure necessary to adapt to climate change, especially in the
more adverse scenarios, implies that resources will be diverted away from innovation and investment
and the social cost of carbon in those states of nature will be high.” Stern and Stiglitz, ‘The
Economics of Immense Risk, Urgent Action and Radical Change’.

315

Most bottom-up models find that the monetised costs of warming of 4ºC are equivalent to a
decline in GDP of around 5% in 2100. The most plausible model I have looked into,
Takakura et (2019), finds that the monetised costs of 4.4ºC are equivalent to around a 6%
reduction in GDP by 2100.
Due to the issues outlined above, this is very likely an underestimate of the true costs ofclimate change. Known tipping points seem important for higher levels of warming, but theprobability of 3.5ºC or more is now low enough that they seem unlikely to make a dramaticdifference to the expected costs of climate change. However, there may be unknown tipping
points which are more important. The literature highlights that the effects would be
disproportionately borne by the worst-off people, who have contributed the least to climatechange.
One reason that estimates in the literature may be overestimated is that they may notaccurately capture the scope for adaptation of future richer societies, which is hard to
predict, but may soften the blow of climate change. Still, I think that impacts are, on thewhole, probably overestimated.
It is difficult to see how plausible changes in the estimates of direct damages found inbottom-up studies could come close to threatening a global catastrophe, or the collapse ofindustrial civilisation. Even once we correct for the problems outlined above, it still seems
like average global living standards will improve. It is very clear from the bottom-up studies
that strong mitigation is justified, but it is also difficult to see how plausible levels of climatechange could cause civilisational catastrophe.
I discuss the import of indirect effects in Chapters 11 to 13.

10.5. Top-down studies
Top down studies attempt to estimate the effect of climate change on aggregate economic
outcomes, like GDP, directly. Much of the recent literature that directly estimates the effects
of climate change on GDP uses panel data and short-term variations in weather to estimate
the relationship between climate variables and GDP based on past experience.453
Heuristically, on this methodology, an economy observed during a cool year is the ‘control’
for that same society observed during a warmer ‘treatment’ year. Because variation in
weather is exogenous, any effects on output are likely to be causal. These studies can also
explore the effects of weather variation on shorter subannual timescales.
Some of these studies have produced markedly higher damage estimates than bottom-up
studies. Before I discuss these studies it is worth exploring the correlation between climate
variables and economic outcomes across space.

453

Council of Economic Advisors, Office of Management and Budget, Climate-Related
Macroeconomic Risks and Opportunities, 4 April 2022, p. 9,
https://www.whitehouse.gov/wp-content/uploads/2022/04/CEA_OMB_Climate_Macro_WP_2022.pdf

316

10.5.1. Cross-sectional correlations
Cross-country correlations
The cross-country correlation between temperature and per capita GDP
There is a strong negative relationship between hot climates and per capita GDP. One cansee this just from eyeballing a map of income per head

Latitude is an imperfect proxy of temperature. Gallup, Sachs and Mellinger (1999) show thatcountries located in the tropics (i.e. between the Tropic of Cancer and the Tropic of
Capricorn) were 50% poorer per-capita in 1950 and grew 0.9 percentage points slower per
year between 1965 and 1990.454
This chart plots ln GDP per capita against population-weighted average temperature:

454

John Luke Gallup, Jeffrey D. Sachs, and Andrew D. Mellinger, ‘Geography and Economic
Development’, International Regional Science Review 22, no. 2 (1 August 1999): 179–232,
https://doi.org/10.1177/016001799761012334.

317

Source: Geoffrey Heal and Jisung Park, ‘Feeling the Heat: Temperature, Physiology & the Wealth of
Nations’ (National Bureau of Economic Research, 2013).

For reference, ln($60,000) =11; ln($8,000) = 9; ln($400) = 6.
This masks the strength of the correlation because it puts income on a log scale. Still, it isclear that at a given temperature, there is a fair bit of variation.
As we saw in the section on heat stress, temperature and humidity contribute to heat stress,
which in turn affects productivity. There is also evidence that higher heat stress is correlatedwith lower GDP per capita:318

Source: Noriko Okamura et al., ‘Evaluating Thermal Comfort in City Life and Its Relation toSocio-Economic Activities’, Asian Journal of Geoinformatics 14, no. 2 (2014).

The cross-country correlation between temperature and growth rates
The ‘growth rate-tropics’ correlation is less clear.

319

Here, the correlation doesn’t seem especially strong. Most notably, in the last few decades,
countries in East and South Asia have recently had much higher rates of growth than theWest despite being much warmer on average. The same is true for several countries in
Sub-Saharan Africa. The table below shows growth rates in selected large countries from2000 up until before COVID-19.
Country

Average growth rate in GDP per capita

Vietnam

>5%

India

>4%

Bangladesh>4%Indonesia~4%

Rwanda>4%

Ethiopia>5%

Source: World Bank
Methodological issues with cross-country regressions
The correlations I have outlined above do not necessarily suggest that there is a causal
relationship between climate and economic indicators. Identifying a causal relationship fromcross-sectional evidence is difficult for several reasons.

320

Firstly, there is omitted variable bias. There are multitudinous other factors that might becorrelated with climate that could influence economic performance, and it will be difficult to
identify and control for all of them properly. For example, in WEIRDest People in the World,
Joseph Henrich argues that historical rates of cousin marriage affect growth rates,455 but I
would wager that few cross-sectional studies have thought to control for that.
Furthermore, controlling for potential confounding variables, such as institutions or
population, risks overcontrolling:“For example, consider the fact that poorer countries tend to be both hot and have
low-quality institutions. If hot climates were to cause low quality institutions, which inturn cause low income, then controlling for institutions... can have the effect ofpartially eliminating the explanatory power of climate, even if climate is the underlying
fundamental cause.”456
Secondly, regressions of climate on economic performance might identify long-run historicaleffects that are not applicable to future climate change. Suppose that, as some scholars
believe, temperature and income are correlated in the cross section today largely because
climate affected the path of agricultural development, technological exchange, and/or
subsequent colonialism. The cross-sectional relationship, which represents a very long-run
equilibrium, may incorporate processes that are too slow to accurately inform the time-scaleof interest, or it may include historical processes (such as colonialism) that will not repeat
themselves in modern times.457
To illustrate, according to Acemoglu et al (2001), the key factor that explains the success of
economies is that patterns of colonialism and institutions were influenced by mortality rates
of settlers between the 16th and 19th century.458 These mortality rates would have been
affected by local climate at the time because climate variables influence disease burden.
This would explain why a hot climate is associated with poor economic performance today.
However, this does not indicate that future warming will damage economic output in the
future since colonialism won’t repeat itself.459

455

Joseph Henrich, WEIRDest People in the World (Picador Paper, 2021).
Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken, ‘What Do We Learn from the Weather?
The New Climate-Economy Literature’, Journal of Economic Literature 52, no. 3 (2014): 6.
457
Dell, Jones, and Olken, 6.
458
Daron Acemoglu, Simon Johnson, and James A. Robinson, ‘The Colonial Origins of Comparative
Development: An Empirical Investigation’, American Economic Review 91, no. 5 (December 2001):
1369–1401, https://doi.org/10.1257/aer.91.5.1369.
459
Acemoglu (2001) is a flawed study. Firstly, the data on settler mortality have been called into
question by Albouy (2012). Secondly, to accept the instrumentation in Acemoglu et al (2001), one has
to assume that disease burden in the countries of interest only had an effect on growth via influencing
settler mortality and the institutions they set up, and not through any other causal mechanism. But
perhaps having lots of malaria in your country is bad for growth for other reasons. David Y. Albouy,‘The Colonial Origins of Comparative Development: An Empirical Investigation: Comment’, American
Economic Review 102, no. 6 (May 2012): 3059–76, https://doi.org/10.1257/aer.102.6.3059.
456

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In light of these methodological difficulties, cross country-regressions are now out of fashionin economics, and the discipline has moved towards studies that can more reliably identify
causal relationships.460
In my view, throwing out all evidence from cross-country correlations is too drastic; although
they should be treated with caution, I think in some cases cross-sectional correlations
provide useful information. The reason for this is that in some cases, economic theory andevidence from other domains may give us reason to believe that a particular relationship is infact causal. Just because a relationship might be confounded does not mean that it isconfounded.
For example, there is a very strong cross-sectional correlation between smoking and lungcancer: smokers are much more likely to get lung cancer than non-smokers. It is true that
this relationship might be confounded by various demographic factors that independently
cause cancer: people of lower socioeconomic status are more likely to smoke, so maybe
their higher rates of lung cancer are driven by other aspects of their lifestyle. But even before
we do a randomised control trial on the effects of smoking, or gather longitudinal evidence
on the timing of increases in lung cancer rates and smoking, the very strong correlation
between smoking and lung cancer should update us towards the view that one causes theother. This is especially true because we have an independently plausible theoreticalexplanation of why smoking causes lung cancer.
In sum, cross-sectional shouldn’t be thrown out entirely but should still be treated withsignificant caution.
One alternative is to combine cross-sectional and panel data approaches.461

Cross-sectional relationship within countries
One of the most important potential confounders of the cross-country relationship between
temperature and income is country-level fixed effects, such as policies, state capacity andinstitutional quality. This is because the policies and institutions of countries are widely
thought to be a major determinant of the economic prospects of different countries. This canbe seen by looking again at recent growth rates in warmer countries. Recent growth460

Durlauf, Steven N. 2000. “Econometric Analysis and the Study of Economic Growth: A Skeptical
Perspective.” In Macroeconomics and the Real World: Volume 1: Econometric Techniques andMacroeconomics, edited by Roger E. Backhouse and Andrea Salanti, 249–62. Oxford and New York:
Oxford University Press.
461"A second approach along these lines represents a marriage of the panel data estimation approach
using short-run weather fluctuations and the Ricardian approach. The concept here is that if one
observes a large number of units (like counties, households, or firms) over a significant number of
periods covering a spatial area with large heterogeneity in climate, one can estimate separate
response functions for subgroups of the individual units using observed short-run weather fluctuations(for example, use within-household variation to identify a short-run response function by zip code). By
controlling for unit- and time-fixed-effects, it is possible to obtain plausibly causal estimates of local
short-run dose response functions. One can then either in a second step regress the slopes of thedose response on climate (for example, long run average summer temperature) across subgroups, or,through an interaction term in a single regression, estimate how the slope of the dose response
function varies across areas with different climates, incomes, and other observables that vary acrossspace.” Maximilian Auffhammer, ‘Quantifying Economic Damages from Climate Change’, Journal of
Economic Perspectives 32, no. 4 (November 2018): 33–52, https://doi.org/10.1257/jep.32.4.33.

322

accelerations in South and East Asia were caused by changes in policy, not by changes inclimate.
One way to control for country-level fixed effects is to examine the cross-sectional
relationship between climate and economic output within countries. For example, there issubstantial variation in annual average temperature across US states, with the hottest statenearly 17°C warmer than the coldest state.

Source: Steve Keen, ‘The Appallingly Bad Neoclassical Economics of Climate Change’,
Globalizations 0, no. 0 (1 September 2020): 1–29, https://doi.org/10.1080/14747731.2020.1807856.

In spite of this variation, in global and historical context, all of these states are very rich.
The findings of some major studies taking a subnational cross-sectional approach aresummarised in the table below:
Study

Findings

Nordhaus (2006)462

Controlling for country fixed effects, this study finds that 20% of the462

William D. Nordhaus, ‘Geography and Macroeconomics: New Data and New Findings’,
Proceedings of the National Academy of Sciences 103, no. 10 (7 March 2006): 3510–17,
https://doi.org/10.1073/pnas.0509842103.

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income differences between Africa and the world’s rich industrial regions
can be explained by geographic variables, which include temperature andprecipitation as well as elevation, soil quality, and distance from the coast
Dell et al (2009)463

A 1°C rise in temperature is related to a 1.2-1.9% decline in municipal
incomes for 7,684 municipalities in 12 countries in the Americas. The
within-country cross-sectional correlation is substantially weaker than any
cross-country correlation.

Greßer et al (2021)464

No negative relationship between subnational temperature and four
different measures of economic development (per capita GDP, growth of
per capita GDP, nightlights and gross cell production). There is noevidence that temperature is non-linearly related to income (with hotter
regions being potentially particularly prone to adverse effects oftemperature on income). There is no robust evidence that the effect oftemperature is especially pronounced in poorer regions.

A key drawback of within-country cross sectional correlations is that they cannot shed lighton how climate change might affect the choice of policies and institutions between different
countries, which is an important limitation.
Nonetheless, they do shed light on some forms of climate impact. Most importantly, theeffects of temperature on labour productivity should show up in within-country
cross-sectional regressions. The lack of effect found in Greßer et al (2020) should update us
towards the effect being small.
Methodological issues with within-country cross-sectional regressions
Within-country cross-sectional studies are also subject to omitted variable bias and toover-controlling. It might be that past climate is correlated with some feature which makes
one subnational region thrive and another not. For instance, climate is correlated with rates
of cousin marriage in different regions in Italy, which, according to Joseph Henrich andothers, contributes to southern Italy’s corruption, mafia problem, and poor economic
performance. But this is a causal product of the willingness of the Church centuries ago toprohibit cousin marriage in northern Italy, which is not relevant to the future impact of climatechange.
Although the within-country correlation might be confounded for specific countries, it is hard
to see why it would be systematically confounded across all countries.

Other problems with cross-sectional evidence
Some aspects of future climate change are not well-captured by current variation in climate
variables across space. This includes things like sea level rise, ocean acidification and CO2
fertilisation.

463

Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken, ‘Temperature and Income: Reconciling
New Cross-Sectional and Panel Estimates’, American Economic Review 99, no. 2 (2009): 198–204.
464
Christina Greßer, Daniel Meierrieks, and David Stadelmann, ‘The Link between Regional
Temperature and Regional Incomes: Econometric Evidence with Sub-National Data’, Economic Policy
36, no. 107 (2021): 523–50.

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Since they measure the effect of climate variables on GDP directly, they also exclude
non-market impacts, such as illness and death.

Overall judgement on the cross-sectional evidence
My conclusions about the cross-sectional evidence are as follows.
Firstly, I don’t put much weight on the current cross-country relationship between
temperature and GDP levels, for several reasons. The sample of countries on which to carryout a regression is not particularly large, which limits statistical power. There are also
numerous potential confounds, which it is very difficult to properly control for.
Secondly, in the longer-term, the climate-growth rates relationship is more important than the
climate-levels relationship. The correlation between growth rates and temperature does notseem particularly strong. If the various countries in the tropics and subtropics experiencing
high economic growth continue to do so, then they will catch up with high-income countries
within a few decades. It is a mistake to categorise the current relative poverty of Bangladesh
as an instance of climatic determinism. Given their current growth rates, they are likely to behigh-income countries soon, and that is due to policy change, rather than climate.
The current global variation in economic growth rates suggests that they are mainly
determined by economic policy. Indeed, economic growth has mainly been a phenomenon ofwhat Lant Pritchett calls the post-1950 ‘development era’, including●●●●

The end of colonisation with the liberation of India, Pakistan and Indonesia
The founding of the Bretton Woods institutions - the IMF and the World Bank
Truman’s Four Point plan to provide technical assistance to developing countries
Overall a concerted effort by economists and sovereign states to increasedevelopment

Growth has only been an independent area of study since this point. This era has brought
more progress than all prior human history combined. Consequently, many countries that are
currently hot will be much richer in the future.
Finally, the within-country cross-sectional regressions shed light on the effects oftemperature on labour productivity.
However, they also miss several avenues of climate impact:
1.
2.
3.
4.
5.

How climate affects the choice between different institutions and policies.
The effects of precipitation change
The transition costs of climate change
The costs of tipping points
The costs of longer-term impacts such asa. Sea level riseb. Ocean acidificationc. CO2 fertilisation
6. Non-market impacts like illness and death325

Overall, this suggests that we should put much more weight on bottom-up studies than on
cross-sectional studies.

10.5.2. Weather and economic performance
Seminal studies by Dell et al (2012) and Burke et al (2015a) have used panel data toestimate the effect of inter-annual changes in weather on economic performance withincountries, and used these to estimate the potential economic effects of climate change.465
After a large number of robustness checks, both papers conclude that there is no evidence
that rainfall has a significant and consistent impact on GDP growth, but higher temperatures
have adverse consequences. The damage estimate of Burke et al (2015a) is far higher thanthat of Dell et al (2012). As I discuss below, this is mainly due to the controls used in the
different studies.
Heuristically, on this methodology, an economy observed during a cool year is the ‘control’
for that same society observed during a warmer ‘treatment’ year. Because interannual
weather change is exogenous, any difference in economic output is plausibly due todifferences in weather rather than country-specific or time trend factors.
This methodological approach avoids the omitted variables and over-controlling problems
associated with cross-sectional approaches. A further advantage of the top-down approachis that it captures feedback between different economic sectors and damage categories,
which are not well-captured when economic sectors and damage categories are estimatedthrough a bottom-up approach and then simply aggregated.466

Methodological drawbacks of weather studies
This approach also has some drawbacks.467
Weather is different to climate changeInter-annual weather change is importantly different to long-term climate change. The chart
below shows the year-to-year change in global average surface temperature between 1950
and 2010.

465

Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Global Non-Linear Effect of Temperature
on Economic Production’, Nature 527, no. 7577 (November 2015): 235–39,
https://doi.org/10.1038/nature15725; Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken,‘Temperature Shocks and Economic Growth: Evidence from the Last Half Century’, American
Economic Journal: Macroeconomics 4, no. 3 (July 2012): 66–95, https://doi.org/10.1257/mac.4.3.66.
466
Council of Economic Advisors, Office of Management and Budget, Climate-Related
Macroeconomic Risks and Opportunities, 4 April 2022, p. 10
https://www.whitehouse.gov/wp-content/uploads/2022/04/CEA_OMB_Climate_Macro_WP_2022.pdf.
467
Dell, Jones, and Olken, ‘What Do We Learn from the Weather?’, 37ff; Council of Economic
Advisors, Climate-Related Macroeconomic Risks and Opportunities, 2022, p. 10-11.

326

Source: John CV Pezzey, ‘Why the Social Cost of Carbon Will Always Be Disputed’, Wiley
Interdisciplinary Reviews: Climate Change 10, no. 1 (2019): e558.

The maximum year-to-year change in global mean surface temperatures during 1950–2010
was only 0.3ºC, which is much smaller than projected climate change.
The chart below is another way to illustrate the difference between climate change andinterannual weather variation:

Between 1997 and 1998, the annual average temperature jumped 1ºC in the US. But climatechange is occurring at a rate of about 0.02ºC per year. The weather studies measure theeffect of year-to-year shifts in the purple line, whereas climate change is very slowly shifting
the distribution of the purple line up over time, which is quite a different effect.
This could introduce bias in either direction. On the one hand, this misses potential tipping
points, as well as effects that do not see sharp shifts on inter-annual timescales, such as
ocean acidification, CO2 fertilisation, and sea level rise.
On the other hand, we have much more time to adapt to slow moving climate change than
we do to changes in average annual temperatures. There would also be more time for327

general equilibrium effects, such as the movement of labour and capital, to reduce thedamage of climate change.468
Noisy proxies
Proxy measures for temperature and precipitation will be quite noisy measures of the true
weather signal. As a covariate becomes noisier, this increases the risk of finding a false
positive in a regression.469 As Auffhammer (2018) says
“Another critique of the panel data approach is that if weather is measured
with error, then as more fixed effects are included in the regression, concerns overmeasurement error loom larger (Fisher, Hanemann, Roberts, and Schlenker 2012).
In the vast majority of locations, weather is measured with error, and the bigger the
distance between weather stations, the bigger measurement error concerns become.
The United States and Europe have tens of thousands of weather stations, but many
locations in sub-Saharan Africa do not have a weather station within hundreds ofmiles. If the measurement error is classical, this is likely to attenuate the response
towards zero.”470
This problem also applies to within-country cross-sectional approaches,471 though to a more
limited extent because studies can reduce noise in regional temperature data by averaging
across long periods of time. For instance, in their within-country cross sectional regressions,
Greßer et al (2021) measure average subnational temperature between 1950 and 2000.472
Non-market impacts
Since top-down panel studies try to measure the effect of climate variables on GDP directly,
they exclude non-market impacts such as illness and death.

468“If both labor and capital are mobile, then this type of macroeconomic readjustment could reduce
the long-run impacts of climate change relative to a short-run panel estimate (although any suchtempering of the impacts would depend on moving costs, the extent to which the marginal product of
capital is location specific, and potentially a host of other factors).” Melissa Dell, Benjamin F. Jones,
and Benjamin A. Olken, ‘What Do We Learn from the Weather? The New Climate-Economy
Literature’, Journal of Economic Literature 52, no. 3 (2014): 740–98.
469“By contrast, multiple regression will typically show the opposite trend: the more unreliable thecovariate, the more the multiple regression actually capitalizes on this unreliability by conflating thedirect and indirect effects of the predictor of interest, leading to biased, inconsistent parameter
estimates and inflated test statistics. The net effect is that, as the reliability of a covariate falls, it
typically becomes easier to reject the null with multiple regression (resulting, as we have alreadyseen, in very high false positive rates when the null is true)...” Jacob Westfall and Tal Yarkoni,‘Statistically Controlling for Confounding Constructs Is Harder than You Think’, PLOS ONE 11, no. 3(31 March 2016): e0152719, https://doi.org/10.1371/journal.pone.0152719.
470
Auffhammer, ‘Quantifying Economic Damages from Climate Change’.
471
Thanks to Danny Bressler for raising this point.
472“The dataset includes a variable measuring regional temperature, originally obtained from theWorldClim database. This variable indicates average temperatures per region between 1950 and
2000.” Christina Greßer, Daniel Meierrieks, and David Stadelmann, ‘The Link between Regional
Temperature and Regional Incomes: Econometric Evidence with Sub-National Data’, Economic Policy
36, no. 107 (2021): sec. III

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Findings of weather studies
Panel studies of weather variation tend to produce higher damage estimates than othertypes of studies, with one panel study - Burke et al (2015a) - a particular outlier.
Burke et al (2015a) finds that SSP5-RCP8.5 would reduce GDP per capita by 23% by 2100
(median estimate), with substantial uncertainty: there is a 5% chance of a 60% loss in GDP
per capita, and in 30% of simulations, they find that climate change increases GDP by 2100.

Source: Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Global Non-Linear Effect ofTemperature on Economic Production’, Nature 527, no. 7577 (November 2015): 235–39,
https://doi.org/10.1038/nature15725.

329

Warming has negative effects in both agricultural and non-agricultural sectors and in bothrich and poor countries. They find that adaptation so far has been limited.

They also find highly varying regional effects, with especially bad outcomes in hotter
countries. In Sub-Saharan Africa and South and Southeast Asia, the costs of 4-5°C ofwarming are around 90% of GDP.

330

331

The finding of Burke et al (2015a) on the country-level costs of climate change has been
adopted in the latest version of the PAGE integrated assessment model,473 which has
historically been used by the US government to estimate the social cost of carbon.
Other panel studies tend to find costs around 5-15% of GDP for 2-4°C of warming.
Newell et al (2021), which I discuss in more detail below, finds costs to GDP levels of 1-3%,
as well as enormous model uncertainty.
GDP growth or GDP levels?
As I discussed above, whether climate change affects the level of GDP or the growth rate isa crucial determinant of the size of climate damages. If temperature shocks merely have alevel effect, then after damage in one bad year, the economy will bounce back in the next
year. But if they have a growth effect, we would expect to see longer lasting effects oftemperature shocks. Thus, we can test for a growth effect by estimating models with lags oftemperature.474 Both Burke et al (2015) and Dell et al (2012) produce equivocal findings onwhether climate change affects the level of GDP or the longer-term growth rate.475
It is also important to consider theoretical reasons explaining why climate change would
damage economic growth. One important possible mechanism is that level effects in one
period could affect the growth in the capital stock, an effect which would be amplified if
productive capital were diverted to costly adaptation measures. Another possibility is thatclimate change affects the speed of technological change, for instance if warmer
temperatures affect the cognition needed to produce innovation.476 There is scope toameliorate this latter effect by using air conditioning.
Adaptation
Burke et al (2015a) find that adaptation measures since 1960 have not fundamentally altered
the relationship between temperature and productivity. In effect, according to Burke et al(2015a), there was no additional adaptation to the negative economic effects of warmer
temperatures between 1960-1989 and 1990-2010, even though average incomes increased
473

Yumashev notes that PAGE-ICE now includes “new economic impact function based on the recent
macro-econometric analysis of the effect of historic temperature shocks on economic growth inmultiple countries by Burke et al., projected onto the 8 major regions of the PAGE model using
population-weighted temperatures, and adapted to fit with the single year consumption-only approachfor climate impacts used in PAGE (also known as level effects).” Dmitry Yumashev, ‘PAGE – ICE
Integrated Assessment Models’, 2020, sec. 2.2.
474
“Moreover, estimating a model with lags of temperature, they find that this large effect is notreversed once the temperature shock is over, suggesting that temperature is affecting growth rates,
not just income levels.22 Growth effects, which compound over time, have potentially firstorderconsequences for the scale of economic damages over the longer run, greatly exceeding level effects
on income, and are thus an important area for further modeling and research (see Section 4.2).”
Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken, ‘What Do We Learn from the Weather? The
New Climate-Economy Literature’, Journal of Economic Literature 52, no. 3 (2014): 740–98.
475
For Dell, see above. For Burke: “Thus, while we can can clearly demonstrate that there is anonlinear effect of temperature on economic production, we cannot reject the hypothesis that thiseffect is a true growth effects nor can we reject the hypothesis that it is a temporary level effect”
Burke, Hsiang, and Miguel, ‘Global Non-Linear Effect of Temperature on Economic Production’, SI
p15.
476
Burke, Hsiang, and Miguel, ‘Global Non-Linear Effect of Temperature on Economic Production’, SI,
p. 10.

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by around $3,000 in this period. The Burke et al (2015) estimate of the damage of climatechange is conditioned on the assumption that future adaptation to climate change will also
be minimal.477
In my view, this finding is difficult to believe. Burke et al (2015a) find that warming damages
economic performance in rich countries that are mainly reliant on services (and not on
agriculture). One of the main causal explanations Burke et al point to that explains this effectis the micro-level evidence on the effects of temperature on labour supply and productivity.478
Since this effect can be eliminated by air conditioning, Burke et al (2015a) implies that as
people get richer and as they experience higher temperatures, they are no more likely toinvest in air conditioning. This is in tension with common sense and with empirical evidence.
Common sense suggests that as people get richer and experience higher temperatures,
they will invest in air conditioning. This includes offices and laboratories that will drive
technological innovation and economic growth.
Empirical evidence also strongly supports the idea that, other things equal, richer people will
buy more air conditioning. Using micro-level data from 16 countries, Davis et al (2021) findthat, within countries, air conditioning increases with income

477“If future adaptation mimics past adaptation, unmitigated warming is expected to reshape theglobal economy by reducing average global incomes roughly 23% by 2100 and widening global
income inequality, relative to scenarios without climate change” Burke, Hsiang, and Miguel, ‘Global
Non-Linear Effect of Temperature on Economic Production’.
478“Numerous basic productive components of an economy display a highly non-linear relationshipwith daily or hourly temperature1 . For example, labour supply4 , labour productivity6 , and crop
yields3 all decline abruptly beyond temperature thresholds located between 20 uC and 30 uC (Fig.
1a–c)” Burke, Hsiang, and Miguel, ‘Global Non-Linear Effect of Temperature on Economic
Production’.

333Source: Lucas Davis et al., ‘Air Conditioning and Global Inequality’, Global Environmental Change 69(1 July 2021): 102299, https://doi.org/10.1016/j.gloenvcha.2021.102299.

The global correlation between air conditioning penetration and temperature is confoundedby the fact that hotter countries tend to be poorer and so less able to afford air conditioning.
However, within countries, there is a strong relationship between temperature and air
conditioning penetration. For instance, as of 2008, the coolest regions in the US had
penetration rates of 40%, while the warmest and most humid regions were nearly completely
saturated.479
Since the finding of Burke et al (2015) conflicts with common sense, empirical evidence andwith one of their main explanations of how temperature affects economic output, this makes
me sceptical that their empirical finding is real.

479“Henderson shows data, including CDD and air conditioner saturation (including both room units
and central systems) for the nine U.S. Census Divisions, plus the four largest states (California,
Texas, New York and Florida), as provided by the U.S. Energy Information Administration’s
Residential Energy Consumption Survey (RECS) for 2001. The data show a clear trend, with thecoolest regions (Pacific and California) having saturation rates of about 40%, and the warm, humid
regions nearly saturated. Henderson references a study which made a fit to U.S. data based on 39
individual cities (Sailor 2003).” Michael A. McNeil and Virginie E. Letschert, ‘Modeling diffusion ofelectrical appliances in the residential sector’, Environmental Energy Technologies Division, August
2010.

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Model uncertainty
One common concern with single studies involving noisy data, limited discipline from theory
and complex econometrics is that researcher degrees of freedom and reporting bias
combine to make it more likely for researchers to find and/or report negative effects, and forthe true extent of model uncertainty not to be presented.
In this vein, Newell et al (2021) argue that the growing literature on weather and theeconomy makes seemingly ad hoc decisions about how to model the relationship betweenchanging climate variables and economic output. Using the same data employed by Burke etal (2015a),480 Newell et al (2021) cross-validate different models by estimating models over a
subset of the data and then testing how well they perform on the rest of the data. They findenormous model uncertainty:“Model uncertainty is comparable in magnitude to sampling uncertainty, yielding
among GDP growth models a 95% confidence interval for GDP impacts in 2100 of-84% to +359%. GDP levels models yield a much narrower 95% confidence intervalof -8.5% to +1.8% and centered around losses of 1-3%, consistent with damage
functions of major integrated assessment models.”
If Burke et al (2015a) had used the Dell et al (2012) method of controlling for country-level
heterogeneity, their model of non-linear temperature effects would have estimated a 45%
gain in GDP by 2100.481
Newell et al’s claim that model uncertainty is much higher than Burke and others
acknowledge seems intuitively plausible. For example, in Sub-Saharan Africa, and Southand Southeast Asia, the 95% confidence interval for damages of 4°C of warming is around70-90% of GDP. Burke et al (2015a) are very confident that warming will almost completely
destroy the economies in these regions, solely on the basis of historical weather data. This isin spite of the fact that such data cannot account well for events usually considered to becatastrophes, such as tipping points or dramatic changes in crop yields or droughts due tolong-term warming.
There is some back and forth on this topic between Marshall Burke (here) and Steve Sexton,a co-author on the Newell et al (2021) paper (here). The debate centres on whether one
thinks cross-validation is a good test of how good a model is at causal inference, or whether
one thinks that there are independent reasons to think the controls used in Burke et al(2015a) identify causal effects.
Overall, I am more sympathetic to Newell et al (2021), though I have not looked into Burke etal’s arguments for their controls. If the model used in Burke et al (2015a) is a good model ofthe causal effect of weather on economic performance, then we should expect the model toperform well in cross-validation, in predicting subsets of the same overall dataset on which

480“Models are estimated using the same data employed by BHM.” Richard G. Newell, Brian C. Prest,
and Steven E. Sexton, ‘The GDP-Temperature Relationship: Implications for Climate Change
Damages’, Journal of Environmental Economics and Management, 20 March 2021, 102445,
https://doi.org/10.1016/j.jeem.2021.102445.
481
This is pointed out by Steve Sexton, a co-author on Newell et al (2021), here.

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the model is based. In his response to Newell et al (2021) Marshall Burke argues that there
is a difference between inference and prediction:“But what if your goal is causal inference? i.e, in our case, in isolating variation intemperature from other correlated factors that might also affect GDP? It's not clear at
all that models that perform the best on a prediction task will also yield the rightcausal results. For instance, prices for hotel rooms tend to be high when occupancy
rates are high, but only a foolish hotel owner would raise prices to increase
occupancy (h/t Susan Athey who I stole this example from). A good predictive model
can get the causal story wrong.”
As Steve Sexton says in his response, since weather is exogenous, it is plausibly causal,
whereas hotel prices are obviously endogenous, so this example does not work.

Overall judgement on weather studies
My overall view on weather studies and in particular Burke et al (2015a) is as follows.
Firstly, I am more sceptical of the econometrics in these studies compared to others, forseveral reasons. There is more scope for researcher discretion when there is complex
econometrics and noisy data. My prior expectation is that due to reporting bias, researchers
will tend not to disclose the true extent of model uncertainty. Newell et al (2021) provides
some confirmation for this.
In addition, there is significant noise and error in the proxies of local temperature andprecipitation used in these studies, which makes the risk of false positives much higher.
Since the finding of Burke et al (2015) conflicts quite strongly with common sense and thewider impacts literature, I am more inclined to believe that the econometrics is mistaken thanthat that their estimate is accurate.
Secondly, panel studies miss some particularly important avenues of impact, including:
1. The transition costs of long-term changes in temperature and rainfall (because these
are importantly different to weather variation)
2. The costs of tipping points
3. The costs of longer-term impacts such as○ Sea level rise
○ Ocean acidification○ CO2 fertilisation
4. Non-market damages like illness and death
Thirdly, the weather studies try to establish a damage function only using statistics. But wealso need a plausible account of the causal mechanism for these damages. In Sub-SaharanAfrica, and South and Southeast Asia, the 95% confidence interval for damages of 4°C isaround 70-90% of GDP: a huge effect. Moreover, they find this result even though they do
not take account of potential tipping points or other avenues of impacts usually considered tobe catastrophic.

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This finding is strongly at odds with bottom-up studies which try to add up the costs of themost important climate impacts identified in the literature. For instance, Takakura et al (2019)find that the costs of SSP5-RCP8.5 in Asia and Africa are 5% to 15% of GDP. For the Burkeet al (2015a) finding to be true, the bottom-up studies must be missing a huge avenue of
impact which can be identified in historical weather data. This is implausible.
For these reasons, I do not put much weight on the very pessimistic estimate found in Burkeet al (2015a). In general, I put more weight on the bottom-up studies than the weather
studies.

10.6. Expert elicitation
One criticism of existing economic models of climate change is that they merely embody thejudgements of the modeller about the costs of climate change, and are therefore arbitrary.
One possible solution to this is to use the opinions of groups of experts for key model
inputs.482 Thus far, expert elicitation studies have asked experts to give their judgement of
the aggregate costs of climate change to GDP. Expert elicitation could in principle also be
used for a bottom-up approach by eliciting the views of experts on different sectoral impacts.

10.6.1. Methodological problems with expert elicitation
Expert elicitation studies have several problems. One overarching problem is that experts
tend to be poor at prediction. The vast majority of experts perform badly at predicting eventsa few years in the future, and almost no experts perform well for events more than five years
out. In Tetlock’s forecasting tournament, discussed in Expert Political Judgment, complex
models and simple algorithms that extrapolated the past to the future outperformed thebest-performing experts.483 Thus, it is not obvious that expert elicitation is superior to formal
models in this domain.

482“For an economist, relying on expert opinion might not seem very satisfying. Economists often build
models to avoid relying on subjective (expert or otherwise) opinions. But remember that the inputs to
IAMs (equations and parameter values) are already the result of expert opinion; in this case themodeler is the “expert.” This is especially true when it comes to climate change impacts, where theory
and data provide little guidance. Also, we would expect that that different experts will arrive at theiropinions in different ways. Some might base their opinions on one or more IAMs, others on theirstudies of climate change and its impact, and others might combine information from models withother insights. The methods experts use to arrive at their opinions is not a variable of interest; whatmatters is that the experts are selected based on their established expertise.” Robert S. Pindyck, ‘The
Social Cost of Carbon Revisited’, Journal of Environmental Economics and Management 94 (2019):
140–60.
483“Translating the predictions of the crude case-specific extrapolation algorithms, as well as thesophisticated time series forecasting equations, into subjective probability equivalents, we discoverthat, whereas the best human forecasters were hard-pressed to predict more than 20 percent of thetotal variability in outcomes (using the DI/VI “omniscience” index in the Technical Appendix), the crude
case-specific algorithms could predict 25 percent to 30 percent of the variance and the generalized
autoregressive distributed lag models explained on average 47 percent of the variance.” “Surveying
these scores across regions, time periods, and outcome variables, we find support for one of thestrongest debunking predictions: it is impossible to find any domain in which humans clearly
outperformed crude extrapolation algorithms, less still sophisticated statistical ones.” Tetlock, Expert
Political Judgment, pp. 53-54.

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Related to this, it is not clear how to select the relevant group of experts on a particular topic.
Forecasting performance seems like the obvious criterion, but few experts have been tested
at forecasting, and when tested, the performance of experts has been relatively poor.484
One possible solution to this is to weight expert forecasts according to their performance on
short-term calibration questions, which is the approach taken in one influential expert
elicitation study on sea level rise by Bamber et al (2019).485 Another possible solution is to
use teams of superforecasters and climate experts. The climate experts could provide
relevant information and the superforecasters could help with expert calibration.
A problem specific to expert elicitation studies of the aggregate economic costs of climatechange is that the effects of climate change are highly diverse and wide-ranging. The
contributors to damages include sea level rise, agricultural impacts, the effects oftemperature on labour supply and productivity, the risk of tipping points, the health costs ofheat stress, increased tropical disease, indirect effects such as conflict and so on. Probably
the best way to aggregate these effects is to elicit expert opinion on each impact, and then to
aggregate the results. In contrast, many studies in this subfield ask experts directly for theirview on aggregate economic impacts. In my view, this approach is likely to be less accurate
than comprehensive enumerative studies using up to date literature.

10.6.2. Findings of expert elicitation studies
The findings of expert elicitation studies on climate damages are summarised below:(hs p9)
Study

Damage estimate (% of GDP)

Nordhaus (1994)486

3°C: -2% to -4% (median, mean). A range of 0% to -20%.
6°C by 2090: -6% to -10%. Range of -1% to -62%

Schauer (1995)487

2.5°C: -3% to -5% (median, mean) with a variance of 71%.

Howard and Sylvan

3°C: -6% to -10% (median, mean). 10% to 20% chance of a loss of484“Figures 2.5 and 2.6 bolster another counterintuitive prediction of radical skepticism. Figure 2.5
shows that, collapsing across all judgments, experts on their home turf made neither better calibrated
nor more discriminating forecasts than did dilettante trespassers. And Figure 2.6 shows that, at eachlevel along the subjective probability scale from zero to 1.0, expert and dilettante calibration curves
were strikingly similar. People who devoted years of arduous study to a topic were as hardpressed ascolleagues casually dropping in from other fields to affix realistic probabilities to possible futures.”
Tetlock, Expert Political Judgment, p. 54.
485
Jonathan L. Bamber et al., ‘Ice Sheet Contributions to Future Sea-Level Rise from Structured
Expert Judgment’, Proceedings of the National Academy of Sciences 116, no. 23 (4 June 2019):
11195–200, https://doi.org/10.1073/pnas.1817205116.
486
William D. Nordhaus, ‘Expert Opinion on Climatic Change’, American Scientist 82, no. 1 (1994):
45–51
487
Michael J. Schauer, ‘Estimation of the Greenhouse Gas Externality with Uncertainty’,
Environmental and Resource Economics 5, no. 1 (1 January 1995): 71–82,
https://doi.org/10.1007/BF00691910.

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(2015 and 2020)488

25% loss of GDP indefinitely

Pindyck (2019)489

Assuming no change in climate policy, by 2066 -10% to -13%. By
2150, around -30%.

Howard and Sylvan(2021)490

For 1.2°C by 2025: 5% chance of an 18% loss by 2025.
3°C: -5% to -9% (median, mean).
5°C: -10% to -16%7°C: - 20% to -25%

Nordhaus (1994)
Nordhaus interviewed 19 experts on climate change (10 economists, four other social
scientists, and five natural scientists), each of whom had a working knowledge of economic
statistics.
This survey seems to be subject to numerous problems.491 Firstly, the survey is small andselection seemed to be arbitrary, as Nordhaus acknowledges.492 Secondly, some of the
respondents did not work on the question at hand.
“At the other extreme are the "other subdisciplines" of economics (those whose
principal concerns lie outside environmental economics); these eight respondents
see much less potential for the calamitous outcome— 0.4 percent, or about one-30thof the magnitude estimated by the natural scientists”493
Thirdly, the damage estimates of the natural scientists were 20-30 times higher than theeconomists, which is evidence of correlated bias in one direction or the other.
Finally, the study asked respondents directly about the aggregate costs of climate change. It
seems more promising to decompose this question into a set of smaller subquestions about
specific impacts.
Schauer (1995)

488

Peter Harrison Howard and Derek Sylvan, ‘Wisdom of the Experts: Using Survey Responses toAddress Positive and Normative Uncertainties in Climate-Economic Models’, Climatic Change 162,
no. 2 (1 September 2020): 213–32, https://doi.org/10.1007/s10584-020-02771-w; Peter H. Howard
and Derek Sylvan, ‘The Economic Climate: Establishing Expert Consensus on the Economics of
Climate Change’ (Institute for Policy Integrity, 2015).
489
Pindyck, ‘The Social Cost of Carbon Revisited’.
490
Peter Howard and Derek Sylvan, ‘Gauging Economic Consensus on Climate Change’ (New York
University School of Law Wilf Hall: Institute for Policy Integrity, 2021).
491
Steve Keen, ‘The Appallingly Bad Neoclassical Economics of Climate Change’, Globalizations 0,
no. 0 (1 September 2020): 8ff, https://doi.org/10.1080/14747731.2020.1807856.
492“In the end, 22 persons (including the author) were invited to participate, but three did not.
Although this selection procedure was arbitrary, it was designed to yield both diversity and informed
opinion. The respondents consisted of 10 economists, four other social scientists and five naturalscientists and engineers.” Nordhaus, ‘Expert Opinion on Climatic Change’.
493
Nordhaus, ‘Expert Opinion on Climatic Change’, 48.

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Schauer (1994) interviewed 14 climate experts. Again, this is a small study and the selectionof experts was not systematic and seems somewhat arbitrary, as Schauer acknowledges.494
This estimate of the social cost of carbon was produced by combining expert estimates ofthe value of different parameters. The experts were solicited to estimate parameter values
only on parameters on which they had expertise.495 However, the damage function itself was
not decomposed into distinct individual damages.
Howard and Sylvan (2015 and 2020)
The expert selection procedure for this study is superior to earlier smaller studies. Howard
and Sylvan reached out to all those who have published an article related to climate change
in a highly ranked, peer-reviewed economics or environmental economics journal since
1994. Of 1,103 experts contacted, 365 responded.
Like the other studies, this suffers from the problem that it does not decompose the damage
function into easier-to-estimate components.
Pindyck (2019)
Pindyck contacted 6,833 experts in economics and natural science, and received around
1,000 responses. The questions focused on emissions, damages and discount rate. The
study does not decompose estimates of the damage function.
Howard and Sylvan (2021)
Howard and Sylvan invited 2,169 PhD economists to participate in the study, of which 738
participated. These economists have all published an article related to climate change in aleading economics, environmental economics, or development economics journal, and theirareas of expertise cover a wide range of issues in climate economics.
The study does not decompose the damage function. Moreover, some of the responses
seem poorly calibrated. The respondents estimate a 1% chance of a 45% loss of global GDP
in 2025, and a 5% chance of an 18% loss due to climate change.496 For context, during theGreat Depression, US GDP fell by 25% and Japanese GDP fell by 50% after the end ofWorld War Two. Having read the impacts literature, I find it difficult to see how climatechange could cause such massive economic damages in the next three years.

494“The experts interviewed were not chosen by a formally established objective process designed toyield a balanced and comprehensive representation of all scientists. Correcting this shortcoming
would improve this work.” Michael J. Schauer, ‘Estimation of the Greenhouse Gas Externality with
Uncertainty’, Environmental and Resource Economics 5, no. 1 (1 January 1995): 71–82,
https://doi.org/10.1007/BF00691910.
495“Authoritative experts who have published in the field were chosen so that those interested could
readily obtain a sense for their dispositions. Since areas of expertise differed, the experts were notasked to estimate every parameter, but rather only those for which they have an expertise”
496“To address outlier estimates and avoid putting our fingers on the scale, we apply a 95th percent
confidence interval trimming methodology, implying a damage range of 0% to -18% in 2025. However,as the upper end of this range implies a permanent catastrophic event with a magnitude akin to theGreat Depression occurring in under five years, we also conducted 90th percentile trimming toanalyze the median estimate. As the median is relatively stable across various trimming ranges, we
focus our analysis primarily on the median estimate after 95th percentile trimming”

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10.6.3. Overall judgement on expert elicitation studies
My overall judgement on expert elicitation studies is as follows.
More recent expert elicitation studies seem to be methodologically superior to studies in the
1990s. However, all studies fall short of the state of the art in the emerging science ofprediction. In all of the studies discussed above, little attempt is made to improve on thecalibration or precision of forecasts, for example with calibration training or weighting
according to performance on resolved calibration questions. No incentives are provided for
accurate forecasts. None of the studies encourage deliberation between experts, and neither
do they encourage respondents to explain their reasoning. None of the studies make use of
professional forecasters who have proven track records in prediction.
The questions asked in expert elicitation studies seem unmanageably large, asking experts
to make aggregated judgements about all climate impacts without explaining their reasoning.
The advantage that these studies have compared to enumerative and statistical studies isthat as things stand in the literature, the estimates are designed to include potential tipping
points and catastrophic impacts. The expert elicitation studies tend to find that mean costs
are greater than median costs, implying that damages are heavy-tailed and strongly
influenced by low probability/high-impact events. However, given the other problems withexpert elicitation studies, it is hard to put much weight on their estimates of these impacts.
The bottom-up studies provide a more reliable guide than current expert elicitation studies.

10.7. Overall view on climate damages
My overall view on climate damages is as follows.
Firstly, there is little indication from any of the climate economics literature that climatechange will do anything close to destroying industrial civilisation or causing civilisational
collapse.
Secondly, I put most weight on bottom-up studies that incorporate a wide range of the mostimportant climate impacts, and use up-to-date scientific evidence. Both top-down andbottom-up studies exclude tipping points and indirect effects, but top-down weather studies
exclude several other important avenues of impact including:
1. The transition costs of long-term changes in temperature and rainfall
2. The costs of longer-term impacts such asa. Sea level riseb. Ocean acidificationc. CO2 fertilisation
3. Non-market damages like illness and death
In addition, the econometrics of some of the weather studies seems to be questionable.

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Thirdly, expert elicitation could in principle be useful, but existing studies are flawed. They
ask experts to estimate aggregate economic damages from climate change without
explaining their reasoning and without any attempt to improve calibration.
Fourthly, bottom-up studies tend to find that the costs of 4ºC are around equivalent to a
counterfactual reduction in GDP of around 5%. However, bottom-up studies are likely tounderstate the direct costs of climate change because they:
1. Don’t include tipping points
2. Aggregate at regional scales and so don’t account for intra-regional inequality
3. Don’t model growth effects
Still, even once we account for these effects, it is difficult to see how plausible levels ofwarming could come close to directly causing civilisational catastrophe.
Fifthly, bottom-up studies also do not capture the potential indirect costs of climate change,
which I discuss in the next three chapters.
Finally, one message that emerges from all of the climate economics literature is that people
living in poor agrarian countries at low latitudes will be especially badly hit by climatechange, even though they have done the least to contribute to the problem. We have strong
humanitarian reasons to reduce emissions. We also have strong humanitarian reasons toencourage economic growth in these countries, which will reduce poverty and will makethem better able to adapt to climate impacts.

10.8. Climate economics and the long-term
Climate economics is useful because it allows us to arrive at ballpark estimates of theaggregate direct costs of climate change. This is useful for several reasons.

10.8.1. Risk of direct extinction or collapse
Climate economics can tell us how plausible it is that climate change will directly cause
extinction or the collapse of industrial civilisation. Climate-economy models try to quantify
and aggregate the costs of climate change, and they do not provide much indication that
direct extinction or civilisational collapse is on the cards.

10.8.2. Size of indirect risks
Although climate-economy models do not directly try to estimate the direct costs of climatechange, they do shed light on the scale of the indirect risks posed by climate change. As arule, the greater the direct effects something has, the greater we should expect its indirect
effects to be. I discuss this in more detail in Chapter 12.

10.8.3. Long-run stagnation
If civilisation stagnates at roughly its current level of technology, this would constitute a hugeloss of potential future value. If climate damages are high, this would increase the risk oflong-term stagnation. However, semi-endogenous growth models suggest that declining342

fertility or progress on AI will be the most important determinant of long-run economic
growth. Climate change is weakly levered on these things.
Another argument is that, conditional on technological stagnation, climate change makes the
future worse because it slows down economic growth for thousands of years. How important
this is depends on whether climate change mainly has a levels effect or a growth effect,
which remains deeply uncertain. Much of the effect of climate change will have diminishedafter 10,000 years, so this is small relative to the expected lifetime of humanity.

10.8.4. The relative geopolitical power of different countries
The relative growth rates of different countries or regions could have important implications
for the long-term. Firstly, one potential contributor to the risk of great power conflict is theprospect that a rising power will surpass the current hegemon. I discuss how climate change
bears on this possibility in Chapter 12.
Secondly, whichever country becomes dominant this century may be able to lock-in theirvalues in perpetuity. The main determinant of this will be progress in AI, and climate change
is a weak lever on AI progress. Climate change also seems a weak determinant of future
growth in the two main contender hegemons for this century, the US and China. I discuss
this in more detail in the Chapter on conflict.

10.8.5. What is the sign of the effect of economic growth on thelong-term?
In my view, the sign of the effect of increased economic growth on the long-term is unclear.
Economic growth may shorten the time of perils, but it is also a driver of technological risks
that threaten the destruction of civilisation and of potential value lock-in by a global
hegemon. It is not clear how these factors trade-off against one another.
There is a difference here between progress at the technological frontier and catch-up
growth. Progress at the technological frontier is more dangerous because it has greater
scope to uncover dangerous new technologies, whereas catch-up growth has less scope todo this. Still, I think catch-up growth does have some effect on emerging technological risk.
For instance, as India grows, it will likely spend more on AI development, on biotechnology
research and on nuclear weapons.

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11. Displacement and migration
The possibility of large numbers of climate refugees or climate migrants is often raised inclimate worst-case scenarios. In particular, climate refugees and migrants are sometimes
raised as potential stressors of war.

11.1. Definitions and trends
Migration is voluntary whereas displacement is involuntary. The line between migration anddisplacement is somewhat fuzzy.

11.1.1. Displacement
Displacement can be driven by conflict and violence, or by weather-related and geophysicaldisasters (like earthquakes and volcanoes). Conflict includes persecution, violence, humanrights violation or events seriously disturbing public order.
The chart below shows the flow of new displaced people between 2011 and 2020, withdisplacements from conflict and violence in orange, and weather-related and geophysicaldisplacements in blue. The vast majority of disasters are weather-related. As this shows,weather-related displacements have been around 23 million per year over the last decade.

Source: IDMC, Global Report on Internal Displacement 2021

The chart below shows the breakdown in weather-related displacements by country and typeof hazard. Storms and floods account for the vast majority of displacements, with extreme
temperatures currently so far responsible for a small minority of displacements. The vast
majority of weather-related displacement occurs in Asia. Sub-Saharan Africa, Latin America
account for a smaller minority of displacements.

344

Source: IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment
Report, 2022.

I am not aware of longer-term data on weather-related displacements. We do however havedata on natural disasters over the 20th Century. Floods and droughts accounted for mostnatural disaster deaths in the 20th Century, but the absolute number of flood and drought
deaths has declined dramatically since the 1920s. Today, earthquakes account for the
majority of deaths from natural disasters.

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The chart at the start of this Chapter shows the flow of new displacements from conflict orviolence. The stock of forcibly displaced people from 2010-2020 is shown below:

As this shows, most forcibly displaced people remain within their national borders.

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11.1.2. Migration
International migrants
The chart below shows trends in the stock of international immigrants - the number of people
in a given country who were born in another country.

This suggests that between 2010 and 2020, the flow of new international migrants wasaround 6 million per year.
In 1960, around 2% of the world population was born in another country.497 This has now
increased to 3.66%

497“However, it is clear that international migration stocks have grown over the last 50 years, both inreal numbers and as a percentage of the world’s population (from 2% in 1960 to 3.1% in 2010).”
Foresight p32.

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Internal migrants
There were an estimated 740 million internal migrants in 2009.498 I am not aware of any
more recent estimates of internal migration, presumably because measurement of internalmigration is much harder than international migration.
I am also not aware of any estimates of the annual flow of internal migrants. Since the stockof internal migrants is roughly threefold higher than international migrants, we can roughly
guess that the flow of internal migrants is around 18 million per year.

Migration to coastal regions
According to the Foresight report for the UK government, between 2000 and 2010, on net,
6.5 million people migrated to coastal regions in Asia per year. This is largely because of
superior economic opportunities in coastal regions.

498“The great majority of people do not migrate across borders; much larger numbers migrate withincountries (an estimated 740 million internal migrants in 2009).2” International Organization for
Migration, ‘World Migration Report 2020’, p.19.

348

Source: Foresight, ‘Migration and Global Environmental Change Future Challenges andOpportunities’ (Government Office for Science, 2011).

11.1.3. The character of migration and displacement
●
●
●

●
●

Environmentally induced population movements are usually internal, temporary andshort term.499
Climate-related migration originates most often in rural areas in low- andmiddle-income countries, and most migrants stay within their own country.500
It is necessary to differentiate between migration caused by slow-onset events, suchas droughts and land degradation, and those caused by fast-onset events, such as
floods, storms or fires. While the former are usually voluntary and often economicallymotivated, the latter are involuntary and tend to be short term.501
The main motivation for migration is usually economic.502
Migration is expensive, so within countries, richer groups are more likely to migrate
than poorer people.503

11.2. How will climate change affect displacement?
The processes by which climate change will affect migration and displacement are very
different. Since migration is voluntary, the main mechanism by which climate change might
499

Brzoska and Fröhlich, ‘Climate Change, Migration and Violent Conflict’.
IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment Report,
2022, Ch. 7, sec. 7.2.6.
501
Brzoska and Fröhlich, ‘Climate Change, Migration and Violent Conflict’.
502
Foresight, ‘Migration and Global Environmental Change Future Challenges and Opportunities’(Government Office for Science, 2011), Ch. 2.
503
Foresight, ‘Migration and Global Environmental Change Future Challenges and Opportunities’(Government Office for Science, 2011), Ch. 2.
500

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have an effect is by affecting relative economic opportunities in different places.
Displacement on the other hand would be caused predominantly by storms and floods.
For this reason, it is much harder to estimate the number of climate change-related migrants
than it is to estimate the number of people displaced by climate, though estimating either isvery difficult. To estimate the number of climate migrants, we would need to build a model
which accurately captures how climate change will affect the economy in all regions. In
contrast, the populations of people affected by storms and floods are much narrower (mainlyin Asia).

11.2.1. Coastal displacements from storms and floods
In Chapter 7, I discussed the modelling study of coastal displacement by Lincke and Hinkel(2021), which found that 1 metre of sea level rise would increase coastal displacements byaround 300,000 people per year relative to 30cm of sea level rise. Displacement will mostly
occur in Asia. Their estimates account for the possibility of adaptation by affected countries.
If this estimate is correct, then migration to coastal regions is likely to outweigh displacementfrom coastal regions. Migration to coastal regions in Asia was about 6.5 million per year
between 2000 and 2010, whereas coastal displacement would be in the hundreds ofthousands.

11.2.2. River flood displacements
Man Kam et al (2021) model the risk of involuntary displacement due to river flooding on
different warming scenarios and population growth. However, they do not account foradaptation by the affected countries,504 which is not realistic. So, their estimate can be seen
as a very high upper bound; it is not clear what figure is more accurate once we account foradaptation. Since countries in Asia are experiencing relatively fast economic growth, weshould expect them to have greater adaptive capacity in the future.
They find that, keeping population constant, for each degree of warming, the global risks of
involuntary displacement due to river floods increase by 50%. So, 4ºC relative topre-industrial times would increase displacements from river floods by 150%. Around 10
million people are displaced by floods each year, but I am not sure how these are divided
between river flooding and coastal flooding; this data is not available in the latest IDMC
report. Floods overall accounted for roughly half of weather-related displacements in 2020(p11). The IDMC definition of floods includes both coastal and riverine floods.505 If weassume that riverine flood displacements account for half of total flood displacements, thenaround 5 million people are displaced by riverine floods each year. The model of Man Kam
et al (2021) implies that 2ºC of warming would increase flood displacements by around 2.5
million people per year, while 4ºC of warming would increase riverine displacements by 7.5
million per year, without adaptation.
504“On the societal impact side, the population projections do not consider future adaptation
measures.” Pui Man Kam et al., ‘Global Warming and Population Change Both Heighten Future Risk
of Human Displacement Due to River Floods’, Environmental Research Letters 16, no. 4 (March
2021): 8, https://doi.org/10.1088/1748-9326/abd26c.
505
See the IDMC codebook.

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11.2.3. Overall displacements from floods and storms
Overall, it is difficult to know how much climate change will increase displacement in the 21stCentury. The Man Tam et al (2021) riverine flood estimates are far higher than the Lincke
and Hinkel (2021) coastal flooding estimates, but the former do not account for adaptation,
which means that the estimate is likely far too high, though it is difficult to know by howmuch.
In the absence of better evidence, I would guess that the increase in displacements fromfloods and storms would be in the low millions per year for 4ºC of warming.

11.3. How will climate change affect migration?
There are no reliable estimates of how climate change has affected migration at the global
level so far.506 According to the IPCC, research so far suggests that weather has mixed
effects on migration patterns, and that the effect is context-specific.507
Scholars are generally hesitant to make quantitative projections of migration anddisplacement because forecasting such complex processes is very hard. Nonetheless, there
are some quantitative estimates in the literature.

Myers estimates
In several places, Myers (1995) and (2002) claimed that climate change would cause massmigration of hundreds of millions of people. These estimates were used in the Stern Review.
These estimates are not taken seriously by the scholarly community.508 Myers (2002) makes
506“Reliable global estimates of voluntary climate-related migration within and between countries are
not available due to a general absence of concerted efforts to date to collect data of this specificnature, with existing national and global datasets often lacking information on migration causation or
motivation.” IPCC, Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment
Report, 2022, Ch. 7, sec. 7.2.6.1.
507“A general theme across studies from all regions is that climate-related migration outcomes arediverse (high confidence) and may be manifest as decreases or increases in migration flows, and leadto changes in the timing or duration of migration, and to changes in migration source locations anddestinations. Multi country studies of climatic impacts on migration patterns in Africa have found that
migration exhibits weak, inconsistent associations with variations in temperatures and precipitation,and that migration responses differ significantly between countries, and between rural and urban
areas (Gray and Wise, 2016);(Mueller et al., 2020). Multidirectional findings such as these are also
common in single-country studies from multiple regions (A.Call et al., 2017);(Nawrotzki et al.,2017);(Cattaneo et al., 2019);(Kaczan and Orgill-Meyer, 2020). The diversity of potential migrationand displacement outcomes reflects (1) the variable nature of climate hazards in terms of their rate of
onset, intensity, duration, spatial extent, and severity of damage caused to housing, infrastructure,and livelihoods; and (2) the wide range of social, economic, cultural, political and other non-climatic
factors that influence exposure, vulnerability, adaptation options and the contexts in which migration
decisions are made (Neumann and Hermans, 2015);(McLeman, 2017);(Barnett and McMichael,2018);(Cattaneo et al., 2019);(Hoffmann et al., 2020) (high confidence)” IPCC, Climate Change 2022:
Impacts, Adaptation and Vulnerability, Sixth Assessment Report, 2022, Ch. 7, sec. 7.2.6.
508“Overall, Myers’ estimate of the number of ‘environmental refugees’ does not rely on any specificmethodology: for each region of the world, the number of internally displaced people is considered.
On the basis of these figures, Myers makes an estimate of the proportion that could have been
displaced because of environmental disruptions. This estimate is based on reports and observationsof environmental degradation in the considered region, but no attention is given to an examination of
the linkages between environmental change and migration behaviour. In an essentialist fashion,
Myers assumes that all people displaced in an area affected by environmental changes have been351

various predictions about drivers of environmental displacement, many of which have turned
out to be false, and ran against historical trends.509

Other estimates
The IPCC’s Sixth Assessment Report cites two other sources that try to quantify future
migration due to climate change.
Rigaud et al (2018) World Bank report
The first is the 2018 World Bank report Groundswell by Rigaud et al.510 Their model
evaluates how climate change will affect water scarcity, crop yields, and sea level rise, andmodels how this in turn will affect economic push and pull factors in Sub-Saharan Africa,
South Asia and Latin America. These regions are among the wors-taffected by climatechange because they are at low latitude and have a relatively low level of socioeconomic
development.
The World Bank estimates that there will be 30 million to 150 million extra internal migrants
by 2050, depending on the SSP and RCP.

The scenarios are:displaced solely because of these changes. Another interesting point to consider is that Myers rulesout the possibility that some could have been displaced outside of their country – internationalmigration is not considered in his estimate.” François Gemenne, ‘Why the Numbers Don’t Add up: A
Review of Estimates and Predictions of People Displaced by Environmental Changes’, Global
Environmental Change, Migration and Global Environmental Change – Review of Drivers of Migration,
21 (1 December 2011): S41–49, https://doi.org/10.1016/j.gloenvcha.2011.09.005.
509“The total number of malnourished people will continue to grow, with at least 100 million destitutes
obliged to live for the most part off imported food.” Norman Myers, ‘Environmental Refugees: A
Growing Phenomenon of the 21st Century’, Philosophical Transactions of the Royal Society of
London. Series B: Biological Sciences 357, no. 1420 (29 April 2002): 609–13,
https://doi.org/10.1098/rstb.2001.0953.https://doi.org/10.1098/rstb.2001.0953. As discussed by Our
World in Data, undernourishment had in fact steadily declined from 1970 to 2000 and has continued
to do so.
510
Kanta Kumari Rigaud et al., ‘Groundswell’, 2018.

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●
●
●

Pessimistic: SSP4, a scenario with high regional inequality, and RCP8.5, which
implies about 2.4°C of warming by 2050
Inclusive development: SSP2, the ‘trends continue’ pathway’, and RCP8.5.
Climate-friendly: SSP4 and RCP2.6, which implies 1.7°C of warming by 2050.

However, they do not consider technological improvements in crop yields, which look set tooutpace the damage of climate change.511 Since this is one of only three factors that drives
migration decisions in their model, I do not put much weight on this study.
The stock of internal climate migrants increases by around 70 million in these regions for
2.4ºC relative to 1.7ºC of warming between 2020 and 2050. This implies that internalmigration increases by around 2.3 million. Given the rough estimate of a flow of 18 million
internal migrants per year, this implies that internal migration would increase by around 12%.
However, the study also excludes much of the world’s population. Overall, the finding could
be significantly biased in either direction.
Hoffmann et al (2020)
The IPCC’s Sixth Assessment also cites Hoffmann et al (2020) which carried out ameta-analysis of studies exploring the effect of climate change on migration at thecountry-level. As the chart below shows, studies have produced mixed and uncertain resultson the sign of the effect of climate change on migration. The majority of studies find that themean effect of climate change on internal migration is positive, while the findings forinternational migration are more equivocal.

511

Alex de Sherbinin, personal correspondence, 14 Jan 2020.

353

Source: Roman Hoffmann et al., ‘A Meta-Analysis of Country-Level Studies on Environmental Change
and Migration’, Nature Climate Change 10, no. 10 (October 2020): 904–12,
https://doi.org/10.1038/s41558-020-0898-6.

The authors produce a funnel plot to test for publication bias, and find no evidence of
publication bias.512
Hoffmann et al construct different regression models which control for different variables. I
discussed the potential issues with omitted variable bias and overcontrolling in Chapter 10.
They find that for each standard deviation change in a climate variable, there is 0.021
standard deviation increase in migration.513
512

Roman Hoffmann et al., ‘A Meta-Analysis of Country-Level Studies on Environmental Change andMigration’, Nature Climate Change 10, no. 10 (October 2020): Si, sec. H,
https://doi.org/10.1038/s41558-020-0898-6.
513“On average, a one standard deviation change in the environmental conditions leads to anincrease in migration by 0.021 standard deviations (95%confidence intervals: 0.0176; 0.0235, random
effects model, Paule-Mandel estimator, heterogeneity measures τ2=0.002, I²=70.0%, H=1.83)48”354

Hoffman et al also argue that there are various contextual factors that influence migration,
including:●●

They confirm other migration research which finds that there is a U-shaped
relationship between socioeconomic development and migration. Environmental
change is smallest among low-income countries, which may be because financial
constraints prevent the poorest people from migrating. These financial constraints
may be compounded by environmental shocks. Migration rates are highest among
middle-income countries.
The extent to which a country depends on agriculture is a strong predictor of theeffect on migration.

They use one of their regression models to project the effects of climate change on migrationin different regions.

Hoffmann et al (2020) do not attempt to calculate the overall effect that climate change will
have on migration.

Summary of migration estimates
While at first glance it seems plausible that climate change will increase migration anddisplacement, the overall net effect we should expect is in fact not clear. There are several
reasons to think that climate change could reduce migration. For example, by undermining
rural livelihoods, it could make costly migration harder for those affected by climate change.
At present, there is large net migration to low lying coastal megacities in Asia. If those cities
become increasingly affected by climate change, then that may discourage potential
migrants.
It is very hard to put a number on overall migration due to climate change.

Roman Hoffmann et al., ‘A Meta-Analysis of Country-Level Studies on Environmental Change andMigration’, Nature Climate Change 10, no. 10 (October 2020): 904–12,
https://doi.org/10.1038/s41558-020-0898-6.

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11.4. Summary of climate change, migration and displacement
My conclusions on migration and displacement are as follows.
Climate-related displacement
The evidence we have suggests that with climate change of 4ºC, migration to coastal
regions is likely to outweigh displacement from coastal regions. Migration to coastal regionsin Asia was about 6.5 million per year between 2000 and 2010, whereas coastal
displacement would be in the hundreds of thousands. Migration to coastal regions is likely toincrease as population increases and living standards improve.
Estimates of displacements from river flooding are less reliable, but my guess would be thatthey would be in the low millions for 4ºC of warming, predominantly people in Asia. This
would increase overall displacements from conflict and weather-related disasters by 5-10%.
Climate-related migration
The literature suggests that climate change will have mixed effects on migration, thoughmost literature suggests that it will increase migration, especially in low- and middle-income
countries. It is very difficult to forecast the amount of future migration caused by climatechange, and existing estimates are flawed in important ways. Estimates that do exist
suggest that climate change would increase the flow of internal migrants by around 10%.
The character of future migration and displacement
Environmentally induced population movements in the future are likely to be similar incharacter to today: they will usually be internal, temporary and short term. There will likely bea U-shaped relationship between economic development and migration, with the poorest
least able to migrate, and middle-income people better able to migrate. Migration caused bysudden onset weather shocks poses fundamentally different challenges to migration causedby slow onset environmental change.
The most important potential effect that displacement and migration might have is increasing
political instability and conflict, which I discuss in the next Chapter.

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12. Conflict
12.1. Context and trends
12.1.1. Definitions
When thinking about conflict risk, it is important to be careful with different definitions ofconflict.514●●

Interpersonal conflict - Some studies refer to violent acts that occur between
individuals usually described as crimes, such as murder, assault, rape, and robbery.
Intergroup conflict encompasses○ Interstate conflict;○ Civil war, defined by >1,000 battle-related deaths, violence against the
government○ Civil conflict, defined by at least 25 battle-related deaths;○ Intercommunal violence (conflict between competing groups within a state)○ Low-intensity conflict or social conflict, e.g., protests and riots○ Political repression.

12.1.2. Trends
Notwithstanding the recent invasion of Ukraine, conflict of all kinds has declined a lot since
the Second World War. Interstate conflict is now very rare. We are in a period known as‘The Long Peace’.

514

Vally Koubi, ‘Climate Change and Conflict’, Annual Review of Political Science 22, no. 1 (11 May
2019): 343–60, https://doi.org/10.1146/annurev-polisci-050317-070830.

357

358

Civil conflicts are now the main type of conflict.

359

12.1.3. Drivers of the Long Peace
There are several theories purporting to explain the ‘long peace’ since 1945, including:
●
●

●
●

Nuclear deterrence. This is one explanation for the ‘long peace’ we have observed
since 1945.515
Growth, trade and globalisation. The share of exported goods as a fraction ofworld GDP has increased substantially since 1945, and some argue that this hashelped to reduce the risk of war.516
Democracy. It is widely accepted in the field of IR that democracies rarely go to
war.517 However, democracies do still go to war with non-democracies.
International organisations. Since World War II, membership of international
institutions such as the UN, WTO and EU has increased. Some evidence suggeststhat countries which share membership in intergovernmental organisations are lesslikely to go to war with each other, though the strength of this effect is smaller thanthe effect of trade or democracy.518

Over the last 200 years, democracy has spread across the globe, as shown on the chart
below:

515“most [IR researchers] would agree that possession of nuclear weapons vastly reduces the
chances of war between two possessors.” Greg Cashman, What Causes War?: An Introduction to
Theories of International Conflict, Second edition (Lanham, Maryland: Rowman & Littlefield
Publishers, 2013), p. 362
516“increasing economic interdependence from the 10th to the 90th percentile reduces the risk of afatal dispute by 32 percent.” Bruce Russett and John Oneal, ‘Causes of Peace: Democracy,
Interdependence, and International Organizations, 1885–1992’, International Studies Quarterly
Vol. 47, No. 3 (Sep., 2003), p. 388
517"[democratic peace theory has] now gained overwhelming support among Western scholars” (p.
258) Greg Cashman, What Causes War?: An Introduction to Theories of International Conflict,
Second edition (Lanham, Maryland: Rowman & Littlefield Publishers, 2013), p. 258.
518
“mutual memberships in intergovernmental organizations (IGOs) make an important independent
contribution, above and beyond those of trade and democracy, to reducing militarized disputes
between pairs of states (though the pacific benefits of IGO membership are smaller than the other two
Kantian variables)” Cited in What Causes War?, p. 270

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12.2. Climate change and pre-industrial strife
There is research suggesting that pre-industrial cold periods are correlated with war,
population decline, dynastic transition and general civilisational strife. The Little Ice Age(1400 to 1900) followed the Mediaeval Warm Period (950 to 1250). In the Little Ice Age,temperatures were often 0.2-0.3°C cooler than they were in the Mediaeval Warm Period.
This was correlated with various instances of food price increase, war, epidemics, andpopulation decline.
This chart from Zhang et al (2011) shows this correlation and packs in a lot of information,
though it is hard to interpret.519

519

David D. Zhang et al., ‘Climate Change and Large-Scale Human Population Collapses in the
Pre-Industrial Era’, Global Ecology and Biogeography 20, no. 4 (2011): 520–31,
https://doi.org/10.1111/j.1466-8238.2010.00625.x.

361

Source: David D. Zhang et al., ‘Climate Change and Large-Scale Human Population Collapses in the
Pre-Industrial Era’, Global Ecology and Biogeography 20, no. 4 (2011): 520–31,
https://doi.org/10.1111/j.1466-8238.2010.00625.x.

The red and blue rectangles denote warm and cool periods respectively. The grey rectangles
are the size of the population decline: most of them happen in cold periods. The warm period
is correlated with variable population effects which are good for certain areas (Europe) but
bad for others (Middle East and North Africa). Conversely, the colder period starting around1550 is bad for the Middle East and North Africa.
This chart from a different 2011 paper by Zhang et al shows the correlation between coolingand civilisational strife in the Northern Hemisphere.520
520

David D. Zhang et al., ‘The Causality Analysis of Climate Change and Large-Scale Human Crisis’,
Proceedings of the National Academy of Sciences 108, no. 42 (18 October 2011): 17296–301,
https://doi.org/10.1073/pnas.1104268108.

362

Source: David D. Zhang et al., ‘The Causality Analysis of Climate Change and Large-Scale Human
Crisis’, Proceedings of the National Academy of Sciences 108, no. 42 (18 October 2011): 17296–301,
https://doi.org/10.1073/pnas.1104268108.

Tol and Wagner (2010) find that the correlation between climate and wars declines up until
the Industrial Revolution:“Agriculture became progressively less important over the period, because of
economic development, and agriculture became less dependent on the weather,because of improved cultivation methods and better fertilizers.”521

521

Richard S. J. Tol and Sebastian Wagner, ‘Climate Change and Violent Conflict in Europe over theLast Millennium’, Climatic Change 99, no. 1 (1 March 2010): 65–79,
https://doi.org/10.1007/s10584-009-9659-2.

363

This seems plausible. For regions experiencing economic growth, we should expect
agriculture to become less dependent on the weather in the future, and for increases in foodprices to be less damaging because people are better off.
Moreover, the main effect found in the literature is that in most regions, cooling not warming
causes civilisational strife, so this doesn’t provide much evidence that warming will cause
civilisational strife.
It is prima facie reasonable to be concerned about the methodological soundness of theaforementioned research due to:
●
●
●
●
●

Uncertain climate proxy data
Uncertain population and other event data
Researcher degrees of freedom in choosing time periods and classifications ofeventsReporting bias
Innumerable confounders

12.3. The contemporary relationship between climate change
and conflict
12.3.1. The correlation
There is a clear correlation between climate indicators and conflict. In general, countries inthe tropics and those suffering water scarcity are more likely to experience civil conflict.

Source: Vally Koubi, ‘Climate Change and Conflict’, Annual Review of Political Science 22, no. 1 (11May 2019): 343–60, https://doi.org/10.1146/annurev-polisci-050317-070830.

364

Obviously, there are lots of confounders of this relationship. These issues are discussed in
more detail in the Chapter on economic costs and institutions.

12.3.2. Overview of the literature
The IPCC’s view on the connection between climate change and conflict in the 2022 Sixth
Assessment Report can be summarised as follows:522
1. There is as yet little evidence linking climate change to interstate conflict.523
Consequently, much of the literature focuses on civil conflict.
2. Climate change is widely agreed to be a risk factor for civil conflict and acts throughdiverse causal mechanisms.524 Changes in agricultural prices are one of the mostimportant mechanisms.525
3. Some studies find that water availability has affected civil conflict, though there isdisagreement about this.526
522This is discussed in sec 7.2.3 and 7.3.3. of IPCC, Climate Change 2022: Impacts, Adaptation and
Vulnerability, Sixth Assessment Report, 2022..
523“Consistent with AR5 findings, there continues to be little observed evidence that climatic variability
or 37 change cause violent inter-state conflict.” IPCC, Climate Change 2022: Impacts, sec. 7.2.3
524“In intra-state settings, climate change has been associated both with the onset of conflict,particularly in the form of civil unrest or riots in urban settings (high agreement, medium evidence).
{Ide, 2020, Multi-method evidence for when and how climate-related disasters contribute to armed
conflict risk} as well as with changes in the duration and severity of existing conflicts (Koubi, 2019)
Climate change is conceptualised as one of many factors that interact to raise tensions (Boas andRothe, 2016) through diverse causal mechanisms (Mach et al., 2019);(Ide et al., 2020) and as part ofthe peace-vulnerability and development nexus (Barnett 2019)(Abrahams, 2020);(Buhaug and von
Uexkull, 2021)”;“Potential pathways linking climate and conflict include direct impacts on physiology from heat, or
resource scarcity; indirect impacts of climatic variability on economic output, agricultural incomes,
higher food prices, increasing migration flows; and the unintended effects of climate mitigation andadaptation policies (Koubi, 2019);(Busby, 2018);(Sawas et al., 2018).Relative deprivation, politicalexclusion and ethnic fractionalisation and ethnic grievances (Schleussner et al., 2016);(Theisen,2017) are other key variables. Research shows that factors such as land tenure and competing land
uses interacting with market-driven pressures and existing ethnic divisions produce conflict over land
resources, rather than a 7scarcity of natural resources caused by climate impacts such as drought.
(high agreement, medium evidence) (Theisen, 2017); (Balestri and Maggioni, 2017);(Kuusaana and
Bukari, 2015);[also Box 8.3]” IPCC, Climate Change 2022: Impacts, sec. 7.2.3.
525“increases in food price due to reduced agricultural production and global food price shocks areassociated with conflict risk and represent a key pathway linking climate variability and conflict(medium confidence) Rises in food prices are associated with civil unrest in urban areas among
populations unable to afford or produce their own food, and in rural populations due to changes inavailability of agricultural employment with shifting commodity prices (Martin-Shields and Stojetz,
2019). Under such conditions, locally specific grievances, hunger, and social inequalities can initiate
or exacerbate conflicts. Food price volatility in general is not associated with violence, but sudden
food price hikes have been linked to civil unrest in some circumstances (Bellemare, 2015);(McGuirk
and Burke, 2020);(Winne and Peersman, 2019). In urban settings in Kenya, Koren et al (2021) foundan association between food and water insecurity that is mutually reinforcing and associated withsocial unrest (although insecurity in either one on its own was not). Analysing global food riots
2007-2008, and 2011, Heslin (2021) stresses the role of local politics and pre existing grievances indetermining whether people mobilise around food insecurity [also Chapter 5].” IPCC, Climate Change
2022: Impacts, sec. 7.2.3.
526“Variation in availability of water has been associated with international political tension andintra-national collective violence (low agreement, medium evidence). Drought conditions have been
associated with violence due to impacts on income from agriculture and water and food security, withstudies focusing predominantly on sub-Saharan Africa and the Middle East (Ide and Frohlich,2015);(De Juan, 2015);(Von Uexkull et al., 2016);(Waha et al., 2017);(Abbott et al., 2017);(D'Odorico365

4. There is high agreement that climate change has been a small driver of civil conflict
relative to other factors, such as socioeconomic development and inter-groupinequality.527 Conflict risk is highly mediated by socioeconomic development.528
5. There is disagreement about the size of the effect future climate change will have on
conflict.529
Most other reviews of the literature find a lack of scientific consensus and generally
conflicting results.530 Sakaguchi et al provide a useful breakdown of neutral, positive andnegative findings for the effect of climate change on violent conflict, though, as I discuss
below, they do not use the same methodological restrictions as other reviews andmeta-analyses:

et al., 2018). A small set of published studies has argued inconclusively over the role of drought incausing the Syrian civil war (Gleick, 2014);(Kelley et al., 2015);(Selby et al., 2017) [also 16.2.3.9]. In
general, research stresses the underlying economic, social and political drivers of conflict. For
example, research on conflict in the Lake Chad region has demonstrated that the lake drying was only
one of many factors including lack of development and infrastructure (Okpara et al., 2016);(Nagarajan
et al., 2018);(Tayimlong, 2020). Fewer studies examine the relationship between flooding (excesswater) and violence and often rely on migration as the causal factor (see below). However, somestudies have shown an association between flooding and political unrest (Ide et al., 2020).” IPCC,
Climate Change 2022: Impacts, sec. 7.2.3.
527“Climatic conditions have affected armed conflict within countries, but their influence has beensmall compared to socio-economic, political and cultural factors (Mach et al., 2019) (high agreement,
medium evidence).” IPCC, Climate Change 2022: Impacts, sec. 7.2.3.
528“Future violent conflict risk is highly mediated by socio-economic development trajectories (highconfidence). Development trajectories that prioritise economic growth, political rights, andsustainability are associated with lower conflict risk (medium confidence, low evidence).” IPCC,
Climate Change 2022: Impacts, sec. 7.2.3.
529“Increases in conflict-related deaths with climate change have been estimated but results areinconclusive (high agreement, medium evidence). Some studies attempted to attribute observed
conflict outbreaks to changes in the physical environment and quantify future conflict risk associatedwith climate change (von Uexkull and Buhaug, 2021);(Theisen, 2017). Burke et al (2015b) concluded
that with each one standard deviation increase in temperature, interpersonal conflict increased by
2.4% and intergroup conflict by 11.3%. However, this kind of approach has been criticised for its
statistical methods and underrepresenting the known role that socioeconomic conditions and conflicthistory play in determining the prevalence of violence (Buhaug et al., 2014);(van Weezel, 2019);(Abelet al., 2019).” IPCC, Climate Change 2022: Impacts, sec. 7.2.3.
530
Koubi, ‘Climate Change and Conflict’; Kendra Sakaguchi, Anil Varughese, and Graeme Auld,‘Climate Wars? A Systematic Review of Empirical Analyses on the Links between Climate Change
and Violent Conflict’, International Studies Review 19, no. 4 (1 December 2017): 622–45,
https://doi.org/10.1093/isr/vix022.

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531

Source: Sakaguchi, Varughese, and Auld, ‘Climate Wars?’

They find that a majority of the included studies (62.3%) found small rises in the risk ofviolent conflict associated with climate change variables. Note that we should expect
reporting bias to overinflate the number of positive findings, a point I discuss in more detailbelow.
However, Hsiang, Burke and others argue that among studies that in their view are notmethodologically flawed, there is actually substantial agreement in the literature.532
This methodological disagreement drives some of the most heated disagreements in theliterature, which are between on the one hand, what I will call the ‘Berkeley economists’ Burke,533 Hsiang and others - and on the other hand scholars at the Peace ResearchInstitute at Oslo - Buhaug and others. This is in part driven by a disagreement about theusefulness of cross-sectional regressions and controls for confounders, which I discuss
below.

12.3.4. Effect sizes found in the literature
Many of the studies in the literature that find relatively strong consistent effects of climatechange are primary research and meta-analyses by the Berkeley economists. Findings for
two key studies are shown below:

Study

Change in relative risk ofintergroup conflict per 1-SD
Study type increase in temperature

Per 1SDchange inprecipitation

Interperson
al violenceper 1SDchange in531

Sakaguchi, Varughese, and Auld, ‘Climate Wars?’“We conclude that there is substantially more agreement and generality in the findings of thisburgeoning literature than has been recognized previously” Solomon M. Hsiang, Marshall Burke, and
Edward Miguel, ‘Quantifying the Influence of Climate on Human Conflict’, Science 341, no. 6151 (13September 2013), https://doi.org/10.1126/science.1235367.
533
Burke was trained at Berkeley but is a professor at Stanford.
532

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climatevariable
Burke et al (2009), ‘Warming
increases the risk of civil war in
Africa’ PNAS

Panel study

27% NA

Burke et al (2015), ‘Climate andConflict’, Annual review of
economics

Meta-analy
sis

11%

NA

3.50%

2.4%

It is not straightforward to translate a standard deviation change into a temperature change.
As Burke et al (2015b) note:“Most studies report changes in climate variables in physical units, such as degrees
of temperature or millimeters of rainfall, but different locations around the world
exhibit different within-location baseline variances in these measures, which is further
exacerbated by differences in the areal extent that is averaged over to computeexposure levels. For example, a 1ºC temperature change is a relatively small change
for average weekly temperature in a US county; however, it is an enormous change
for annual average temperature in an African country. To adjust for these large
differences in baseline climate variance, we convert all physical measures of climate
into standardized measures based on the within-location standard deviation inclimate”
On the A1B emissions scenario, on which there is warming of around 2.5°C by 2050, allregions experience an increase in temperature of 2-4 standard deviations. So, it would bereasonable to assume that for 5°C of warming by 2100, regions would experience warmingof 4-8 standard deviations.
These claims about intergroup conflict are highly controversial,534 and have led to heateddebate between the Berkeley economists and the PRIO researchers.
Here, I will carry out a ‘minimal trust’ investigation of the two studies mentioned above: I willsuspend my trust in the field and dig as deeply into the question as I can.

12.3.5. A review of Burke et al (2009)
Burke et al (2009) has been cited more than 1,000 times and argues that >1,000 death civil
conflict will increase in Africa by 4.5 percentage points per degree of warming, and that thiseffect will outweigh the effects of growth and democratisation. This is a panel study which
examines the effects of interannual weather variation on conflict risk. Heuristically, an
economy observed during a cool year is the ‘control’ for that same society observed during a
warmer ‘treatment’ year. Because interannual weather change is exogenous, any differencein conflict risk is plausibly due to differences in weather rather than country-specific or timetrend factors. This methodological approach avoids the omitted variables and534

H. Buhaug et al., ‘One Effect to Rule Them All? A Comment on Climate and Conflict’, Climatic
Change 127, no. 3 (1 December 2014): 391–97, https://doi.org/10.1007/s10584-014-1266-1; Halvard
Buhaug, ‘Climate Not to Blame for African Civil Wars’, Proceedings of the National Academy of
Sciences 107, no. 38 (21 September 2010): 16477–82, https://doi.org/10.1073/pnas.1005739107.

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over-controlling problems associated with cross-sectional approaches or that try to control
for confounders.
As discussed in the Chapter on economic costs, this panel study approach relies on theassumption that interannual weather variation is relevantly similar to long-term climatechange, which is not obvious.
I have spent a significant amount of time focusing on this study because much of the posited
relationship between climate change and conflict depends on studies of Africa, a point I
discuss in more detail below.
There has been vigorous debate about this study with the following critiques and responses
●
●
●
●
●
●
●

Buhaug (2010) - ‘Climate not to blame for African civil wars’, PNAS
Burke et al (2010) - ‘Climate robustly linked to African civil war’, PNAS
Burke et al (2010) - ‘Climate and civil war: is the relationship robust’, working paper(companion piece to the above)
Buhaug (2010) - ‘Reply to Burke et al.: Bias and climate war research’ PNAS
Buhuag et al (2010) - ‘Sensitivity analysis of climate variability and civil war’, working
paper (expands on the claims in the above Buhaug paper)
Hsiang and Meng (2014) - ‘Reconciling disagreement over climate–conflict results in
Africa’, PNAS
Buhaug (2014) - ‘Concealing agreements over climate–conflict results’, PNAS

My overall judgements are as follows
1. The PRIO researchers do not provide a convincing response to the criticisms of theireconometric approach by the Berkeley economists.
2. The Burke et al result is highly sensitive to the choice of time period and becomesmuch weaker or disappears after 2002, in a way that is inconsistent with one of
Burke et al’s projections.
3. The Burke et al result is highly sensitive to the definition of civil war.
4. The Burke et al projection is one about severity, not onset, and the severity ofconflicts is noisy and declining on the most plausible measures.

Failure to predict decline in civil conflict incidence
Burke et al argue that civil conflict will decline even on an optimistic scenario for growth anddemocratisation. Specifically, they consider“an ‘‘optimistic scenario,’’ in which the annual per capita economic growth rate is 2%
and the increase in democracy is the same as during 1981–2002, a period of
substantial democratic reform in Africa (see Methods).”535
Even on this optimistic scenario, there is little effect on their mean conflict projections
535

Marshall B. Burke et al., ‘Warming Increases the Risk of Civil War in Africa’, Proceedings of the
National Academy of Sciences 106, no. 49 (8 December 2009): 20670–74,
https://doi.org/10.1073/pnas.0907998106.

369

The optimistic scenario is depicted in the bottom box and whisker diagram. They argue thateven on the optimistic scenario, climate change would lead to increased war by ~50% by2030. If we assume that the increase is linear, the increase would be 18% by 2010 and 36%
by 2020. The bottom fifth percentile is a 40% decline by 2030. As they note:“Furthermore, the adverse impact of warming on conflict by 2030 appears likely tooutweigh any potentially offsetting effects of strong economic growth and continued
democratization.”536
The Burke et al (2009) study only uses data on conflict up to 2002.
However, as Buhaug (2010) points out, between 2002 and 2008, >1000 death civil conflicts
declined by about 50% in Africa by 2008, despite warming.537
536

Burke et al.
Buhaug, ‘Climate Not to Blame for African Civil Wars’; Halvard Buhaug, ‘Reply to Burke et al.: Biasand Climate War Research’, Proceedings of the National Academy of Sciences 107, no. 51 (2010):
E186–87.
537

370

538

The light blue is state-based conflicts with >25 deaths. These have not increased by a thirdby 2012, which is what one might expect on the Burke et al model.
The chart below presents the trend up to 2019 for wars (>1,000 battle-related deaths) andconflicts (25–999 battle-related deaths)

Source: Palik et al, ‘Conflict Trends in Africa, 1989–2019’, PRIO paper, 2020

538

Marshall Burke et al., ‘Climate and Civil War: Is the Relationship Robust?’ (National Bureau of
Economic Research, 2010).

371

As this shows, the trend in >1000 death civil wars was low and stable between 2002 and2019.
Applying the Burke et al model to the 2003-2008 period, the relationship between
temperature and war now disappears. This is in model 2 in table 4 below

Soure: Marshall Burke et al., ‘Climate and Civil War: Is the Relationship Robust?’ (National Bureau of
Economic Research, 2010).

In response to this, Burke et al (2010) attribute this to increased economic growth anddemocratisation (as measured by the Polity Score) since 2000.

Source: Marshall Burke et al., ‘Climate and Civil War: Is the Relationship Robust?’ (National Bureau of
Economic Research, 2010).

However, in Burke et al (2009), their optimistic scenario assumed that the effects of climatechange would outweigh the effects of economic growth and democratisation, which is notwhat has happened. Their optimistic scenario assumed that the increase in democratisation
followed trends from 1981-2002. But the increase in the polity score in Sub-Saharan Africa
has actually been slower since 2002.
372

Source: Robert H. Bates, Ghada Fayad, and Anke Hoeffler, ‘The State of Democracy in Sub-SaharanAfrica’, International Area Studies Review 15, no. 4 (2012): 323–38.

On the other hand, economic growth in reality was 0.8% above the assumed rate in theiroptimistic scenario. Still, this effect would not be large enough to produce the decline inconflict that is so far outside their model. Their bottom fifth percentile was a decline of 40%
by 2030. In fact, conflict declined by 50% in only the next 6 years. Reality was very different
to their predictive model.
Burke et al (2010) say:
“Unlike our baseline climate dataset (CRU 2.1), the UDel data allow us to extend ouranalysis through 2008. We do not think that changing the study time period
constitutes a “robustness test” but rather an answer to a slightly different question:i.e., were African economic and political institutions as sensitive to variation in climate
over the most recent decade as they were over the previous two? Africa was clearlya different continent over the last decade compared to the two decades previous(Miguel 2008). As Table 5 shows, average annual per capita GDP growth over
2003-2008 was six times higher than the 1981-2002 period (where it was near zero),and the Polity Score (a -10 to +10 measure of democratic political institutions)improved an average of more than 4 points between the two periods.”539
Extending the dataset through to 2008 is not “answering a different question”: they
themselves had asked this question in their original paper.
In a working paper, Buhaug et al illustrate the sensitivity of the Burke et al (2009) result tothe time period studied. Burke et al (2009) study the effect of the period from 1981 to 2002.
Here is how the coefficient would be affected by different date ranges:
539

Burke et al., ‘Climate and Civil War’.

373

Source: Halvard Buhaug, Haavard Hegre, and Haavard Strand, ‘Sensitivity Analysis of Climate
Variability and Civil War’, PRIO Paper, 2010.

A size of coefficient comparable to the Burke et al (2009) paper is found only in a certain
cluster of date ranges, and is almost always (much) lower if the end date is after 2004.
Longer time periods also tend to produce lower coefficients.

Sensitivity to civil war cut-off
As Buhaug notes, the finding in Burke et al (2009) is not one about civil war onset, but rather
one about civil war severity, defined in a particular way. Burke et al (2009) measure theprevalence of civil war and count only conflict years that caused a minimum of 1,000 direct
casualties. This approach has some strange implications:“For example, consider the civil war in Sierra Leone. This conflict is widely accepted
as lasting from March 1991 until the ceasefire and resulting Abuja Agreement in late
2000.* However, the Burke et al. article considers Sierra Leone at war in 1998–1999
only, the only 2 y in which direct annual casualty estimates crossed the 1,000 deaths
threshold. Using climate statistics for 1997–1998 to explain a war that had caused374

somewhere between 2,000 and 5,500 battle deaths by 1998 (14), however, makes
little sense.”540
Other definitions of conflict seem reasonable, such as onset of civil wars of >1000 deaths,
prevalence of >25 death conflicts, or battle deaths. If one uses any of these definitions, theresult found in Burke is no longer statistically significant, and the mean effect is negative or
close to zero.

Source: Solomon M. Hsiang and Kyle C. Meng, ‘Reconciling Disagreement over Climate–Conflict
Results in Africa’, Proceedings of the National Academy of Sciences 111, no. 6 (11 February 2014):
2100–2103, https://doi.org/10.1073/pnas.1316006111.

(Note that these numbers are different to those found in Buhaug (2010) because that paper
did not adjust for baseline risk of the different types of conflict).
Hsiang and Meng (2014) argue that these results are not necessarily inconsistent with thoseof Burke because the confidence intervals are wide enough to include the Burke results, so
any difference could be due to sampling variation.541 On the basis of this, they argue that
Buhaug’s work is consistent with Burke’s results.
I don’t find this test plausible. Firstly, we should be interested in the mean effect, not whether
an effect is within the 95% confidence interval of another effect. While we may not be able torule out any difference with 95% confidence, the key question is whether, in expectation, thiscalls into question the posited causal link between climate change and conflict, which it
does.
Secondly, it seems like what we should be interested in from a frequentist point of view iswhether we can reject the null with 95% confidence, not whether we can reject a previous
finding in the literature with 95% confidence. Hsiang and Meng’s sensitivity analysis shows
540

Buhaug, ‘Climate Not to Blame for African Civil Wars’.
“In Table 2, we test whether the results in each of these models is different from the main result
presented in Burke et al. (1) using seemingly unrelated regression (SUR), an approach thatallows us to formally test whether or not two different regression models return statistically different
results (14) (also 15, p. 153). Intuitively, this approach asks whether the “regression lines”
describing the relationship between temperature and conflict in Burke et al.’s and Buhaug’s (8)analyses are statistically different from one another, while taking into account the fact that thestudies are using related conflict outcomes where disturbances may be correlated” Solomon M.
Hsiang and Kyle C. Meng, ‘Reconciling Disagreement over Climate–Conflict Results in Africa’,
Proceedings of the National Academy of Sciences 111, no. 6 (11 February 2014): 2100–2103,
https://doi.org/10.1073/pnas.1316006111.
541

375

that we can reject the null on other plausible operationalisations of conflict. Hsiang andMeng’s approach makes the question of whether a finding has been refuted or not dependon the order in which papers are published. If Buhaug had published his paper first andfound no effect, then Burke would have been unable to refute that with their data.
This shows that the Burke et al finding is highly sensitive to different definitions of conflict.
Indeed, it seems like the measure we should care about is battle deaths in civil conflict. The
relationship between climate and battle deaths is also noisy and uncertain.
Contemporaneous temperature and lagged temperature each have a negative effect on
battle deaths that is not statistically significant.

Source: Buhaug, ‘Reply to Burke et al.’(Note that I'm not sure whether the coefficients in Models 2 and 3 are comparable to those in
Model 1 because I’m not sure if they adjust for baseline risk. Nevertheless, again this does
illustrate that if the thresholds of 500+ deaths or 2000+ deaths had been used, the effect oftemperature on conflict would be ~0. This also raises some theoretical concerns about theposited mechanism by which climate change increases conflict. Burke et al (2009) argue thattemperature causes conflict by affecting economic performance. It is not clear why climate
damage to economic performance would cause conflicts with more than >1000 deaths, but
not with >500 deaths or >2000 deaths.)
The coefficient for the number of battle deaths is negative (temperature reduces battle
deaths) but the coefficient for log(battle deaths) is positive. This could happen because thenumber of deaths coefficient could be affected by extreme outliers, and the effect of outliers
is limited with log transformation. Neither effect is statistically significant and there is a lot ofnoise.
Burke et al (2009) note that average battle deaths in conflicts in which >1000 died from
1981–2002 are 39,455 deaths/year.542 However, average deaths per civil conflict in
sub-Saharan Africa (so excluding North Africa) declined by a factor of 4 between the 1980s
542

Burke et al., ‘Warming Increases the Risk of Civil War in Africa’, SI p3.

376

and the 2000s, despite warming.543 The chart below shows the trend in the number of civil
wars and of battle deaths. There was a large decline after 1999 and since then the trend has
been fairly stable.

Source: Palik et al, ‘Conflict Trends in Africa, 1989–2019’, PRIO paper, 2020

Aggregated spatial and temporal scales
O’Loughlin et al (2014) write:“The sub-Saharan Africa data used by Burke et al. (1) and the replications arecountrywide data and averaged by year. Such a coarse spatial and temporal
resolution limits our ability to uncover explanations for any relationships that emerge
in statistical analyses. Increasingly in the field of conflict studies, researchers use
subnational geographic data for single-country studies and cross national inquiries.
Relying on a fine spatial resolution for the analysis of political violence allows
intergroup social dynamics within a country to emerge. In the original Burke et al.
data, countries as large as Sudan or Democratic Republic of Congo are single units,with political, economic, and climate characteristics that are uniform acrossenormous territories. This is a bold and naïve assumption, a view increasingly
rejected by most conflict researchers because civil war tends to be concentrated incertain regions (e.g., southern Sudan and eastern Democratic Republic of Congo)(4).544

Qualitative analysis
The Burke et al (2009) result is highly dependent on six conflicts in Guinea-Bissau, Sierra
Leone, Chad (in 1990), Congo, and Chad (in 1987). If these conflicts are removed, thecoefficient falls close to zero.545 However, according to Buhaug et al, all of these conflicts
involved foreign intervention. It might be that temperature influenced conflict in the country,

543

Buhaug, ‘Reply to Burke et Al.’John O’Loughlin, Andrew M. Linke, and Frank D. W. Witmer, ‘Modeling and Data Choices Sway
Conclusions about Climate-Conflict Links’, Proceedings of the National Academy of Sciences 111, no.
6 (11 February 2014): 2054–55, https://doi.org/10.1073/pnas.1323417111.
545
Buhaug, Hegre, and Strand, ‘Sensitivity Analysis of Climate Variability and Civil War’, 5.
544

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which led to foreign intervention, but I would want to see a careful argument to that effectbefore putting much weight on the Burke et al (2009) study.
Because the result is driven by so few data points, qualitative analysis of the posited
climate-economy-conflict link seems important. According to Buhaug et al:“Another apparent commonality between these cases is the scarcity of references toclimate anomalies and loss of agricultural income in news reports and narratives of
the conflicts.”546

Overall view
I have a sceptical prior when dealing with individual social science studies that produce
eye-catching and controversial findings using noisy data. My concerns especially centre
around statistical significance, reporting bias and researcher degrees of freedom or the‘garden of forking paths’. I think these problems will be especially severe for something as
noisy as the relationship between climate and conflict. A deeper analysis of the Burke et al(2009) paper confirms that these concerns apply in this case.
This is important because, as I discuss below, most of the posited effect of climate change
on conflict is driven by conflicts in Africa.

12.3.6. A review of the Burke et al (2015b) meta-analysis
The Berkeley economists have produced two meta-analyses on climate and conflict. I
assume that the more recent Burke et al (2015b) paper is more representative of theircurrent view, so will focus on that in what follows.

Intergroup conflict includes a lot
The meta-analyses showing a link between climate and intergroup conflict include quite
different things in the definition of conflict, including:
●
●
●
●
●
●
●

Civil warCommunal violence involving 25 or more deaths
Riots
Coup probability
Leadership exit
Gang homicide
Institutional changeThis is important to bear in mind as the import of urban riots is quite different to the import ofcivil war. Nonetheless, most of the temperature studies concern civil conflict, involving >25
battle deaths. Note also that the effect sizes refer to the effect on the relative risk of eachtype of conflict, and so these types of conflict are not all aggregated.

Study inclusion criteria
In their primary research and in their meta-analyses, Burke, Hsiang and others rule out:
546

Buhaug, Hegre, and Strand, ‘Sensitivity Analysis of Climate Variability and Civil War’.

378

●

●

Cross sectional studies that compare rates of conflict across different locations.
○ This risks omitted variable bias and over-controlling, as discussed in Chapter
10 on economic costs.547
Studies that attempt to control for confounders, such as average income, institutional
quality, and so on.
○ This can also lead to overcontrolling because populations differ in unobserved
ways that become artificially correlated with climate when the control variable
is included.548

Instead, they favour panel studies that promise superior causal identification. I agree with
Burke et al that cross-sectional regressions should be interpreted with extreme caution, andthat properly controlling for confounders is challenging. I would gloss their position as
follows. Burke et al want to be able to make causal judgements with high confidence on thebasis of the study alone, i.e. without introducing theoretical considerations or evidence fromother domains.
However, I think that, from a Bayesian point of view, we should sometimes update fromstudies that require us to make theoretical judgements or use evidence from other domains.
Thus, discarding all non-panel studies is too drastic. For example, income per head isstrongly negatively correlated with conflict risk. It is true that income is also correlated withlots of other things that might affect conflict, but we also have a good theoretical explanationof why income might have a causal effect on conflict, so it would not be rational to discard
such evidence. If income per head were not correlated with conflict risk, that would be alarge update.
547“One approach to the above problem would be to assume that populations or societies inhabiting
different locations are identical to one another in all respects except their climate, usually after
regression adjustment for observable economic, social, and political correlates of conflict. For
example, Buhaug (2010a) compares the rate of civil war across different countries in Africa. Yet it
seems implausible that the conditions needed for causal inference are met in this setting: There aremany ways in which populations and societies differ from one another (e.g., culture, history), many ofthem unobserved or hard to measure, so we cannot infer whether a climatic treatment has a causal
effect (Wooldridge 2002, Angrist & Pischke 2008). In the above example, the cross-sectional analysis
by Buhaug (2010a) compares average rates of civil conflict in South Africa and Nigeria (among many
comparisons), attributing observed differences to the different climates of these countries—despite
the many other potentially important ways in which these countries differ.” Marshall Burke, Solomon
M. Hsiang, and Edward Miguel, ‘Climate and Conflict’, Annual Review of Economics 7, no. 1 (2015):
577–617, https://doi.org/10.1146/annurev-economics-080614-115430.
548“Some studies expand Equations 3 and 4 to explicitly control for potential confounding factors,
such as average national income. For example, Buhaug (2010a) alters the analysis of atemperature–war association studied by Burke et al. (2009) to include indices for political exclusion
and average income. Although well intentioned, this approach may introduce bias in the coefficients
describing the effect of climate on conflict because these controls are endogenously determined andmay themselves be affected by climate variation. This can cause the signal in the climate variable ofinterest to be inappropriately absorbed by the control variable or the estimate to be biased because
societies differ in unobserved ways that become artificially correlated with climate when the control
variable is included. This approach is commonly termed bad control (Angrist & Pischke 2008) and is aparticular difficulty in this setting because climatic variables may affect so many of the socioeconomic
factors commonly included as control variables, such as crop production, infant mortality, population(via migration or mortality), and even political regime type. To the extent that these outcome variables
are used as covariates, studies might draw mistaken conclusions about the relationship between
climate and conflict… In what follows, we modify estimates that rely on this method by excluding bad
controls in our reanalysis.” Burke, Hsiang, and Miguel.

379

Moreover, showing that a relationship could be confounded does not show that it is likely to
be confounded or that the effect is large. For example, it is true that climate change might
influence average income and so if we control for average income, the signal from theclimate variable would be inappropriately absorbed by the control variable. However, wemight also be able to establish with other types of evidence that climate change is at best aminor influence on average income. In that case, controlling for average income would still
reduce the bias of the estimate.

Study inclusion and coding decisions
I have doubts about some of the study inclusion and coding decisions in Burke et al (2015b).
Firstly, this may not be particularly important, but it is somewhat unclear how to disaggregate
the findings in different papers included in the meta-analysis. Burke et al (2015b) include
Burke et al (2009) discussed above, as well as the Buhaug (2010) finding, which I think is
the one using the >25 battle death definition of conflict. Burke et al (2015b) counts this as
two contrasting findings, but there were many other findings in the back and forth regarding
Burke et al (2009), including null effect at >500 death or >2000 death thresholds or for battle
deaths. Following the logic of including one of the Buhaug (2010) results, one could include
multiple different null results. This problem arises from the expansive definition of intergroupconflict. I’m not sure what the correct solution to this problem is, but it is worth noting.
Secondly, Burke et al (2015b) only includes the effect found in Burke et al (2009) on
temperature, but not on precipitation. But Burke et al (2010) notes that extending the yearsof conflict to 2008, precipitation has almost zero effect on conflict prevalence, and that theeffect is not statistically significant.549 The null finding for precipitation does not make it into
the meta-analysis, even though Burke et al (2009), on which Burke et al (2010) is based, isincluded in the meta-analysis.
Finally, I have looked into some of the other included studies and I find some of the coding
decisions difficult to understand. For example, Bruckner and Ciccone (2011) finds that“negative rainfall shocks lead to significant democratic improvement and, in particular, atightening of executive constraints, greater political competition, and more open andcompetitive executive recruitment” [my emphasis].550 Burke et al (2015b) code this as a formof intergroup conflict - ‘institutional change’ specifically. The terms ‘death’, ‘fatalities’,‘casualties’, ‘oppression’ and their cognates are not used in the Bruckner and Ciccone paper,and ‘conflict’ is only mentioned in discussion of the methodology of other papers in a passing
footnote.551 The paper is just not about intergroup conflict, and in fact shows that weather
variability has positive effects.
A 2010 study by Paul Burke (different to Marshall Burke, who wrote the meta-analysis) andAndrew Leigh finds that weather shocks to GDP increase the chance of democratisation.552
549

Burke et al., ‘Climate and Civil War’, Table 1.
Markus Brückner and Antonio Ciccone, ‘Rain and the Democratic Window of Opportunity’,
Econometrica 79, no. 3 (2011): 923–47.
551
Brückner and Ciccone, fn4.
552
Paul J. Burke and Andrew Leigh, ‘Do Output Contractions Trigger Democratic Change?’, American
Economic Journal: Macroeconomics 2, no. 4 (2010): 124–57.
550

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Burke et al (2015b) again code this as a form of intergroup conflict involving institutionalchange. Burke and Leigh (2010) note that
“The result that stronger economic growth reduces the short-run likelihood of
democratic change needs to be considered alongside the finding of Miguel,Satyanath, and Sergenti (2004) that stronger growth reduces the short-run likelihoodof civil conflict in Africa. The combined evidence indicates that output contractions
trigger anti-government resistance activities. Further research into the conditions
which determine whether output contractions lead to peaceful or to violent opposition
to incumbent regimes may be of considerable value.”
Burke and Leigh (2010) is not about violence or conflict and in fact shows that weather
shocks have positive social effects.
A 2012 study by Paul Burke on the effects of weather variability on leadership exits is
included. There are two findings in the paper: one for regular exits which occur when aleader leaves office according to the prevailing rules, provisions, conventions and norms ofthe country; and one for irregular exits, which occur when a leader is removed from office incontravention of rules and conventions (for example, by coups, assassinations, military
power struggles, or removal by domestic rebel forces or revolts). Only the latter kind of exit
can be classed as demonstrating intergroup conflict. However, as I understand it, though I
am not sure, the Burke et al (2015b) meta-analysis includes the effect of the weather on both
regular and irregular forms of political exit.553
These are all important errors and they seem to point towards a bias in favour of including
studies that find a positive effect on conflict and excluding studies that find a negative effect.

Reporting bias
Burke et al (2015b) demonstrate evidence of reporting bias in the papers included in theirmeta-analysis, but they do not adjust for this when making their estimates of the effect ofconflict. They use the following tests of reporting bias:
A. If there is no reporting bias, across the set of studies the statistical power ofstudies should increase with their sample size.
B. If there is large reporting bias, then large sample sizes should provide no benefit
in terms of statistical power. (Maximal reporting bias would be when sample size andstatistical power are perfectly anti-correlated).
Eventuality A is shown by the 45 degree diagonal line on the figure below, and eventuality B
is shown on the horizontal line. (Maximal reporting bias would be shown by a downward
sloping 45 degree line).

553

Compare Figure 3 of Burke et al (2015) with Table 12 of Paul Burke (2012). The latter shows asmall and non-significant effect for irregular exit, whereas Burke et al (2015) shows a larger marginallysignificant effect.

381

Source: Burke, Hsiang, and Miguel, ‘Climate and Conflict’.

As this shows, the trend line across studies for temperature is much closer to the horizontal
than the 45 degree line: the correlation between sample size and statistical power is quite
weak (0.36 in the case of temperature). Burke et al (2015b) say this about this result:“We strongly reject a slope of zero for both the full sample (P value < 0.01) and thetemperature subsample (P value < 0.05) and marginally reject a zero slope for the
precipitation estimates (P value < 0.10). And although we can also reject a
one-to-one relationship for each sample, studies with larger sample sizes on average
do have larger t-statistics in the climate and conflict literature we survey, suggestingthat authors with large samples are not simply searching through specifications ordata mining to find significant effects at exactly the 95% confidence interval. We notethat for both samples, the upward relationship stands in sharp contrast to the resultsof Card & Krueger (1995), with the negative slope they estimate. Our estimates are
more similar to that of Disdier & Head (2008), who interpret their results as ruling out
any large role for publication bias in the trade literature they survey.”Firstly, just as they strongly reject a slope of zero, they even more strongly reject a slope of
45 degrees. Contra Burke et al, this is indeed evidence that authors with large samples aresearching through specifications or data mining to find significant effects at the 95%
confidence interval. Finding that the correlation between sample size and statistical power is
not zero is a low bar test for reporting bias.

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Secondly, I don’t think the relevant benchmark for reporting bias adjustment should be thepractice of other economists.
Given that they found clear reporting bias, Burke et al should have adjusted for it in theirmeta-analysis.

Implausibly precise estimates
The meta-analytic finding in Burke et al (2015b) is as follows:

The 95% confidence interval for the mean effect is shown in the grey box on the right by thewhite circle with whiskers. This means that Burke et al (2015b) are extremely confident thatthe true effect per standard deviation increase in climate change is between 9 and 13%; it isalmost impossible to be <7% or >16%. This seems implausibly precise for two things asnoisy, variable, multi-causal and complex as climate change and conflict.
Indeed, they find an effect of precipitation on conflict that is (a) tiny - a 3.5% increase inrelative risk per standard deviation change; and (b) statistically significant. This result isdifficult to believe.
I think what is going on here is that they are estimating an effect size on the assumption that
their model is correct, but in reality there is much more uncertainty about their model thanthere is uncertainty conditional on their model.
One can visually see the effect of publication bias in the chart above. The meta-analytic
effect size is meant to be 3.5% for precipitation, so we should expect to see precipitation

383

studies dotted either side of zero. Instead, the included precipitation studies (in blue)
overwhelmingly find a positive effect, with numerous studies finding an effect of more than
10%. The mean estimated effect of temperature (in red) on conflict is 11%, but there are no
studies finding a negative effect and most of the studies find an effect greater than 20%. One
would expect to see at least some negative studies for an effect this small in comparison toother factors, solely due to noise. (For comparison, civil conflicts in Africa declined by 50%
between 2003 and 2008, and deaths per conflict declined by 400% in Sub-Saharan Africa
between the 1980s and the 2000s).

Africa focus
In Burke et al (2015), if we include studies on the El Nino Southern Oscillation, there are 10
studies of the effect of temperature on civil conflict, 7 of which are in Africa, 2 global and 1
focuses on Indonesia. I now discuss the non-Africa focused studies.
Caruso et al (2016) - Climate Change, Weather Shocks and Violent Conflict: A Critical Lookat the Evidence
Caruso et al (2016) studies the effect of climate change on rice production and subsequent
violence in Indonesia over a ten year period from 1993 to 2003.554 Although Burke et al code
this study as being about civil conflict, which is standardy defined as conflict between thestate and others involving >25 battle deaths, it is actually about something more expansive:“violence perpetrated by a group on another group (as in riots), by a group on an
individual (as in lynchings), by an individual on a group (as in terrorist acts), by thestate on a group, or by a group on organs or agencies of the state.”555
I don’t put much weight on this study. Firstly, the study period is only ten years, so it is hard
to know whether the result is robust. Secondly, climate change is projected to have mild
effects on rice yield, with adaptation, on average, in both temperate and tropical regions.

554

Burke et al (2015) discusses the earlier working paper of the Caruso et al (2016) paper.
Raul Caruso, Ilaria Petrarca, and Roberto Ricciuti, ‘Climate Change, Rice Crops, and Violence:
Evidence from Indonesia’, Journal of Peace Research 53, no. 1 (1 January 2016): 66–83,
https://doi.org/10.1177/0022343315616061.
555

384

Source: Toshihiro Hasegawa et al., ‘A Global Dataset for the Projected Impacts of Climate Change on
Four Major Crops’, Scientific Data 9, no. 1 (16 February 2022)
https://doi.org/10.1038/s41597-022-01150-7.

This suggests that the mechanism of climate change => damaged rice production in Asia =>civil conflict is extremely weak. For comparison, rice yields have increased by more than
200% over the last 60 years.
Dell et al (2012) - Temperature Shocks and Economic Growth: Evidence from the Last Half
Century’, American Economic Journal: Macroeconomics
The findings of the only global temperature study on civil conflict - Dell et al (2012) - areshown below (columns 5 and 6):385

Source: Dell, Jones, and Olken, ‘Temperature Shocks and Economic Growth’.

This shows that at the global level, the mean effect of temperature is to reduce civil conflictoutbreak (though the effect is not statistically significant), but in poor countries (with average
income below the median - therefore mainly countries in Africa and South Asia), temperature
increases civil conflict (though the effect is not statistically significant).
Hsiang et al (2011) - Civil conflicts are associated with the global climate, Nature
Hsiang et al (2011) is a global study that argues that the warmer and dryer conditions
associated with El Nino events are associated with civil conflict onset. However, Klomp and
Bulte (2013) do not find an El Nino effect. Klomp and Bulte (2013) is not included in the
Burke et al (2015b) meta-analysis, so perhaps Klomp and Bulte use an excluded
methodological approach. However, they do say that a “When we further probe therobustness of the Hsiang et al. result and use their exact model specification and statisticalprogram in a rolling regression framework (so not the standard logit model), we also do notfind any robust result of the ENSO effect in Africa or Asia”.556 I’m not sure what explains that.
All of this suggests that it would be premature to infer anything from Burke et al (2015b)
about the risk of intergroup conflict from increased temperature outside of Africa.
556

Jeroen Klomp and Erwin Bulte, ‘Climate Change, Weather Shocks, and Violent Conflict: A Critical
Look at the Evidence’, Agricultural Economics 44, no. s1 (2013): 63–78.

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This is one instance of a more general problem in the literature, which is that researchoverwhelmingly focuses on Africa, which is not necessarily where climate stress is greatest.
Adams et al note:“In contrast, the sampling of countries to be studied seems to be barely informed by
the independent variable. A high exposure and a high vulnerability to climate change
according to the ND-GAIN index23 are negatively, but not significantly, correlatedwith the number of times a country is mentioned.”557

Hsiang and Burke respond to this“Adams et al.’s error arises because they confuse sampling observations within agiven study based on the dependent variable (a major statistical violation) with theobservation that there are more studies in locations where the average of adependent variable, the conflict rate, is higher (not a violation). Nowhere does Adams
et al. provide evidence that any prior analysis contained actual statistical errors.”
The problem with this is that Burke and Hsiang make claims about a global increase in
conflict risk due to climate change. Take this extreme example. Suppose all of the literature
focused on conflict in Africa. Even if all of this literature was methodologically sound, it would
be dubious to produce a meta-analysis saying that climate has global effects on conflictbecause you would have ignored many of the data points where (i) climate stress is alsohigh and (ii) rates of conflict may be different.
The neglect of Asia is especially important because, as of 2014, Asia was home to nearly
half of the world’s active civil wars, and had the highest density of armed conflicts per
country in the world.558

557

Adams et al., ‘Sampling Bias in Climate–Conflict Research’. See also Cullen S. Hendrix, ‘The
Streetlight Effect in Climate Change Research on Africa’, Global Environmental Change 43 (1 March2017): 137–47, https://doi.org/10.1016/j.gloenvcha.2017.01.009.
558
Gerdis Wischnath and Halvard Buhaug, ‘On Climate Variability and Civil War in Asia’, Climatic
Change 122, no. 4 (1 February 2014): 710, https://doi.org/10.1007/s10584-013-1004-0.

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Other issues with panel studies
Panel studies on conflict have the same problem as the panel studies for economic costs,
discussed in Chapter 10. Climate change is different to inter-annual weather variation. It is
not clear in which direction this biases the finding.
However, panel data does allow good causal identification. Buhaug and others typically wantto control for things like GDP and institutions, which introduces the risk of omitted variablebias and overcontrolling.

Overall judgement on these estimates
I think the Burke and Hsiang meta-analytic estimates of the effect of climate on conflict aresubstantial overestimates. In other domains, it has been shown that meta-analyses that try
to correct for publication bias typically substantially overestimate effect sizes.
●●●

In psychology, Kvarven et al (2020) find that meta-analytic effect sizes are nearly
three times greater than those found in large pre-registered randomised control trials,
even accounting for meta-analytic methods that try to adjust for publication bias.559
Simulations suggest that meta-analyses that try to control for reporting bias using
trim and fill will typically overestimate effect sizes, usually by a factor of 2 or more.560
In medicine, where there are requirements for pre-registering large RCTs, there is afalse positive and false negative rate of one third in meta-analyses.561

Despite evidence of publication bias, Hsiang and Burke’s meta-analyses do not try to correct
for publication bias. Given information from other fields, my best guess is that, due topublication bias, their meta-analytic effect sizes are likely overestimated by a factor of morethan 2, which suggests that the effect of climate change on civil conflict is <5% per standard
deviation. The other factors I have discussed above also suggest that their estimates are toohigh.
This puts the adjusted estimates more in the ballpark of other quantitative estimates
identified by Sakaguchi et al (2019) in their systematic review.562

559

Amanda Kvarven, Eirik Strømland, and Magnus Johannesson, ‘Comparing Meta-Analyses andPreregistered Multiple-Laboratory Replication Projects’, Nature Human Behaviour, 23 December
2019, 1–12, https://doi.org/10.1038/s41562-019-0787-z.
560
Uri Simonsohn, Leif D. Nelson, and Joseph P. Simmons, ‘P-Curve and Effect Size: Correcting for
Publication Bias Using Only Significant Results’, Perspectives on Psychological Science 9, no. 6(2014): fig. 3.
561“According to our analysis, if there had been no subsequent randomized, controlled trial, themetaanalysis would have led to the adoption of an ineffective treatment in 32 percent of cases (100percent minus the positive predictive value) and to the rejection of a useful treatment in 33 percent ofcases (100 percent minus the negative predictive value).” Jacques LeLorier et al., ‘Discrepancies
between Meta-Analyses and Subsequent Large Randomized, Controlled Trials’, New England Journalof Medicine 337, no. 8 (21 August 1997): 536–42, https://doi.org/10.1056/NEJM199708213370806.
562“Of the forty-three studies that found a positive relationship, some studies report results in terms ofa percentage change in conflict risk (Burke et al. 2009; Hsiang, Meng, and Cane 2011; Fjelde and von
Uexkull 2012; Hendrix and Salehyan 2012). They find the risk of conflict rises between 0.5 percent
and 4.5 percent relative to a unit change in the climate indicator.” Sakaguchi, Varughese, and Auld,‘Climate Wars?’388

Overall, I think that if the Burke et al (2015b) meta-analysis properly adjusted for study
inclusion and publication bias, they would find that the panel studies would, like the studies
using other methodologies, also find a mixture of results on the climate-conflict link.

Implications of the estimates
Burke et al estimates taken at face value
Suppose that you take the Burke et al (2015b) estimates at face value and say that the riskof intergroup conflict in Africa will increase by 11% for each SD increase in warming.
Assume that by 2100, each region will experience a 8 standard deviation increase intemperature, which is what we might expect in a 5 degree world.
Since 2000, battle deaths in Africa have been on the order of 5,000 per year.

Source: Palik et al, ‘Conflict Trends in Africa, 1989–2019’, PRIO paper, 2020

This suggests that battle deaths will increase to 40,000 by 2100, other things equal. This isbad, but much smaller than other public health problems. For instance, more than a million
people die in road accidents every year.
In essence, this would return Africa to the civil conflict prevalence and severity seen in theearly 1990s.
My favoured estimates
As I have said, I think the Burke and Hsiang meta-analyses overestimate the effect ofclimate change on conflict. If the effect on conflict is a 5% increase per standard deviation
change, then we end up with civil conflict rising by 40% by 2100, which would increase
deaths to around 6,000 by 2100, other things equal.

Mechanisms of impact
Burke et al (2015b) discuss several potential mechanisms by which climate could affectconflict, including:563●
563

Damaging economic output

Burke, Hsiang, and Miguel, ‘Climate and Conflict’, sec. 3.

389

●
●

●

Affecting population density by increasing rural to urban migration
Climate change might affect the chance of a successful attack○ This might be due to logistics - for example floods might affect the roadavailable to military vehicles○ Or it might be because economic damage reduces the security of incumbent
leaders, as illustrated by the evidence that climate change affects leadershipexit.
Temperature might increase aggression via a physiological mechanism.

Burke et al (2015b) believe that the psychological mechanism is the most plausible because
economic conditions and most other plausible alternative mechanisms usually do notrespond to the climatic conditions on the timescale analysed.
I do not find it plausible that climate change could increase conflict risk by systematicallyincreasing the probability of a successful attack. Firstly, as discussed above, Burke et al(2015b) seem to misinterpret the evidence on leadership exit, which shows noisy effects
close to zero. Indeed, many studies suggest that climate change increases democratisation,
which is widely agreed to reduce conflict risk. Secondly, climate change is set to alter
precipitation patterns, which will mean more floods in some locations and less in others. The
effects of climate change on logistics also seem to be at most a small driver of conflict.
The effect on rural-urban migration raises some interesting questions. As I discuss below,
population size is a correlate of civil conflict risk, but population growth is also correlated with
reduced per capita conflict risk.
Thus, it seems that the economic mechanism and the direct physiological mechanism seemto be the most plausible candidates among those raised by Burke et al. I discuss the direct
physiological mechanism in the next Chapter.

12.3.7. Climate less important than other factors
Despite disagreement in the literature, all major players in the debate seem to agree thatclimate change has been much less important than other factors as an influence on conflict.
An expert elicitation study including Buhaug and Burke found almost no-one putting climate
variability as the most influential determinant of war so far, with socioeconomic development,
state capability, intergroup inequality and a recent history of violent conflict being the mostimportant.

390

Source: Katharine J. Mach et al., ‘Climate as a Risk Factor for Armed Conflict’, Nature 571, no. 7764
(July 2019): 193–97, https://doi.org/10.1038/s41586-019-1300-6.

Most experts held that around 10% of civil conflict risk has been related to climate change so
far.

391

Looking to the future, most of the surveyed experts felt that, assuming current levels ofsocioeconomic development, population and government capacity, 2ºC would have anegligible or moderate effect on civil conflict, with a plurality finding negative change. For
4ºC, there would be a moderate increase. A ‘substantial’ increase is defined in the elicitationas ‘involving severe and widespread effects’. Since it is not clear what the definition means,
it is not clear how to interpret the chart below:

392

There is robust evidence and high agreement that socioeconomic development is the single
most important covariate in cross-sectional studies and time series.564 Fearon and Laitin(2003) find a strong negative cross-country correlation between income per head andconflict propensity. Between 1945 and 2003, the chance of having a civil conflict for
countries at different levels of income was as follows:●●●

10th percentile in income per head ($580): 72% chance
A country at the median ($2,671): 44% chance.
A country at the 90th percentile ($8,650): 2.7% chance.565

564

Mach et al., SI p2.
James D. Fearon and David D. Laitin, ‘Ethnicity, Insurgency, and Civil War’, The American Political
Science Review 97, no. 1 (2003): 16.
565

393

In a future of low socioeconomic development and poor governance in low income countries,
conflict is likely to be high anyway, regardless of climate change.

Future conflict predictions
There are interesting forecasting studies projecting levels of conflict forward based on
different socioeconomic trends.
Hegre et al (2016) ‘Forecasting Civil Conflict along the Shared Socioeconomic Pathways’,
Environmental Research Letters
Hegre et al - scholars at PRIO - note
“The conflict research community has identified a handful of robust country-level
correlates of civil conflict, the three most powerful of which are a history of priorconflicts, a large population, and a low level of socioeconomic development”566
They quantify the size of these effects by analysing the correlates of major civil conflict(>1000 battle deaths).567
Population
The estimates for log population indicate that the odds of both minor andmajor conflict increase by 39% when population is increased by a factor ofe≈2.7 – more populous countries have more frequent conflicts, but
considerably less conflict per capita than smaller ones.
IncomeAmong countries currently at peace, increasing GDP per capita from e.g.
USD 1,000 to USD 2,700 decreases the odds of major conflict by 20%.
Education
Controlling for the effect of GDP per capita, increasing YMHEP education by
0.1 (e.g., changing the proportion of the male population between 20 and 24
years from 30 to 40%) further reduces the odds of conflict by 20%.
Hegre et al (2016) models civil war risk (>1000 battle deaths) on the SSPs using theirpredictors of income per head, population size and history of conflict. Note this is excluding
the effect of climate change - this is because (contra Burke et al who used a different
methodology) they couldn’t identify an effect on conflict in their statistical analysis.568 Burkeet al would say that Hegre et al (2016) do not account for omitted variable bias.

566Håvard Hegre et al., ‘Forecasting Civil Conflict along the Shared Socioeconomic Pathways’,
Environmental Research Letters 11, no. 5 (April 2016): 054002,
https://doi.org/10.1088/1748-9326/11/5/054002.
567
Hegre et al., SI.
568“In a final test, we investigated a possible separate effect of temperature anomalies on conflict risk.
This test revealed a weak and insignificant effect in the historical sample, and accounting fortemperature anomalies did not improve the predictive performance of the model (section S5 in
supplementary information)” Hegre et al., ‘Forecasting Civil Conflict along the Shared Socioeconomic
Pathways’.

394

As this shows, on all the SSPs except SSP3 and SSP4, conflict declines overall. Hegre et al(2013) produces similar findings of a decline in conflict, assuming that socioeconomic
development follows past trends.569 In all regions, conflict declines the most on SSP5, thehigh growth future. Recall that SSP3 and SSP4 assume that there is no improvement ineducation in most/all countries. But they all also assume some convergence growth, which is
at odds with the historical trend.
Low socioeconomic development and increased population in SSP3 and SSP4 leads toincreased civil conflict in Africa by 200% by 2100. Note however that increasing population
reduces conflict per capita.
Independently of climate change, if socioeconomic improvement is low, conflict will probably
increase.
Witmer et al (2017) ‘Subnational violent conflict forecasts for sub-Saharan Africa, 2015–65,
using climate-sensitive models’, Journal of Peace Research
Witmer et al (2017) projects future episodes of subnational political violence, which includes“riots, protests, violence against civilians, and battles between rebel and government
factions”570

569Håvard Hegre et al., ‘Predicting Armed Conflict, 2010–20501’, International Studies Quarterly 57,
no. 2 (1 June 2013): 250–70, https://doi.org/10.1111/isqu.12007.
570
Frank DW Witmer et al., ‘Subnational Violent Conflict Forecasts for Sub-Saharan Africa, 2015–65,
Using Climate-Sensitive Models’, Journal of Peace Research 54, no. 2 (1 March 2017): 175–92,
https://doi.org/10.1177/0022343316682064.

395

To generate forecasts of future violence, they compile historical data on the factors known tobe the most important influences on violent conflict. In their approach, they control for keysocial and political variables, an approach the Berkeley economists think is mistaken.571 The
drivers of violence they consider are political rights, infant mortality, temperature andpopulation growth.
Their results are as follows:

They find that political rights and population growth have a much greater influence on future
conflict than climate change to 2065, which implies warming of around 3°C. For example, theeffect size of poor governance is 20x larger than higher temperature.
571“In modeling and forecasting violence across all of subSaharan Africa at a 1 (degree) grid
resolution, we explicitly consider the effects of temperature variability, while controlling for temporalreporting bias in the coding of our violent event data. Additionally, we account for a number of keysocial and political variables that have known associations with violence” Witmer et al.

396

Each of these projection studies are dependent on choices about data, some of which theBerkeley economists would disagree with. Nonetheless, they do illustrate that other factors
are considerably more important for civil conflict risk.

12.4. Concluding thoughts on climate change and civil conflict
I have argued that some prominent studies on the connection between climate change andcivil conflict are flawed. This does not necessarily mean that there is no connection. The
question of how climate change affects conflict is inherently difficult to study, though someinsights can be drawn from the literature:
1. Importance of agriculture. The most plausible mechanism through which climatechange could affect conflict is by harming agriculture. This will primarily be a problem
for countries that remain poor and agrarian in the future. As I discussed in Chapter 5,given historical growth trends, there is a good chance that many countries in
Sub-Saharan Africa and some in South Asia will remain poor and agrarian for muchof the 21st Century. Climate change would be an additional stressor of conflict inthose countries, but it seems unlikely to play a major role in countries with moreadvanced agricultural techniques and where food spending takes up a smaller shareof average income.
2. Socioeconomic development is key. The first point implies that socioeconomic
development will be the most important driver of trends in conflict in the 21st Century.
If there is limited socioeconomic development, then civil conflict will remain a problem
in Africa. If there is strong socioeconomic development, the prevalence of civil
conflict seems likely to fall, as will the potential role of climate change as a stressor ofconflict.

12.5. Climate, conflict and Great Power War
Unfortunately, the existing literature on climate change and conflict does not shed much lighton the risk of future interstate conflict. Because interstate warfare is now so rare, the397

literature has focused on intrastate civil conflicts. Moreover, among interstate conflicts,
conflicts between the major powers pose by far the largest expected risk to humanity. This isbecause the major powers have far more destructive weaponry and have the capacity toalter the trajectory of humanity in other ways.
I have argued so far that the direct risks of climate change do not come close to causing
permanent stagnation, civilisational collapse or human extinction. Economic models which
add up the direct effects of climate change across different sectors find that the monetised
costs of warming of 4ºC are equivalent to a 5-10% of GDP in 2100 relative to a
counterfactual without climate change (i.e. not relative to today). There is much less
literature on the impacts of >5ºC, so any judgments on the effects of that level of warming
are much more speculative. Still, I find it difficult to see how even warming of 8ºC could
directly cause civilisational collapse or human extinction.
Consequently, if climate change could cause civilisational collapse or extinction, it would
have to do so via its indirect effects, and the main candidate indirect effect that is large
enough is a Great Power War. It is therefore crucial to assess the extent to which climatechange is a stressor of Great Power War.
For people aiming to prioritise resources in order to have the greatest possible positive
impact on the world, we first and foremost need to understand the size of the indirect risks ofclimate change relative to the total risk posed by other problem areas, such as nuclear
security, AI safety and biorisk.

12.5.1. How would Great Power War affect the long-term future?
There is no universally accepted list of Great Powers, but it seems clear that given somecombination of likely economic and military power, and the willingness to deploy it, over thecourse of the 21st Century, the list should include the US and other NATO members, China,
Russia, and India. Here, I will also discuss Pakistan because it is a nuclear power which
stands to be hit especially hard by climate change.
Great Power War would not itself cause a global catastrophe, but it is a risk factor for global
catastrophe. It increases the risk of catastrophe from other causes, which includes nuclear
war, engineered pathogens, and advanced AI. Toby Ord roughly estimates that if there wereno Great Power War this century, existential risk would fall by around one tenth.572 Since heestimates that existential risk this century is around 17%, Great Power War accounts for
upwards of one percentage point of the existential risk this century.
In his analysis for the Forethought Foundation, Stephen Clare estimates that there is arounda 1% to 2% chance of an existential disaster due to a Great Power War this century. This isalmost entirely driven by the war and the build-up to the war causing states to develop anddeploy dangerous weapons of mass destruction, including engineered bioweapons,misaligned AI systems, and currently unknown technologies. On Clare’s model, a small
fraction of the risk is due to nuclear war because it seems very unlikely that nuclear war
could lead to human extinction.
572

Toby Ord, The Precipice: Existential Risk and the Future of Humanity (Bloomsbury Publishing,2020), 177.

398

Conflict dynamics in World War II led major powers to build atomic weapons. In the Cold
War, countries hugely expanded their nuclear arsenals and built hydrogen bombs, and theSoviet Union built a huge bioweapons program. If tensions between the major powers
increase again, major powers could once again race to develop even more destructive
weapons.
Another important factor to consider is that Great Power War could be a key determinant of
which country becomes the global hegemon this century, which could be the main
determinant of which values get locked-in for the future. This could be comparably importantto the direct extinction risks from war.

12.5.2. Theories of interstate war
Before we examine the causes of war, it is worth pausing to consider different theories ofInternational Relations, as these have different implications for which causal drivers of war
are most important.
The three main theories of the causes of interstate war are realism, liberalism andconstructivism. All of these theories assume that states are unitary actors aiming to realise a
grand strategy. An alternative view is public choice theory, which holds that states are notunitary actors with identifiable goals and strategies.

Realism
The realist family of theories hold that key actors in world politics are sovereign states that
act to rationally advance their own security and power relative to other states in an anarchic
international system. Anarchy, in this sense, refers to the absence of an international
government authority to regulate disputes and enforce agreements between states and otheractors. Despite their broad agreements, there are many different forms of realism, whichsometimes disagree about the true causes of war and about the sign of the effect of different
policies.573
Rational states?
The combination of international anarchy and states acting in their own interests mightsuggest that realists will be pessimistic about the prospects of global peace, and somescholars indeed contend that this is an implication of realism.574 However, in his classic
article ‘Rationalist explanations for war’, James Fearon (1995) notes that the central puzzle

573

Jack S. Levy and William R. Thompson, Causes of War, 1st edition (Chichester, West Sussex,
U.K. ; Malden, MA: Wiley-Blackwell, 2010), Ch 2.
574“intentions of other states, has enormous consequences. It induces insecurity and a continuouscompetition for power, which makes the international system inherently confl ictual. Givenomnipresent threats, political leaders tend to focus on short - term security needs and adopt worst case thinking. They often utilize coercive threats to advance their interests, infl uence the adversary,and maintain their reputations. Anarchy does not automatically lead to war, but it creates a permissive
environment for war by creating a system of insecurity, confl icts of interest, and international rivalries.
Realists tend to have a pessimistic worldview, and they tend to be skeptical of grand schemes forcreating and maintaining a peaceful international order.” Levy and Thompson, Causes of War, p. 29.

399

of international relations is that wars are costly but nevertheless occur.575 It should usually becheaper to negotiate a settlement than to go to war. So, if states are rational unitary actors,
as posited by realism, war should be rare. Fearon outlines three potential causes of war,
assuming that states are rational
1. Rational miscalculation due to private information about relative capabilities orresolve and incentives to misrepresent such information in order to deter attack.
2. Rationally led states may be unable to arrange a settlement that both would prefer towar due to commitment problems, situations in which mutually preferable bargains
are unattainable because one or more states would have an incentive to renege on
the terms. This is especially acute
3. There may be issue indivisibilities which do not admit compromise.
Fearon is more sceptical about the last possibility as a true rationalist explanation for war
because rational states should be able to compensate one another for other issues thatseem indivisible.
An alternative and reasonable alternative to the rationalist view, but one not compatible withrealism as traditionally stated, is that sometimes states are not rational. This might bebecause leaders or voters are simply irrational about what would advance the national
interest. Common sense and history suggest that this happens regularly.
Another possibility is the unchecked interests of leaders.576 The costs of war are the main
incentive for peace, but when the people who decide on war aren’t accountable to the othersin their group, they can ignore some of the costs and agony of fighting. These leaders willtake their group to war too frequently. Sometimes they expect to gain personally fromconflict, and so they’re enticed to start fights. Blattman comments that “unchecked rulers likethese are one of the greatest drivers of conflict in history.” This account has overlap withpublic choice theory, which I discuss below.

Liberalism
Like realists, liberal theories of international relations assume that states seek to maximise
utility. But liberal theories are more hopeful about the scope for cooperation between rational
states when mutual gains are possible, and about the role of international institutions inpreventing conflict.577
Liberals make several substantive claims about the causes of war. First, like realists, they
accept that international anarchy is a problem. However, they believe the problem can be
reduced by the construction of international institutions like the EU and the UN.
Secondly, contra realists, they believe that not all states are equally likely to wage war.
Democracies are less likely to wage war than autocracies, so the spread of democracy
should reduce war.
575

James D. Fearon, ‘Rationalist Explanations for War’, International Organization 49, no. 03 (1995):
379–414.
576
Christopher Blattman, Why We Fight: The Roots of War and the Paths to Peace (New York: Viking,2022), Ch. 2.
577
Levy and Thompson, Causes of War, p. 69ff; Cashman, What Causes War?, p. 170ff.

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Third, economic interdependence through international trade can help to reduce the risk of
war by increasing the opportunity cost of war and creating a constituency of special interests
who stand to lose out from war and so would lobby against it.

Constructivism
Constructivists emphasise the role that identities and norms play in determining a state’s
actions.578 For example, a state’s identity—perhaps as democratic, or developing, orhegemonic—will influence which norms it sees itself as following. Constructivist scholars
emphasise that their approach allows them to consider a broader range of variables thatbear on state decision-making.
From a constructivist point of view, states are not rational maximisers of the interests of theircitizens. Norms and values determine the kinds of actions that states will pursue. For
instance, human rights norms constrain the actions of states not because of considerationsof power capabilities (and of consequent sanctions), but because human rights in part
constitute the identity of states, especially states that are democratic.579
The structure of the international system - of states pursuing their material interests in astate of international anarchy - is a much weaker determinant of state behaviour thanrealists suggest. Constructivism mainly aims to explain war by explaining changes inindividual beliefs, which are determined by shared norms and values. For instance, one
explanation of why interstate war has declined is that there is now a widespread norm that
offensive war is wrong.580

Public choice theory
According to public choice theory, contra realism, liberalism and (arguably) constructivism,
states are not unitary rational actors that try to optimise a particular goal. Rather, state action
is driven by people or factions who have a concentrated interest in the outcome, especially
special interests in the defence establishment and weapons industries, and politicians trying
to get re-elected. Decisions are made mostly on the basis of short-term politicalconsiderations, with a playing field tilted by concentrated interests.581 This means that
actions a state takes will often have limited consistency with any overall unified strategy, andwill often foreseeably not help to enhance their power relative to other states, or to advancetheir citizens’ interests.
From a public choice perspective, the most important drivers of conflict are politicians
seeking to get re-elected or otherwise hold on to power, people in government defence
departments, people in defence think tanks who are often government funded and regularly
rotate between governments and think tanks, and finally weapons industries. Because thedefence establishment wants to expand its budget, and military contractors want to make578

Cashman, What Causes War?, p. 461ff.
Cashman, What Causes War?, p. 463.
580
Cashman, What Causes War?, p. 488-489.
581
Richard Hanania, Public Choice Theory and the Illusion of Grand Strategy: How Generals,
Weapons Manufacturers, and Foreign Governments Shape American Foreign Policy, 1st edition(Routledge, 2021), p. 4.
579

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money selling weapons, each group has an incentive to inflate external threats and to argue
against diplomatic solutions which might reduce defence spending.582 Voters only weakly
constrain the preferences of these concentrated interests because voters are uninformed
about foreign policy and lack the incentives to learn more, meaning that they can be easily
led by groups in the foreign policy establishment who are perceived to have expertise.583
On this view, military conflict will happen much more often than it would if states rationallypursued their self-interest; conflict is usually not justified by a rational analysis of its costs
and benefits to the state or its citizens.
It should be stressed that the public choice theory is different to the view that states wage
war in order to gain access to natural resources to benefit capitalists. Noam Chomsky is one
prominent proponent of this analysis of the goals of US foreign policy: he argues that the US
often goes to war to gain oil from other countries.584 But this theory assumes that the US is a
unitary actor with a coherent strategy, which the public choice point of view denies.

12.5.3. How might climate change affect the risk of Great Power War?
I can think of four main mechanisms by which climate change might affect the risk ofinterstate war and, indirectly, war between great powers:
1. Conflict over water resources: Climate change changes the availability of water
resources across the world, leading to conflict between countries over shared water
resources.
2. Economic costs: Climate change imposes large economic costs, especially on lowand middle-income countries, which causes internal upheaval.
3. Civil conflict: Climate change increases the risk of civil conflict, which in turnincreases the risk of interstate war.
4. Mass migration: Due to the economic and agricultural disruption in low- andmiddle-income countries, and to increased storms and flooding, there is a large
increase in migration and displacement within and between countries, which
increases political instability, increasing the risk of conflict.
All of these problems would be most severe in low and middle-income countries that are
reliant on rainfed agriculture, and countries susceptible to riparian and coastal flooding.

Conflict over water resources
The possibility of ‘water wars’ sometimes comes up in discussions of how climate change
might affect the risk of conflict. There currently are around 310 international river basins thatare shared by 150 countries. They cover 47% of the world’s land surface and are home to
52% of the world’s population.585 Climate change will affect the supply of these resources,
582

Hanania, Public Choice Theory and the Illusion of Grand Strategy, 53-54.
Hanania, Public Choice Theory and the Illusion of Grand Strategy, 45-46.
584
Chomsky, Noam, and David Barsamian, 2010, Imperial Ambitions: Conversations on the Post-9/11
World, Metropolitan Books .
585Thomas Bernauer and Tobias Böhmelt, ‘International Conflict and Cooperation over Freshwater
Resources’, Nature Sustainability 3, no. 5 (May 2020): 350–56,
https://doi.org/10.1038/s41893-020-0479-8.
583

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which could, it is argued, lead to increased interstate conflict. However, the evidence
suggests that water resources are a weak driver of conflict.
Actual militarised conflict over water is extremely rare, and the overwhelming response toshared water resources has been cooperation. Wolf et al (2003) note that
“The only recorded incident of an outright war over water was 4500 years ago
between two Mesopotamian city-states, Lagash and Umma, in the region we now callsouthern Iraq. Conversely, between the years 805 and 1984, countries signed morethan 3600 water-related treaties, many showing great creativity in dealing with thiscritical resource”586
Wolf et al (2003) categorise all instances of water conflict and cooperation between 1948
and 2000 using the Basins at Risk (BAR) scale. The Basins at RISK scale ranges from +7
for unification into one nation, to -7 for formal war.

Source: Wolf, Aaron T., Shira B. Yoffe, and Mark Giordano. “International waters: identifying basins at
risk.” Water Policy 5, no. 1 (2003): 29-60.

As this shows:●●●

There were no instances of formal war over water.
Most interactions were mild, ranging between -2 and +4.
The overwhelming pattern is one of cooperation, with international water treaties
strongly outweighing conflict.

586

Wolf, Aaron T., Shira B. Yoffe, and Mark Giordano. “International waters: identifying basins at risk.”
Water Policy 5, no. 1 (2003): 29-60.

403

The instances of conflict are not recent and largely occurred in the Middle East:“Of the 37 cases of acute conflict, 30 are between Israel and one or other of its
neighbors, violence which ended in 1970. Non-Middle East cases account for only
five acute events.”587
The pattern of cooperation has been confirmed by more recent data.(see also de stefano)
Bernauer and Böhmelt (2020) find that since 1948, states’ interaction over freshwater
catchments is, on global average of all catchments and countries in a given year,
characterised by more cooperation than conflict.

587

Wolf et al, “International waters: identifying basins at risk.”, 39.

404

Source: Thomas Bernauer and Tobias Böhmelt, ‘International Conflict and Cooperation overFreshwater Resources’, Nature Sustainability 3, no. 5 (May 2020): 350–56,
https://doi.org/10.1038/s41893-020-0479-8.

Since 1950, the net global average Basins at Risk scale has been above zero, implying
more cooperation than conflict.
There is evidence suggesting that cooperation over shared water resources increases the
chance of broader cooperation between states. Ide et al (2018) found that “For the period
1956–2006, we find that a higher number of positive, water-related interactions in the405

previous ten years makes a shift toward more peaceful interstate relations more likely. Thisis particularly the case for state pairs that are not in acute conflict with each other”.588
Wolf et al (1998) notes that water acts as both an irritant and a unifier.
“The historical record shows that water disputes do get resolved, even among bitter
enemies, and even as conflicts rage over other issues. Some of the most vociferous
enemies around the world have negotiated water agreements or are in the process of
doing so. The Mekong Committee has functioned since 1957, exchanging data
throughout the Vietnam War. Secret “picnic table” talks have been held between
Israel and Jordan since the unsuccessful Johnston negotiations of 1953–55, even as
these riparians, until only recently, were still in a legal state of war. The Indus River
Commission survived through two wars between India and Pakistan. And all ten Nileriparians are currently involved in negotiations over cooperative development of thebasin.”589
Overall, it is not clear what the sign of the effect of shared water resources is. If states dotend to cooperate over water, and that in turn leads to broader cooperation, shared water
resources seem on balance to be a driver of cooperation rather than conflict.
From a theoretical point of view, it is also difficult to see why countries would go to war overwater, rather than negotiate a settlement. As Wolf (1998) says “War over water seems
neither strategically rational, hydrographically effective, nor economically viable.”“Water is neither a particularly costly commodity nor, given the financial resources totreat, store and deliver it, is it particularly scarce. Full-scale warfare, on the other
hand, is tremendously expensive. A "water war" simply would not cost out.
This point was probably best made by the Israeli Defense Forces analyst responsible
for longterm planning during the 1982 invasion of Lebanon. When asked whether
water was a factor in decision-making, he noted, "Why go to war over water? For the
price of one week's fighting, you could build five desalination plants. No loss of life,
no international pressure, and a reliable supply you don't have to defend in hostile
territory".”590
This is one reason that the prospect of conflict over water is less plausible than conflict overmore expensive commodities like oil and gas.

Economic costs
A commonly held belief is that states are more likely to go to war during times of economic
distress. This might be because:591

588

Tobias Ide and Adrien Detges, ‘International Water Cooperation and Environmental Peacemaking’,
Global Environmental Politics 18, no. 4 (2018): 63–84.
589
Wolf, A.T. (1998). ‘Conflict and cooperation along international waterways’, Water Policy, 1(2),251-265
590
Wolf (1998). ‘Conflict and cooperation along international waterways’.
591
Cashman, What Causes War?, 190ff.

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●
●
●
●

War may enable states greater access to markets and to resources.
Leaders think war will stimulate the economy.
Leaders may seek war to divert attention away from hard times.
Leaders may be more willing to take risks during times of economic distress.

The evidence for this is mixed, and there is some evidence that the risk of interstate war isgreatest during economic upswings. One reason for this might be that wars are morefinancially feasible in economic upswings.
A study of global economic cycles (called “long cycles”) and war from 1495 to 1975 by
Joshua Goldstein finds a strong and consistent correlation between the severity of war andeconomic upswings. Although wars have occurred in roughly equal numbers throughout
history in the upswing and downswing phases, the most severe wars have taken place inupswing phases. From 1495 until 1918 each peak in war severity occurred near the end of
an upswing phase.592
Thompson (1982) used data on British, American, French and German business cycle
phases and several types of war initiations from 1792 to 1973. He found that there was no
association between war outbreak and the business.593
Cashman concludes that:“Probably the most we can say about the business cycle is that it may play a role inthe development of war, but its effects are less than clear. Some wars have broken
out in hard times, others have occurred in good times. Neither economic weakness
nor prosperity seem to prevent war.”594

Civil conflict and upheaval
A variety of studies have shown that there is a connection between internal upheaval andthe risk of interstate war.595 Cashman (2009) summarises some of the quantitative evidence“Later studies using more sophisticated methodology and larger databases haveshown much more support for the linkage between internal and external conflict. For
instance, Geller’s analysis of a variety of internal and external conflict variables for
thirty-six states for the years 1959–68 shows that states with high levels of internal
instability are more likely to be involved in external conflict than states with lower
levels of instability. 5 Mansfield and Snyder find a very strong and statisticallysignificant relationship between the presence of civil wars and interstate wars (butnot world wars and not extra-systemic or colonial wars) for the period between 1816
and 1992.6 As part of their research they perform an analysis of the probability that agiven dyad (a pair of countries) will be involved in an interstate war with each other.
Their results indicate that if one country in the pair is undergoing civil war, thisstrongly increases the probability of war. 7 Another analysis by Houweling and592

Cashman, What Causes War?, 191.
William R. Thompson, ‘Phases of the Business Cycle and the Outbreak of War’, International
Studies Quarterly 26, no. 2 (1 June 1982): 301–11, https://doi.org/10.2307/2600653.
594
Cashman, What Causes War?, 191.
595
Cashman, What Causes War?, Ch. 6.
593

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Siccama demonstrated that civil wars and interstate wars cluster in both time andspace, implying that the presence of one increases the probability of the other. 8
Cashman and Robinson’s case studies of seven wars in the last one hundred years(though based on an unscientific sample) found that in five of the six wars between
equals, domestic political variables helped to explain the initiation and escalation of
conflict. In these cases, one of the participant states had significant domestic politicalinstability and/or ruling elites perceived themselves to be politically vulnerable. (Thecases are Iran prior to the Iran-Iraq War, Pakistan prior to the 1971 war with India,
Japan in the 1930s and 1940s, Ethiopia in 1998, and Germany and Austria in
1914.)”596
The theoretical explanations for this are as follows:597
●

●

●

●

Diversionary: leaders could use war to divert public attention away from internalturmoil. This is especially risky for enduring rivals.
○ Cashman (2009) comments that “Ultimately, it is not entirely clear whether thetheory of diversionary wars—applicable to a wide variety of countries andrepresenting a general pattern—has much basis in reality, and if so whatkinds of countries are most likely to use diversionary force and under what
conditions. The support for the theory is certainly not very robust.
Diversionary war appears to be a distinct path to war, but one that is very little
traveled”598
The “kick ‘em while they’re down” theory: predatory states could exploit states who
are weakened by internal upheaval.
○ Cashman (2009) notes that “The kick-’em-while-they’re-down theory certainly
is not an explanation that can be widely applied; only a small percentage of allwars fall into this category.”599
Revolutions alter the balance of threats and power between states and revolutionary
states often face a hostile political environment.
○ There is good evidence that violent revolution increases the risk of war. Of
nine violent revolutions between 1789 and 1979 highlighted by Stephen Walt,,
six led to wars within five years; the other three were near-misses that
resulted in militarized disputes short of full-scale war.600
The internationalisation of civil conflicts in part due to external intervention.
○ Gleditsch, Salehyan and Schultz (2008) found that states experiencing civil
wars are substantially more likely to engage in military disputes than others.601
They find that the presence of a civil war increased the probability of amilitarised interstate dispute (which falls short of war) in a dyad by between
50 percent and 80 percent over the baseline predicted probability dependingon the civil war data set used. They argue that “intervention, externalization,and unintended spillover effects are important sources of international

596

Cashman, What Causes War?, 200.
Cashman, What Causes War?, Ch. 6.
598
Cashman, What Causes War?, 210.
599
Cashman, What Causes War?, 214.
600
Cashman, What Causes War?, 215-218.
601
Kristian Skrede Gleditsch, Idean Salehyan, and Kenneth Schultz, ‘Fighting at Home, Fighting
Abroad: How Civil Wars Lead to International Disputes’, Journal of Conflict Resolution 52, no. 4(2008): 479–506.
597

408

friction”. The increased probability of a militarised interstate dispute due to theexistence of a civil war was roughly equal to the decreased probability of adispute due to joint democracy.

Mass migration, displacement and conflict
I discussed the potential effects of climate change on migration and displacement in the last
Chapter. To recap:
1. The character of future environment-related population movementa. Most environment-related population movement is short-term, internal andtemporaryb. Most environment-related population movement is likely to occur in low- andmiddle-income countries.
2. Displacementa. Weather-related displacement is likely to predominantly occur in Asia, mainly
due to storms and flooding.
b. Weather-related coastal displacement for 4ºC is likely to be strongly
outweighed by migration to coastal regions in Asia.
c. Evidence on displacement from river flooding is weaker, but my roughestimates suggest that warming of 4ºC would increase overall displacementfrom conflict and environmental disasters by around 10% relative to today.
3. Migrationa. It is much harder to quantify the overall effect of climate change on migrationb. Studies have found that environmental change can both increase anddecrease migration.
c. The overall evidence suggests that climate change will probably increase
migration, though the size of the effect is very unclear.
d. The estimates we do have suggest that climate change would increaseinternal migration by around 10%, though this estimate may be substantiallybiased in either direction.
In short, the overall connection between climate change and population movement is unclear
and disputed. The connection between population movement and violent conflict is also
disputed.
The mechanism
There are two main mechanisms proposed in the literature by which migration or
displacement increases the risk of civil (as opposed to) conflict. These are:
1. The Malthusian idea that economic migrants increase conflict over resources.602

602“The influx of large numbers of “environmental migrants” is likely to burden economic and resourcebases in the receiving areas, thus promoting contests over scarce resources.” Vally Koubi, ‘Climate
Change and Conflict’, Annual Review of Political Science 22, no. 1 (11 May 2019): 343–60,
https://doi.org/10.1146/annurev-polisci-050317-070830.

409

2. Population movement could inflame ethnic or cultural tensions with the hostpopulation.603
The plausibility of the mechanisms depends in part on the nature of the migration or
displacement. Migration due to a short-term climatic event is less likely to cause civil conflict
than migration due to a long-term climatic event such as a drought. Brzosky and Fröhlichsummarise the risks associated with different types of environmental migration:

Source: Michael Brzoska and Christiane Fröhlich, ‘Climate Change, Migration and Violent Conflict:
Vulnerabilities, Pathways and Adaptation Strategies’, Migration and Development 5, no. 2 (3 May
2016): 190–210, https://doi.org/10.1080/21632324.2015.1022973.

Salehyan (2008) discusses two mechanisms by which international refugees could increase
the risk of interstate conflict:604

603“In addition, environmental migration could lead to conflict by stirring ethnic tensions that arise
when migrants and residents belong to different ethnocultural groups and the arrival of newcomers
upsets an unstable ethnopolitical balance” Vally Koubi, ‘Climate Change and Conflict’.
604
Idean Salehyan, ‘The Externalities of Civil Strife: Refugees as a Source of International Conflict’,
American Journal of Political Science 52, no. 4 (2008): 787–801,
https://doi.org/10.1111/j.1540-5907.2008.00343.x.

410

1. Refugees constitute a negative externality borne by receiving states, and thesestates may launch military actions to seal their borders, threaten sending regimes
with violence, and even invade the sending state to prevent further flows.
2. Refugee-sending states may violate the sovereign territory of their neighbours inorder to attack political and/or ethnic rivals that have fled across the border as well as
punish the states that harbour them.
The size of the effect on conflict
Unsurprisingly, there is disagreement about the size and sign of the effect of migration anddisplacement on the risk of both civil and interstate war. The IPCC’s Sixth Assessment
Report has the following findings on the connection between climate-related population
movement and conflict:605
1. There is some evidence of an association between climate-related rural-urban
migration and civil unrest such as riots and protests in urban areas.
2. There is evidence that climate-related internal migration has been associated withthe prolongation of conflict in migrant-receiving areas.
3. There has been no association established between international migration andconflict.
The evidence we have suggests that displaced people are less likely to cause civil conflict
than climate-affected migrants because displaced people are likely to be the victims ofstorms and floods, which mainly cause short-term disruption.
However, the findings of the literature on the connection between migration and conflict are
mixed.606
Although not mentioned by the Sixth Assessment Report, Salehyan (2008) explores theconnection between cross-border flows of refugees and ‘militarised interstate disputes’.607
Militarised interstate disputes are events that involve at least two states in which there was a
605

Source: IPCC, Impacts, Sixth Assessment Report, Ch 7, sec. 7.2.7.
“While these and other models are plausible, they need to be substantiated through empiricalevidence. Empirical support so far is scant. The available empirical evidence, while generally
supporting the importance of mediating factors identified in the theoretical literature on the causal
pathway from climate change to migration to violent conflict, crucially points to the under-complexity ofexisting models. As mentioned above, effects of climate change on local livelihoods differ in several
dimensions. Furthermore, only in extreme cases are decisions to migrate shaped by livelihood
conditions in regions affected by climate change, and even then, factors unrelated to climate change
may be important, such as the availability of external humanitarian assistance or existing social
networks. In most cases, the decision to migrate will be shaped by many factors beyond changes inresource availability in a location, including conditions in potential receiving regions but also group,
family and gender relations. Similarly, case studies point to the complexity of the relationship between
population movements and violent conflict. Even where violent conflict has often been seen as most
likely to erupt, such as in the Sahel zone, where farmers and herders compete for scarce resources,
the evidence is mixed, and it remains difficult to explain reality with linear models linking migration toviolent conflict (Benjaminsen, Alinon, Buhaug, & Buseth, 2012; Raleigh et al., 2010). Moreover, casestudies seemingly contradict each other, making generalized statements difficult.” Michael Brzoska
and Christiane Fröhlich, ‘Climate Change, Migration and Violent Conflict: Vulnerabilities, Pathways
and Adaptation Strategies’, Migration and Development 5, no. 2 (3 May 2016): 190–210,
https://doi.org/10.1080/21632324.2015.1022973.
607
Salehyan, ‘The Externalities of Civil Strife’.
606

411

threat, display, or use of military force. Thus a military interstate dispute need not actually
involve war.
Before presenting his quantitative analysis, Salehyan (2008) outlines two illustrative casestudies: the Indian invasion of East Pakistan (now Bangladesh) in 1971, and the Rwandan
invasion of Zaire in 1996. In both cases, cross-border refugees seemed a plausible cause ofor contributing factor to interstate conflict. Salehyan (2008) constructs a regression model
which attempts to isolate the effect of refugee inflows on the probability of conflict between
pairs of countries that either share a boundary or one is a major power.608
The model produces the following results

100,000 refugees is roughly the mean amount of refugees in the dataset for all nonzero
observations. So, the baseline risk for a pair of countries which share a border, are notdemocracies and have no civil wars is around 1.2%. An increase in refugees by 100,000
increases the risk of interstate dispute by around one percentage point. This is larger thanthe effect of both countries becoming democracies, which reduces the risk of dispute by 0.6
percentage points.
Quantifying the overall effect of migration and displacement on interstate war is very difficult
given how rare interstate war now is.

12.5.4. The main drivers of interstate war
All of the theories outlined above have different views about the most important drivers of therisk of interstate war. Wars are usually due to a complex mixture of factors that interact with

608

“Because many pairs of states are unlikely to be involved in military conflicts with one another (e.g.,
Peru and Tanzania), following convention, I restrict the analysis to politically relevant dyads where thestates are either contiguous or involve at least one major power” Michael Brzoska and Christiane
Fröhlich, ‘Climate Change, Migration and Violent Conflict: Vulnerabilities, Pathways and Adaptation
Strategies’, Migration and Development 5, no. 2 (3 May 2016): 190–210,
https://doi.org/10.1080/21632324.2015.1022973.

412

one another, and the factors will vary from case to case.609 Research has however identified
certain drivers and triggers that increase the risk of war. Below, I highlight those most
relevant to the risk of Great Power War.610 All of these factors are correlated andendogenous. Each of the theories outlined above will have different views on how importantthese factors are, or indeed whether they matter at all.

War usually involves contiguous neighbours
For obvious reasons, neighbouring countries are more likely to fight one another. If war does
not involve contiguous neighbours, then it is likely to involve a major power.

A large proportion of wars involve enduring or strategic rivals
Rivals are pairs of states who see each other as threatening.611 Relations between thesestates tend to be fraught with hostility and they tend to have had repeated instances of
diplomatic and military conflict over time. Obvious examples include India and Pakistan, andIsrael and Egypt.

Security dilemma and conflict spiral
A conflict spiral occurs when states respond to hostility with increased hostility. As the levelof hostility escalates, conflict spirals may develop that eventually lead to war. This is closely
related to the security dilemma, which is, as Jervis puts it, that most means of self-protection
simultaneously menace others.612 In a situation of international anarchy, where there is nocentral authority to restrain states from violence, each state is compelled to provide for its
own security. However, the security policies of states are interdependent; greater security for
one state may mean relatively less security for others.
According to Levy and Thompson, “a large array of scientific studies provide evidence tosupport a conflict spiral theory of international conflict”.613 Cashman claims that “most wars
are preceded by militarized disputes or crises that involve escalatory behavior preceding theoutbreak of war that looks like a conflict spiral—though the temporal length of the spiral mayvary considerably”.614

Unresolved territorial disputes
The most likely cause of a conflict spiral is an unresolved territorial dispute. This is especiallytrue for neighbouring countries.

Misperceptions about relative power
Misperceptions about the opponent’s actions, intensions and capabilities and hence about
the degree of threat to one’s own security may create the conditions necessary for the onsetof a crisis.615 In fact, misperceptions by national leaders have frequently been cited as the609

Cashman, What Causes War?, 478.
These are factors identified in Cashman, What Causes War?, Ch. 13; Levy and Thompson,
Causes of War.
611
Cashman, What Causes War?, 252.
612
Levy and Thompson, Causes of War, 290.
613
Levy and Thompson, Causes of War, 289
614
Cashman, What Causes War?, 479.
615
Cashman, What Causes War?, 492.
610

413

immediate cause of war. World War I, Vietnam, Korea, the Middle East Wars of 1967 and1973, the first Gulf War, and the Iraq War of 2003 are typically seen as examples. Cashman
and Robinson look at seven cases of interstate war in the last 100 years and find that “inalmost no case did the leaders in the initiating country, operating under what we have called
the fog of prewar, accurately perceive the situation in which they found themselves.”616
According to Cashman:“Once the crisis begins, these misperceptions may accelerate and exacerbate thelevel of tension. Particularly important are the combination of an over-perception of
the rival’s hostility, treachery, and threat, coupled with an under-perception of boththe capabilities of the rival and the amount of risk involved. Unwarranted confidence
in the ability to compel one’s opponents to back down short of war, or of one’s ability
to defeat the adversary with little cost if war does begin, seem to be an important partof the picture.”617
Misperceptions are not necessarily irrational. They can arise because each side has private
information about relative capabilities, and has incentives to misrepresent such information.

Power transitions
One of the most important and oft-discussed potential drivers of Great Power War is powertransition.618 According to power transition theory, the most dangerous and war-prone
situation is one in which a state that is rising and dissatisfied with the status quo begins toapproach the strength of the leading state in the system and threatens to surpass it in power.
Conflict is more likely to occur when the competing powers are roughly equal in power, so
there is a window in the transition process when conflict is especially likely.
Power transitions could be driven by differential rates of economic growth or military
accumulation, or new strategic alliances by the rising power.
There are several reasons to think that conflict is more likely during power transitions. Firstly,
there is a greater risk of the security dilemma and conflict spirals. As the rising power gains
more economic and military power, they may prompt the hegemon to further increase theirown military power. This could in turn lead to escalating conflict
Secondly, at times of great change in power, there will also be great uncertainty about therelative power of each actor. Misperceptions about relative power are a key driver of war.
Thirdly, commitment problems are severe during power transitions. The rising power could
promise the current hegemon that they will not take advantage of their newfound status once
they are on top, but there is no way to enforce such a promise. This creates incentives forthe current hegemon to pre-emptively strike before it loses hegemonic status.
616

Cashman, What Causes War?, 99.
Cashman, What Causes War?, 492.
618
“Serious great-power crises have in the past been most likely to occur during periods of transitionin the international system (or in regional subsystems) where there are significant shifts in the balanceof capabilities, especially between the dominant power in the system and its major rival(s)—but also
between any set of rivals.” Cashman, What Causes War?, 485.
617

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12.5.4. The influence of climate change on the risk of Great Power War
In this section, I will outline the main drivers of potential conflicts between the major powers
this century

The US and China
As of 5th August 2022, the community forecasting platform Metaculus puts the chance of a
war between the US and China before 2035 at 16%.619
By far the most discussed drivers of the risk of war between China and the US are: (1) the
possibility of power transition - that China will surpass the US as the global hegemon in thenext few decades,620 and (2) flashpoints about contested territories, including Taiwan and the
Senkaku Islands. This assessment is widely shared in US foreign policy circles.621
It is difficult to see how climate change could have much of an effect on these factors. On
both the economic models that I think are most plausible and the pessimistic study by Burkeet al (2015b), climate change of 4ºC would have only small effects on the economic
prospects of the US and China. There is little reason to think that climate change would havemuch effect on the absolute or relative economic performance of the US and China.
It is also clear that climate change has only negligible relevance to the most pressing
territorial disputes relevant to US-China relations. China’s views about Taiwan being part of
One China, for instance, are at best tangentially related to climate change.
From a public choice point of view, much of the risk of war between China and the US isdriven by special interests who have a vested interest in threat inflation, defence spending
and bellicosity. For example, Richard Hanania, a proponent of the public choice theory,
argues that defending Taiwan does not serve American interests. Nonetheless, concentrated
interests have an incentive to inflate the importance of this conflict. The risk is, therefore,
driven by the domestic political economy of the US and the fact that China has a strong
ideological commitment to Taiwan being part of One China. Again, it is difficult to see how
climate change is relevant to either of these factors.

The US and Russia
As of 5th August 2022, forecasters on Metaculus put the chance of a war between the US
and Russia before 2050 at 25%.
619

Note that for these forecasts, the resolution criteria may be different to what some people intuitively
think of as war.
620
Graham T. Allison, Destined for War: Can America and China Escape Thucydides’s Trap?
(Houghton Mifflin Harcourt, 2018).
621“Destined for War, published in 2017 and named a notable book of the year by the New YorkTimes, Financial Times, and Times of London, is the most influential work pushing the Thucydides
Trap as a way to understand current international politics. The book has garnered praise from thelikes of former CIA director David Petraeus, former Secretary of State Henry Kissinger, former
Senator Sam Nunn, and former Secretaries of Defense Ash Carter and William Cohen. It also inspired
the Harvard Thucydides’s Trap Project, an ongoing effort to expand on and facilitate discussion about
Allison’s findings, created by the scholar himself. Cited nearly 800 times as of this writing, perhaps nointernational relations book of the last decade has had as much impact” Richard Hanania, ‘Graham
Allison and the Thucydides Trap Myth’, Strategic Studies Quarterly 15, no. 4 (2021): 13–24.

415

Although there was no Great Power War after 1945, the US and the Soviet Union cameclose to nuclear war on several occasions. The drivers of these near-misses usually centredaround (1) attempts to alter the balance of power, for example during the Cuban Missile
Crisis, and (2) accidents with nuclear weapons, especially mistaken beliefs that the opposing
side had launched a nuclear first strike.622 The risk of such accidents was particularly acuteduring times of crisis.
After the end of the Cold War, tensions between the US and Russia declined, but they haveincreased again recently with Russia’s invasion of Ukraine, which substantially increased therelative risk of a nuclear exchange between NATO powers and Russia.
There are two main causal explanations for Russia’s invasion. One is that the war is aconsequence of the US and its allies refusing to rule out Ukraine eventually joining NATO,
which, from the Russian point of view, imposes intolerable security risks on Russia.623
Proponents of this theory have argued that NATO promised Russia not to expand eastwards
after the fall of the Berlin Wall, but they broke that promise. So, Russia had incentives tostrike before Ukraine had the chance to join NATO.
Another view is that the conflict is a product of Russian nationalistic aggression that would
have occurred even if NATO membership was ruled out for Ukraine.624 The argument is that
Putin does not see Ukraine as an independent sovereign state and so has violated thepost-World War II norm against territorial conquest.
These two different theories will likely give different accounts of the future drivers of the riskof US-Russia war. On the former account, most of the risk stems from potential eastward
expansion of NATO to include countries like Sweden and Finland. Finland shares a borderwith Russia and the two countries have a history of conflict.
The main alternative view is that future conflicts will be driven by increasing Russian
nationalism and unprovoked military aggression against its neighbours, which Putin uses asa tool to cement his domestic dominance.
It is difficult to see how climate change is particularly relevant on either view of the future
drivers of US-Russia conflict risk.

India and Pakistan
As of 5th August 2022, forecasters on Metaculus put the chance of a violent conflict between
India and Pakistan killing at least 100 people by 2050 at 61%. There are no forecasts on therisk of all-out war between the two countries.

622

Toby Ord, The Precipice: Existential Risk and the Future of Humanity (Bloomsbury Publishing,2020), chap. 4.
623
John Mearsheimer, ‘The Causes and Consequences of the Ukraine Crisis’, The National Interest,
June 2022, https://nationalinterest.org/feature/causes-and-consequences-ukraine-crisis-203182.
624
Tanisha Fazal, ‘The Return of Conquest? Why the Future of Global Order Hinges on Ukraine’,
Foreign Affairs, June 2022,
https://www.foreignaffairs.com/articles/ukraine/2022-04-06/ukraine-russia-war-return-conquest.

416

The rivalry contains many of the ingredients for conflict. First, the two countries have ahistory of violent conflict. They have engaged in major conflict three times over the past 60
years (1948, 1965, 1971), and a more limited war over Kargil in 1999.625 Tensions remainhigh today.
“In February 2019, an attack on a convoy of Indian paramilitary forces inIndian-controlled Kashmir killed at least forty soldiers. The attack, claimed by
Pakistani militant group Jaish-e-Mohammad, was the deadliest attack in Kashmir inthree decades. Two weeks later, India claimed to have conducted air strikes targetinga terrorist training camp inside Pakistani territory. Pakistan retaliated a day later with
air strikes in Indian-administered Kashmir. The exchange escalated into an aerial
engagement, during which Pakistan shot down two Indian military aircraft andcaptured an Indian pilot; the pilot was released two days later.”626
Second, the countries share a border. Thirdly, the countries have an unresolved territorial
dispute over Kashmir, which was the cause of two of three major Indo-Pakistan wars in 1947
and 1965, as well as the smaller 1999 war.627 Fourthly, while both countries are currently
democracies, Pakistan only became a democracy in 2003 and its status seems quite fragile.
If either country becomes an autocracy, the risk of war would increase.
The character of the tensions between India and Pakistan changed dramatically once eachcountry acquired nuclear weapons - India in 1974 and Pakistan in 1998. The stakes ofconflict are now much higher, but conflict is also less likely.
It is much more plausible that climate change could play a role in future conflicts between
India and Pakistan than for the other potential conflicts I have considered. Around 40% of
the labour force in each country is in agriculture, a highly climate-exposed sector. Bothcountries are at low latitudes and so will be hit especially hard by rising heat stress,
especially for 4ºC of warming. Thus, there is more scope for the climate-related drivers of
war - economic disruption, migration and civil conflict - to play a role.
Water wars
I argued above that conflicts over water resources are a weak driver of interstate conflict. In
the case of India and Pakistan, the Indus River Commission which manages water
resources between the two countries, survived through two wars between India andPakistan.
Economic performance
I argued above that the connection between economic disruption and war seems weak. But
independent of that, different climate-economy models produce very different estimates of
the costs of climate change for India and Pakistan. One approach is bottom-up: to add up
the costs of climate change sector by sector using the impacts literature. As I discussed in
Chapter 10, two such examples are Kompas et al (2018) and Takakura et al (2021).
Alternatively, one can take a top-down approach. The most pessimistic study, Burke et al625

Levy and Thompson, Causes of War, 4.
See Global Conflict Tracker
627
See Global Conflict Tracker
626

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(2015a) calculates the effects of climate change by inferring from the past effects on
economic output of interannual weather variation.
The tables below show the effects on GDP per capita, according to these different models,
first for 2.5ºC warming, which is most likely on current policy, and then for 4ºC, which has aroughly 5% chance on current policy.628
Warming vspre-industrial

2.5ºC

DamageGDP/person in 2100 vs. no climatechangeGDP/person in 2100 vs. today (SSP4)

Study

Kompas

KompasTakakura

Burke

Takakura

Burke

India

-8%

-4%

-50%

+1595%+1668%+821%

Pakistan

-5%

-4%

-50%

+2275%+2300%+1150%

Warming vspre-industrial

4ºC

DamageGDP/person in 2100 vs. no climatechangeGDP/person in 2100 vs. today (SSP4)

Study

Kompas

KompasTakakura

Burke

Takakura

Burke

India

-15%

-8%

-80%

+1466%+1595%+268%

Pakistan

-9%

-8%

-80%

+2175%+2200%+400%

Source: Calculations are in this google sheet

As this shows, on the bottom-up models, the effects of climate change are fairly small
relative to overall economic growth in the 21st Century. If these models are correct, climatechange will be a relatively small driver of economic disruption in these countries.
Alternatively, on the Burke et al (2015a) estimate, the effects of climate change on the future
economic prospects of India and Pakistan are huge.
I explained in Chapter 10 why in my view, the bottom-up approach is more plausible, but
probably understate the costs of climate change. I could imagine the damage estimates for
4ºC being wrong by a factor of 2-4. However, the probability of 4ºC now seems to be 5%, if
not lower. Even if you think the costs of 4ºC would be 40% of GDP, the expected economic
costs would be around 2% of GDP, making climate change a small factor relative to the other
determinants of war.
Moreover, there is limited evidence that poor economic performance is a major driver of therisk of interstate war.
628

Note that Takakura et al (2021) measure the monetised cost of non-market damages, so their
damage estimate has a different meaning to the other two studies. Note also that Kompas et al and
Takakura et al measure the effect on GDP not GDP per capita. However, in their models, climatechange only affects the numerator of GDP per capita, so this does not affect the results.

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Mass migration and civil conflict
The other two climate-related drivers I discussed in 12.5.3 were mass migration and civil
conflict. These two factors are related because one way that migration might be thought toinfluence the risk of conflict is by influencing the risk of civil conflict.
India will be one of the countries worst-hit by displacement from coastal flooding, with on theorder of tens of thousands of extra people displaced per year, with adaptation, for 1 metre ofsea level rise relative to 30cm (see Chapter 7). The evidence suggests that coastal
displacements will be outweighed by migration to coastal regions. However, due to progressin emissions, 75cm of sea level rise rather than 1 metre now seems like the most likely
scenario. So, this is likely something of an overestimate of the increase in coastal
displacements due to climate change.
Data on displacements from river flooding is worse, and it is hard to give an overall figure,but my own guess based on the limited data is that river flood displacements would becomparable to coastal displacements.
In 2020, in India 3.9 million people were displaced by weather-related and geophysicaldisasters, and in Pakistan the number was 829,000.629 Thus, the increased displacementsfrom storms and flooding from climate change in these countries seem likely to account for a
relatively small fraction of overall displacements.
It is much harder to predict how climate change will affect voluntary migration within India
and Pakistan. Since economic factors are the main driver of migration, the size of the effectdepends mostly on the size of the effect climate change will have on economic factors inIndia and Pakistan, which I discussed above. On the models that in my view are moreplausible, climate change is a relatively small factor relative to the other determinants ofeconomic growth in those countries, especially given recent progress in emissions.
The final part of the migration causal chain is ‘migration => conflict’. As I discussed in
Chapter 11 and earlier in this chapter, there is great uncertainty about the size of the effect of
migration on conflict. The two mechanisms discussed in the literature are:(a) Cross-border refugees => interstate conflict(b) Migration => civil conflict => interstate conflict
On (a), there is also evidence that large flows of refugees across borders increase the risk of
conflict between states and indeed that was a driver of one previous conflict between India
and Pakistan in 1971, when refugees fled what was then East Pakistan (now Bangladesh)
into India following conflict between the Pakistan army and Bengali separatists.630 However,most weather-related migration is typically short-term, internal and temporary. The vast
majority of weather-related migrants in Pakistan or India are likely to stay in-country.
Cross-border India-Pakistan migration is likely to be a small fraction of overall climate-related
migration.

629
630

IDMC, Global Report on Internal Displacement 2021, p. 51.
Salehyan, ‘The Externalities of Civil Strife’, 792-793.

419

On (b), there is some evidence that mass migration increases civil unrest and protest in
urban areas and prolongs ongoing civil conflicts. This might indirectly increase the risk ofpolitical upheaval in India and Pakistan and therefore the risk of interstate war.
Other climate impacts might also increase the risk of civil conflict, as discussed earlier in thischapter. If you take the Burke et al (2015b) estimate of the effect of climate change on
conflict at face value, then 2ºC would increase the risk of civil conflict by around 50%, while
4ºC would increase it by around 100%. However, this estimate is mainly informed by datafrom Africa and I think it is a substantial overestimate, probably by a factor of two or more.
Below, I will construct a rough model that will bring all of these factors together to estimate
the size of the effect of climate change on conflict.

India and China
As of 5th August 2022, forecasters on Metaculus put the probability of a war between India
and China before 2035 at 20%.
The relationship has many of the ingredients necessary for conflict. Firstly, India isdemocratic and China is authoritarian. Secondly, the two countries have a history of conflictover disputed territory on their shared border. India and China went to war in 1962 overdisputed border territories around Aksai Chin.631 Indian forces were soundly defeated, 7,000
men having been killed or captured. The border dispute continues to this day.632 Fighting in2020 resulted in the deaths of both Indian and Chinese soldiers, and shots were fired in
September 2020 for the first time in 45 years.633
India is also a member of the Quad group of countries (along with US, Japan and Australia)
and it is unclear what role they will play in defending Taiwan if China ever does invade.634
Climate change seems like a weaker driver of this potential conflict than the India-Pakistan
conflict. The main reason for this is that China is now relatively wealthy and so will be able toadapt to climate damages, and models suggest that it will not be hit especially hard byplausible levels of climate change. Moreover, climate change seems unlikely to affect thebalance of power between the two countries: China’s GDP per capita is already tenfold
higher than India’s, and China is experiencing much higher rates of economic growth. The
only plausible mechanism of impact is if climate change causes civil unrest in India, which
increases the risk of interstate war.
As discussed above, climate change might also drive migration and displacement in eachcountry, but the vast majority of climate-related migrants are likely to remain in their home
631

Britannica, Sino-Indian War 1962.
‘India-China Dispute: The Border Row Explained in 400 Words’, BBC News, 25 January 2021, sec.
Asia, https://www.bbc.com/news/world-asia-53062484.
633‘First Time in 45 Years, Shots Fired along LAC as Troops Foil China’s Bid to Take a Key Height’,
The Indian Express, 9 September 2020, 45,
https://indianexpress.com/article/india/first-time-in-45-years-shots-fired-along-lac-as-troops-foil-chinas-bid-to-take-a-key-height-6588658/.
634
Huynh Tam Sang and Trang Huynh Le, ‘How Far Could the Quad Support Taiwan?’, The Diplomat,
August 2022.
632

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country. In the absence of political repression, it is difficult to see why there would besubstantial climate-induced cross-border refugee movements between India and China.

12.5.5. Which conflicts would do the most damage?
Some of the conflicts outlined above have the capacity to be far more destructive thanothers, and have the potential to have a disproportionate impact on how the future will go.
How potentially important these conflicts are depends in large part on the overall level of therisk posed by the different direct risks (AI, biorisk and nuclear war) of which Great Power
War is a stressor.
In The Precipice, Toby Ord argues that AI and biorisk account for the majority of direct
existential risk in the next 100 years.635
●
●
●
●
●

AI accounts for 10 percentage points
Biorisk accounts for 3pp
Unforeseen anthropogenic risks account for 3pp
Other anthropogenic risks account for 3pp
Nuclear war accounts for 0.1pp.

For Ord, most of the existential risk we face this century will be driven by emerging
technologies with destructive power, especially AI and biotechnology. Similarly, on Clare’smodel, the indirect risk of Great Power War is driven by the prospect that the competing
powers would develop and deploy novel weapons of mass destruction.

AI
Insofar as Great Power War is a stressor of the risk from AI, this must almost entirely be
driven by the risk of war between the US (and its allies) and China. The US and China
account for around 60% of all AI publications and 90% of highly cited AI publications.636 The
US, China and the EU account for 84% of private investment in AI.637 It seems highlyplausible that transformative AI systems will be developed either by American companies or
the Chinese government.
One concern is that increasing tension between the US and China could cause each ofthese countries to race to develop advanced AI systems that would enable them to gain
strategic dominance over the other. This could lead to an existential catastrophe either due
to accidents as each country skimps on safety, or deliberate misuse by the controlling
government.
Since climate change is a weak lever on the risk of US-China conflict, it is also a weak leveron the risk of AI-induced existential catastrophe.
635

Toby Ord, The Precipice: Existential Risk and the Future of Humanity (Bloomsbury Publishing,2020), 167.
636
Ashwin Acharya Brian Dunn, ‘Comparing U.S. and Chinese Contributions to High-Impact AI
Research’, CSET, Jan 2022, Fig. 1,
https://cset.georgetown.edu/publication/comparing-u-s-and-chinese-contributions-to-high-impact-ai-research/637
This data is from Statista.

421

Biorisk
The capacity to invest in bioweapons is more evenly spread across the world. Despite
having relatively low GDP per capita, the Soviet Union developed Biopreparat, the world’s
largest bioweapons program, which started in 1974 and at its height employed more than
9,000 scientists to weaponise various diseases, including smallpox and anthrax.638 It is,therefore, possible for relatively poorer countries to develop dangerous bioweapons
programs if they are in a conflict situation and perceive there to be security benefits fromdoing so.
The development of such weapons seems strategically irrational, but such programs cannevertheless exist.
The extent to which Great Power War might be a stressor of biorisk depends on the natureof future biorisk. The existential risk from biotechnology might come from:
1. Accidental release from public health-focused research in university or governmentlabs
2. Accidental or deliberate release from a state bioweapons program
3. Deliberate release by terrorists
If most of the risk will come from public health-focused research in university or government
labs, then the US, China and the EU account for most of the risk as they have the greatest
capacity to fund such research, and are its main funders today. The vast majority ofBiosafety Level-4 labs, which handle the world’s most dangerous pathogens, are in the EU,
China and the US.639 This seems likely to remain true in the coming decades, given theeconomic power of these regions. It is not clear whether increased conflict would increase
the risk of this kind of research. If it does, then climate change is a weak lever on thismechanism because it is a weak lever on the risk of US-China conflict.
It is difficult to know how state bioweapons programs might develop over the coming century,
especially in the face of increasing tension between great powers. The capacity to build statebioweapons programs is likely to be concentrated in the US, the EU and China given theireconomic power and dominance of current biotechnology research.
However, it is unclear whether they will have the greatest willingness to develop bioweapons
programs. Russia has shown the greatest historical willingness to develop a substantial
bioweapons program, and the US believes that it still has one today.640 Insofar as Russia
drives much of the future risk of accidental or deliberate release, the main conflict-related
driver of this risk is the conflict between NATO powers and Russia, which is Russia’s main
security concern. As I argued above, climate change seems like a weak lever on this conflict.
638

Toby Ord, The Precipice, 202, 132.
Filippa Lentzos and Gregory Koblentz, ‘Fifty-nine labs around world handle the deadliest
pathogens – only a quarter score high on safety’, 14 June 2021, King’s College,
https://www.kcl.ac.uk/fifty-nine-labs-around-world-handle-the-deadliest-pathogens-only-a-quarter-score-high-on-safety
640“The United States assesses that the Russian Federation (Russia) maintains an offensive BW
program and is in violation of its obligation under Articles I and II of the BWC. The issue of complianceby Russia with the BWC has been of concern for many years.” US state department.
639

422

As technology develops, bioweapons will become increasingly accessible to countries withmore limited economic power, and some of these countries may be willing to develop
bioweapons programs. For example, there is some evidence that China and Pakistan arecollaborating on a program which will develop novel biological weapons.641 So, it may well bethat increased international tension could drive developing countries to develop dangerous
bioweapons programs.
In the future, the capabilities of terrorists to develop bioweapons may also be spread acrossthe world. Tension between the Great Powers is a weaker lever on this than on statebioweapons programs, but may nevertheless have some effect, as it might reduce the abilityof states to cooperate to reduce bioterror.

Nuclear weapons
At present, the US and Russia hold the vast majority of the world’s nuclear warheads.
Assuming that this remains true in the future, the US and Russia account for the majority of
the risk from nuclear war in the next 100 years. However, it is possible that if tensions rise,other states will also start to build up their nuclear arsenals and so the picture may change inthe future. Indeed, China looks set to increase its arsenal in the future.642

Other technological risks
China, the EU and the US have the greatest capacity to invest not just in AI but otherpotentially destructive technologies. China, the US and the EU account for the vast majorityof global spending on R&D.643 Thus, these regions plausibly account for most of the risk fromnovel weapons of mass destruction this century.

12.5.6. Quantifying the indirect risks of climate change
It is extremely difficult to provide reliable quantitative estimates of the risk of Great Power
War caused by climate change. Nonetheless, I have built a model that attempts to put somenumbers on the arguments I have discussed so far. I think this is valuable for several
reasons. Firstly, it clarifies the cruxes of disagreements and allows focused discussion on
those cruxes. Secondly, it allows us to prioritise different problems. If we do not quantify, we
will still have judgments about how important different considerations are. Models makethese considerations precise.
The downside of quantitative models is that they can cause false precision and anchor
readers, even if the model is not good and has not been subject to scrutiny. Many of theconsiderations I have discussed are very difficult to quantify because there is essentially noliterature on them.

641‘China Entered Covert Deal with Pakistan Military for Bio-Warfare Capabilities against India,
Western Countries: Report’, The Times of India, 24 July 2020,
https://timesofindia.indiatimes.com/india/china-entered-covert-deal-with-pakistan-military-for-bio-warfa
re-capabilities-against-india-western-countries-report/articleshow/77139556.cms.
642
Tong Zhao, ‘What’s Driving China’s Nuclear Buildup?’, Aug 2021, Carnegie Endowment for Peace.
643
This data is summarised by Wikipedia here.

423

With those caveats in my mind, my model is here. Given the discussion in this Chapter, I
assume that all of the risk stems from the India v Pakistan and India v China conflicts, and inturn that most of the risk of existential catastrophe stems from AI, biorisk and currently
unforeseen technological risks. My best guess estimate is that the risk of ‘climate change =>great power war => existential catastrophe’ is on the order of 1 in 100,000. My pessimistic
estimate puts the figure at 1 in 1,000. The pessimistic estimate makes the following
assumptions, among others:●●

Conflict between India and Pakistan accounts for:
○ 10% of the risk from ‘Great Power => AI’.
○ 30% of ‘Great Power War => biorisk’○ 20% of ‘Great Power War => other anthropogenic risk’Climate change accounts for a third of civil conflict risk in India and Pakistan, and civil
conflict accounts for 20% of the total risk of war between India and Pakistan thiscentury.

12.5.7. Overall judgement on climate change and Great Power War
Overall, climate change seems likely to impose burdens on developing countries that will
increase the risk of instability and civil conflict. This in turn is a plausible stressor or risk
between major powers: worlds with greater instability and fewer economic opportunities
broadly seem likely to be ones with more interstate war. Thus, I think the overall sign of theeffect of climate change on interstate conflict is clear.
One highly controversial but commonly discussed example of this kind of effect is the Syrian
Civil War. Some scholars argue that climate change-induced multi-year droughts played amajor role in driving this conflict, though this is heavily disputed.644
During the conflict, the US and Russia provided military support to the opposing sides, andcarried out air strikes against anti-Assad and pro-Assad forces, respectively. US commandos
destroyed a force of Russian mercenaries with strong links to the Russian government,645
though the risk of a hot war between the US and Russia still seemed small.646
Even if climate change did not in fact play a major role in the Syrian Civil War, it is certainlyplausible that it could contribute to increasing civil conflict in the future. Thus, this does
illustrate one way that a domestic conflict could spill over to increase tensions between
major powers.
644“A small set of published studies has argued inconclusively over the role of drought in causing the
Syrian civil war (Gleick, 34 2014);(Kelley et al., 2015);(Selby et al., 2017) [also 16.2.3.9].”“Migration from drought-stricken areas to local urban centres has been used to suggest a climate
trigger for the Syrian conflict (e.g.(Ash and Obradovich, 2020)). However, this link has 22 beenstrongly contested by research that contextualizes the drought in wider political economic approaches
and existing migration patterns (De Châtel, 2014);(Fröhlich, 2016);(Selby, 2019) [16.2.3.9].” IPCC,
Climate Change 2022: Impacts, Adaptation and Vulnerability, Sixth Assessment Report, 2022, Ch. 7,
sec. 7.2.3
645
Thomas Gibbons-Neff, ‘How a 4-Hour Battle Between Russian Mercenaries and U.S. Commandos
Unfolded in Syria’, The New York Times, 24 May 2018, sec. World,
https://www.nytimes.com/2018/05/24/world/middleeast/american-commandos-russian-mercenaries-sy
ria.html.
646
There is an overview of the Syrian Civil War in the Encyclopaedia Britannica here.

424However, I also think it is true that climate change is a relatively weak lever on futureinterstate conflict. It is difficult to come up with clear causal stories which explain why levelsof climate change that now seem plausible would make states go to war. This is especiallytrue of many of the most potentially destructive conflicts, between the US and Russia andthe US and China. But it also seems true for conflicts between India and Pakistan, countries
with nuclear weapons, a history of conflict, and which will be relatively badly affected byclimate change. Although they will be badly affected, the levels of climate change that now
seem likely have relatively little impact relative to the other determinants of conflict risk
between those countries.
Moreover, working on climate change seems like a highly indirect way to work on reducing
the risk of Great Power War. It seems to be more impactful to work directly on the mostthreatening conflicts and on the levers that are widely recognised to drive them, which
include:●●●●●Finding the right balance between deterrence and the conflict spiral, especially withrespect to disputed territories
Avoiding race dynamics for military technology and weapons of mass destruction
Strengthening international management of emerging weapons of mass destruction
Making a clear national strategy to manage power transitions
Etc.

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13. Interpersonal conflict, aggression and violence
There is some evidence in the literature that climate change could affect levels of aggression
and violence at the person level. One might worry about this because climate change has
global effects and raising impulsivity, aggression and violent inclination across the whole
global population seems robustly bad.

13.1. Context, base rates and trends
Homicide rates have been declining in Western Europe and the US for hundreds of years.

Rates of violent crimes vary substantially on short timescales. For example between 1990
and 2000, the homicide rate in the US dropped by 40%.

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13.2. Cross-sectional relationshipThe cross-sectional relationship between temperature and rates of violent crime withinjurisdictions and between jurisdictions is at best not very strong. For example, within the US,
this is the relationship between temperature and violent crime - just from eyeballing, therelationship seems quite weak:427

Source: Matthew Ranson, ‘Crime, Weather, and Climate Change’, Journal of Environmental
Economics and Management 67, no. 3 (1 May 2014): 274–302,
https://doi.org/10.1016/j.jeem.2013.11.008.

The cross-country correlation between temperature and homicide is negative across the 37
European countries with populations greater than one million (r = −0.43, 95% confidence
interval: −0.66, −0.12),647 i.e. the higher temperature is, the lower is crime.
There is a mild cross-country correlation between homicide and latitude in the Northern
Hemisphere, but almost none for the Southern Hemisphere

647

Martin Daly and D. B. Krupp, ‘The Importance of Being Explicit’, Behavioral and Brain Sciences 40(2017).

428

Source: Agustin Fuentes et al., ‘The CLASH Model Lacks Evolutionary and Archeological Support’,Behavioral and Brain Sciences 40 (2017).

Across Russia, there is a negative correlation between regional violent crime rates andaverage annual temperature:429

Source: Pavel N. Prudkov and Olga N. Rodina, ‘Russian Data Refute the CLASH Model’, Behavioral
and Brain Sciences 40 (2017).

Of course, there are lots of confounders for these relationships. But this is indicative
evidence that temperature is not a major driver of rates of violence or aggression.
Regarding the issue of confounders, consider this example. Rates of violent crime are higher
in Southern US states compared to Northern states, but some scholars attribute this tohonour culture, which developed in response to the herding economy of frontier states in theSouth.648 As discussed above, trying to control for confounders in cross-sectional studies islikely to suffer from omitted variable bias and over-controlling.

13.3. Experimental and quasi-experimental evidence
The connection between temperature and interpersonal violence and aggression seems to
be much less controversial than the connection between temperature and intergroup conflict,with most research finding a positive relationship.649

648

Paul AM Van Lange, Maria I. Rinderu, and Brad J. Bushman, ‘Aggression and Violence around theWorld: A Model of CLimate, Aggression, and Self-Control in Humans (CLASH)’, Behavioral and Brain
Sciences 40 (2017): 5.
649“Research has found overwhelming evidence for a positive relationship between violence andtemperature.” Dennis M. Mares and Kenneth W. Moffett, ‘Climate Change and Interpersonal Violence:
A “Global” Estimate and Regional Inequities’, Climatic Change 135, no. 2 (1 March 2016): 297–310,
https://doi.org/10.1007/s10584-015-1566-0.

430

There are experimental studies which suggest that higher temperatures increase
aggression.
“Other experiments have found that participants are more likely to be hostile, more
likely to perceive others as being hostile, and act more aggressively toward anotherperson after being randomly assigned to sit in an uncomfortably hot room instead ofa room at a comfortable temperature. One study manipulated temperature in a police
training program. They found those police officers randomly assigned to an
uncomfortably hot condition were more likely to respond to a burglary scenario by
drawing their weapon and by firing it than those who were in a
comfortable-temperature condition.”650
There is also strong evidence that property and violent crime is highly seasonal, with morecrime occurring in the summer.651
There are also studies exploring natural experiments or quasi-experiments in order to isolate
the causal effect of temperature. As discussed above, these studies can correct for theomitted variable and over-controlling problems by holding cultural and political effects
constant and exploiting exogenous variation in weather.
“In such time-period studies, we also find a consistent pattern: the hotter the period,
the higher the rate of violence. As noted, this can range from a few hours to days,
months, or years.”652“This behavior might contribute to results of natural experiments that use versions ofEquation 3, in which it is generally found that crimes between individuals - particularly
violent crimes such as assault, murder, rape, and domestic violence - tend toincrease at higher temperatures in Australia (Auliciems & DiBartolo 1995), India(Blakeslee & Fishman 2014, Iyer & Topalova 2014), Mexico (Baysan et al. 2014), thePhilippines (Wetherley 2014), Tanzania (Burke et al. 2014), and the United States(Anderson et al. 1997, 2000; Cohn & Rotton 1997; Rotton & Cohn 2000; Bushman etal. 2005; Jacob et al. 2007; Card & Dahl 2011; Mares 2013; Ranson 2014).”653
The meta-analysis by Burke et al (2015b) collects together studies of this type and finds thatper standard deviation increase in temperature, there is a 2.3% increase in interpersonal650

Andreas Miles-Novelo and Craig A. Anderson, ‘Climate Change and Psychology: Effects of Rapid
Global Warming on Violence and Aggression’, Current Climate Change Reports 5, no. 1 (2019):
36–46.
651“Put simply, crime has a strong seasonality that needs to be properly accounted for in any
examination of the relationship between weather and crime” Ryan D. Harp and Kristopher B.
Karnauskas, ‘The Influence of Interannual Climate Variability on Regional Violent Crime Rates in the
United States’, GeoHealth 2, no. 11 (2018): 356–69, https://doi.org/10.1029/2018GH000152.
652
Andreas Miles-Novelo and Craig A. Anderson, ‘Climate Change and Psychology: Effects of Rapid
Global Warming on Violence and Aggression’, Current Climate Change Reports 5, no. 1 (2019):
36–46.
653
Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Climate and Conflict’, Annual Review of
Economics 7, no. 1 (2015): 577–617, https://doi.org/10.1146/annurev-economics-080614-115430.

431

conflict, while a standard deviation change in rainfall causes a 0.59% increase ininterpersonal violence.654

13.3.1. Methodological issues
Some of the methodological issues with the intergroup conflict studies recur for theinterpersonal violence studies.

Weather different to climate
Panel studies test weather variability rather than climate change, and these processes might
be importantly different.

Heavy US focus
The studies are quite geographically limited and heavily US focused. Of the 18 interpersonal
conflict studies, 9 are based in the US, with the rest covering Australia, India, Mexico andother countries.655 The relevance for the rest of the world is therefore not entirely clear.

Implausible precision
The estimates of the mean effect in Burke et al (2015b) seem implausibly precise. They
estimate an effect of around 2.3%, with almost no chance that the true effect is <1% or >4%.
This level of precision seems implausible given the noisiness of the dependent andindependent variable and the many other factors that could affect these variables. I think
what is going on here is that they are estimating an effect size on the assumption that theirmodel is correct, but in reality there is much more uncertainty about their model than there isconditional on their model.

Reporting bias
As discussed in the Chapter 12, Burke et al (2015b) find evidence of reporting bias, but do
not correct for it. This likely biases the estimate upwards. One can see the publication bias
visually in the figure below.

654

Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Climate and Conflict’, Annual Review of
Economics 7, no. 1 (2015): Table 2, https://doi.org/10.1146/annurev-economics-080614-115430.
655
Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Climate and Conflict’, Annual Review of
Economics 7, no. 1 (2015): Table 1, https://doi.org/10.1146/annurev-economics-080614-115430.

432

Source: Marshall Burke, Solomon M. Hsiang, and Edward Miguel, ‘Climate and Conflict’, Annual
Review of Economics 7, no. 1 (2015): https://doi.org/10.1146/annurev-economics-080614-115430.

The mean effect is shown by the black horizontal line. Since the mean effect found is so
close to zero, one would expect studies to be dotted either side of zero. In fact, all of thestudies find a positive effect and the vast majority are statistically significant.

13.4. Mechanisms
The two most popular theoretical explanations for the effect of temperature on interpersonal
violence are Routine Activity Theory and General Aggression Theory.656
Routine Activity Theory - Increasing temperatures likely increases the frequency ofinteractions between people, thereby raising the number of violent encounters. For
656

Dennis M. Mares and Kenneth W. Moffett, ‘Climate Change and Interpersonal Violence: A “Global”
Estimate and Regional Inequities’, Climatic Change 135, no. 2 (1 March 2016): 297–310,
https://doi.org/10.1007/s10584-015-1566-0.

433

instance, during pleasant temperatures people exit their homes and go to bars or
restaurants where they are more likely to encounter others and drink alcohol.
General Aggression Theory - Explains increases in violence as a result ofheightened aggressive emotions brought on by warmer conditions. On this theory,
heat raises irritability levels, which leads to increased aggressiveness.
There does not appear to be any consensus in the literature about whether these twotheories or any others are true.
Adjudicating between the truth of these two theories is relevant to long-termism because if
the General Aggression Theory is true, then the results we see in the quasi-experimental
studies reflect an increase in aggression that would be across the entire global population if
temperatures were to increase. This is a worse outcome than if the Routine Activities Theory
is true.

13.5. Effect size
The literature generally suggests that the effect size of climate, temperature, precipitation
and weather on aggression and violence is at most very small.
The cross-sectional studies suggest that temperature is not a major driver of interpersonal
violence.
According to Ferguson and Dyke (2012), the effect sizes found in laboratory studies of theGeneral Aggression Theory tend to find effect sizes between r = 0 and r = 0.2, though they
don’t provide references, which is frustrating.657 This means the General Aggression Theory
could explain at most 4% of the variation in aggression. Similarly, in a review of the evidence
on the General Aggression Theory, Anderson et al (2000), who are generally supportive ofGeneral Aggression Theory, note that laboratory studies have produced inconsistent results:“hot temperatures sometimes increase and sometimes decrease aggressive behavior inlaboratory settings".658 In a meta-analysis of the laboratory studies, Anderson et al note thatacross the studies “there was no consistent effect on temperature”, though there was aconsistent effect if other conditions obtained, such as the contextual presence of negative or
neutral affect. Of course, it is natural to be concerned about the methodological quality ofthese sorts of psychology studies.
Burke et al (2015b) estimate that a one standard deviation change in a climate variable
causes a 2.3% increase in interpersonal violence. So, on RCP8.5, following an 8 standard
deviation in temperature in 80 years, we would expect an 18% increase in interpersonal
violence, other things equal.

657

Christopher J. Ferguson and Dominic Dyck, ‘Paradigm Change in Aggression Research: The Time
Has Come to Retire the General Aggression Model’, Aggression and Violent Behavior 17, no. 3(2012): 220–28.
658
Craig A. Anderson et al., ‘Temperature and Aggression’, in Advances in Experimental Social
Psychology, vol. 32 (Academic Press, 2000), 63–133,
https://doi.org/10.1016/S0065-2601(00)80004-0.

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For comparison, as mentioned above, the US homicide rate dropped by 40% between 1990
and 2000, and many European countries have seen comparable declines between 1990 and2017, despite warming of about 0.5ºC. Other factors seem much more important as driversof aggression. For example, Reyes (2007) argues that a reduction in childhood lead
exposure in the 1970s and 1980s accounted for more than half of the violent crime decline of
the 1990s.659 Levitt argues that increases in the number of police, the rising prison
population, the waning crack epidemic and the legalisation of abortion all explain the decline
in US crime in the 1990s.660
Overall, the effect of temperature on aggression seems like a negative effect of climatechange, but one that is very small and will be swamped by the other determinants ofaggression.

659

James J. Feigenbaum and Christopher Muller, ‘Lead Exposure and Violent Crime in the Early
Twentieth Century’, Explorations in Economic History 62 (1 October 2016): 52,
https://doi.org/10.1016/j.eeh.2016.03.002.
660
Steven D. Levitt, ‘Understanding Why Crime Fell in the 1990s: Four Factors That Explain the
Decline and Six That Do Not’, Journal of Economic Perspectives 18, no. 1 (March 2004): 163–90,
https://doi.org/10.1257/089533004773563485.

435

Appendix 1. The WCRP estimate of climate
sensitivity
Prior selection
What we ought to believe depends on the beliefs we had prior to considering new evidence,and the new evidence. This is the insight of Bayesian epistemology. Many of the models that
input into the IPCC’s Fifth Assessment Report estimates of climate sensitivity have very
heavy right tails, suggesting a non-negligible probability of very high climate sensitivity. This
seems to be mainly a product of the fact that these posterior distributions are updated from a
uniform prior over climate sensitivity with an arbitrary cut-off at 10°C or 20°C. I checkedsome of the papers for IPCC models of climate sensitivity that have a long tail and theyeither: explicitly use a uniform prior which makes a large difference to tail behaviour,661 or do
not say whether or not they use a uniform prior (but I would guess that they do). When this iscombined with the likelihood ratio from the data and evidence that we have, we end up witha posterior distribution with heavy right tails.
However, as Annan and Hargreaves (2011) have argued, the use of a uniform prior isunjustified. Firstly, climate scientists use these priors on the assumption that they involve“zero information”, but this is not the case. Secondly and relatedly, the cut-off is arbitrary.
Why not have a cut-off at 50°C?
Thirdly, it is not the case that before analysing modern instrumental and paleoclimatic dataon the climate, we would rationally believe that a doubling from pre-industrial levels of
280ppm to 560ppm would be equally likely to produce warming of 3°C or 20°C. In 1896,
Arrhenius estimated that climate sensitivity was between 5 and 6°C. In 1906, he revised thisdown to 4°C. Before analysing modern data sets, scientists had settled on a 67% confidence
range of between 1.5 and 4.5°C in 1979.662 As Annan and Hargreaves note:“This estimate was produced well in advance of any modern probabilistic analysis of
the warming trend and much other observational data, and could barely have been
affected by the strong multidecadal trend in global temperature that has emergedsince around 1975. Therefore, it could be considered a sensible basis for a credible
prior to be updated by recent data.”663
Arguments from physical laws also suggest that extreme values of 10°C or 20°C are
extremely unlikely.

661

Olson Roman et al., “A Climate Sensitivity Estimate Using Bayesian Fusion of Instrumental
Observations and an Earth System Model,” Journal of Geophysical Research: Atmospheres 117, no.
D4 (February 21, 2012), https://doi.org/10.1029/2011JD016620; Lorenzo Tomassini et al., “Robust
Bayesian Uncertainty Analysis of Climate System Properties Using Markov Chain Monte Carlo
Methods,” Journal of Climate 20, no. 7 (April 1, 2007): 1239–54, https://doi.org/10.1175/JCLI4064.1.
662
J. D. Annan and J. C. Hargreaves, “On the Generation and Interpretation of Probabilistic Estimatesof Climate Sensitivity,” Climatic Change 104, no. 3–4 (February 1, 2011): 429–30,
https://doi.org/10.1007/s10584-009-9715-y.
663
Annan and Hargreaves, 429–30.

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Sherwood et al (2020) corrects for this by testing the exploring the implications of a range ofdifferent priors.

Using multiple lines of evidence
Many lines of evidence are relevant to climate sensitivity, including (1) the physics offeedback processes, (2) the historical climate record (since 1800), and (3) the paleoclimate
record (millions of years into the past). Because the underlying science spans many
disciplines, individual scientists generally only fully understand one or a few of the strands.
Consequently, scientists have tended to produce estimates of climate sensitivity using only
one strand of evidence. Each approach therefore leaves out potentially important
information. Sherwood et al (2020) corrects for this problem by formally taking account of all
relevant lines of evidence.664

Formally producing a posterior distribution
Our posterior beliefs are what we believe once we have updated our prior beliefs given newevidence. For instance, in advance of considering the historical data, we might believe thatclimate sensitivity is between 2 and 7°C. Once we learn of new evidence, this might narrow
our uncertainty to between 3 and 6°C. The correct way to update our prior beliefs is throughformal Bayesian updating.
The last IPCC report produced its estimate of climate sensitivity in an informal way, while
trying to account for all relevant lines of evidence.665

664“Quantifying ECS is challenging because the available evidence consists of diverse strands, noneof which is conclusive by itself. This requires that the strands be combined in some way. Yet, because
the underlying science spans many disciplines within the Earth Sciences, individual scientists
generally only fully understand one or a few of the strands. Moreover, the interpretation of each strand
requires structural assumptions that cannot be proven, and sometimes ECS measures have been
estimated from each strand that are not fully equivalent. This complexity and uncertainty thwarts
rigorous, definitive calculations and gives expert judgment and assumptions a potentially large role.”
p2
665“As a consequence, in this chapter, statements using the calibrated uncertainty language are a
result of the expert judgement of the authors, combining assessed literature results with an evaluationof models demonstrated ability (or lack thereof) in simulating the relevant processes (see Chapter 9)
and model consensus (or lack thereof) over future projections” IPCC, Climate Change: The PhysicalScience Basis, 1040.

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