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2/26/2017
1
Energy Perspectives & Environment
Today
• Introduction
• Technological Change (Primer)
• Energy and the Environment (Overview)
• Integrative Perspectives: Scenarios(Illustration: Climate Change)
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Introduction: Energy Systems
Interaction between:
‐‐ Society
‐‐ Economy
‐‐ Technology
‐‐ Policy
that shape both
‐‐ Demand
‐‐ Supply
in terms of quantity, quality, costs, impacts.
Part 1: Technology
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Technology• Main determinant of energy systems(essence: conversion for/and service provision)
• Only “man‐made” resource available, determines:‐‐ resource availability (what and how much)‐‐ costs‐‐ environmental impacts/remediation
• Key concepts:Innovation process and technology lifecycleReturns to scaleKnowledge (learning and unlearning)
Technology
τεχνε λογοσ
Origin: the science of the art of the practical
A systems of means to particular ends that employs both technical artifacts
and (social) information
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Technology is...
H ‐ Hardware (artifacts, “machines”)
+
S ‐ Software (know‐how, “know‐why”)
+
O ‐”Orgware” (institutions, regulation, “rules of the game”)
The “black box” of Technology
BasicR&D
AppliedR&D
Demon‐stration
Nichemarkets
Diffusion
Product / Technology Push
Market / Demand PullLearning
Public Sector
Private Sector
DisembodiedTechnology(Knowledge)
EmbodiedTechnology(plant,equipment,..)
funding
funding incentives,standards, regulation,subsidies, taxes
investments,knowledge andmarket spillovers
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A Stylized Technology Life Cycle Modelakin Diffusion Curve
Technological Change: Life Cycle Model
Stage
Invention
Innovation
Niche markets
Commercialization
Pervasive diffusion
Measure/Mechanism
Basic R&D, breakthrough
Applied research, demonstration plants
Investment, learning‐by‐doing and using
Standardization, mass production, economies of scale
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Technology Growth Determinants(both positive and negative impacts)
• New knowledge generation/depreciation (R&D)
• (Dis‐)Economies of scale(unit, manufacturing, market)
• Learning by doing and using (knowledge)positive & negative learning
• Innovation System functioning (+/‐):‐‐ Knowledge generation & uncertainty reduction‐‐ Complementary institutional & social settings‐‐ Resource mobilization (investments, financing)
R&D Intensity:(propensity to innovate * probability of success * expected return)
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Creating New Knowledge: Scherer’s Ruleof compounding uncertainties
Probability an R&D project gets selected** ??
Probability of technical success (once selected)* .57Commercialization (given technical success)* .67Financial success (given commercialization)* .74
Aggregate probability .27
Magnitude of financial success(private AND social Rates of Return)** ??
* Based on Mansfield et al.’s empirical study of R&D project histories in US enterprises
in chemical, pharmaceutical, electronics, and petroleum industries
** Largest uncertainties!
Assuming 0.5 probability for the unknown, compound probability is 0.5x0.27x0.5 = 7 % success rate
5 Phases in Scaling‐up Technology:Example Coal Power Plants
0
250
500
750
1000
1250
1900 1925 1950 1975 2000
cum
GW
in
sta
lled
0
10
20
30
40
GW
ca
pa
city
ad
de
d
cum capcity GW
cap. additions GW
0
500
1000
1500
1900 1925 1950 1975 2000
MW
0
20
40
60
80
100
120
140
160
180
200
nu
mb
er
avg size
max size
scale frontier
numbers built
1: build many (small) units
2: scale‐up units:2.1. at frontier2.2. average
3: build many (large) units
4: scale‐up industry
5: grow outside core markets(globalize)
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Size of Wind Turbines
818 ETISArnulf Grubler
818 Technology Arnulf Gruebler86025 Energy Systems Analysis Arnulf Grubler
1990 1991 1993 1998
Diameter, m 23 31 44 63
kW 150 300 600 1500
DM per kW 2538 2410 2135 1752
Million DM .381 .723 1.281 2.628
Estimate .682 1.294 2.766
Difference (actual/estimate)
+6.0% -1.0% -4.6%
Declining Costs per kW of German Wind Turbines: Pure Economies of Scale: $t = (kWt/kWt‐1)
0.84 x $t‐1
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Improved Economics: Prices vs. Costs
Ford Model T
y = -0.214x + 3.8738
R2 = 0.9765
y = -0.1194x + 3.6024
R2 = 0.7333
4
5
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
log (cum. production, million)
log
(1993$/c
ar)
Prices
Costs
Data Source: Abernathy&Ward, 1975
PR= 86%LR= 14%
PR= 92%LR= 8%
Learning/Experience Curve TerminologyCosts: CLearning Rate: LR
(% cost decline per doubling of output)Progress Ratio: PR = 1 – LR
(remaining fraction of initial costs after doubling of output)Learning parameter: bOutput: OLearning investment: Cumulative expenditures above break‐even value
Ct = C0 * (Σ0tO)‐b
PR = 2‐b
LR = 1 ‐ PR
e.g. 30% cost reduction per doubling of output:Co=100 Ct = 70 Oo =1 Ot = 2 LR = .3 PR = .7 b = ‐.51477
Mind: energy economics literature expresses cumulative Output often per cumulative Capacity installed. With increasing unit scales this confounds economies of scale and LbD, resulting in overestimation of learning rates.
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y = ‐0.02ln(x) + 0.0822 R² = 0.33179
‐40%
‐30%
‐20%
‐10%
0%
10%
20%
30%
40%
1.E‐04 1.E‐03 1.E‐02 1.E‐01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04
De‐scaled Learning Rate (C
umula
ve Number
of U
nits)
Average Unit Size (MW)
Healey, S. (2015). Separating Economies of Scale and Learning Effects in Technology Cost Improvements. IR-15-009.International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
smaller units
‐> more units
‐> more opportunities to experiment
‐> more learning
geothermal
nuclear
Learning Rates after considering Economies of Scale
Drivers of Technology Improvements
Source: G. Nemet, 2008.
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Global Resource Mobilization forEnergy Technology Innovation & Diffusion
(Billion $)
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 non-OECD ~20 ~30 ~400 - ~1500
non-OECD share >40% <20% 40% - 30%
* hydrogen, fuel cells, other power & storage technologies, basic energy research
GEA, 2012
Public Policy‐induced ETIS Investmentsbillion US$2005
Wilson et al. Nature CC, 2012
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UK Energy History – Innovation‐driven Growth (LbD=Learning by Doing)
818 Technology Arnulf Gruebler
Synthesis Technology
• Main driver for past evolution
• Main driver for future scenarios
• Creating technological knowledge:Systemic process: Actors/institutions + resource mobilization + knowledge generation = innovation and improvements
• Requirements for policy: Alignment, stability, allow experimentation (and failure), globalize
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Part 2: Environment
Energy & Environment(Energy dominant stressor in 9/18 cases)
814 Energy Systems Analysis Arnulf Grubler
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814 Energy Systems Analysis Arnulf Grubler
A Taxonomy of Environmental Problems (after WB, 1992):
814 Energy Systems Analysis Arnulf Grubler
Environmental Problems of Energy
1: Poverty, ignorance, and lack of capacity
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814 Energy Systems Analysis Arnulf Grubler
Particulate Concentrations and Human Exposure in 8 Environments
Exposure = People x Time x Concentration
814 Energy Systems Analysis Arnulf Grubler
Environmental Problems of Energy
2: Industrialization, growing awareness, regionalization
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London ‐ December 1952Urban Air Quality and Mortality
China ‐ Air Pollution (SO2) Exposure
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Sulfur Cycle Local, Regional, and Global Impacts
Wetdeposition
Drydeposition
Regional: Acidification
Local:Air quality(health,corrosion)
Global: Stratosphere (cooling)
Based on Crutzen&Graedel, 1986.
* hydrogen sulfide (land)dimethyl sulfide (ocean)
*
Sulfur Deposition (gS/m2)
Europe, ca. 1990
China, A2 Projection for 2020
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814 Energy Systems Analysis Arnulf Grubler
Percent losses
Not assessed
No damage
< 10%10 - 33%33 - 75%
> 75%
A2 Acidification Impacts on Food Production Loss
814 Energy Systems Analysis Arnulf Grubler
World – Sulfur Emissions by Region
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814 Energy Systems Analysis Arnulf Grubler
IPCC WGI AR5Radiative Forcing
Change1750‐2011
814 Energy Systems Analysis Arnulf Grubler
Environmental Problems of Energy
3: Affluence, deep uncertainty, globalization
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Atmospheric CO2 Concentration (Mauna Loa)1 ppm= 2.12 GtC (7.8 GtCO2), change 2015/2014: +2.3 ppm
atmospheric increase = emissions x Airborne Fraction= 10.7 GtCx2.12=5 ppm x 0.46 =2.3 ppm
NOAA, 2017.
814 Energy Systems Analysis Arnulf Grubler
Carbon Emissions by RegionGtC (Gigatons elemental carbon) x 3.67 = tons CO2
GtC
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Energy – Environment Strategies
814 Energy Systems Analysis Arnulf Grubler
Improvements in Efficiency and Decarbonization: Diverse Paths
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Synthesis Environment
• Multiple impacts, drivers as organizing principle (not environmental media):poverty, industrialization, affluence
• Environmental strategies:‐ generic (efficiency improvements, decarbonization)‐ “add on” (end‐of‐pipe)‐ remediation (“fix”)
• Largest policy successes:‐ demonstrated benefits for human health‐ better alternatives and business models available‐ uncertainty “managed” (e.g. insurance, research)
Hence: climate change as biggest challenge to date
Part 3: Energy Perspectives (Scenarios)
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Why Look into the Future?
• Planning for R&D, investment, marketing• Reconcile growing mismatch in temporal rhythms of society:‐‐ accelerating rates of change in innovation
and knowledge obsolescence‐‐ slowing rates of change in social systems
and technological infrastructures (increasing inertia)
• Anticipate and plan for disruptive change(e.g. climate change)
• Exercise pro‐active rather than re‐active management
US Energy Demand Projections
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
1975 1980 1985 1990 1995 2000
Exa
joul
es
9b
1c
8b
4
8f15
5f
1a
35e
9a
1110
13
16
17a,b
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Source: Shell, 2001
World Economic MapAreas of Regions Proportional to 1990 GDP (mer)
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Alternative Scenario Formulations
Models
Stories
Scenarios
Quantitati
ve
Qualitativ
e
How to Deal with Scenarios
• Robustness: Which trends unfold across even a wide range of scenarios?(Demographic ageing, geo‐econoomic shift to “South”, urbanization)
• Divergence: Which short‐ to medium‐term trends/actions yield long‐term differences across scenarios
• Implications: Which of “bifurcation triggers” are external (e.g. oil prices) or internal (e.g. innovation) for my firm/sector/country?
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Futures: Major Uncertainties
• Demographic (growth & composition)
• Economic (growth, structure, disparities)
• Social (values, lifestyles, policies)
• Technologic (rates & direction of change)
• Environmental (limits, adaptability)
• Geopolitical (globalization vs. regionalization)
Summary of IIASA‐GGI Scenario Characteristics
1800 1900 2000 2100
Population(billion)
1 1.6 6 7-12
GDP(trillion $)
0.5 2 35 190-330
Primary Energy (EJ)
13 40 440 1050-1750
Emissions Energy (GtC)all GHGs (GtC-equiv)
00.3
0.51.0
711
7-2710-35
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Population Density: 1990 and 2070 B1 and A2R
GDP mer 1990
World Economic MapAreas of Regions Proportional to 1990 GDP (mer)
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GDP mer 2100
GDP mer 2050
GDP mer 1990
An Intermediary Scenario to 2100
World Primary Energy Supply
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Global Primary Energy Scenarios
Carbon Emissions: IPCC‐SRES Scenarios andIPCC‐TAR Stabilization Profiles
25
20
15
10
5
0
1800 1900 2000 2100 2200
S450
GtC
S550
S650
Stabilizationat 450, 550, 650ppmv CO 2
S450S550S650
B2
B1
A2
35 Gt in 2100
A1B
A1FI (A1C & A1G)
A1T
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814 Energy Systems Analysis Arnulf Grubler
IPCC Projected Temperature Change in Absence of Climate Policies
Uncertainty:50 % climate (sensitivity) modeling25% emissions (POP+GDP) 25% emissions (TECH influence)
Policy uncertainty (success of climate stabilization efforts
Δ Temperature (high A2 scenario)
2070
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1900 1950 2000 2050 2100
Glo
ba
l CO
2 e
mis
sio
ns (G
tCO
2)
-20
0
20
40
60
80
100
120
140
IPCC AR5 2 DegreesRCP 6.0RCP 4.5RCP 2.6RCP 8.5GEA (SE4ALL)
RCP 2.6
RCP 4.5
RCP 6.0
RCP 8.5
Global CO2 Emissions IPCC & GEA Scenarios
SRES range
1850 1900 1950 2000 2050
Pri
ma
ry E
nerg
y S
uppl
y [E
J/yr
]
0
200
400
600
800
1000
1200SavingsGeothermalSolarWindHydroNuclearGas wCCSGas woCCSOilCoal wCCSCoal woCCSBiomass wCCSBiomass woCCS
Advancedtransportation
Conventionaltransportation
GEA Efficiency
Un
rest
rict
ed P
ort
folio
No
Nu
clea
rN
o B
ioC
CS
No
Sin
ksLi
mit
ed B
io-e
ner
gy
Lim
ited
Ren
ewab
les
No
CC
SN
o N
ucl
ear
& C
CS
Lim
. Bio
-en
erg
y &
Ren
ewab
les
No
Bio
CC
S, S
ink
& li
mB
io-e
ner
gy
Un
rest
rict
ed P
ort
folio
No
Nu
clea
rN
o B
ioC
CS
No
Sin
ksLi
mit
ed B
io-e
ner
gy
Lim
ited
Ren
ewab
les
No
CC
SN
o N
ucl
ear
& C
CS
Lim
. Bio
-en
erg
y &
Ren
ewab
les
No
Bio
CC
S, S
ink
& li
mB
io-e
ner
gy
Supply Side Innovations
Demand Side Innovations
GEA Transition Pathways
Transitions entail bothdemand‐ and supply‐side innovations
GEA, 2012
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GEA Supply
1850 1900 1950 2000 2050
Pri
ma
ry E
nerg
y S
uppl
y [E
J/yr
]
0
200
400
600
800
1000
1200SavingsGeothermalSolarWindHydroNuclearGas wCCSGas woCCSOilCoal wCCSCoal woCCSBiomass wCCSBiomass woCCS
Advancedtransportation
Conventionaltransportation
Un
rest
rict
ed P
ort
folio
No
Nu
clea
rN
o B
ioC
CS
No
Sin
ksLi
mit
ed B
io-e
ner
gy
Lim
ited
Ren
ewab
les
No
CC
SN
o N
ucl
ear
& C
CS
Lim
. Bio
-en
erg
y &
Ren
ewab
les
No
Bio
CC
S, S
ink
& li
m B
io-e
ner
gy
Un
rest
rict
ed P
ort
folio
No
Nu
clea
rN
o B
ioC
CS
No
Sin
ksLi
mit
ed B
io-e
ner
gy
Lim
ited
Ren
ewab
les
No
CC
SN
o N
ucl
ear
& C
CS
Lim
. Bio
-en
erg
y &
Ren
ewab
les
No
Bio
CC
S, S
ink
& li
m B
io-e
ner
gy
Lack of innovation in efficiency,constrains supply side flexibility
GEA Transition Pathways
GEA, 2012
New CC Policy Perspectives• Traditional CC policy framework:‐‐ “additionality”‐‐ opportunity costs (crowding out)‐‐ costs & benefits separated(in space and time)
• New perspectives:‐‐ integration of policy frameworks‐‐ significant synergies possible(if CC is used as entry point)
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Synergies between Climate Change, Energy Security,and Air Pollution Policy Objectives
McCollum et al., 2013Climatic Change DOI 10.1007/s10584‐013‐0710‐y
Synergies between Climate Change, Energy Security,and Air Pollution Policy Objectives
McCollum et al., 2013Climatic Change DOI 10.1007/s10584‐013‐0710‐y
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Cumulative Investments for single & multiple policy objectives
0
50
100
150
200
250
300
only security only pollution only climate all 3 objectivesCu
mu
lati
ve in
vest
men
ts 2
010-
2050
(T
rill
ion
US
$20
05)
Policy Objectives Fulfilled
end-use high
end-use low
conservation (policy induced)
pollution control & CCS
other conversion
non-fossil electrcity
fossil electricity
non-fossil supply
fossil supply
Some Scenario Findings
• Demographics: Lowering of population projections, ageing, education, urbanization
• Geopolitics: Pervasive move to “South”(urban population, GDP, energy, emissions,…)
• Structural Change in Energy I: Change is constructed via R&D and investment choices
• Structural Change in Energy II: Demand (& lifestyle) management (change) is key in energy security, resilience, and emissions reduction
• Climate Change: Minimum committed warming: ~1.5 °C• Mitigating Climate Change: Uncertainty in targets, early action, induced technological change, and policy integration can lower costs substantially
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Main Messages Scenarios
• Forecasting both impossible and innovation counterproductive
• Use of scenario techniques instead• Opportunities and threats vary depending on which alternative future will unfold
• Response strategies:‐‐ Uncertainty needs optimal portfolio of
measures and contingency (best AND worst case planning)
‐‐ innovation as key technique, in:technology (R&D), human capital (education)institutional (adaptation)
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