energy and technology...

Energy and Technology Transitions

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Hunter College, NYC, December 5, 2012

Energy Transitions: Past

Source: adapted from V. Smil, 1994

Energy Transitions: Drivers & Outcomes

• Overcome constraints:- availability- density- lack of diversity

• Technological change enables to:- provide novel energy services- improved efficiency- expanded & diversified supply- lessened trad. environmental impacts(indoor air pollution, “decarbonization”)

• Demand & economic growth, andnew environmental impacts (climate)

Energy Transitions: Present (unfinished business)

Source: Global Energy Assessment (GEA) KM1, 2012

Energy Transitions - The Traditional View:Measuring Primary Energy Inputs


SHARES INPRIMARYENERGY

historical

1990

1850

40%

1900

1950

1920

60%

20%

Renewables/Nuclear100%80%60%40%20%

1970

60%40%

Coal

80%

100%

20% 80%

Oil/Gas

0%0%

100%0%

1850-2010

World Primary Energy Shares

2010

Power Densities of Energy Supply & Demand


The End-use/Service Output Perspective of Transitions. Example: UK Lighting

Data: R. Fouquet, 2008

0.0001

0.001

0.01

0.1

1

10

1700 1750 1800 1850 1900 1950 2000

Mtoe fin

al ene

rgy whale‐oil

candles

town‐gas

kerosene

electricity

TOTAL

1

10

100

1,000

10,000

100,000

1,000,000

1700 1750 1800 1850 1900 1950 2000

Billion

Lum

en‐hrs

whale‐oil

candles

town‐gas

kerosene

electricity

TOTAL

1

10

100

1,000

10,000

1700 1750 1800 1850 1900 1950 2000

UK‐Po

unds/M

ilion

lumen

‐hrs

whale‐oil

candles

town‐gas

kerosene

electricity

AVERAGE

Energy input

Service output

Service price

Factors of Increase & driverssupplyenergy input: x 300efficiencyservice output: x 150,000learning, scale economiesservice price: ÷ 6,500

Lessons from Energy Transition Research

• Lesson 1: Energy-end use key• Lesson 2: Multiple, interacting drivers• Lesson 3: Technology, efficiency,

costs, and welfare gains interact• Lesson 4: Powerful patterns

(scaling, rates of change,…)• Lesson 5: Impact of policies mixed

Lesson 1: Energy End-use Key

• End-use: dominant form of energy conversion and dominant mobilizer of energy investments (and jobs!)

• Least efficient part of energy system (highest improvement potential)

• Largely “market” driven but with important externalities and barriers (information gaps, myopia, principal-agent problems, social & environmental externalities)

• Decentralized and “granular”decisions & technologies

• Stepchild of “silver bullet”, “top-down”(supply-side biased) policies

Capacity of US Energy Conversion Technologies

GW (rounded) 1850 1900 1950 2000

stationary thermal (furnaces/boilers) 300 900 1900 2700end-use mechanical (prime movers) 1 10 70 300

electrical (drives, appliances) 0 20 200 2200

mobile animals/ships/tra ins/a ircraft 5 30 120 260end-use automobiles 0 0 3300 25000

stationary thermal (power plant boilers) 0 10 260 2600suppply mechanical (prime movers) 0 3 70 800

chemical (refineries) 0 8 520 1280

TOTAL 306 981 6440 35140

Energy end-use = 30 TW or 87% of all energy conversion technologies= 5 TW or 50% when excluding automobiles

Source: GEA KM24, 2012

Exergy Efficiency of OECD Energy Systems (as % of primary exergy by conversion stage)

Traditional focus of energy transition research Source: GEA KM1, 2012

Investment into Energy Technology by Category and Life-cycle Stage. World in 2005 (billion US$)

innovation market diffusion(RD&D) formation

End-use & efficiency >>8 5 300-3500Fossil fuel supply >12 >>2 200-550Nuclear >10 0 3-8Renewables >12 ~20 >20Electricity (Gen+T&D) >>1 ~100 450-520Other* >>4 <15 n.a.Total >50 <150 1000-<5000* hydrogen, fuel cells, other power & storage technologies, basic energy research


Lessons 2&3: Interacting Drivers and Linkages income↔costs↔market size↔innovation↔TFP growth

• Micro-level:- demand pull AND supply push- extended development cycles, vulnerable

to policy intermittency and knowledge obsolescence

• Macro-level:major interactions between economy, technology, and energy services (but poorly documented)

US Solar Thermal Virtuous Development Cycle

Source: GEA KM24, 2012 based on G. Nemet, 2011

UK Energy HistoryA story of interlinked positive feedback loops

1

10

100

1000

10000

1800 1825 1850 1875 1900 1925 1950 1975 2000

Mill

ion

popu

latio

n, B

illio

n US$

2005

GDP

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

US$

2005

GD

P pe

r cap

ita

0.1

1

10

100

1800 1825 1850 1875 1900 1925 1950 1975 2000

GJ

PE p

er G

J or

per

ser

vice

leve

l

heating-domestic GJ PE/GJ

power-average GJ PR/GJ

transport-passengerGJ/10e3pass-kmlighting GJ/10e6 lumen-hrs

1

10

100

1,000

10,000

100,000

1800 1825 1850 1875 1900 1925 1950 1975 2000

US

$200

5/G

J or

per

uni

ts s

ervi

ce le

vel

heating-domestic $/GJ

power-industry $/GJ

transport-passenger $/500pass-km

transport-freight $/250 ton-km

lighting $/5000 lumen-hrs

0

2

4

6

8

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177 185 193 201 209

EJ

lightfreight-transportpassenger-transport(mechanical) powerheat-industryheat-domestic

1800 200019001850 1950

Population and Income Innovation and LbD:efficiency of end use

+Market growth and LbD:cost of energy services

Energy service demands

LbD=Learning by DoingSource: GEA KM1, 2012

Lesson 4: Powerful Patterns

• Path dependencies (multiple authors)but discontinuities remain enigma

• Scaling patterns and dynamics(C. Wilson)

• Lessons from systems and diffusion theories (IIASA)

• New concept: “granularity” (GEA KM24)smaller unit scale of technology=smaller investment risk, more learning/experimeningpossibilities

Path Dependency & Historical Discontinuitiesin Energy Use vs Wealth

0

100

200

300

400

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

GDP per capita (2005US$ MER and 2005Int.$ PPP)

GJ

per c

apita

prim

ary

ener

gy

USA 1800-2008Japan 1885-2008China (PPP) 1950-2008China (MER) 1950-2008India (PPP) 1950-2008India (MER) 1950-2008UK 1800-2008UK final energy 1800-2008


Path Dependent Energy Intensitiestotal & commercial (only), per $ MER and $ PPP

1

10

100

1000

100 1000 10000 100000

GDP per capita 2005US$ MER or 2005Int$ PPP

MJ

per

2005

US$

ME

R o

r 20

05In

t$ P

PP

JAPAN total MER JAPAN commercial MERJAPAN total PPP JAPAN commercial PPPUSA total USA commercialINDIA total MER INDIA commercial MERINDIA total PPP INDIA commercial PPPCHINA total MER CHINA commercial MERCHINA total PPP CHINA commercial PPP


5 Phases in Scaling-up of a Technology:Example Coal Power Plants (Source: C. Wilson, 2009)

0

500

1000

1500

1900 1925 1950 1975 2000

MW

0

20

40

60

80

100

120

140

160

180

200

num

ber

avg sizemax sizescale frontiernumbers built

1: build many (small) units

1. ExperimentationDecades long process of experimentationand learning with small-scale technologies diverse designs and multitude of actors, many failures


0

500

1000

1500

1900 1925 1950 1975 2000

MW

0

20

40

60

80

100

120

140

160

180

200

num

ber



2: scale-up units:2.1. at frontier2.2. average

2. Scaling at unit scale levelemerging standardized designallows scaling and economiesof scale effects, risks of pre-maturestandardization or “too big too early”


0

500

1000

1500

1900 1925 1950 1975 2000

MW

0

20

40

60

80

100

120

140

160

180

200

num

ber




3: build many (large) units

3. Market growth in coreAfter reaching unit scale-frontiergrowth by selling many large units

Aircraft Sales


0

250

500

750

1000

1250

1900 1925 1950 1975 2000

cum

GW

inst

alle

d0

10

20

30

40

GW

cap

acity

add

ed

cum capcity GWcap. additions GW

0

500

1000

1500

1900 1925 1950 1975 2000

MW

0

20

40

60

80

100

120

140

160

180

200

num

ber




3: build many (large) units

4: scale-up industry

5: grow outside core markets(globalize)

Market Size (normalized index) vsDiffusion Speed (∆t) of Energy Technologies

Source: C. Wilson, 2009, e-bikes courtesy of Nuno Bento, IIASA, 2011

Technology Scaling Patterns Past and Scenarios (GGI)(8 Scenarios: A2r/B1/B2 * base/670/480)

• Scenarios aremore conservative as durations (and extents) increase

• Closer relationship just for power techs historically dotted black

line1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

0 25 50 75 100 125

Normalised

K (ind

ex)

Δt (yrs) of cumulative total capacity

Cumulative Total Capacity (OECD): normalised K vs ΔtALL TECHS: HISTORICAL & SCENARIOS ‐ semi‐log

SCENARIOS (All Techs)

Historical (Core)

Historical (Core) ‐POWER only ‐ no WIND

Expon. (SCENARIOS (All Techs))

Expon. (Historical (Core))

Expon. (Historical (Core) ‐ POWER only ‐no WIND)

Learning rates and cumulative experience(# of units produced/sold): Importance of “Granularity”

category technology data for: cumulative production (units) learnin# exp period rate

energy Transitors World >1 10^18 1960-2010 40end-use DRAMs World >1 10^11 1975-2005 16 - 24 Automobiles World >2 10^9 1900-2005 9 - 14

Washing machines World >2 10^9 1965-2008 33 ±9Refrigerators World >2 10^9 1964-2008 9 ±4Dishwashers World >6 10^8 1968-2007 27 ±7Freezers (upright) World >6 10^8 1970-2003 10 ±5Freezers (chest) World >5 10^8 1970-1998 8 ±2Dryers World >3 10^8 1969-2003 28 ±7Hand-held calculators US >4 10^8 early 1970s 30CF light bulbs US >4 10^8 1992-1998 16A/C & heat pumps US >1 10^8 1972-2009 18 ±1Air furnaces US >1 10^8 1953-2009 31 ±3Solar hot water heaters US >1 10^6 1974-2003 -3

average for end-use technologies 10^9 20energysupply PV modules World >1 10^10 1975-2009 18-24

Wind turbines World >1 10^5 1975-2009 10-17Heat pumps S, CH <1 10^5 1982-2008 2 - 21Gas turbines World >4 10^4 1958-1980 10-13Pulverized coal boilers World >6 10^3 1940-2000 6Hypropower plants OECD ~5 10^3 1975-1993 1Nuclear reactors US, France <1 10^3 1971-2000 -20 - -47Ethanol Brazil <1 10^3 1975-2009 21Coal power plants OECD <1 10^3 1975-1993 8Coal power plants US <1 10^3 1950-1982 1 - 6Gas pipelines US <1 10^3 1984-1997 4Gas combined cycles OECD <1 10^3 1981-1997 10Hydrogen production (SRM) World >1 10^2 1980-2005 27LNG production World >1 10^2 1980-2005 14

average for suppy technologies 8average for supply, excluding nuclear 1210^4

Source: Wilson et al., 2012

Lesson 5: Influence of Policies?

• No single hammer for all nails!• Good evidence at micro- and meso scale

(Dehli CNG buses, cat cars, UK smokeless zones)• Key for policy successes: patience, predictability,

credibility, alignment, documentation of success• Successful transitions: repeat of established

patterns along the energy ladder (more energy services via higher exergy quality carriers),no large-scale “leapfrogging” examples yet

• Global scale: significant transition slowdown since 1970s (conflicting policy objectives?)

World - Primary Energy Substitution

0

25

50

75

100

1850 1875 1900 1925 1950 1975 2000 2025

Perc

ent i

n Pr

imar

y En

ergy

traditional biomass

coal

modern fuels:oil, gas,electricity

0

25

50

75

100

1850 1875 1900 1925 1950 1975 2000 2025

Perc

ent i

n Pr

imar

y En

ergy

traditional biomass

coal

modern fuels:oil, gas,electricity

Begin of energy policy focusand transition slowdown


A Next, Sustainability Energy Transition?

• Unfavorable baseline (no structural change)• Innovation systems (GEA ETIS) broken:

- portfolio bias (efficiency short-rifted)- spillovers blocked (BRICs exclusion)- wrong policy framework: “demand pull” (only)

(“cost buy down” leading to cost escalation)• Lack of consistent, aligned, and holistic policies

- “boom & bust” (stimulus vs. ITC phase-out)- lack of externality pricing- social returns ignored(despite “green growth” rhetoric)

AC

TOR

S &

INS

TITU

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TEC

HN

OLO

GY C

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RA

CTE

RIS

TICS

KNOWLEDGE

RESOURCES

learninggenerationsh

ared

ex

pect

atio

nsen

trepr

eneu

rs /

risk

taki

ng cost

resourceinputs

public policy & leverage

performance

ETISEnergy Technology Innovation System

CLIMATE MITIGATION

AC

TOR

S &

INS

TITU

TIO

NS

TEC

HN

OLO

GY C

HA

RA

CTE

RIS

TICS

KNOWLEDGE

RESOURCES


ared

ex

pect

atio

nsen

trepr

eneu

rs /

risk

taki

ng cost

resourceinputs


performance

R,D&D(public $)

Diffusion Support

Market Formation

Social Rate of Return

Analysis & Modelling

Future Needs

CLIMATE MITIGATION

AC

TOR

S &

INS

TITU

TIO

NS

TEC

HN

OLO

GY C

HA

RA

CTE

RIS

TICS

KNOWLEDGE

RESOURCES


ared

ex

pect

atio

nsen

trepr

eneu

rs /

risk

taki

ng cost

resourceinputs


performance

key

Roadmaps & Portfolios

Technology Collaborations

Learning Effects

Directable(Inputs)

Non-Directable(Outputs)

R,D&D(public $)

Diffusion Support

Market Formation

Social Rate of Return

Analysis & Modelling

Future Needs

CLIMATE MITIGATION

supply : end-use(relative effort)

AC

TOR

S &

INS

TITU

TIO

NS

TEC

HN

OLO

GY C

HA

RA

CTE

RIS

TICS

KNOWLEDGE

RESOURCES


ared

ex

pect

atio

nsen

trepr

eneu

rs /

risk

taki

ng cost

resourceinputs


performance

key

Roadmaps & Portfolios

Technology Collaborations

Learning Effects

Directable(Inputs)

Non-Directable(Outputs)

References & Additional Reading• Global Energy Assessment (GEA), 2012,

Chapter 1. Energy Primer,Chapter 24, Policies for the Energy Technology Innovation System,Online at: www.globalenergyassessment.org

• Wilson, C., Grubler, A., Sims-Gallagher, K., and Nemet, G., 2012, Marginalization of end-use technologies in energy innovation for climate protection. Nature Climate Change 2, 780–788 (2012) doi:10.1038/nclimate1576

• Grubler, A., 2012, Energy transitions research: Insights and cautionary tales, Energy Policy 50:8-16. doi:10.1016/ j.enpol.2012.02.070.

• Wilson, C., and Grubler, A., 2011, Lessons from the history of technological change for clean energy scenarios and policies. Natural Resources Forum, 35(3), 165-184.

• Grubler, A., 2008, Energy transitions, in Encyclopedia of Earth, Cutler J. Cleveland (ed.), online: http://www.eoearth.org/article/Energy_transitions

• Grubler, A., 2004, Transitions in energy use, Encyclopedia of Energy, Vol. 6, 163–177. Elsevier.

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