86025 energy systems analysisarnulf grubler climate change: addressing uncertainty, inertia, and...

86025 Energy Systems Analysis Arnulf Grubler

Climate Change: Addressing Uncertainty, Inertia, and Equity


The Greenhouse EffectE. Boeker, Environmental Physics

#updated for 2005 ann.average CO2 conc. CDIAC

Gas conc. ppm GWP factor G. Warming (ºK)

H2O 5000 0.2 20.6CO2 380# 1 7.2O3 0.03 3900 2.4N2O 0.3 310 0.8CH4 1.7 21 0.8

HFC-134 ~ 0.03 1000 0.6

Total 32.4


Source: IPCC AR4 WG1 2007


Temperature Change (over 1901-1950 mean): Observations and Modeled by Including all Positive and

Negative Forcings. Source: IPCC AR4 WG1 2007

EU Regional Climate Variability:Observations (b)modeled for present (c)and future (d)conditions.

Note 2003 heat wave (a) being far outside both observational and model range.

IPCC uncertainty terminology (adopted from Schneider and Moss) :<1% probability =“exceptionally unlikely” (but 2003 happened)

Height of Pasterze glacier in 1960

Current CC Impacts:80 meters thinning of Pasterze glacier,Austria

But…uncovering 5000 yrold vegetation


Global Carbon & Warming Budgets


Climate Change: Major Uncertainties

• Demographic (growth & composition)• Economic (growth, structure, disparities)• Social (values, lifestyles, policies)• Technologic (rates & direction of change)• Environmental (limits, adaptability)• Valuation (discounting, non-market damages and

benefits)

• Addressing uncertainties:ScenariosResearchSocial choice


A Taxonomy of SRES Scenarios(“baselines”, no climate policies)

Emissions vs. Energy Use & Technologyin IPCC SRES Scenarios

Uncertainty 1: Population and GDP growth, prices, policies

Un

certa

inty 2: R

eso

urce availa

bility

, tech

no

log

y

INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC)

Example Climate Change:Projected Global Mean Temperature Change

and Sources of Uncertainty

IPCC WG1 TAR: “By 2100, the range in the surface temperature response acrossthe group of climate models run with a given scenario is comparableto the range obtained from a single model run with the different SRES scenarios”

Example Climate Change:Projected Global Mean Temperature Change

and Sources of Uncertainty

Baseline Uncertainty:50 % climate (sensitivity) modeling25% emissions (POP+GDP influence)25% emissions (TECH influence)

Uncertainty on extent and success of climate policies

Minimum committed warming1.5 °C (certainty)

0

2

4

6

SRES scenarios cumulative emissions 1990 - 2100, GtC

<100 1000 2000 3000

ppm CO2

300 600 1000

70 2.5

° C global mean temperature change1 2 3 4 5 6

MAJOR CLIMATE CHANGE UNCERTAINTIESSocioeconomic (Future Cumulative Emissions SRES Scenarios)

Carbon Cycle (Resulting CO2 Concentration) andClimate Sensitivity (ºC for 2CO2)

Vulnerability: low high

0

5

10

15

20

199020002010202020302040205020602070208020902100

Carb

on

em

issi

on

s (G

tC y

r -1)

Year

550 ppmv (F = 1.1)

550 ppmv (F = 0.6)

550 ppmv (F = 2.6)

F = 0.6 T2x = 4.5

F = 2.6 T2x = 1.5

(2.6, 1.5)

(2.6, 2.5)

(1.1, 2.5)

(0.6, 2.5)

(0.6, 4.5)

(a)

0.1

1

10

100

1,000

10,000

199020002010202020302040205020602070208020902100

CO

2 sh

ad

ow

pri

ce (

$ p

er

ton

C)

Year

550 ppmv (F = 1.1)

550 ppmv (F = 0.6)

550 ppmv (F = 2.6)

F = 0.6 T2x = 4.5

F = 2.6 T2x = 1.5

(not shown as zero)

(b)

Uncertainties (F=carbon cycle; Δt2x=climate sensitivity)in Stabilizing Climate Change at +2.5 ºC by 2100

Emissions Shadow Prices


North -- South

• Responsibility: Mostly in Annex-I• Vulnerability: Mostly in “South”

• Adaptation capacity: Mostly in Annex-I• Future emission growth: Mostly in “South”

• Near-term mitigation potential: highest in Annex-I

• Near-term mitigation costs: lowest in “South”


1990 Per Capita CO2 by Source vs. Population

Distribution of Industrial CO2 Emissions since 1800


The Greenhouse “Barometer”


Agricultural Impacts for Alternative Climate Change Scenarios.

Source: IIASA LUCC, 2000.


Environmental Change: Development vs. Climate

• More ecosystems will be destroyed by economic development than by the climate change this development induces

• Far more human lives are threatened by a lack of development than by any climate change resulting from a closure of the development gap

• Baselines: “business-as-usual” + climate control vs sustainability paradigm


Vulnerability to Economic Development vs. Seal Level Rise. Source: IPCC AR4 WG2, 2007


Reducing CC Vulnerabilities

• Economic & Social Developmentun-targeted and asymmetricalpoverty vulnerability: -affluence vulnerability: +

• Adaptation targeted to CC

• Emissions reduction (mitigation)lowering CC but not eliminating it


Vulnerability to CC by 2050 (IPCC AR4 WG2 2007)

A2 current adaptive capacity Improved adaptive capacity

Mitigation only (550 stab) Mitigation + improved adaptation


Mitigation Options• Demographic change• Economic development• Social behavior

• Efficiency Improvements• Low carbon intensity• Zero carbon (solar, nuclear)• Carbon removal

• Non CO2 gases (agriculture&industry)

• End deforestation• Sink enhancements• “geo-engineering”


Emissions & Reduction MeasuresMultiple sectors and stabilization levels

0%

20%

40%

60%

80%

100%

400600800100012001400

CO2 eq. Concentration in 2100, ppm

Sh

are

of

cum

ula

tiv

e e

mis

sio

n r

ed

uc

tio

ns

by

sec

tor

(20

00

-21

00)

B1A2r

Energy & Industry

Forestry

Agriculture

0%

20%

40%

60%

80%

100%

400600800100012001400

CO2 eq. Concentration in 2100, ppm

Sh

are

of

cum

ula

tive

em

issi

on

red

uct

ion

s b

y g

as (

2000

-210

0)

B1A2r

CO2

CH4

N2O

Other Gases

Source: Riahi et al., TFSC 2007

Costs of Different Baselines and Stabilization ScenariosSource: IIASA, 2000.

Deployment rate of efficiency and low-emission technologies

Σ: The lower baseline emissions (efficiency, clean supply)the easier to achieve (currently unknown) climate targets

Stabilization Costs(% GDP loss, top,; and carbon price, $/tCO2, bottom) by 2100 as a Function of Baseline,

Model, and Stabilization Level Differences

IPCC AR4 WG3 2007

Technology as Source and Remedy of Climate Change:IPCC Baselines and 550 ppmv Stabilization Scenarios (in GtC), Source: IIASA, 2002.

BASELINE WITHFROZEN EFFICIENCYAND TECHNOLOGY

Σ: With “frozen” efficiency and technology improvementsemissions grow “through the roof”. Even with continued improvements,additional emission reduction is needed for climate stabilization

Baseline Emissions vs. Reductions in Illustrative 550 ppm Stabilization Scenarios (Source: Riahi, 2002)

CO2 reductions by source from A1 to A1-550

0

10

20

30

1990 2010 2030 2050 2070 2090

CO

2 e

mis

sio

ns

[GtC

]

Demand reduction

Fuel switching(mainly shifts awayfrom coal)

CO2 scrubbing andremoval

A1

A1-550

CO2 reductions by source from B2 to B2-550

0

10

20

30

1990 2010 2030 2050 2070 2090

CO

2 e

mis

sio

ns

[G

tC]

Demand reduction



B2

B2-550

CO2 reductions by source from A1C to A1C-550

0

10

20

30

1990 2010 2030 2050 2070 2090

CO

2 e

mis

sio

ns

[G

tC]

Demand reduction



A1C

A1C-550

CO2 reductions by source from A2 to A2-550

0

10

20

30

1990 2010 2030 2050 2070 2090

CO

2 e

mis

sio

ns

[G

tC]

Demand reduction

Fuel switching(mainly shiftsaway from coal)

CO2 scrubbingand removal

A2

A2-550

Σ: The higher the baseline emissions, the more reliance on “silver bullet” backstop technologies like CCS

Emission Reduction Measures Riahi et al. TFSC 2007

Emissions reductions due to climate policies

Improvements incorporated inbaselines

0 500 1000 1500 2000 2500 3000 3500 4000

F-gases

N2O

Switch to natural gas

Sinks

Other renewables

Fossil CCS

CH4

Consevation & efficiency

Nuclear

Biomass (incl. CCS)

Energy Intensity Improvement (Baseline)

Carbon Intensity Improvement (Baseline)

Cumulative contribution to mitigation (2000-2100), GtC eq.

1390 ppm

1090 ppm

970 ppm

820 ppm

670 ppm

590 ppm

520 ppm

480 ppm

0 500 1000 1500 2000 2500 3000 3500 4000

F-gases

N2O


Sinks

Other renewables

Fossil CCS

CH4


Nuclear

Biomass (incl. CCS)

Energy Intensity Improvement (Baseline)

Carbon Intensity Improvement (Baseline)

B1

B2

A2

820 ppm

670 ppm

590 ppm

520 ppm

480 ppm

Σ: Technological change in Baseline best hedging against target uncertainty

Emission Reduction Measures Riahi et al TFSC 2007

0 50 100 150 200 250 300 350

F-gases

N2O


Sinks

Other renewables

Fossil CCS

CH4


Nuclear

Biomass (incl. CCS)

Cumulative contribution to mitigation (2000-2100), GtC eq.

1390 ppm

1090 ppm

970 ppm

820 ppm

670 ppm

590 ppm

520 ppm

480 ppmA2B1 B2

RF = 0.7

RF = 0.3

RF = 0.2

RF = 0.1

RF = 0.5

RF = 0.1

RF = 0.3

RF = 0.7

RF = 1.0 RF = 0.2

(0.9 incl. baseline)

RF = Robustness factor of options across scenario uncertainty is highest for:F-gases and N2O reduction, energy conservation & efficiency, and biomass+CCS “wildcard” (if feasible)

Mitigation Technology Portfolio Analysis

• Paramount importance of Baseline • Costs matter• Diffusion time constants matter• Differences in where technology is developed

and where it is deployed• Technological interdependence and systemic

aspects important in “transition” analysis• Non-energy, non-CO2 can help, but cannot

solve problem

Σ: Popular “wedge” analysis fails on all above accounts!


Energy Carbon and Climate: How far to go?

• Energy: 20 (5% exergy efficiency)• Carbon: Zero (H2-economy)

• Damages: committed warming (>1.5 C?)• Non-linear (catastrophic) change: ???

• “Collateral damages”-- Geoengineering, e.g. aerosol cooling (white sky)-- sequestration (leakage, marine ecology)-- biomass (soil carbon, biodiversity, agriculture)-- solar (albedo changes)


Policy Conundrums

• Equitable quantitative targets at odds with economics or infeasible

• Cost optimal emission reduction: Start with inefficiencies in DCs but requires new instruments (CDM+)

• Separation of equity and efficiency (e.g. via tradable permit allocation) might be politically infeasible (unprecedented N-S resource transfers)

• Uncertainties cannot be ignored (soil C, avoided deforestation)

• Mitigation technology innovation “recharge” chain broken (declining R&D)


Allocating Emissions Reductions or Access to Global Commons

86025 energy systems analysisarnulf grubler climate change: addressing uncertainty, inertia, and...

Documents

equity slide

declining rd slide

solar albedo changes

ipcc ar4 wg1

present c

ipcc uncertainty terminology

nonlinear catastrophic

observations b