Energy Perspectives Environment...

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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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A Taxonomy of Environmental Problems (after WB, 1992):


Environmental Problems of Energy

1: Poverty, ignorance, and lack of capacity

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Particulate Concentrations and Human Exposure in 8 Environments

Exposure = People x Time x Concentration



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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Percent losses

Not assessed

No damage

< 10%10 - 33%33 - 75%

> 75%

A2 Acidification Impacts on Food Production Loss


World – Sulfur Emissions by Region

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IPCC WGI AR5Radiative Forcing

Change1750‐2011



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.


Carbon Emissions by RegionGtC (Gigatons elemental carbon) x 3.67 = tons CO2

GtC

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Energy – Environment Strategies


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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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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