FEEM’S INPUT TO CliMares Arctic WP 310 NERSC, BERGEN, 22-23 October 2009 Andrea Bigano.

FEEM’S INPUT TO CliMares Arctic WP 310

NERSC, BERGEN, 22-23 October 2009

Andrea Bigano

CliMares Arctic2

WP310: Economic impact studies

WP310 in particular is the one in which FEEM models can be applied. It is however crucial to understand how these models can interact with the model already proposed (i.e. MADIAMS-ARCTIC modelling tool)

The ICES model can be useful to unravel the economic interconnections among the sectors analysed in the other WP within a computable general equilibrium (CGE) model.

The WITCH model could instead be used to assess the effect of the positive feedback effects of climate change on the Arctic on mitigation policies and costs (albedo reduction , CH4 release from permafrost thawing etc. on the basis of the physical impact assessment results of other WPs. This issue seems very relevant to us, but not touched in the current version of the proposal at least after a very quick view of the document.

In theory we believe that, according to what we know already of the MADIAMS model, there should be an innovative stream of research open and to be explored in this project in which we should work on the coupling of multiscale models from top-down CGE models to bottom-up agent based models, with MADIAMS, somehow in the middle, with the capability of taking advantage of large scale signals provided by the FEEM models cited above and integrate also those coming from bottom-up approaches, such as the approach of the paper by Berman et al., attached to this.

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WITCH

The use of Integrated Assessment Modelsfor studying Climate Change

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IAMs

Climate Change is an intrinsically long term problem

Nightmare of any modeler: action taken now affects variables decades or centuries from now

Integrated Assessment Models (IAMs) combine economy-energy-environment

It is then possible to study how these three macro areas interact among themselves and try to assess policies for counteracting climate change

Two broad classes: Top-down (TD): macroeconomic models with little energy detail Bottom-up (BU): energy system models without a proper description of the

macroeconomic environment Hybrid: try to combine the two classes above to grasp a better understanding

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Main features/1

Ramsey-type neo-classical optimal growth (dynamic, perfect foresight)

Detailed energy input specification (BU)

Hard-link (stand-alone optimization) hybrid

World, 12 regions, interacting strategically (open-loop game)

Endogenous Technical Change

Climate module feedback

Solved numerically

WITCH: a “TOP DOWN” optimization framework, with a energy sector description as a downward expansion of the energy input.

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“TOP DOWN “ perspective: strategic optimal investment strategy both on a time dimension (inter-temporality) and a regional dimension (non-cooperative game)

“BOTTOM UP” component: enables a strategic assessment of the optimal investment in different energy technologies

Electric and non electric energy use 6 Fuels types (Oil, Gas, Coal, Uranium, Trad. Biofuels, Adv. Biofuels) 7 Technologies for electricity generation (+ 1 Backstop Technology)

Optimal intertemporal investment strategies in all fuel technologies and R&D are determined as a dynamic Nash equilibrium of the game among the 12 regions (free riding incentives on CO2 , fuel prices, LbD, LbR).

When choosing optimal investments agents are forward looking (take into account future events, including future policies) and behave strategically (take into account present and future policy measures in the other regions).

Main features/2

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Regional Disaggragation/1

World disaggregated into 12 macro regions, clustered on the basis of geography, income and the structure of energy demand.

Each region behaves strategically in a non-cooperative game, interacting through 5 channels:

CO2 externality (climate damage feedback)

Fossil Fuel prices (aggregate demand)

Investment costs of W&S (Learning by Doing)

Price of Advanced Biofuels (work in progress)

Global CO2 permit market (when emission caps are imposed)

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Regional Disaggregation/2

CAJANZ (Canada, Japan, New Zealand)USALACA (Latin America, Mexico and Caribbean)OLDEURO (Old Europe)NEWEURO (New Europe)MENA (Middle East and North Africa)SSA (Sub-Saharan Africa excl. South Africa)TE (Transition Economies)SASIA (South Asia)CHINA (including Taiwan)EASIA (South East Asia)KOSAU (Korea, South Africa, and Australia)

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Output and Climate Damage

Gross output produced via capital, labour (=population) and energy services.

Climate Damage

(3) tntnESntnLtnKntnTFPtnY nnC ,,))(1(,,)(,,

/1)()(1

Climate Module

EmissionsCO2 emissions

CO2 concentrations

Radiative Force

Temperature

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Primary Energy Supply. BAU

0

50000

100000

150000

200000

250000

300000

350000

2000 2020 2040 2060 2080 2100

Trad Biofuels

Adv Biofuels

Trad Biomass

Nuclear

Hydro

Oil

Gas

Coal

W&S

TW

h

Continued use of fossil fuels Most significant increase for oil Role for nuclear Limited scope for renewables (wind&solar electricity, biofuels etc)

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BAU: World Electricity Mix

World Electricity Generation

TW

h

2000 2020 2040 2060 2080 21000

1

2

3

4

5

6x 10

4

BackNuclearHydroelectricOilGasAdv Coal+CCSCoal deSOX deNOXOld CoalWind&Solar

Electricity increases steadily Coal supplies most of the demand Nuclear plays an important role

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Emissions by Region. BAUCO2 Emissions by Region

GtC

2020 2040 2060 2080 21000

5

10

15

20

25

LACAEASIACHINASASIASSAMENATECAJANZKOSAUNEWEUROOLDEUROUSA

20 GtC by 2100Developing Asia the biggest emitter

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Mitigation Target: 450 and 550 ppmv

Carbon Concentrations (CO2 only)

300350400450500

550600650700750

pp

mv

BAU

550ppmv CO2 only (650 ppmv CO2 eq)


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Mitigation Target: 450 and 550 ppmv

Radiative Forcing

0

1

2

3

4

5

6

7

W/m

^2

BAU



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Trajectories in the energy intensity/carbon intensity wrt 2002

-10%

0%

10%

20%

30%

40%

50%

60%

0% 20% 40% 60% 80%

Reduction in Energy Intensity wrt 2002

Re

du

cti

on

in

Ca

rbo

n

Inte

ns

ity

wrt

20

02

BAU


-10%

0%

10%

20%

30%

40%

50%

60%

0% 20% 40% 60% 80%


Re

du

cti

on

in

Ca

rbo

n

Inte

ns

ity

wrt

20

02

550

BAU

Energy and Carbon Intensities


-10%

0%

10%

20%

30%

40%

50%

60%

0% 20% 40% 60% 80% 100%


Re

du

cti

on

in

Ca

rbo

n

Inte

ns

ity

wrt

20

02

450

550

BAU

2100

2100

2100

Energy efficiency

Dec

arbo

nisa

tion

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Power generations mix

World Electricity Generation Shares

2000 2020 2040 2060 2080 21000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

NuclearHydroelectricOilGasIGCC+CCSTrad CoalWind&Solar

World Electricity Generation Shares

2000 2020 2040 2060 2080 21000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

NuclearHydroelectricOilGasIGCC+CCSTrad CoalWind&Solar

550ppmv 450ppmv

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ICES

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ICES Overview

Recursive dynamic general equilibrium model used for assessment of economic and climate change: Impacts Policies

Top-Down modelInter sectoral factor mobilityInternational tradeInternational investment flowsGHG emissions

CO2 (Carbon dioxide) CH4 (Methane) N2O (Nitrous oxide) SO2 (New - non GHG)

Geographical coverage – up to 87 world regions

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RiceWheatCereal CropsVegetable FruitsAnimalsForestryFishing

CoalOilGasOil ProductsElectricityEnergy Intensive industries

WaterOther industriesMarket ServicesNon-Market Services

Sectoral Aggregation: 17 Sectors

GTAP DatabaseVersion 6

57 sectors

ICES17 sectors

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1° Level

2° Level

3° Level

4° Level

5° Level

6° Level

Supply

OutputOutput

V.A. + Energy

V.A. + Energy Other Inputs

Other Inputs

Domestic

Domestic Foreign

ForeignNatural

Resources

Natural Resource

s LandLand

LabourLabour Capital

+ Energy

Capital +

Energy

CapitalCapital

EnergyEnergy

Non Electric

Non Electric Electric

Electric

CoalCoalNon

Coal

Non Coal

GasGas

OilOil

Petroleum Products

Petroleum Products

Domestic

Domestic Foreign

Foreign

Domestic

Domestic Foreign

Foreign

Domestic

Domestic Foreign

Foreign Domestic

Domestic Foreign

Foreign

DomesticDomestic

ForeignForeign

Reg 1

Reg 1

Reg n

Reg n

Reg ..

Reg ..

Reg 1

Reg 1 Reg

n

Reg n

Reg ..

Reg ..

Reg 1

Reg 1 Reg

n

Reg n

Reg ..

Reg ..

Reg 1

Reg 1 Reg

n

Reg n

Reg ..

Reg ..

Reg 1

Reg 1 Reg

n

Reg n

Reg ..

Reg ..

Region 1Region 1

Region ...

Region ...

Region nRegion n

Representative Firm - cost minimizingLeontief

CES VAE D

CES KE

CES =0.5 D

CES =1

D

CES =1

D

D

D

M

M

M

M

M

M

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Demand

UtilityUtility

Private Consumptio

n

Private Consumptio

nSavings

Savings

Domestic

Domestic Foreign

Foreign

Region 1Region 1

Region nRegion n

Region ...

Region ...

Item …Item … Item m

Item mItem 1

Item 1

Public Consumptio

n

Public Consumptio

n

Domestic

Domestic Foreign

Foreign

Region 1Region 1

Region nRegion n

Region ...

Region ...

Item …Item … Item m

Item mItem 1

Item 1

Representative consumer: Regional Household

Cobb-Douglas

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Dynamics

Endogenous Dynamics

> Capital Accumulation> Debt Accumulation> Stock of Natural Resources

Exogenous Dynamics (from external data sources)

> Population> Labor stock> Labor productivity> Land productivity

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Endogenous DynamicsEndogenous DynamicsExogenous DynamicsExogenous Dynamics

ICES Recursive Simulation

Preliminary Step BasedataCalibration year

1997

BasedataCalibration year

1997

Step 0Step 0

UpdatedBasedata 1Updated

Basedata 1Step 1Step 1

UpdatedBasedata 2Updated

Basedata 2Step 2Step 2Step 2Step 2

Step 1Step 1

UpdatedBasedata tUpdated

Basedata tStep tStep tStep tStep t

Recursive Simulation

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Introducing Climate Change shocks

Evaluate economically climate change impacts in a general equilibrium perspective (first and higher order

welfare costs substitution mechanisms + market interdependencies)

Initially one category of impact at a time

Then jointly (e.g study tourism + SLR)

Use and development of the CGE model

Calibrate a climate change damage function

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This approach is innovative

The predominant approach in climate change impact assessment is that of “direct costing” Partial

equilibrium:

Damage = “price” x “quantity”

This method is applied in “sectoral studies” see e.g. Nichols (2005) -> SLR, Aldy and Viscusi (2003) -> Health

risk and in global or aggregated studies (see e.g. Nordhaus (1995, 1999), Manne and Richels (1995, 2004),

Tol (1995 – 2002))

This disregards rebounds and feedback: social economic sytems adapt and “shocks” propagate

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Integrated Assessment and Modularity

Global Circulation

Models

Climate Change and Variability

Environm.Impact Models

• Temp. increase• Temp. rate of change• Precipitation• Sea level rise

Disentangle Climate Change in (some) Physical Impacts

• Loss of land (sq. Km.)• Changes in tourism flows • Changes in crop yields• .......................

Economic (GE)

Model

Provides welfare evaluation of physical

impacts+

Feedback on the environment

(e.g. emissions)

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Regional Increase in TemperatureRegional Increase in Temperature

Changes in Households’ demand for “energy” commodities. (Changes in Heating/cooling patterns)

Changes in Households’ demand for “energy” commodities. (Changes in Heating/cooling patterns)

Changes in Households’ preferences for some locations

Changes in Households’ preferences for some locations

Changes in Tourism flowsChanges in Tourism flows

Changes in regional incomeChanges in regional income

OriginOrigin Effect/modelled asEffect/modelled as

Changes in demand for “Market Services” commodity

Changes in demand for “Market Services” commodity

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The difference between direct and general equilibrium cost

-0.012

-0.010

-0.008

-0.006

-0.004

-0.002

0.000

USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM

Land loss value as % of GDP

Final impact on GDP

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

SAS SEA CHI NAF SSA SIS

Land loss value as % of GDP

Final impact on GDP

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Tourism Alone

Imposed Endogeno

us USA -0.874 -1.259 1.457 -0.365 -0.0015 -0.511 -0.626CAN 0.459 0.755 -1.381 0.211 -0.0004 0.420 -0.116WEU 0.883 1.357 -2.287 0.378 0.0556 0.331 0.238JPK 5.639 8.096 -14.760 2.779 -0.1768 3.768 3.810ANZ -1.530 -2.096 3.475 -0.696 0.0493 -0.063 -0.654EEU -3.172 -4.683 3.255 -1.169 -0.1068 -0.803 -0.999FSU -0.024 -0.073 0.052 -0.011 -0.0311 -0.135 -0.390MDE -5.974 -8.600 8.295 -2.074 0.0030 -2.279 -1.960CAM -5.519 -7.980 7.518 -2.387 -0.1139 -1.030 -1.805SAM -1.521 -2.015 1.583 -0.558 -0.0027 -0.100 -1.161SAS -1.532 -1.794 1.102 -0.453 0.0251 0.596 -0.507SEA -5.452 -7.057 6.854 -1.629 -0.0324 -0.825 -0.620CHI -6.777 -8.020 2.731 -1.129 -0.0442 -1.127 -0.854NAF -3.204 -4.179 1.314 -0.646 -0.1614 -0.795 -0.640SSA -3.068 -4.122 2.993 -1.053 -0.0079 -0.359 -0.951SIS -12.251 -18.984 17.001 -5.990 -0.5330 -7.522 -7.852

Market services demand

Other goods/services dem.

Income transfers

GDP Terms of

TradeInvest. Flows

Reference Year 2050: % changes wrt baseline

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Tourism and sea level jointly

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-90

-70

-50

-30

-10

10

30

SLR&TOU SLR TOU %D SLR&TOU-SUM

FEEM’S INPUT TO CliMares Arctic WP 310 NERSC, BERGEN, 22-23 October 2009 Andrea Bigano.

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Transcript of FEEM’S INPUT TO CliMares Arctic WP 310 NERSC, BERGEN, 22-23 October 2009 Andrea Bigano.