evaluating co2 mitigation options for the 21 st century - insights from the model mind cdmc seminar,...

Evaluating CO2 mitigation options for the 21st century -

Insights from the model MINDCDMC seminar, July 12, 2006

Elmar KrieglerEPP, Carnegie Mellon University (visiting research scholar)

supported by EU 6th framework programme for research, Marie Curie Actions

Credit for MIND due to:

Ottmar Edenhofer (PI), Nico Bauer (PD), Kai Lessmann (PSA)Potsdam Institute for Climate Impact Research

E. Kriegler: Insights from the model MIND, CDMC seminar, July 12, 2006

Integrated assessment of climate policy

GHG emissions Climate damages

Climate system

Socio-economic system

Mitigation

Adaptation




Climate system


Mitigation

Adaptation

Types of integrated assessment analyses:

Simulation: explore effect of predefined policies (e.g. IMAGE) Cost-benefit: find policy minimising welfare losses from mitigation and climate damages (e.g. DICE)



Climate system


Mitigation

Climate guardrailCO

2 stabilisation (e.g. 450 ppm)

Warming limit (2oC since preind.)


Simulation: explore effect of predefined policies (e.g. IMAGE) Cost-benefit: find policy minimising welfare losses (e.g. DICE) Cost-effectiveness: find policy minimising welfare losses from mitigation for complying with guardrail (e.g. MERGE)



Climate system


Mitigation

Climate guardrailCO

2 stabilisation (e.g. 450 ppm)

Warming limit (2oC since preind.)


Simulation: explore effect of predefined policies (e.g. IMAGE) Cost-benefit: find policy minimising welfare losses (e.g. DICE) Cost-effectiveness: minimising losses to comply with guardrail (e.g. MERGE) Tolerable windows: find bundle of all policies compliant with guardrails (e.g. ICLIPS)

Socio-economic guardrailon production / consumption(e.g. Minimum growth rate)


Energy

intensity

E / GDP

Carbon

intensity

CO2 / E

CO2

emissions

CO2(A)/CO2

Population

Pop

Per capitaproduction

GDP / Pop

Climate

change

Carbon capture

& sequestration(CCS)

Substitution byrenewables /

nuclear

Increasing

energyefficiency

Objective of the model MIND

Decreasing

productivity?

Limiting population

growth?

Climate

guardrail

Malthusian options Technological Technological options + Lifestyle

Calculate cost-effective portfolio of mitigation options (over time!) to reach ambitious climate protection targets


Energy

intensity

E / GDP

Carbon

intensity

CO2 / E

CO2

emissions

CO2(A)/CO2

Population

Pop

Per capitaproduction

GDP / Pop

Climate

change

Carbon capture

& sequestration(CCS)

Substitution byrenewables /

nuclear

Increasing

energyefficiency

Objective of the model MIND

Decreasing

productivity?

Limiting population

growth?

Climate

guardrail

Malthusian options Technological Technological options + Lifestyle

MIND focuses on technological options (no population and lifestyle dynamics)

MIND1.0 (2001-03)(O. Edenhofer, N. Bauer, E. Kriegler, 2005, Ecological Economics 54)

• R&D sectors for labour and energy productivity

• Renewable & fossil energy sectors with learning-by-doing

• and depreciation of fossil resource base

• Used in 2003 WBGU report (www.wbgu.de)

MIND1.1/1.2 (2003-05) (O. Edenhofer, K. Lessmann, N. Bauer, 2006, Energy journal special issue)

Carbon Capture and Sequestration endogenized (N. Bauer, 2005, Ph.D. Thesis)

Coupled to atmospheric chemistry-climate model ACC2 (K. Tanaka, T. Bruckner)

Used in the InterModel Comparison Project (IMCP)

Model history

MIND1.2 (2006) (in test phase)

Coupling of MIND1.1 to ACC2 with upgraded carbon cycle (K. Tanaka) and new ocean diffusion energy balance temperature model (E. Kriegler)

Work in progress

Emissions

Concentrations

Forcing

Temperature


Use conventional (neo-classical) climate – macroeconomic growth models (e.g. DICE) as reference point

Keep it as simple as possible by adding most important processes step by step

Construct hierarchy of models for

→ tracking model behaviour in enlarging parameter space

→ identifying benchmarks (ideal worlds) as reference models

Model philosophy


KapitalK(t)

Production Y(t) = Consumption C(t) + Investment I(t)

ProductionY(t) =(t) K(t) L(t)1-

Welfarebased on per capita

consumptionObjective

Decision

LabourL(t)

Climate change

EmissionsMitigation

cost function

Damage cost functionCoupled

System

DICE model (Nordhaus, 1994, 2000)

Energy efficiencyincrease


Exogenuous assumptions about growth of energy and labour productivity

→ unable to model effect of investment decisions on growth dynamics (both in BAU and climate policy case)

Model solution: → include energy as factor of production→ include R&D sectors for improving energy and labour productivity

First improvement: R&D sectors


Growth Rate of Labour and Energy Productivity: USA

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

1870 1890 1910 1930 1950 1970 1990 2010

Year

natu

ral l

ogsc

ale

ln(GNP/L) Predicted ln(GNP/L) ln(GNP/E) Predicted ln(GNP/E)

Predicted ln(GNP/E) = -11.10282 + 0.00588 * yearR² = 0.5800Test Intercept = 0: t=-5.23; p<.0001Test Slope = 0: t=5.38; p<.0001

Predicted ln(GNP/L) = -22.55142 + 0.01180 * yearR² = 0.9491Test Intercept = 0: t=-18.55; p<.0001Test Slope = 0: t=18.83; p<.0001

USA

Structure of MINDIntertemporal welfare

function

Optimize ConstraintGuardrail

Per capitaconsumption

Budget

Industrialproduction

Labour efficiencyunits

Energy efficiencyunits

Labour

Labourknowledge

capital

Energy Energyknowledge

capital

Renewableenergy

Secondaryfossil

energyTraditionalnon-fossil energy

(exog.)

Capital

Learning by doing

Capital stockren. energy

sector

Capital stockfossil energy

sector

Fossilprimaryenergy

carbonintensity (exog.)

Fossil fuelextraction

Capital stockextraction

Learning by doingResource scarcity

CO2concentration

Land usechange

CO2 emission

Fossil fuelCO2

emissions

SO2 emissions

Leakage

Energy

Capital stocksequestration

sector

Rad. forcing ofother GHG (exog.)

Global mean temperature change

Total radiative forcing

Captured CO 2and SO2

Desulphurization(exog.)


Competing energy technologies with qualitatively different dynamics

→ renewables experience large learning rates

→ fossil fuel use will be limited by resource base

Model solution: → separate energy sector into renewable and fossil fuel sector→ include learning-by-doing and resource scarcity

Second improvement: Energy sector

Source: IEA (2000): Experience Curves for Energy Technology Policy; p. 21


function



Budget




Labour

Labourknowledge

capital


capital

Renewableenergy

Secondaryfossil


(exog.)

Capital

Learning by doing


sector


sector

Fossilprimaryenergy





CO2concentration

Land usechange

CO2 emission

Fossil fuelCO2

emissions

SO2 emissions

Leakage

Energy


sector






Carbon capturing and sequestration has become an important option to neutralise climate impact of fossil fuels

Model solution: → include CCS sector (Nico Bauer, Ph.D. Thesis, download at opus.kobv.de/ubp/volltexte/2006/654/pdf/bauer.pdf)

5 capturing techologies: low cost, post-combustion (coal, cement, iron), oxyfuel/syngas

models for transportation (pipelines), compression, injection

6 sequestration sites: oil & gas fields (166 GtC), trapped aquifers (55 GtC), untrapped aquifers (3500 GtC); onshore or offshore

Third improvement: CCS


function



Budget




Labour

Labourknowledge

capital


capital

Renewableenergy

Secondaryfossil


(exog.)

Capital

Learning by doing


sector


sector

Fossilprimaryenergy





CO2concentration

Land usechange

CO2 emission

Fossil fuelCO2

emissions

SO2 emissions

Leakage

Energy


sector






MIND results for WBGU climate window

Results calculated with MIND1.1 (latest version of ACC2 not coupled)


Mitigation gap for WBGU window

Energy production and GWP losses


Carbon capturing and sequestration


Investment dynamics in the energy system


Influence of individual mitigation options

CPP: All options (climate protection path)EE: Only energy efficiency improvementsREN: Only renewable energy sourcesCCS: Only CCSNONE: Neither EE nor REN nor CCS


Mitigation costs

Mitigation costs

0

0.5

1

1.5

2

2.5

3

300 400 500 600 700 800

CO2 stabilization level [ppmv]

Pre

se

nt

va

lue

of

wo

rld

G

WP

los

s [

%] AIM A1B

MARIA A1T

MARIA A1B

MiniCAM A1Fl

MIND CPP


Sensitivity Analysis – GWP

Energy sector

Resource extraction

Macro-economy

Source: Edenhofer, Lessmann and Bauer, Energy Journal, 2006


Impact of Macroeconomic efficiency



Impact of energy sector



Energy-environment-economy models

general equilib.market models

CGEWIAGEM (DIW)

AIM (NIES)

IMACLIM-R (CIRED)

DEMETER-1CCS (IVM)

endogenous growthIAM

FEEM-RICE (FEEM)

ENTICE-BR (D. Popp)

Hybrid modelsMIND 1.1 (PIK)

MERGE-ETL (PSI)

simulationmodels

TIMER/IMAGE (RIVM)

energy systemmodels

DNE21+ (RITE)

GET-LFL (CUT)

MESSAGE (IIASA)

E3MG (CamEcon)

Models in the IMCP


Timing of mitigation options (450 ppm)

Bullets are set 20 years apart. Source: Edenhofer, Lessmann et al., Energy Journal, 2006

with ITC without ITC


Shadow prices (450 ppm)

With ITC Without ITC

Source: Edenhofer, Lessmann et al., Energy Journal, 2006


Current and future developments at PIK

Regional resolution (5-10 world regions): REMIND

Sectoral resolution of energy sector: GENERIS(Coal / Gas / Oil / Nuclear / Hydro / Wind / Solar / Biomass; Electricity / Heating / Transportation)

Resolving actors (household / firm / government) to model policy instruments

Adding agricultural sector to MIND to include all major sources of greenhouse gases

evaluating co2 mitigation options for the 21 st century - insights from the model mind cdmc seminar,...

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