Integrated Assessment Models - IIASAgruebler/Lectures/skku_2009/handouts/esa_16.pdf · “bottom-up” assessment Consistency of land-price, urban areas, net primary productivity,

1

Energy Systems Analysis Arnulf Grubler

Integrated Assessment Models

esa_16


IAM: Integrated Assessment Models

• Modeling/study of interlinked systems• For:

-- simulating possible futures-- address range of policy questions-- identifying research needs/priorities

• Through:-- “hard” linkage of models, or “compact” IA models-- “soft” linkages (successive iterations) of large models

• Using:-- simulation-- cost-benefit analysis-- optimization (e.g. min costs for var. targets)

• Considering uncertainty via:-- scenarios of deterministic models-- using stochastic models

2


Climate IAM:Concept and Main Model Linkages

society

atmosphere climate

ecosystems

GHG emissions

GHGconcentrations

radiative forcingtemperature changesea level rise

damages


Bill Nordhaus’ DICE Model: Overview

Avoided damage

-(AEEI)

+ Solow

Remaining damage

(Consumption = Q – I)

3

Nordhaus’s DICE Model: Most Compact Form

atmosphere climate

ecosystemsMacrogrowth modelQ= f(K, L, A, Ω)

C emissions

CUM C

Damages Ω

Labor, A (C/GDP decoupling) - exogenous

Max C* (=Q-K-TC-Ω)

* = discountedTC = mitigation costs

Damage functionΩ = f(CUM C)

C-cycle

stock (CUM C) and flow variables (all other)units: C (emissions, concentration), $ (all other)

production function: GDP = Q = K*L = C+I

objective (goal)function


Illustrative DICE Result: Global policy optimumis low emission reduction/carbon taxes

“do nothing”, i.e. ignore climate change

keep climate constant (no further change)

“optimal solution”balancing costs (abatement)vs avoided costs (damages)

4


DICE – Assumptions Determining Results

• Modeling paradigm:-- utility maximization (akin cost minimization)-- perfect foresight (akin no uncertainty)-- social planner (when-where flexibility, strict

separation of equity and efficiency) • Abatement cost and damage functions,

calibrated as %GWP vs. GMTC (°C) [CUM C] →• Discount rate (for inter-temporal choice, 5%)

matters for damages (long-term) vs abatement costs (short-term)

• No discontinuities (catastrophes) →


Treatment of Uncertainty

• Model sensitivity analysis

• Scenarios

• Stochastic modeling

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DICE Model - Analytically Resolved (99% of all solutions by 2100). Source: A. Smirnov, IIASA, 2006

(analytically based model sensitivity analysis)

abatement costs

damage costs


Attainability Domain Analysis ofClimate Change Policy

• Analytical analysis of all possiblestates of Nordhaus’ DICE model

• Two policy variables:investment & emissions (abatement)

• First performed by IIASA YSSP Alexey Smirnov • Successive overlays of

-- objective function, revealing “indifference”…space (linguistic ambiguity of “optimality”)-- risk surfaces of catastrophic event …(thermohaline shut-down with different …climate sensitivities based on Keller et al.)

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Attainability Domain of DICE with original Optimality Point2100


DICE Attainability Domain and Isolinesof Objective Function Surface

Percent of max. of objective function.Note the large “indifference” area

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Risk Surface of Thermohaline collapse(years of exposure 1990-2100)

climate sensitivity = 4 ºC

Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces




• Scenarios


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IIASA Integrated Assessment & Scenario Analysis


4 Examples from IIASA IAM• Sectorial and GHG species mitigation share

across scenarios• Mitigation technology portfolios across

scenarios• Biomass and forest-based mitigation

potentials and deployment across scenarios, identifying potential land-use conflicts

• Technology targets under ex ante climate targets with probabilistic uncertainties (climate sensitivity)

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Mitigation Scenario AnalysisSource: Riahi et al., TFSC 74(2007)

0

5

10

15

20

25

30

35

40

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Annu

al G

HG e

mis

sion

s, G

tC e

q. A2r

A2r - 4.5W/m2

1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Energy conservation and efficiencyimprovementSwitch to natural gas

Fossil CCS

Nuclear

Biomass (incl. CCS)

Other renewables

Sinks

CH4

N2O

F-gases

CO2

0

5

10

15

20

25

30

35

40

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Annu

al G

HG e

mis

sion

s, G

tC e

q.

B2

B2 - 4.5W/m2

Σ: Mitigation “wedges” are baseline and target dependent

Emissions & Reduction MeasuresMultiple sectors and stabilization levels for 2 scenarios

Source: Riahi et al., TFSC 74(2007)

0%

20%

40%

60%

80%

100%

400600800100012001400

CO2 eq. Concentration in 2100, ppm

Sha

re o

f cum

ulat

ive

emis

sion

redu

ctio

ns b

y se

ctor

(200

0-21

00)

B1A2r

Energy & Industry

Forestry

Agriculture

0%

20%

40%

60%

80%

100%

400600800100012001400

CO2 eq. Concentration in 2100, ppm

Shar

e of

cum

ulat

ive

emis

sion

red

uctio

ns b

y g

as (2

000-

2100

)

B1A2r

CO2

CH4

N2OOther Gases

Σ: Energy CO2 remains main problem and target for mitigation

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Cost Savings through Multi-gas Approach(example of B2 with intermediary 670 ppmv-e climate target)

Source: Rao and Riahi, 2006

0

200

400

600

800

1000

2000 2020 2040 2060 2080 2100

Shad

ow p

rice

of C

O2

($/tC

)

CO2-only Multigas

Biomass Potentials

Dynamic GDP maps (to 2100) Dynamic population density (to 2100)

Development of bioenergy potentials “bottom-up” assessment

Consistency of land-price, urban areas, net primaryproductivity, biomass potentials (spatially explicit)

“Top-down”Downscaling

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Bioenergy and C-sink Modeling

MESSAGE

Systems Engineering

Energy Model

Exogenous drivers for CH4

& N2O emissions:

N-Fertilizer use, Bovine Livestock

Bottom-up mitigation

technologies for non-CO2

emissions,

Black carbon and organic carbon

emissions coefficients

Forest Sinks Potential, FSU

050

100150200250300350

0 100 200 300 400 500 600 700 800Rate of carbon sequestration MTC

Incr

ease

in P

rices

21002000

2050

Data Sources :Obersteiner & Rokityanskiy, FOR

Data Sources: Fischer & Tubiello,LUC

Data Sources:USEPA, EMF-21

Data Sources: Klimont & Kupiano, TAP

Agricultura l residue pote ntia ls

01000200030004000500060007000

19 9020 00

20 1020 20

20302040

20 5020 60

20 7020 80

20 90

PJ

NAMWEUPAOFSUEEUAFRLAMM EACPASASPAS

Data sources: Fischer & Tubiello, LUC

Data Sources: Obersteiner & Rokityanskiy, FOR; Tubiello & Fischer, LUC

Biomass supply A2:WEU

0

2

4

6

8

10

12

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Bio

ener

gy p

oten

tial (

EJ)

Ag. residues

Biomass from forests

1$/GJ

6$/GJ

4$/GJ5$/GJ

3$/GJ




• Scenarios


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What is a pdf?

pdf = probability density function = (subjective) outcome probabilitiesattached to given variables, e.g. climate sensitivity = °C warming for doubled CO2

Source: M. Meinshausen, 2005


PDFs of Climate Sensitivity Applied to EU Climate Target of 2 C°Warming by 2100. Meinshausen&Hare, 2007

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Global Zero-Carbon Primary Energy Shares and the Probability of Meeting a 2°C Target for 2 Scenarios

0%

20%

40%

60%

80%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Share of zero-carbon energy (2050)

Pro

babi

lity

of s

tayi

ng b

elow

the

targ

et

2°C

3°C

2000

A2r B1r

Source: Keppo et al., 2007

Today

52-58 % > 75%

Note: zero-carbon shares include CCS

Stochastic Optimization

Given ex ante assumed pdf’s of uncertainties, what is the optimal risk hedging portfolio in energy technology investments (here: globalprimary energy supply), given different degrees of risk aversion(willingness to pay for “hedging” risks)?Source: Krey and Riahi (in press)

deterministic solution (no uncertainty) stochastic optimization with full uncertainty no “risk premium” and highest “risk premium” (5% of total costs)

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Integrated Assessment Models: What they can do

• Full cycle analysis: Economy – Energy – Environment

• Multiple scenarios (uncertainties)• Multiple environmental impacts (but

aggregation only via monetarization)• Cost-benefit, cost-effectiveness

frameworks• Value and timing of information

(backstops)


Integrated Assessment Models: What they cannot do

• Resolve uncertainties• Optional “hedging” strategies vis à vis

uncertainty (→stochastic optimization)• Resolve equity-efficiency conundrum

(→agent based, game theoretical models)

• Address implementation issues(e.g. building codes, C-trade, R&D, technology transfer)

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Reading ListJ. Weyant et al., 1995 (IPCC SAR), Integrated Assessment of Climate

Change, An Overview. Posted on class server

Rotmans, J., Dowlatabadi, H., 1998. Integrated Assessment Modeling.In:Rayner, S., Elizabeth Malone, (Eds.), Human Choice and Climate Change, Vol. 3. Battelle, Columbus, pp. 292–37. See Arnulf for a copy.

B. Nordhaus, 2008, A Question of Balance, Pre-pub version and GAMS code of DICE-2007 Model at:http://nordhaus.econ.yale.edu/DICE2007.htmspreadsheet version of DICE-99 & RICE-99 models at:http://www.econ.yale.edu/~nordhaus/homepage/dice_section_V.html

A. Grubler et al., Integrated Assessment of Uncertainties in GHG Emissions and their Mitigation, Introduction and Overview(Special Issue of Technological Forecasting & Social Change)all 9 articles at:http://www.iiasa.ac.at/Research/GGI/publications/index.html?sb=12


Software Overview for the “to-be” Modeling Student

• GAMS (e.g. DICE): http://www.gams.com/

• Vensim: http://www.vensim.com/download.html

• LEAP: http://www.sei-us.org/LEAP/index.html

• R: http://www.r-project.org/

• Markal family: http://www.etsap.org/ , http://www.etsap.org/markal/main.html

• CPLEX (for MESSAGE):http://www.ilog.com/products/cplex/ ,http://www.ilog.com/products/oplstudio/

Documents

Integrated Assessment Models - IIASAgruebler/Lectures/skku_2009/handouts/esa_16.pdf · “bottom-up” assessment Consistency of land-price, urban areas, net primary productivity,