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Understanding Model Estimates of the Economic Costs of Climate Policy EPRI Global Climate Change Research Seminar May 22, 2008 Tom Wilson Senior Program Manager

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Understanding Model Estimates of the Economic Costs of Climate Policy

EPRI Global Climate Change Research SeminarMay 22, 2008

Tom WilsonSenior Program Manager

2© 2007 Electric Power Research Institute, Inc. All rights reserved.

U.S. Climate Policy Proposals Focused on Cutting Emissions Significantly Below Historic Levels

3© 2007 Electric Power Research Institute, Inc. All rights reserved.

Elements of Lieberman-Warner Climate Security Act of 2007

• Economy-wide Coverage – about 87% of 2005 emissions– Downstream on coal (units > 5,000 tons/yr)– Upstream on oil, gas, F-gases, N2O

• Targets for Covered Sectors (in MtCO2e)2012: 5,7752020: 4,9242030: 3,8602050: 1,732 (~ 70% below 2005 level)

• Cap-and-trade system– 22.5% auction phasing to 69.5% by 2030– Permits/auction revenues designated for a wide array of uses

• Provisions for limited use of offsets• Cost containment via a Carbon Market Efficiency Board

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And the Cost of Lieberman-Warner 2007 is ….

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Topics

• A Digression – A Perspective on Using Models• Models and Modelers• Different Assumptions Often Make the Biggest Differences• Concluding Thoughts

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Part I:What Are Models Good For?

“It’s hard to make predictions, especially about the future”

-Yogi BerraFamous American philosopher (and baseball player)

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The Challenge of Modeling (Thanks to Jae Edmonds)

“I’m amused year after year by your high emissions scenarios – and happy to see others now chuckle at them too!”

– Note Jae got from a friend in 1983

Examine Jae and John’s Forecasts of 2000 CO2Emissions + Oil Prices

… Made in 1982

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Projected and Actual 2000 Global CO2Emissions

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1975 1980 1985 1990 1995 2000

Mill

ions

of M

etric

Ton

s of

Car

bon

Per Y

ear

ForecastCDIAC Historical Global CO2 EmissionsExpon. (CDIAC Historical Global CO2 Emissions)

Source: Edmonds and Reilly -- Global Energy Production And Use To The Year 2050. Institute for Energy Analysis, Oak Ridge Universities, 1982

9© 2007 Electric Power Research Institute, Inc. All rights reserved.

Projected and Actual 2000 World Oil Prices

$0.00$10.00$20.00$30.00$40.00$50.00$60.00$70.00$80.00$90.00

$100.00

1975 1980 1985 1990 1995 2000

Cru

de O

il D

omes

tic F

irst P

urch

ase

Rea

l Pric

e 96

$$/

barr

el

Actual PriceER 1982 Scenario

Conventional Wisdom

Source: Edmonds and Reilly -- Global Energy Production And Use To The Year 2050. Institute for Energy Analysis, Oak Ridge Universities, 1982

10© 2007 Electric Power Research Institute, Inc. All rights reserved.

The Magic of Countervailing Errors –Geographic

Year 2000 Global Primary Energy

0

20

40

60

80

100

120

ER 2000 scenario Est. 2000 historical

Exa

joul

es p

er Y

ear

US

Canada & Western Europe

EE&FSU

China, South & East Asis,& Africa

Japan, Australia & NZ

Latin America

China, South & East Asia,& Africa

Exa

joul

espe

r Yea

r

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The Magic of Countervailing Errors – Technology Choice

2000 Global Energy Consumption by Fuel (ER

Scenario)

32%

17%

31%

12%

8%

OilGasCoalNuclearHydro

`

2000 Global Energy Consumption by Fuel (EIA

Historical)

39%

23%

24%

6%

8%

OilGasCoalNuclearHydro

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Insights from This Example

• Important stuff happens, especially over 10, 20, or 40 year time horizons– Models can provide very useful insights … but are less

useful for providing “predictions”– Some things are easier to “predict” than others

• What could happen to change “predictions” dramatically?– Extended recession?– Rebound of the US$?– Technology accident?– Technology breakthrough?

• What modeling insights seem robust?

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Part II: Modelers and Models

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Models with Public Estimates of Lieberman-Warner

• EIA NEMS=National Energy Modeling System• CATF NEMS• ACCF NEMS

• EPA ADAGE = Applied Dynamic Analysis of the Global EconomyIGEM = Intertemporal General Equilibrium ModelIPM = Integrated Planning ModelOthers

• MIT EPPA = Emissions Prediction and Policy Analysis Model• CRAI MRN-NEEM = Multi-Region National Model +

North American Electricity and Environment Model

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Selected Model Characteristics(Draft)

Model/Modeling Model/Modeling EffortEffort Geographic ScopeGeographic Scope SectorsSectors Time HorizonTime Horizon ForesightForesight

Electric Electric Sector DetailSector Detail

EIA/CATF/ACCF

• NEMS U.S. – Regional All, Process 2030 Recursive Plant type

EPA

• ADAGE Global(?) All, Trade1 2050 Perfect Production function

• IGEM U.S. All, Trade1 2050 Perfect Production function

• IPM U.S. – Regional Electric 2025 Perfect Plant type/unit

MIT: EPPA Global All, Trade1 2050 Recursive Production function

CRAI: MRN-NEEM U.S. – Regional All, Trade1 2050 Perfect Plant type/unit

1Based on IMPLAN state-level, SIC code trade data with energy trade adjusted to match EIA physical trade data and reference scenario calibrated to EIA-AEO projections.

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None of the Models Address Price Volatility …A Critical Factor for Some

EU CO2 Allowance (EUA) Prices (2005–2007)

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Part IIIDifferent Assumptions Often Make the Biggest Differences

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Recall the Cost of Lieberman-Warner 2007 is ….

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Cost Estimates from Application of NEMS by EIA, CATF, and NAM/ACCF

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Key Assumptions IncludeUncertainties and Policy Levers

I will talk about four uncertainties:• Reference Case• Technology Cost• “Non-economic” limits on technology deployment• Offsets

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Policy Cost Depends on the Reference Case…Reference Cases Change Over Time

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Energy Technology Costs Are Highly Uncertain:All Nuclear and CCS Costs are Engineering Estimates

Pleasant Prairie Chilled Ammonia, Post-Combustion Capture Demo

Late 2007 photo, courtesy of Alstom

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100

110

120

130

140

150

160

170

2000 2001 2002 2003 2004 2005 2006 2007 2008

Mid-Year

Inde

x Va

lue

GDP Deflator

EIA Generation Data –PC & IGCC

Chemical Engineering Plant Cost Index -CEPCI

Marshall & Swift Equipment Cost Index

Handy-Whitman –Electricity Utility Construction

RS Means Construction Cost Index

IHS-CERA Downstream Construction Cost Index -DCCI

Nelson Farrar Refinery Construction Inflation Index

Construction Cost Escalation Continues(Comparison of Several Cost Indices, Mid-2000 = 100)

Energy Technology Costs Are Highly Uncertain:Are Today’s Costs a New “Plateau” or a “Bubble”

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Reported and Guesstimated Technology Cost Estimates for Analyses you Will See Today

Overnight Capital Cost (2008$/kW)

Caveats: Converted to 2008$

CATF= AEO2007, EPA/IPM= AEO2005, CRAI= updated AEO2007

Some costs decline rapidly over time, e.g., CRAI Coal CCS to $3203/kW by 2050

0500

100015002000250030003500400045005000

Nuclear Coal CCS

$/kW

EIA

CATF

ACCF

EPA

MIT

CRAI

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Technology Deployment is Even More Uncertain

PublicPerception

Cost

Leakage

Regulation

LegalLiability

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Cumulative Capacity Additions of Coal CCS, Nuclear and Renewables

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EPA AnalysesOffset Provisions of S. 2191 Significantly Influence Costs

Marginal Cost of GHG Abatement in 2030 - Sensitivity Cases

82%

31%

34%

0%

-26%

-71%

93%

-100% -75% -50% -25% 0% 25% 50% 75% 100%

Nuclear and Biomass Constrained, No CCS before 2030

Nuclear and Biomass Constrained to Reference

No Domestic Offsets or International Credits

15% Domestic Offsets, No International Credits

15% Domestic Offsets, 15% International Credits

Unlimited Domestic Offsets, 15% International Credits

Unlimited Domestic Offsets and International Credits

% Change from Core S. 2191 Scenario*

Need increased attention on offset modeling

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MIT -- Banking Strategy is Not Obvious

Banking and Borrowing: Offsets

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

2012 2017 2022 2027 2032 2037 2042 2047

mm

t-CO

2e

Banking and Borrowing: No Offsets

-1000

-800

-600

-400

-200

0

200

400

600

800

2012 2017 2022 2027 2032 2037 2042 2047

mm

t-CO

2e

With Offsets

Without Offsets

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Concluding Thoughts

• US climate policy analyses likely to be released frequently over the next few years

• All of the models here are credible• None are great predictors; but all can provide important

insights about policy• Differences in cost estimates appear to represent true

uncertainties (e.g., technology deployment) and effects of policy choices (e.g., use of offsets) more than differences in models

I would be more worried and feel less-informed if there were little diversity in the cost estimates

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May 8 Workshop Presentations at www.epri.com