This document discusses key open issues regarding catastrophic impacts of climate change. It focuses on the debate around whether temperature impacts economic output through level effects or growth effects. The growth effect specification suggests much larger potential economic losses from climate change compared to the level effect specification. However, there are several statistical and theoretical problems with the growth effect approach. Improving the resolution of economic data and further research is needed to better understand the relationship between temperature and economic output. Settling this debate is important for accurately assessing climate change impacts, particularly the potential for catastrophic economic impacts.

1. Catastrophes in Climate Change:
Key Open Issues
William Nordhaus
Yale University
October 18, 2021
OECD Conference on Economic Modeling of
Climate and Related Tipping Points
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2. Categories of Catastrophes
• Micro catastrophes (low-lying islands and
regions, extreme storms, health, …): not discussed
here
– Virtually certain at different scales (I, II, … V, …)
– Significant analytical and empirical work here
• Complex catastrophes (uncertainty, fat tails,
irreversibility, hysteresis)
• Macro catastrophes (global scale)
– Geophysical
– Ecological
– Human (economic, non-market) 2

3. Geophysical
• “Tipping elements” discussed by Lenton and
others and analyzed recently by Dietz et al.
Key research issues:
• Need improvement in “tipping” metrics
– Almost sure that tipping is not passing a T threshold so
not a good rationale for x °C targets
– For ice sheets, better metric is something like T-years.
• Need to improve coupling of economic and
geophysical models
– Example of Greenland Ice Sheet
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4. 4
Worst Economic Declines, 1950 - 2019
Country Ten-year decline End year Cause
Venezuela 88% 2019 Civil strife
Nigeria 87% 1995 War
Liberia 85% 1996 War
Tajikistan 81% 2000 Empire collapse
Zimbabwe 77% 2006 Civil strife
Kuwait 76% 1991 Oil
Qatar 75% 1986 Oil
Kyrgyzstan 73% 2000 Empire collapse
Iraq 72% 1991 War
Georgia 70% 2000 War
DR Congo 70% 1998 War
Source: Data on per capita real income from Penn World
Table 10 (adjusted for Venezuela with IMF data.

5. Economic impacts of Climate Change
• Much work here, but little consensus
• Some studies suggest human economic
catastrophes due to major climate change.
• Key unresolved issue is the T → Y linkage:
… Is it [level, level]: T → Y
... or [level, growth]: T → g(Y)
– Much current work uses the (level, growth)
specification
– That implies that rapid climate change might
lead to be a human economic catastrophe
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6. Take as example results of Dell, Jones, Olken (2012)
Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken, “Temperature Shocks
and Economic Growth: Evidence from the Last Half Century,” American
Economic Journal: Macroeconomics 2012, 4(3): 66–95
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7. Simulation
• Take the DJO coefficients.
• Assume temperature rises at 4 °C per century
• Then calculate the damages for each of the two
specifications:
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Level effect: (1 ) = 4% at 3 C
Growth effect: g( ) ( ) )
= - 0.325 % point/ C [All countries]
= - 1.655 % point/ C [Poor countri
base
t t t
base
t t t
Y Y T
Y g Y T
α α
β
β
β
= −
= −



es]
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8. Results over time
-100%
-80%
-60%
-40%
-20%
0%
2000 2020 2040 2060 2080 2100
Income
Loss
(%
global)
Level effect
Growth: All
Growth: Poor
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9. Implicit damage function
-100%
-80%
-60%
-40%
-20%
0%
0 1 2 3 4 5 6
Income
loss
(%
global)
Temperture increase (°C)
Growth: All
Level effect
Growth: poor
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10. Predictions for late 2010s
• According to (level, growth) model, growth in poor
countries should have declined about 1% points
relative to rich countries over 2010-2019.
• In fact, bottom countries grew about 2% point
faster in 2010-2019.
• Difference of impact in 2100 for poor countries:
– 8% income loss for level
– 97% income loss for growth
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11. Reflections on the level v growth issue
Problems with T → g(Y):
• Several channels for T to affect output level
(agricultural experiments and production fns)
• Deep issue of weather v. climate in damages
• Several statistical problems with using countries
as data points.
• Perhaps some tipping points in poor countries?
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12. Reflections on the level v growth issue
Problems with T → g(Y):
• No plausible and empirically validated mechanism
for T to affect total factor productivity (in either
Romer or learning model): next slide
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13. Romer model of technological change
Unclear how climate would enter into g(R,S)
function. Possibly through input and output prices,
but these are missing from the damage model.
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1
(.)
( , ,...)
= total factor productivity
= inventive inputs
basic advances in fundamental science
(.) damages, other factors
t t t t
t t t t
t
t
t
Y A K L
A A g R S
A
R
S
α α
−
= Ω
=
=
Ω =


14. The problem with using countries as data
COUNTRY/Region Grid cells
Russia 3,492
Canada 2,210
US 1,369
China 1,094
Australia 815
Ecuador 44
Coted'Ivoire 41
Iceland 41
Laos 40
BurkinaFaso 39
Gambia 4
Luxembourg 4
Bahrain 2
HongKong 2
Singapore 2 14

15. Possible approach
• Need higher resolution economic data
• Yale GEcon project to compile sub-national data on
output and integrate with geophysical data.
• Stay tuned ….
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16. Reflections on the level v growth issue
Settling the level v growth impact of climate on
output is a first-order issue in understanding
impacts, and particularly potentially catastrophic
impacts of climate change.
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