Climate Change, Economics and Integrated Assessment Modelsweb.uvic.ca/~kooten/resource/ClimateChange.pdf · Monthly Data for the Pacific Ocean/Western Americas 60 0 U.S. Historical

Climate Change, Economics and Integrated Assessment Models

G Cornelis van Kooten

Climate Change: Initial issues of interest

• Condemnation of climate ‘sceptics’ as deniers • Scientific consensus: Where does the 97% come from? Derogatory term: ‘deniers’• Rejection of near unanimous consensus regarding GMOs.

• Attribution of climate change solely to humans (anthropogenic) and solely to CO2 emissions (coal is THE target, but more recently all fossil fuels are targeted)

• Summary for Policy Makers (SPM): • Appears before the scientific report is completed

• Hype surrounding increase in number of extreme weather events: • extreme precipitation, droughts, hurricanes & typhoons, wildfires, winters without snow,

earthquakes (?), psychosis (?) • https://www.amazon.com/Rightful-Place-Science-Disasters-Climate/dp/0999587749/ (Roger

Pielke Jr. 2018)

• Fear of famine (food security), biodiversity loss (polar bear), disease (malaria), sea level rise, coral bleaching, climate refugees, etc.

• Driven by extremely well-funded environmental movement (billionaires) and government science funding (e.g., entire Environment Canada budget went to climate model at Uvic)

Roy Spencer: “Global Warming Skepticism for Busy People.”

Five questions that must be answered ‘yes’ for action on global warming to occur:

1. Is warming and associated climate change mostly human caused?

2. Is the human-caused portion of warming and associated climate change large enough to be damaging?

3. Results from climate models are used to guide energy policy. Do the climate models accurately predict climate change?

4. Would the proposed policy changes substantially reduce climate change and resulting damage?

5. Would the policy changes do more good than harm to humanity?

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Theme of 1126 Papers Investigated to Find Consensus on IPCC Position (# of studies)

Explicitly endorse consensus

Implicitly endorse but focus on impacts

Mitigation proposals

Methods

Paleoclimate analyses

Reject/doubt consensus

Natural factors

Unrelated to climate change but included

words "global climate change"

97% ??

No position on AGW

Explicitly endorse AGW

(>50%)

Explicitly endorse AGW, not

quantified

Explicitly reject AGW

(<50%)

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quantified

Implicitly reject AGW

The large green-shaded ‘No position on AGW’ was

subsequently ignored in establishing the 97% consensus

The 97% Consensus of Scientists who Support AGW

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Still Consensus???

Another study found that there was no consensus whatsoever

Quotes from book by Spencer:

“Why don’t more papers tackle the thorny issue of determining how much warming is natural versus anthropogenic? For at least three reasons:

1. We cannot separate human from natural causes of warming (there are no human fingerprints)

2. We have only a poor understanding of natural causes of climate change.

3. We cannot compute how strong human-caused warming is from first physical principles (the climate sensitivity problem, discussed later).

In Chapter 13, “Why is Warming not Progressing as Predicted?,” Spencer addresses the big problem of IPCC’s reliance on climate models:

“Climate models [in use today] probably over-predict warming because they[the models] produce too much positive feedback, which is necessary for high climate sensitivity. The small amount of direct warming from a doubling of CO2 (a little over 1oC) is magnified by about a factor of three in climate models due to warming-induced changes in clouds and water vapor, while the [actual] observations suggest there is little magnification at all.

“The positive feedback processes contained in climate models are very uncertain, yet are responsible for most (about 2/3) of the warming the models produce.”

cont…

“While the models are indeed mostly made up of fundamental physical principles that are pretty well established, it is these few poorly known feedback processes that determine how serious the global warming problem will be. Out of hundreds of thousands of lines of computer code making up the models, it could be that only a few lines of code representing very uncertain assumptions about the climate system are mainly responsible for producing too much [predicted] warming.

“This is why I call the climate research community’s defense of the current climate models as ‘bait and switch’. The well-understood basic physical principles the models are built on produce only about 1 deg. C of warming in response to 2×CO2 [a doubling of CO2], while the additional 2 deg. C of warming they produce from positive feedbacks is very speculative. They sell you on the well understood physics supporting the 1 deg. C of direct warming, but then switch to the full 3 degrees of warming the models produce as similarly reliable.

“How clouds might change with warming (cloud feedback) is particularly uncertain, a fact that is admitted by modelers. The climate models cannot include the actual physics of cloud formation and dissipation because computers are not nearly fast enough to be run with the fine detail contained in clouds. In fact, we don’t even understand some of the microphysical details of what happens in clouds, preventing us from modelling them even if computers were fast enough.”

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Average: 9.27oC

Mark Steyn, Ross McKitrick, Steve McIntyre, and Anthony Watts on the same stage

https://youtu.be/FHUHsBnpCj8

Check about 31 minutes into the program where Anthony Watts talks about weather stations.

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Role of Humans

• IPCC attributes climate change solely to human causes. Section A (“Understanding Global Warming of 1.5oC”) of the Summary for Policy Makers: “A.1 Human activities are estimated to have caused approximately 1.0°C of global warming above pre-industrial levels, with a likely range of 0.8°C to 1.2°C.”

• Temperatures have risen by about 1oC since pre-industrial times, so one can only conclude that humans have been responsible.

• Causes of the Medieval Warm Period and Little Ice Age unexplained, although anthropogenic CO2 emissions clearly not responsible.

• Infamous hockey stick

Five warnings of disaster due to climate change (all bogus):

1. Warming causes declining human birth rates (Morning Consult).

2. We have “12 years left to live” because of it (Alexandria Ocasio-Cortez—and lots of her acolytes in and out of Congress).

3. Warming causes more tornadoes (Ocasio-Cortez, Al Gore, and others).

4. Warming causes Indian tigers to eat more people (website “The Conversation”).

5. Warming is causing mass extinctions. (see here)

Total Wildland Fires and Acres (1926-2019)Source: National Interagency Wildfire Centerhttps://www.nifc.gov/fireInfo/fireInfo_stats_totalFires.html

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Appears that fire suppression might play bigger role than climate change

REST OF CANADA

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Total and Severe Hurricanes Impacting the United States, 10-year Moving Average, 1851-2018

(Source: http://www.aoml.noaa.gov/hrd/hurdat/All_U.S._Hurricanes.html)

Period Average Period Average

1851-1900 0.54 1951-2018 0.56

1851-1950 0.61 1976-2018 0.49

1851-1975 0.62 2000-2018 0.53

1951-2000 0.56 Entire period (1851-2018) 0.59

Average Annual Hurricanes of Categories 3, 4, or 5 Impacting the United States, 1851-2018

Source: Calculated from NOAA data

Decadal Precipitation Trends in the UK, 1780-2017(No trend toward greater or lesser precipitation events)

Contributions of Economists to:

• Instrumental temperature reconstructions

• Paleoclimatic temperature proxies

• Hockey stick (hide the decline)

• Modeling

• Emission scenarios

• Validity of climate models

• Cost-benefit analysis & precautionary principle

• Economic policy to control CO2 emissions

• Conclusions

How does one create a temperature series free of contamination?

• How does one correct for contamination from socioeconomic development when averaging across all weather stations in a region? Globally?

• How does one homogenize weather station temperatures to obtain gridded temperatures that are unaffected by socioeconomic or non-climatic factors?

Global weather stations

• There are 22,231 stations that report average daily temperatures

• Of these, 8,152 report the daily maximum and minimum temperatures

• Stations are provided in the next slide

• Global Historic Climatology Network (GHCN) (7280)

• US Historic Climatology Network (USHCN) (1221)

• National Center for Atmospheric Research (NCAR)

• NOAA’s National Climate Data Center (NCDC)

# of stations providing:

Data Source Mean temp Max/Min temp

NCAR’s World Monthly Surface Station Climatology 3,563 0

NCDC’s Max/Min Temperature Data Set 3,179 3,179

Deutscher Wetterdienst’s Global Monthly Surface

Summaries Data Set2,559 0

Monthly Climatic Data for the World 2,176 0

World Weather Records (1971-80) 1,912 0

World Weather Records (1961-70) 1,858 0

U.S. Summary of the Day Data Set 1,463 1,463

U.S. Historical Climatology Network 1,221 1,221

Climatological Database for N Hemisphere Land Areas 920 0

Australian National Climate Center's Data Set 785 785

North American Climate Data, NCDC 764 764

Bo-Min's Data Set for the People's Republic of China 378 0

USSR Network of CLIMAT stations 243 0

Daily temperature & precipitation data for 223 USSR

stations (NDP-040)223 223

Global Historical Climate Network Monthly Temperature Sources

# of stations providing:

Data Source Mean temp Max/Min temp

Two long-term databases for People's Republic of China

(NDP-039)205 60

ASEAN Climatic Atlas 162 162

Pakistan's Meteorological and Climatological Data Set 132 132

Diaz’s Data Set for High-Elevation Areas 100 0

Douglas’ Data Set for Mexico 92 0

Ku-nil’s Data Set for Korea 71 71

Jacka’s Data Set for Antarctic Locales 70 0

Monthly Data for the Pacific Ocean/Western Americas 60 0

U.S. Historical Climatology Network (Alaska) 47 47

Muthurajah’s Data Set for Malaysia 18 18

Hardjawinata’s Data Set for Indonesia 13 13

Fitzgerald’s Data Set for Ireland 11 11

Sala’s Data Set for Spain 3 0

Al-kubaisi’s Data Set for Qatar 1 1

Al-sane’s Data Set for Kuwait 1 1

Stekl’s Data Set for Ireland 1 1

GHCN Monthly Temperature Sources (cont)

Keepers of the Climate Data

• Surface Data

• Hadley Centre: UK Meteorology Office and Climate Research Unit, University of East Anglia, UK

• North American Space Agency and Goddard Institute for Space Studies (NASA – GISS): group formerly led by James Hansen

• National Oceanic & Atmospheric Administration (NOAA)

• Berkeley Earth Surface Temperature (BEST) – established 2011

• Richard Muller

• Satellite Data

• NASA’s Marshall Space Flight Center and University of Alabama at Huntsville (Spencer and Christy)

• Remote Sensing System in Santa Rosa, California

More on surface temperature data

• As of 2010, 7,350 weather stations globally, plus 14 ships: only 6,257 stations are actually used to determine global climate information

• ARGUS buoys less than decade old

• 1,221 high-quality weather stations in U.S., one-fifth of the global total

• Two balloon series for tropics

• Most other stations are located in Europe, New Zealand, Australia, Canada, Russia and parts of Asia

• Most of the globe is poorly covered

Effect of Weather Station Numbers on Average Global Temperature, 1950 – 2000

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Quality of USHCN Stations

Climate Reference Network Rating

# of Stations % Description

CRN1 19 2% No heat source within 50 m

CRN2 76 8% No heat source within 30 m

CRN3 142 15% No heat source within 15 m

CRN4 578 61% Heat source within 10 m radius

CRN5 133 14% Poorly sited

Weather Station Location and Relation to Temperature

temperature

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Weather station location

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Average Annual and Ten-Year Moving Average of Temperatures in Central England, 1659-2010

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Average Annual Global Temperature Anomaly, GISS and Hadley CRUT3 Temperature Series, 1850-2010

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What about satellite data?

• Satellite Data

• NASA’s Marshall Space Flight Center and University of Alabama at Huntsville (Spencer and Christy)

• Remote Sensing System in Santa Rosa, California

• Satellite data are not contaminated

• Problem (?): Observations only available since December 1978

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Flat line last 20 years

Contamination from Non-climatic factors

• HadCRUT3 and CRUTEM (land only) temperature product are supposed to be free of non-climatic factors, but McKitrick & Michaels (2004, 2007) and de Laat and Maurellis (2004, 2006) found that population growth, changes in per capita GDP, coal consumption (which increased particulates as well as CO2) explained rising temperatures

• As much as half of the rise in temperatures since 1980 is due to non-climatic factors

• Not found in the satellite data or an ensemble of temperature data from climate models for the same period

van Kooten, G.C., 2013. Climate Change, Climate Science and Economics. Dordrecht, NL: Springer. Chapter 2. (Available free to students through UVic’s SpringerLink.)

IPCC Response

“McKitrick and Michaels (2004) and De Laat and Maurellis (2006) attempted to demonstrate that geographical patterns of warming trends over land are strongly correlated with geographical patterns of industrial and socioeconomic development, implying that urbanisation and related land surface changes have caused much of the observed warming. However, the locations of greatest socioeconomic development are also those that have been most warmed by atmospheric circulation changes …, which exhibit large-scale coherence. Hence, the correlation of warming with industrial and socioeconomic development ceases to be statistically significant. In addition, observed warming has been, and transient greenhouse-induced warming is expected to be, greater over land than over the oceans …, owing to the smaller thermal capacity of the land” (IPCC 2007, Chapter 3, p.244).

Also see: http://www.globalwarming.org/wp-content/uploads/2010/03/ross.gatekeeping.pdf

THE HOCKEY STICK CONTROVERSY

Brian Fagan:MWP: 800 – 1300LIE: 1300 – 1850

IPCC 1990 Report:

Why is the Medieval Warm Period Problematic?

• Cannot be attributed to human influence as this was a period well before the industrial age

• If the MWP existed, then it needs to be explained. What caused it to happen?

• Requires climate models to mimic the warm period, as well as the subsequent cold period

• Problem: It is assumed that the climate has historically been in equilibrium and that it is now out of equilibrium because of human activities

This is the assumption built into current thinking about climate:

The Hockey Stick

Temperature Proxies• Consider tree ring data. Tree ring width is correlated

with temperature as long as there is sufficient precipitation

• Calibration period: What effect does temperature have on tree rings? Estimate the following response function:

Rt = β0Tt + β1Tt–1 + β2Tt–2 + … + δiZi,t + … + εt

• Climate scientists tend to estimate simpler version:

Rt = β Tt + εt

• Verification Period: We want to check whether the calibration is correct.

• Data on tree rings or ice cores available from 1850-2000

• Calibration period might be 1940-2000

• Verification period is then 1850-1939

• To determine temperatures from tree rings requires the following transfer function:

Tt = γ0Rt + γ1Rt–1 + γ2Rt–2 + ξt

• It is also possible to estimate the response function and thus derive the transfer function as:

Tt = (1/β)Rt + (1/β) εt

Deriving Paleoclimatic Temperatures

• In practice, every tree ring, ice core and lake bed series that is considered somehow to be statistically valid leads to a paleoclimatic temperature series. The result is a whole bunch of what have been labeled “spaghetti graphs”

• Principal component (PC) analysis is then used to find a single temperature series that “best represents” (accounts for the greatest variation in) the spaghetti graphs. This series is then plotted as the paleoclimatictemperature series.

Some Problems

• Sometimes second, third, fourth PCs are chosen

• To determine PCs, one subtracts means of each series, but climate scientists appear to have subtracted the means of the instrumental record (rotated the data about the recent means, not the means of the entire series)

• Inclusion of the bristle cone pine series in ALL reconstructions

• Other statistical problems have been identified by Wegman and others.

A Comparison of the McIntyre-McKitrick Historical Temperature Reconstruction (blue line) with the Original MBH Hockey Stick (red line), 10-year Moving Average

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Source: http://www.climateaudit.org/data/MM05.EE/emulation.txt

Hide the Decline

• Likely one of the greatest tricks of scientific deception in order to rig results in favor of an ideological position.

• Richard Muller of UC Berkeley indicates on YouTube that he would never again read anything the climate scientists involved would write or the journals in which their papers appeared.

• The ‘trick’ came to light in ClimateGate emails, those involved tried to (and continue to) circumvent FOI requests for the data, there have been several controversial investigations, and a number of lawsuits are pending.

Hide the Decline: Original Data

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Hide the Decline: Truncate Original Series and Add Weather Station Data

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Contributions to climate modeling

Special Report on Emission Scenarios

• No surprise: The IPCC provides the emission scenarios that go into climate models

• Report produced in 2001 and used as basis for the analyses in the 2007 and subsequent IPCC Reports

• Six integrated assessment models (IAMs) are used to develop the emission scenarios, with the IPCC telling the modelers what to assume

Six integrated assessment models

• Asian Pacific Integrated Model (AIM): National Institute of Environmental Studies, Japan

• Atmospheric Stabilization Framework Model (ASF): U.S. consulting firm

• Integrated Model to Assess the Greenhouse Effect (IMAGE): Institute for the Environment (RIVM) and Central Planning Bureau (CPB), The Netherlands

• Multiregional Approach for Resource and Industry Allocation (MARIA): Science University of Tokyo, Japan

• Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE): International Institute of Applied Systems Analysis (IIASA), Austria

• Mini Climate Assessment Model (MiniCAM): Pacific Northwest National Laboratory (PNNL), U.S.

Economic Structure• Each model has exogenous assumptions regarding:

• Population growth

• GDP growth

• Technological change

• Natural resource base

• Some models employ constrained optimization subject to constraints

• Others are pure simulation models• Production and consumption of goods/services are exogenous as a

fixed proportion of population or income; in some, these are endogenously determined by supply and demand

• Models may or may not link to other models

• Number of sectors in models differ; all have an energy sector, but some focus only on energy and details about this sector vary across models

Special features of emission scenarios

• Models not permitted to include any actions (policies) to reduce CO2 emissions

• Assumptions about the rate of technological change (emissions of CO2 per person or $GDP) provided

• Population growth assumptions given

• Economic growth exogenous not endogenous:

• Assumptions about rate of growth in each economy

• Developing countries expected to grow significantly AND per capita incomes of poor and rich countries converge

• UN development policy requires economic growth and convergence

Four Storylines

• A1: rapid economic growth, global population peaking halfway through the 100-year forecast period, rapid introduction of new and more efficient technologies; social and cultural ties lead to rapid convergence in incomes – elimination of regional disparities

• A2: world continues to be highly heterogeneous; convergence in per capita incomes and reduction in fertility rates are slow, global population continues to grow throughout the 21st century; technological change and adoption are not homogeneous across regions

Four Storylines (continued)

• B1: Regional convergence in incomes and fertility rates; technological change and adoption rapid; population peaks mid century and then declines; structures of economies change from material to services and information; major developments in clean and resource-efficient technologies; global cooperation in solving economic, social and environmental problems; more optimistic than the A1

• B2: solutions to economic, social, environmental problems are not addressed globally, but locally or regionally; global population rises throughout 21st century, but slower than in A2; technological change is less rapid but more diverse than in B1 and A1; a more environmentally friendly set of assumptions

Year A1F1 A1T A2 B1 B2

Global population (×109)

1990 5.3 5.3 5.3 5.3 5.3

2020 7.6 7.6 8.2 7.6 7.6

2050 8.7 8.7 11.3 8.7 9.3

2100 7.1 7 15.1 7 10.4

Global GDP (1012

1990 US dollars)

1990 21 21 21 21 21

2020 53 57 41 53 51

2050 164 187 82 136 110

2100 525 550 243 328 235

Ratio of rich to poor per capita incomes

1990 16.1 16.1 16.1 16.1 16.1

2020 7.5 6.2 9.4 8.4 7.7

2050 2.8 2.8 6.6 3.6 4

2100 1.5 1.6 4.2 1.8 3Average global per capita income ($US1990)

1990 3,962 3,962 3,962 3,962 3,962

2020 6,974 7,500 5,000 6,974 6,711

2050 18,851 21,494 7,257 15,632 11,828

2100 73,944 78,571 16,093 46,857 22,596

Average per capita income of poorest countries ($US1990)

1990 246 246 246 246 246

2020 930 1,210 532 830 871

2050 6,732 7,677 1,099 4,342 2,957

2100 49,296 49,107 3,832 26,032 7,532

Assumptions Regarding GDP and Population for Selected IPCC Emission Scenarios

Assumed Rates of Technical Change in Energy Use for Selected IPCC Emission Scenarios

Year A1F1 A1T A2 B1 B2

Final energy intensity (106 Joules per US$)

1990 16.7 16.7 16.7 16.7 16.7

2020 9.4 8.7 12.1 8.8 8.5

2050 6.3 4.8 9.5 4.5 6

2100 3 2.3 5.9 1.4 4

Primary energy use (1018 Joules per year)

1990 351 351 351 351 351

2020 669 649 595 606 566

2050 1431 1213 971 813 869

2100 2073 2021 1717 514 1357

Share of coal in primary energy (%)

1990 24 24 24 24 24

2020 29 23 22 22 17

2050 33 10 30 21 10

2100 29 1 53 8 22

Share of zero carbon in primary energy (%)

1990 18 18 18 18 18

2020 15 21 8 21 18

2050 19 43 18 30 30

2100 31 85 28 52 49

Year A1F1 A1T A2 B1 B2

Carbon dioxide from fossil fuels (tonnes×109)

1990 22.0 22.0 22.0 22.0 22.0

2020 41.1 36.7 40.3 36.7 33.0

2050 84.7 45.1 60.5 42.9 41.1

2100 111.1 48.0 106.0 19.1 50.6

Cumulative 7803 3806 6501 3626 4253

Carbon dioxide from land use (tonnes×109)

1990 4.0 4.0 4.0 4.0 4.0

2020 5.5 1.1 4.4 2.2 0.0

2050 2.9 0.0 3.3 -1.5 -0.7

2100 -7.7 0.0 0.7 -3.7 -1.8

Cumulative 224 227 326 -22 15

Cumulative carbon dioxide TOTAL (tonnes×109

= 1Gt)

1990-2100 2189 1068 1862 983 1164

Expected Emissions of Carbon Dioxide for Selected IPCC Emission Scenarios

How well do models predict?

• McKitrick & Christy sought to answer this by comparing model generated data for the mid and lower troposphere (the region most sensitive to climate change) with 60 years of actual data from two balloon data series

• Finding: Temperatures from climate models are 2-4 times higher, depending on model and location (lower or mid troposphere), than the observed data; differences statistically significant at 99% level

McKitrick, R. and J. Christy, 2018. A Test of the Tropical 200‐ to 300‐hPa Warming Rate in Climate Models, Earth and Space Science 5: 529-536.

How scientific are model forecasts?

• Green and Armstrong (2007) examined extent to which predictions from climate models followed scientific forecasting procedures, as laid out by International Institute of Forecasters for example

• Conclusion: “those forecasting long-term climate change have no apparent knowledge of evidence-based forecasting methods”

• Important forecasting principles were simply ignored, including establishing cause and effect between CO2 and temperatures

Green, J.C. and J.S. Armstrong, 2007. Energy and Environment 18: 997-1021.

IPCC Response

• “In fact there are no predictions by IPCC at all. And there never have been. The IPCC instead proffers “what if” projections of future climate that correspond to certain emissions scenarios. There are a number of assumptions that go into these emissions scenarios. They are intended to cover a range of possible self consistent ‘story lines’ that then provide decision makers with information about which paths might be more desirable. But they do not consider many things like the recovery of the ozone layer, for instance, or observed trends in forcing agents. There is no estimate, even probabilistically, as to the likelihood of any emissions scenario and no best guess” (Kevin Trenberth, IPCC Lead Author)

Equilibrium or Effective Climate Sensitivity (ECS) Parameter

• ECS is the temperature increase from a doubling of atmospheric CO2

compared to pre-industrial times

• IPCC reports state a likely range of 2.0˚C to 4.5˚C, with a best estimate of 3.0˚C

• Recall that we already have approximately 1oC warming to date

• Recent studies found much lower values of ECS, ranging from 2oC down to less than 1oC (in which case observed ECS to date is not solely caused by humans): one study finds ECS is closer to 0.5˚C.

• Mauritsen, T. & R. Pincus, 2017. Nature Climate Change 7(July 31): 652–655.

• Lewis, N. & J.A. Curry, 2015. Climate Dynamics 45: 1009-1023.

• Lewis, N. & J.A. Curry, 2018. Journal of Climate 31(August): 6051-6071.

Equilibrium Climate Sensitivity Parameter

• Pretis (2019) estimates values of ECS of 1.37-1.67 K in his main model, but finds a value of 2.16 K in his ‘preferred model’.

• He estimates TCR values of 1.24-1.38 K if f2×CO2 is assumed to equal 3.7 W/m2, and 1.15-1.28 K if f2×CO2 equals 3.44 W/m2, with his preferred model yielding the higher values.

• Similar values of ECS and TCR have been found by others based on observational data.

• If low values, then climate change is no longer a problem

• A low ECS and no significant increase in global temperatures over the past 20 years has caused the IPCC to warn about the potential catastrophe that a 1.5oC increase in temperatures might cause (prior they sought to restrict temperature increase to 2oC)

0

50

100

150

200

250

300

2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

So

cia

l C

ost o

f C

arb

on

($

per t

CO

2)

3.1 oC

2.0 oC

1.0 oC

Path of the Social Cost of Carbon, 2015-2100, for Three Values of the Equilibrium

Climate Sensitivity Parameter, DICE Model (US$2005 per tCO2)

Cost-benefit analysis

Copenhagen Consensus: Cline’s (2004) use of DICE

Item Scenario

Optimal

carbon tax

Kyoto

Protocol

Value-at-risk

carbon tax

Benefits (×1012

) $271 $166 $1,749

Costs (×1012

) $128 $94 $458

Benefit-cost ratio 2.12 1.77 3.82

Annualized benefits (×1012

) $0.90 $0.55 $5.83

Annualized costs (×1012

) $0.43 $0.31 $1.53

Cline’s results (higher than other economists using DICE):

- Reduce emissions immediately by 35-40%; nearly 50% by 2100:

63% by 2200

- Optimal carbon: $35 (1990$) per t CO2 in 2000; $46/tCO2 in 2005,

$67/tCO2 by 2025, $100/tCO2 by 2050, $355/tCO2 in 2200

DESPITE THIS: Expert panel of 8 economists (3 Nobel laureates)

ranked global warming last of 10 global problems

POLICY RAMPSNordhaus (2008) Results: Costs ($2005) to prevent temperatures rising 2.3oC

• Relative to BAU emissions of greenhouse gases:• 15% reduction in 2010-2019

• 25% reduction in 2050

• 45% reduction in 2100

• Optimal carbon tax:• $9.50 per t of CO2 ($35 per ton of carbon) in 2005

• $25/t CO2 in 2050

• $56/t CO2 in 2100

• In terms of the price at the pump: 12¢ per gallon of gasoline in 2005 to 70¢ per gallon by 2100

• Optimal path of tax predicated on unmitigated damages of 3% of global output in 2100 and 8% in 2200

Other Approaches

• Goklany (2008, 2009) begins with IPCC emission scenarios

• He finds negative impacts of climate change are offset by rising incomes, so much that the overall climate impact is essentially negligible

• Goklany (2009) finds net biome productivity will increase as a result of climate change and that less wildlife habitat will generally be converted to cropland as a result of global warming (Sohngen et al. 1999 find the same thing).

IPCC Scenarios

Item A1F1 A2 B2 B1

1. Population in 2085 (×109) 7.9 14.2 10.2 7.9

2. Average global per capita

GDP in 2085 ($)a 78,600 19,400 29,900 54,700

3. Average per capita GDP in

2100, Industrialized countries

($)a

160,300 69,000 81,300 108,800

4. Average per capita GDP in

2100, Developing countries

($)a

99,300 16,400 26,900 60,000

5. Technological change Rapid Slow Medium Medium

6. Energy use Very high High Medium Low

7. Energy technologies Fossil fuel

intensive

Regionally

diverse

“Dynamics

as usual” High efficiency

8. Land-use change Low-medium Medium-high Medium High

9. Atmospheric CO2

concentration in 2085 (ppmv) 810 709 561 527

10. Global temperature change

in 2085 (oC) 4.0 3.3 2.4 2.1

11. Sea level rise in 2085 (cm) 34 28 25 22

12. Change in total mortality

in 2085 compared to

baselineb,c

–2,064,000 +1,927,000 –1,177,000 –2,266,000

13. Total population at risk

due to water stress compared

to baselinec

299,000 5,648,000 2,746,000 857,000

14. Average net global loss in

coastal wetlands by 2085

compared to baselinec

13% 9% 9% 10%

In the world of renewable energy nothing is what it seems. “Environmentally

friendly” turns out to be devastating to the natural world. “Cheap” is expensive.

“Local support” is found to be at a distance. “Sustainable” is, strange to say,

short lived and unaffordable. A “contract” is non-binding. “Secure” is actually

unreliable. Love is hate, black is white, and “Green” is a murky shade of brown.

….. Daily Telegraph, March 3, 2020

Further Copenhagen Consensus: Part II

• Gary Yohe argues that non-market values of the damages avoided are not sufficiently taken into account in IAMs

• He calculates the benefits of mitigating global warming by examining the reduction by 2080 in the number of people at risk from hunger, water scarcity and coastal flooding

• Using the DICE model, Yohe finds costs of mitigating climate change generally exceed benefits (see next table); when non-market values are added, however, he argues benefits greatly exceed costs.

• Again climate change shows up low on the list of global problems that should be addressed

Scenarios

Scenario description 1 2 3 4 5

Climate sensitivity 3oC 1.5oC 5.5oC 3oC 1.5oC

Carbon tax (/tCO2) $13.70 $1.40 $20.50 $27.30 $2.20

Tax starts in 2006 2006 2006 2016 2016

Number with less risk of hunger in 2080 (×106) 26 19 48 26 19

No. with less risk of water scarcity in 2080 (×106) 2,070 1,160 2,680 2,070 1,160

No. with less risk of coastal flooding in 2080 (×106) 74 16 76 74 16

High discount rate (3% social rate of time preference; DICE – effective 5% declining to 4%)a

Discounted costs (×1012) $12.73 $0.22 $19.23 $16.14 $0.73

Net present value (×1012) –$0.46 –$0.44 –$0.74 –$0.59 –$0.44

Per person cost to reduce risk of hunger $17,69 $24,444 $15,417 $22,692 $24,444

Per person cost to reduce risk of water scarcity $222 $379 $266 $285 $204

Per person cost to reduce risk of coastal flooding $6,216 $27,500 $9,737 $7,973 $27,500

Cost-benefit ratio (lower bound) 0.96 2.00 0.96 0.96 1.91

Low discount rate (0% social rate of time preference; DICE – effective 2% declining to 1%)a

Discounted costs (×1012) $110.81 $2.22 $141.52 $129.43 $2.33

Net present value (×1012) –$0.76 –$0.20 –$1.07 –$0.95 –$0.02

Per person cost to reduce risk of hunger $29,231 $1,111 $22,292 $36,538 $1,111

Per person cost to reduce risk of water scarcity $367 $17 $385 $459 $9

Per person cost to reduce risk of coastal flooding $10,270 $1,250 $14,079 $12,838 $1,250

Cost-benefit ratio (lower bound) 0.99 0.99 0.99 0.99 0.99

Costs and Benefits of Mitigating Climate Change: Further Results from the DICE Model (US$1995)

Cost-benefit analysis & the precautionary principle

• William Nordhaus (and Richard Tol)• Low discount rate

• Damages = aT + bT2

• Policy ramp

• Nicholas Stern• Very low interest rate

• Very low costs and very high damages

• Immediate action is required

• Martin Weitzman• Interest rate similar to Tol and Nordhaus

• Fat tails

• High damages = exp(aT + bT2)

• Low carbon tax to fund a put-a-man-on-the-moon type of project

Consensus: Policy Ramp

• Economic researchers favor a policy ramp whereby a carbon tax is imposed, with the tax increased slowly over time.

• Advantages:

• Let the market decide

• Does not lock one into a ‘negative’ technology such as hydrogen vehicles (with infrastructure) that could discourage electric vehicles

Economic Policy: Strategies for lowering Atmospheric CO2

Economic Instruments for Addressing Climate Change

1. Carbon tax

2. Cap and trade

• Auction emission permits

• Grandfather emission permits

• A weaker version of cap and trade that allows certified emission reductions (CERs) from elsewhere (e.g., via Clean Development Mechanism, sink activities, etc.). Referred to as ‘credit trading’

3. Regulation

4. Subsidies (flip side of a tax)

Cap and Trade

• An emissions cap creates a rent

• Who collects the rent?

• Auction permits: government collects the rent much like the case of a carbon tax

• Grandfathered permits: large emitters collect the rent

• Rent-seeking behavior

• By large emitters who want a cap with grandfathered permits

• By financial intermediaries who earn a commission on every trade (the market could be $1 trillion)

Examples of emission permits to set against a firm’s or country’s emissions

• AAUs – Assigned Amount Units are emission permits in excess of what a country needs to achieve its Kyoto commitment

• RMU – removal unit for carbon sinks.

• tRMU – temporary RMU.

• CER – Certified Emission Reduction

• tCER – temporary Certified Emission Reduction

• ERU – Emission Reduction Unit is an earned credit for participation in joint implementation activities

• Other• REC – Renewable Energy Credit (California)

• tREC – transferable REC

• VER – Voluntary Emission Reduction

European Trading System(Only carbon market in the world)

0

5

10

15

20

25

30

Jun-05 Feb-06 Sep-06 May-07 Dec-07 Jul-08 Mar-09 Oct-09 Jun-10 Jan-11 Aug-11

€ p

er t

CO

2

European Allowances

CERs (thin line)

Carbon Taxes: Effects on Various Fuels, U.S., 2010

Coal Oil Natural Gas CO2 emissions 2.735 tCO2/t coal 0.427 tCO2/barrel 1.925 tCO2/m

3×10

3

Average price $45.50/t coal $70.69/barrel $423.78/m3×10

3

Carbon tax per unit of fuel $10 per tCO2 $27.35 $4.27 $19.25 $30 per tCO2 $82.05 $12.81 $57.75 $100 per tCO2 $273.50 $42.70 $192.50 % increase in price of fuel from carbon tax $10 per tCO2 60.1% 6.0% 4.5% $30 per tCO2 180.3% 18.1% 13.6% $100 per tCO2 601.1% 60.4% 45.4% Carbon tax as % of tax-adjusted fuel price $10 per tCO2 37.5% 5.7% 4.3% $30 per tCO2 64.3% 15.3% 12.0% $100 per tCO2 85.7% 37.7% 31.2%

McKitrick’s Optimal Tax• Optimal carbon tax rate derived by ignoring costs and minimizing

discounted present value of damages subject to the effect that carbon emissions have on a ‘state’ variable.

• State-contingent pricing rule that he derives is as follows:

τt = γ s(t)

τt ≈ optimal tax at time t; γ = marginal damage rate; et = current level of emissions

= moving average of emissions over k periods, where k is the number of periods required for CO2 to leave the atmosphere (or the half life of CO2residency in the atmosphere)s(t) = value of the state variable – temperature in troposphere where the first evidence of global warming is expected to appear.

t

t

e

e

te

Optimal tax rule

• Not a prescription for optimal policy (not an optimal policy ramp) but only a rule relating tax to temperature

• Marginal damage rate and k need to be known. Suppose:

• marginal damages = $25 per tCO2;

• 29,227 Mt of CO2 emitted globally;

• average emissions over k =50 are 17,835 Mt of CO2;

• HadCRUT3 temperature anomaly was 0.482oC.

• Solving tax rule gives γ = 31.65.

• Use this result to derive optimal taxes since 1850 given knowledge about CO2 emissions based on fossil fuel use. The graph is provided on next slide.

Optimal Tax Rate ($ per tCO2) to Address Global Warming, Annual (thin line) and Five-Year Moving Average (thick line)

0

5

10

15

20

25

30

1850 1870 1890 1910 1930 1950 1970 1990 2010

Ta

x (

$ p

er tC

O2

)Only time tax exceeds $25 per tCO2 is 1998.

Advantages of McKitrick’s tax rule

• Costs ignored because the tax is a signal to emitters of CO2

• Information from tax trends is used by decision makers to judge the credibility of IPCC forecasts of climate change. Market decides the credibility of the science: those who decide wrongly incur costs that make them less competitive.

• Investors use trend in tax rates (i.e., market) to guide decisions, much like with other commodity prices that fluctuate over time.

• Tax rate appeals to two groups:

• those who fear catastrophic global warming because the tax will escalate rapidly with rising temperatures.

• those who do not believe in catastrophic anthropogenic climate change because, if their view is correct, the tax will either rise very slowly or not at all, or even fall to zero

Kaya Identity

E

C

Y

E

N

YNC

C = CO2 emissions, N = population, Y = GDP, E = energy use

Ways to reduce emissions of carbon dioxide:

1. Manage population;

2. Limit the generation of wealth (reduce GDP);

3. Generate the same or a higher level of GDP with less energy;

4. Generate energy with less CO2 emissions; or

5. Some combination of the first four factors.

Ways to reduce CO2 emissions

Rewrite the Kaya Identity as:

Emissions = Y × C/Y = GDP × technology

In 2006, C/Y was 0.62 tCO2 per $1000 GDP, down from 0.92 in 1980; France went from 0.42 to 0.30 in 20 years. For the UK to meet its climate target and go from 0.42 to 0.30 in five years requires 40 nuclear power plants of 1100 MW capacity.

“Japan's commitment in June [2009] to cut greenhouse gas levels 8 percent from its 1990 levels by 2020 was scoffed at for being far too little. Yet for Japan – which has led the world in improving energy efficiency – to have any hope of reaching its target, it needs to build nine new nuclear power plants and increase their use by one-third, construct more than 1 million new wind-turbines, install solar panels on nearly 3 million homes, double the percentage of new homes that meet rigorous insulation standards, and increase sales of ‘green’ vehicles from 4 percent to 50 percent of its auto purchases. Japan's new prime minister was roundly lauded this month [September 2009] for promising a much stronger reduction, 25 percent, even though there is no obvious way to deliver on his promise. Expecting Japan, or any other nation, to achieve such far-fetched cuts is simply delusional. Imagine … that at the climate conference in Copenhagen in December [2009] every nation commits to reductions even larger than Japan’s, designed to keep temperature increases under 2 degrees Celsius. The result will be a global price tag of $40 trillion in 2100, to avoid expected climate damage costing just $1.1 trillion, according to climate economist Richard Tol. … That phenomenal cost, calculated by all the main economic models, assumes that politicians across the globe will make the most effective, efficient choices. In the real world, where policies have many other objectives and legislation is easily filled with pork and payoffs, the deal easily gets worse.” (Lomborg, Washington Post, September 28, 2009)

Addressing Climate Change: 2010 Copenhagen Consensus Results

Rank and Solution Solution category

Individual Ranking

Values

Rank

Score

1. Cloud whitening research Engineering 15 15 13 14 8 65

2. Energy R&D Technology 13 12 11 13 13 62

3. Carbon storage research Technology 12 7 15 15 12 61

4. Stratospheric aerosol research Engineering 14 13 14 11 7 59

5. Planning for adaptation Adaptation 11 14 9 7 15 56

6. Research in Air Capture Engineering 10 6 12 12 9 49

7. Technology transfers Tech transfer 9 10 10 8 11 48

8. Expand and protect forests Forestry 5 11 8 9 14 47

9. Stoves in developing nations Cut black carbon 2 9 4 6 10 31

10. Methane reduction portfolio Cut methane 4 8 7 4 5 28

11. Diesel vehicle emissions Cut black carbon 3 4 5 5 6 23

12. $20 OECD carbon tax Cut CO2 8 5 6 1 1 21

13. $0.50 global CO2 tax Cut CO2 6 3 2 10 2 23

14. $3 global CO2 tax Cut CO2 7 2 3 3 4 19

15. $68 global CO2 tax Cut CO2 1 1 1 2 3 8

Worst options

Best options

Climate Policy

• July 2009 meeting of G8 countries in L'Aquila, Italy: • Limit increase in global average temperature to 2°C above pre-

industrial levels

• reduce global greenhouse gas emissions by 50% and own emissions by 80% or more by 2050

• European Union had in place a ‘20-20-20 target’ (binding) –• 20% reduction in CO2 from 1990 levels by 2020

• 20% of energy to come from renewable sources

• Updated since Paris Agreement (2015): see next slide

• U.S. House of Representatives American Clean Energy and Security Act (passed June 26, 2009)• Cap-and-trade scheme

• Reduce CO2 emissions • 3% below 2005 levels by 2012

• 17% by 2020

• 42% by 2030

• 83% by 2050.

Climate Policy (cont)

• Paris Agreement:

• Drafted December 2015

• Came into effect April 2016 when signed by 195 countries/blocs

• NDCs : Nationally Determined Contributions

• European Parliament declared ‘climate emergency’ in November, 2019, prior to COP25 in Madrid (2019). COP26 to take place in Scotland in December, 2020.

• European Union 2030 climate & energy framework sets three targets for 2030:

• At least 40% cuts in greenhouse gas emissions (from 1990 levels) (binding)

• At least 27% share for renewable energy

• At least 27% improvement in energy efficiency

https://ec.europa.eu/info/energy-climate-change-environment/overall-targets/2030-targets_en

Current: EU bureaucrats desire to pass an EU-wide “climate law” that would enshrine a legally binding target of net-zero carbon emissions by 2050, with Europe’s greenhouse gas emissions halved by 2030. Attempt to somehow appease eastern EU members.

Climate Policy (cont)

• Latest IPCC target

• Limit temperature increase to 1.5oC rather than 2oC agreed to in 2009

• Implies immediate action is needed

• UK set a net zero target by 2050 (June 2019 Climate Act) as recommended by the Committee on Climate Change, an independent climate advisory body.

• Currently 42% below 1990 emissions.

• Annual rate of emissions reduction must be 15 MtCO2e /year (3% of 2018 emissions)

• 50% higher than its previous 2050 target

• 30% higher than achieved on average since 1990

• BUT … how much is due to elimination of coal and movement of manufacturing off shore?

• U.S. has failed to pass binding climate legislation under both Democratic and Republican presidents. It is loss of sovereignty and freedom that prevents passage of binding legislation.