Representing Technology and Technological Change in IAMs

Leon Clarke, Jae EdmondsAugust 5, 2008

Snowmass

2

Accurately represent technological processes so that IAM research is robust

The processes of technological changeInduced R&D, learning processes, economies of scale, spilloversThe transition to Nth of a kind technology

Regional variations in technologyDiscounting issues & associated market failures (energy efficiency gap)“Other” obstacles to deployment

Nuclear waste, safety, securityCCS infrastructure and implementation issuesBioenergy interactions with agricultural policy and food security

The “realism” of technology representations and assumptionsWind supplies, distribution system and intermittencyEnergy end use technology representations

Interactions between technologies, and between technologies and other systems

Hydrogen & CCSRenewables, transportation, storage, and distributionDedicated energy crops and other uses of land

3

Inform a range of questions about technology-focused policies

The Policy Portfolio:What policy portfolio will be most useful in stimulating technology? (e.g., Basic R&D, applied R&D, RPS, incentives, emissions constraints)

The Appropriate Level of ActionHow much should be spent on R&D? How aggressive should technology-focused deployment policies be?What’s the appropriate carbon price?

The Allocation of ActionHow should R&D funds and focused technology policies be directed? (e.g., among technologies, basic versus applied R&D)

or, “How do we get from here to there?”

4

There has been a tendency to conflate modeling endogenous technological change with effective modeling of technology

Traditionally, models have taken technological change as an input (Exogenous).

A number of models have allowed technological change to respond to model variables, and therefore to be induced by policies (Endogenous).

Source: Grubler, et al. (1999)

5

What questions have been explored in IAMs with endogenous technological change?

Optimal emissions trajectories with a focus on near-term action.Optimal policy portfolio: mix of technology and emissions instruments.The costs of emissions mitigation.The possible character and implications of path dependence.

Analyses have largely been exploratory and confined to reduced-form IAMs.

Endogenous technological change has not become part of the standard toolkit of IAM modelers.

Analyses have largely been exploratory and confined to reduced-form IAMs.

Endogenous technological change has not become part of the standard toolkit of IAM modelers.

6

Why hasn’t endogenous technological change become a mainstream part of IAMs?

Source: Clarke and Weyant. 2002. “Modeling Induced Technological Change – An Overview,” RFF

7

How does an IAM modeler think about technological change?

1. R&DBasic versus appliedPrivate versus publicEnergy versus non-energy

2. ProductionGenerally meant to capture learning effects

1. R&DBasic versus appliedPrivate versus publicEnergy versus non-energy

2. ProductionGenerally meant to capture learning effects

The activities that drive technological change

1. Within the industry Responsive to policies applied to the industry.

2. Outside the industrySpilloversLess responsive to policies applied to the industry

1. Within the industry Responsive to policies applied to the industry.

2. Outside the industrySpilloversLess responsive to policies applied to the industry

The location of technology-enhancing activities

8

How does the IAM modeler think about technological change?

May be largely unresponsive to climate policies in some sectors

May be largely unresponsive to climate policies in some sectors

The direct effect of policies applied to the industry and its supply chain

The direct effect of policies applied to the industry and its supply chain

Creating a pool of knowledge that can be exploited by own-industry activities

Creating a pool of knowledge that can be exploited by own-industry activities

9

The literature on technological change indicates that spillovers are a big deal

“In spite of these difficulties, there has been a significant number of reasonably well done studies all pointing in the same direction: R&D spillovers are present, their magnitude may be quite large, and social rates of return remain significantly above private rates." (Griliches, 1992, p. S43)

From Australian Industry Commission (1995). Research and Development. Australian Government Publishing Service.

Study Country Time Period IndustryFirms in Other Industries National

Bernstein & Nadiri (1988)* USA 1958 to 1981 19 to 37 2 to 145 21 to 172Bernstein & Nadiri (1991)* USA 1957 to 1986 25 to 39 (32) 0 to 113 (24) 28 to 142 (55)Goto & Suzuki (1989) Japan 1978 to 1983 26 80 106Griliches & Lichtenberg (1984) USA 1959 to 1978 11 to 31 (24) 69 to 90 (80) 41 to 62 (55)Scherer (1982) USA 1948 to 1978 19 to 43 64 to 147 103Scherer (1984) USA 1973 to 1978 29 74 to 104 103Sveikauskas (1981) USA 1959 to 1969 7 to 25 50 57 to 75Terleckyj (1974) USA 1948 to 1966 12 to 37 (26) 45 to 187 (92) 73 to 107 (90)Terleckyj (1980) USA 1948 to 1966 25 to 27 (26) 82 to 183 (108) 107 to 110 (108)Wolff & Nadiri (1987) USA 1947 to 1972 11 to 19 (15) 10 to 90 (50) 21 to 109 (65)Bernstein (1989)* Canada 1963 to 1983 34 to 57 (42) 0 to 70 (26) 39 to 104 (68)Hanel (1988) Canada 1971 to 1982 50 100 150Mohnen & Lepine (1991) Canada 1975 to 1983 15 to 285 (67) 2 to 90 (29) 21 to 329 (45)Sterlacchini (1989) UK 1954 to 1984 2 to 33 (16) 7 to 32 (15) 18 to 56 (45)

* Net rate of return converted to a gross rate of return assuming a depreciation rate of 10 percent.

Rate of return to:

Note: Numbers in parentheses represent unweighted arithmetic means

10

The literature on technological change indicates that spillovers are often indirect

From Klevorick, A., R. Levin, R. Nelson, and S. Winter (1995). On the sources and significance of interindustry differences in technological opportunities. Research Policy 24, 185-205.

Source of opportunity High opportunity sectors Low opportunity sectors

electronic components

aircraft and missiles drugs

stone, clay and glass

metal products

non-electrical machinery

Basic and applied sciences Biology 1.8 1.2 6.8 1.4 1.3 1.4 Chemistry 6.0 3.8 6.6 5.8 4.6 3.5 Physics 6.5 5.6 3.3 4.2 4.6 4.5 Computer science 6.2 6.3 5.1 4.4 4.7 3.8 Material science 6.6 6.4 3.1 5.7 6.0 4.9 External contributions Material suppliers 5.1 5.1 3.2 3.9 4.7 4.3 Equipment suppliers 5.8 5.1 3.7 4.4 5.1 4.5 Users 4.9 4.9 4.2 3.5 4.4 4.6 University research 4.0 3.1 5.4 2.7 2.7 2.8 Government laboratories 3.6 4.1 4.8 2.1 2.3 2.5 Natural trajectories Mechanization/Automation 5.1 5.1 4.0 5.1 5.0 4.9 Improving process yield 6.4 5.0 5.5 5.1 4.8 4.5 Improving input materials 5.4 5.4 4.5 4.6 4.7 4.4 Changes in product dimensions 6.1 3.7 3.4 3.3 3.6 3.7 Improving product performance 6.5 6.2 5.1 5.2 5.1 5.8 Designing for market segments 5.3 5.7 3.3 4.4 4.4 5.1

11

Experience curves are valuable but should be approached with caution

Source: Grubler, et al. (1999)

1

10

100

1 10 100 1,000 10,000

Indexed Cumulative ProductionPr

ice

($/k

Wh)

Wind (mid-tech)Progress ratio = 0.94

Solar (mid-tech) Progress ratio = 0.87

Solar (high-tech)Progress ratio = 0.84

Empirical experience curves Model Results: Exogenous Technological Advance

12

How can we better inform R&D strategy: (1) how much effort is appropriate?(2) how should it be allocated?

Total Policy Cost

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

$12.0

$14.0

-0.25

% E

Effic

Base

_Stab

Wind

& S

olar

BioTe

ch

Geol C

arb C

CS

+0.25%

EEf

ficH2 E

nd U

ses

H2 Wind

&Solar

+0.25%

EEf

fic H

'2

Carb

CCS

& H

2H2 &

BioT

ech

+0.25%

EEf

fic H

2 Wind

&Solar

H2 & C

CS &

BioT

ech

+0.25%

EE

H2 C

CS B

io W

&S

Cost

(Tr

illio

n $9

0US) Individual Technologies

Binary Technologies Cominations

Multiple Technologies Cominations

Value & Implications of Technology

Integrated Assessment Models

R&D Allocation Under Uncertainty

Portfolio Models, Decision Analysis

ISSUES

TOOLS

1-p

p

1-p

p

Potential for Advance, R&D Impact on Advance

Technology AssessmentsHistorical Analysis

The Normative R&D Strategy Challenge:Issues and Tools

13

How can we better inform R&D strategy: (1) how much effort is appropriate?(2) how should it be allocated?

Total Policy Cost

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

$12.0

$14.0

-0.25

% E

Effic

Base

_Stab

Wind

& S

olar

BioTe

ch

Geol C

arb C

CS

+0.25%

EEf

ficH2 E

nd U

ses

H2 Wind

&Solar

+0.25%

EEf

fic H

'2

Carb

CCS

& H

2H2 &

BioT

ech

+0.25%

EEf

fic H

2 Wind

&Solar

H2 & C

CS &

BioT

ech

+0.25%

EE

H2 C

CS B

io W

&S

Cost

(Tr

illio

n $9

0US) Individual Technologies

Binary Technologies Cominations

Multiple Technologies Cominations

Value & Implications of Technology

Integrated Assessment Models

R&D Allocation Under Uncertainty

Portfolio Models, Decision Analysis

ISSUES

TOOLS

1-p

p

1-p

p

Potential for Advance, R&D Impact on Advance

Technology AssessmentsHistorical Analysis

The Normative R&D Strategy Challenge:Issues and Tools

What is the role of IAMs in addressing this

challenge?

What is the role of IAMs in addressing this

challenge?

14

Starting Point: Detailed Portfolio Assessments(note: this is old data from last year’s template)

* In view of various hypothetical RD&D portfolios and other factors. Key: Very Likely (90-100%); Likely (60-90%); Maybe (40-60%); Unlikely (10-40%); Very Unlikely (0-10%)

15

5-Year RD&D Investment, Technology Readiness Acceleration, and Expected U.S. Climate Change Benefits(CCTP Goals & Risk Factors; to 2100)

Transportation12

Industry6

Fossil5

Bio-Based Fuels

Solar3

Buildings3Wind

5

Nuclear5

Transportation12

Industry15

Fossil23

Buildings8

Wind6

Solar3

Bio-BasedFuels

3

Nuclear15

0

1

2

3

4

5

6

2000 2010 2020 2030 2040 2050 2060 2070

Technology Readiness (When Annual Deployment Reaches 10% of Peak Deployment)

U.S

. Cum

ulat

ive

Inve

stm

ent (

5-Y

ear B

udge

t Pla

n)

Area = Expected Benefits in GtC to 2100

Budget Option D1

Budget Option A

Focus U.S. Federal RD&D Investmenton High Return Areas

Greatest impact on emissions

- Long-term large-scale investments

- High Risk

Near-term (5-15 years)

- Policy dependent

- Lower risk

16

Rest of World

Fossil w/ CCS

143

Rest of World

Nuclear

75

Rest of World

Industry

61

Keep Eye on Potential Influence of Major EmittersU

.S. C

umul

ativ

e In

vest

men

t (5

Year

Bud

get P

lan)

17

Ongoing CCTP Scenarios: A Richer Set of Evolutionary Technology Pathways

0

200

400

600

800

1000

1200

1400

1600

1800

2005 2020 2035 2050 2065 2080 2095

$/tC

200

5$

ReferenceNuclearEnd UseEnd Use & RenewablesCCSBio & CCSSupply & HydrogenAdvancedNo Nuclear, No BioEnergy Ref

Draft results: subject to change

Carbon Price (450 ppmv) under a Technology Pathways

IAMs provide information on the value of technology for addressing climate change.

18

The interactions between bioenergy, CCS, and terrestrial carbon

-100

-75

-50

-25

0

25

50

75

100

125

150

175

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

direct buildings direct Industryelectricity refined liquidshydrogen synthetic gasenergy reduction

-100

-75

-50

-25

0

25

50

75

100

125

150

175

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

direct buildings direct Industryelectricity refined liquidshydrogen synthetic gasenergy reduction

Global Biomass Consumption: w/CCS

Global Biomass Consumption: No CCS

Bioenergy crops are increasingly used in liquid fuel applications,

& the value of carbon in land holds back bioenergy consumption

Bioenergy crops are increasingly used in liquid fuel applications,

& the value of carbon in land holds back bioenergy consumption

Bioenergy crops are primarily used in electricity applications with CCS,

& there is a large expansion of bioenergy consumption.

Bioenergy crops are primarily used in electricity applications with CCS,

& there is a large expansion of bioenergy consumption.

19

The Role of Technological Advances in Agriculture

Cumulative Emissions 2005 to 2095

0.5%/yr crop productivity growth:

11 PgCNo crop productivity growth:

162 PgC

-1,500

-1,000

-500

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

TgC/

yr

0.5%/yr Crop Productivity GrowthNo Crop Productivity Improvement

Land Use Change Emissions

IAMs can provide insights into the roles of technologies that are not always considered as part of the climate technology portfolio

20

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

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0

50

100

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200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

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0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

Global biomass consumption under differing technology and international policy assumptions (500 ppmv scenarios)

Delayed participation by developing regions

Full where and when flexibility

A renewables, nuclear, and

efficiency future

A bioenergy and CCS future

BioCCSFull Participation

RNEFull Participation

RNEDelayed Participation

BioCCSDelayed Participation

21

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

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0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

Global biomass consumption under differing technology and international policy assumptions (500 ppmv scenarios)

Technology drives total biomass consumption

Technology drives total biomass consumption

RNEDelayed Participation

BioCCSFull Participation

RNEFull Participation

BioCCSDelayed Participation

22

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

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0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

0

50

100

150

200

250

1990 2005 2020 2035 2050 2065 2080 2095

EJ/y

r

Non-Annex 1

Annex 1

Global biomass consumption under differing technology and international policy assumptions (500 ppmv scenarios)

International policy determines the distribution of biomass consumption

International policy determines the distribution of biomass consumption

23

Technology is even more important in an imperfect world

0

2

4

6

8

10

12

14

16

Ref RNE BioCCS All

Tri

llion

200

0$ (

Dis

coun

ted)

Total

Non-Annex 1

Annex 1

0

2

4

6

8

10

12

14

Ref RNE BioCCS All

Tri

llion

200

0$ (

Dis

coun

ted)

Total

Non-Annex 1

Annex 1

Total Discounted Cost of Mitigation, 2005 through 2095

Delayed ParticipationFull Participation

Reduction in Mitigation Costs

24© 2006 Electric Power Research Institute, Inc. All rights reserved.

Global Discounted Sum of Economic Cost

30

25

20

15

102000

$US

Trill

ions

5 0.5%3B1B

Optimistic

1.0%

1.5%

2.0%

2.5%

% G

DP Loss from

Reference

4.7 Target 3.4 Target

3B1B

3B

1B

Pessimistic

Optimistic

3B

1B

Pessimistic

At 5% through 2200

25

Where should we be concentrating our efforts in IAM development?

The long-term processes of technological changeInduced R&D, learning processes, economies of scale, spilloversThe transition to Nth of a kind technology

Regional variations in technologyDiscounting issues & associated market failures (energy efficiency gap)“Other” obstacles to deployment

Nuclear waste, safety, securityCCS infrastructure and licensingAgricultural policy and food security

The “realism” of technology assumptionsInteractions between technologies

Hydrogen & CCSRenewables, transportation, and storageDedicated energy crops and other uses of land