CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 1 Dottorato Climate Change and Policy Modelling...

CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI

1

Dottorato

Climate Change and Policy Modelling Assessment:

Impacts in Modelling

Francesco Bosello

2CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI

The typical structure of a IIA exercise

Climatic drivers

Environmentalimpacts

• Δ Temp. • Δ Preci.• Δ SLR• ………

• Δ Tourism Flows• Δ Energy demand

SocialEconomic

impacts

Economic Assessment

• Δ flood. land• Δ desert. land• Δ crop yield• Δ mort./morb.•……………..

• Δ Agr. Prod.• Δ Health care expenditure• Δ Labour prod.• ……………..


Steps before the economic assessment

Quantify “impacts”

Translate them into meaningful economic variables

Choice of a “convenient” baseline on which impacts can be imposed. Assess changes respect to a “no climate change scenarios”

“Static baselines” status quo

“Dynamic baselines” - - evolving according to exhogenous storylines (IPCC SRES)- - evolving according to endogenous mechanisms


IPCC and exhogenous storylines

A1: rapid economic growth and technological dev.pm. Low population growth.

A2: heterogeneous world, preservation of local id, economic growth but more fragmented technological progr. High population growth.

B1: convergent world, low population growth, development towards a high tech and service society. Emphasis on sustainability.

B2: like B1, but with more emphasis on local solution.Source: IPCC, Climate Change

2001, “The Scientific Basis”


The scenario issue

IPCC approach: emissions scenarios stem from exogenous storylines proposed by/incorporated in a set of soft-linked models.

Replicating “soft linked” emissions with hard link models may => unrealistic economic assumptions; alternatively using model-consistent economic assumptions may => different emissions paths!

Problematic for hard linked models to replicate those storylines as the “storyline” is endogenously embedded: in fact it is the model itself

The same problem with model comparison and harmonization

Crucial role of the baseline it “determines the impact”


Quantifying impacts (1) – Sea Level Rise, some literature

Low land in coastal countries with elevation < 5m. (Source: EEA, 2005)



0

50

100

150

200

250

300

350

Austria

Belgiu

m

Cypru

s

Czech

Repu

blic

Denm

ark

Estonia

Finla

nd

France

Ger

man

y

Gre

ece

Hungar

y

Irela

nd Italy

Latria

Lithuan

ia

Luxem

bourg

Mal

ta

Nether

lands

Poland

Portugal

Slova

kia

Slove

nia

Spain

Swed

en U K

Km

2

0500100015002000250030003500400045005000

Km

2

Low High

Land loss in 2085. Source Nicholls 2007

Population living in coastal flood plain in 2080. Nicholls (2004)SLR impacts (+1 m.) in selected EU countries


Quantifying impacts. Sea level rise @ ICES

Data set 1: Sq. Km. of land lost due to erosion, if

there is no protection for different SLR scenarios. Country detail.

CombiningAreas at risk Basis is the 1993 Global Vulnerability Analysis by Delft Hydraulics

andLand Loss Nicholls and Leatherman (1995).

Aggregated for the regions of interest, calculated in 2050 for

25 cm of SLR

5000

1053

767 1022

874 3681

2307

31465

20416

8750

39427

10510

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

USA

OLD

EURO

NEWEURO

KOSAU

CAJANZ TE

MENA

SSA

SASIA

CHINA

EASIA

LACA

Km

2

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

%

Land Loss to SLR Km2 Land loss to SLR %


Quantifying impacts (2) – Health, some literaturePossible Changes in the Distribution of Death Rates from Heat Related Mortality in Europe – 2000 to A2 Scenario 2100, based on the climate signal alone.

Source: PESETA project (2007) at CEC (2007)


Quantifying impacts (2) – Health, some literature


Quantifying impacts (2) – Health @ ICES

Change in

Morbidity(n° of years diseased)

Mortality(n° of deceases)

Health Care Expenditure

Due to Climate Change (ΔT)

Calculated for five classes of diseases: - Malaria, - Schistosomiasis, - Dengue, - Diarrhoea,- Cardiovascular and Respiratory.

Meta Analysis



0

t

GDP PC3100

GDPPC31001)(

3100$)Income0(

TBM

PCifmortality

i

58.1

0

t14.1

GDP PC

GDP PC)(

TBM

)( TfBM

Change in base mortality: additional n° of deceased people: Examples

Vector Borne Diseases

Diarrhoea

Applied to UP > 65

25.0

0

0GDPPC

GDPPC6565

t

t PP

00

00

tt

tt0

PD0.011-GDP PC0.031

PD0.011-GDP PC0.0311

PD0.011-GDP PC0.0311

PD0.011-GDP PC0.031UPUPt

Cardio Vascular



)()DiseasedYearsAdd.( 10 GDPPC

Additional Mortality Additional years of life diseased

Additional Health Care Expenditure

2

10)DiseasedYearsAdd.(

GDPPC

Additional Health Exp. VBD+Diarr.

Additional Health Exp. Cv and

Resp.

From the literature



Additional Mortality (1000) in 2050 for + 0.93°C wrt 2000 (static baseline)Additional years of life diseased in 2050 for + 0.93°C wrt 2000 (static baseline)

MALARIA SCHISTO DENGUE DIARR. CARDIOV. RESP. TOTUSA 0.00 0.00 0.00 11.53 -179.20 4.16 -163.51CAN 0.00 0.00 0.00 0.00 -37.47 0.00 -37.47WEU 0.00 0.00 0.00 2.28 -203.91 3.43 -198.20JPK 0.00 0.00 0.00 0.55 -86.37 7.77 -78.04ANZ 0.00 0.00 0.00 0.02 -2.58 1.71 -0.86EEU 0.00 0.00 0.00 0.61 -61.34 0.11 -60.63FSU 0.00 0.00 0.00 3.23 -257.69 5.16 -249.30MDE 0.95 -0.10 0.05 14.57 -50.56 54.85 19.76CAM 0.01 0.00 0.01 16.61 -6.27 11.53 21.89LAM 0.00 0.00 0.00 37.42 -10.78 24.27 50.91SAS 3.45 -0.25 5.74 292.84 -122.66 213.23 392.36SEA 0.62 -0.03 0.30 61.32 -11.89 55.14 105.47CHI 1.03 -0.23 0.32 22.23 -784.11 1.48 -759.29NAF 3.36 -0.15 0.10 129.64 -21.30 40.79 152.44SSA 177.81 -1.52 0.19 2906.00 -25.40 110.07 3167.16ROW 0.14 0.00 0.01 7.13 0.17 6.12 13.57TOT 187.38 -2.29 6.72 3505.98 -1861.35 539.82 2376.26

MALARIA SCHISTO DENGUE DIARR. CARDIOV. RESP. TOTUSA 0 0 0 477328 -172197 36466 341597CAN 0 0 0 0 -36005 0 -36005WEU 0 0 0 98898 -195944 30022 -67025JPK 0 0 0 10288 -88561 97874 19601ANZ 0 0 0 1030 -2479 14954 13505EEU 0 0 0 28191 -55120 1277 -25651FSU 0 0 0 177241 -231556 60929 6613MDE 12880 -3170 34 78871 -68024 1128592 1149183CAM 26 -71 7 64906 -7868 255480 312480LAM 10 -9 3 142422 -13530 537641 666536SAS 22186 -1917 460 1137947 -157963 2440712 3441424SEA 2039 -203 131 287115 -16419 1101174 1373838CHI 15518 -235 97 271653 -1087878 16604 -784239NAF 83483 -7777 97 309980 -28669 889716 1246830SSA 656819 -447383 0 5249676 -33782 2375943 7801273ROW 540 -344 13 33766 189 134910 169074TOT 793503 -461109 841 8369312 -2195806 9122293 15629034



Additional Health Care Expenditure LEVELS

Is split between public and private additional expenditure LEVELS (using WHO 2003)

These then calculated as % of GDP consistent with the original database (Tol) %

The % is reported to GTAP GDP LEVELS consitent with GTAP GDP

These levels are calculated in % of GTAP public and private demand for Non Market Services shocks in % change



LabourProd.

Public Health Care

Exp.

Private Health Care

Exp.

USA -0.04 -0.18 -0.017

OLDEURO 0.05 -0.30 -0.011

NEWEURO 0.10 -0.45 -0.009

KOSAU -0.30 0.80 0.048

CAJANZ 0.09 0.00 -0.001

TE 0.11 -0.23 -0.007

MENA -0.30 1.72 0.120

SSA -0.35 0.46 0.063

SASIA -0.10 0.33 0.084

CHINA 0.13 0.60 0.055

EASIA -0.11 1.39 0.092

LACA -0.13 0.73 0.071

Labour Productivity and Health Care

Final impacts on labour productivity and health care expenditure as shocks for the ICES model (+1.5°C wrt 1980-1999 average)

Labour Prod.

Public Health Care

Exp.

Private Health Care

Exp.

USA -0.05 -0.094 -0.011CAN 0.20 -0.361 -0.021WEU 0.07 -0.212 -0.009JPK 0.04 0.118 0.006ANZ -0.05 0.349 0.019EEU 0.07 -0.139 -0.004FSU 0.09 -0.196 -0.010MDE -0.23 0.723 0.006CAM -0.16 0.435 0.032LAM -0.13 0.351 0.038SAS -0.16 0.111 0.026SEA -0.15 0.496 0.044CHI 0.09 0.064 0.005NAF -0.42 0.763 0.182SSA -0.69 0.190 0.030ROW -0.29 1.040 0.060

“old baseline static model”


Quantifying impacts (5) -- Energy

Heating effect: higher temperatures in cold seasons lead to a lower demand for energy for heating purposes

Cooling effect: higher temperatures in warm seasons lead to a higher demand for energy for cooling purposes

Climate Change affects energy demand through changes in temperature

Both effects are likely to weight differently at different geographical locations Hot countries vs Cold countries

Econometric investigation on panel data performed to identify the “elasticity of energy demand to temperature”



- 31 countries (OECD and non-) from 1978 to 2000- The dataset includes:

Real GDP per capita (IEA) Residential demand for oil products, electricity and gas (IEA) Fuel prices (IEA) Seasonal Temperature (Hadley Center UEA High Resolution Gridded

Dataset)

Balanced panel with the following observations:

- Electricity: 550 (T = 22; N = 25)

- Natural gas: 418 (T = 19; N = 22)

- Oil products: 418 (T = 19; N = 22)

Data



Cluster analysis used to identify temperature clusters GROUP 1 – mild

Austria, Belgium, Denmark, France, Germany, Ireland, Luxembourg, Netherlands, New Zealand, Switzerland, Greece, Hungary, Italy, Japan, Korea, Portugal, South Africa, Spain, Turkey, United Kingdom, United States;

GROUP 2 – hot

Australia, India, Indonesia, Mexico, Thailand, Venezuela;

GROUP 3 – cold

Canada, Finland, Norway, Sweden.



Cooling effect for electricity is present in hot and mild countries in summer and spring

Heating effect for all fuels in winter and mid-seasons


Quantifying impacts (9) – Tourism, some literature

Vulnerab04.shp

-100 - -50

-50 - -30

-30 - -10

-10 - 10

10 - 30

30 - 50

50 - 100

View1

Source: PESETA project (2007) at CEC (2007)

Green => Increased climatic attractivenessRed => reduced climatic attractiveness

Europe: Changes in Tourism Climate Index (climate attractiveness) 2071-2100 rt 1961-1990 A2 scenario


Quantifying impacts (9) – Tourism @ ICES

Using a World tourism Model (HTM13, Tol et al., 2005)

Which assesses changes in domestic and international tourist flows with a country detail

The model is calibrated on 1995 data and explains tourism flows with: population, income, temperature, coastal lenghts, travel distance.


An example for Italy(% changes wrt no climate change)

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

5

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

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2080

2085

2090

2095

2100

A B2

A B1

A A2

A A1

-2

0

2

4

6

8

10

12

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

2075

2080

2085

2090

2095

2100

D B2

D B1

D A2

D A1

International Arrivals

Domestic Tourist Trips

-35

-30

-25

-20

-15

-10

-5

0

5

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

2075

2080

2085

2090

2095

2100

T B2

T B1

T A2

T A1

Total Tourism Demand


Formulas for tourism


Quantifying impacts (12) – Agriculture, some literature

Source: IPCC, (2007)


Quantifying impacts (10) -- Agriculture

Rosenzweig and Hillel (1998) report detailed results from an internally consistent set of crop modelling studies

Wheat, maize, rice, soybean Australia, Brazil, Canada, China, Egypt, France, India, Japan, Pakistan,

Uruguay, USSR, USA 3 GCMs; with and without CO2 fertilisation 3 levels of adaptation

Data extended to the regions of the economic model and to different climate change scenarios main yield drivers: regional T and CO2 concentration parameterization as reported by Tol (2002).


Quantifying impacts (10) -- AgricultureCO2 fert. EffectT 4.2 4 5.2 4.2 4 5.2Model GISS GFDL UKMO GISS GFDL UKMOAustralia -18 -16 -14 8 11 9Brazil -51 -38 -53 -33 -17 -34Canada -12 -10 -38 27 27 -7China -5 -12 -17 16 8 0Egypt -36 -28 -54 -31 -26 -51France -12 -28 -23 4 -15 -9India -32 -38 -56 3 -9 -33Japan -18 -21 -40 -1 -5 -27Pakistan -57 -29 -73 -19 31 -55Uruguay -41 -48 -50 -23 -31 -35USSR Wi -3 -17 -22 29 9 0USSR Sp -12 -25 -48 21 3 -25USA -21 -23 -33 -2 -2 -14World -16 -22 -33 11 4 -13

Rice World -24 -25 -25 -2 -4 -5Maize World -20 -26 -31 -15 -18 -24

Soybean World -19 -25 -57 16 5 -33Cereals World -22 -25 -34 -5 -9 -18

Other crops World -22 -24 -33 3 1 -9

Wheat

CropNo Yes (555 pm)

Source: Rosenzweigh and Hillel, (1998)

T Cereals Other crops Model4.2 -22 -22 GISS4 -25 -24 GFDL5.2 -34 -33 UKMO4.2 -5 3 GISS4 -9 1 GFDL5.2 -18 -9 UKMO4.2 -2 3 GISS4 -6 1 GFDL5.2 -13 -8 UKMO4.2 1 5 GISS4 3 1 GFDL5.2 -6 -3 UKMO

No CO2

CO2 (555 pm)

Adaptation 1

Adaptation 2


Quantifying impacts – Agriculture @ ICES

TYY TC

5.2

5.2

)(),2( 5.25.2 CC YmediaAdCOY

))2((),2()2( 5.25.2 CC COWoutYmediaAdCOYCOY

5.2/))2(),2(()( 5.21 COYAdCOYTY CC

)(2)2()()2,( ppmCOCOYTTYCOTY


Quantifying impacts – Agriculture @ ICES

Wheat RiceCer

CropsOther Food Productions

USA -5.66 -6.19 -8.18 -6.68

OLDEURO -5.31 -5.18 -7.17 -5.88

NEWEURO -1.13 -2.64 -4.60 -2.79

KOSAU -7.78 -2.90 -3.11 -4.60

CAJANZ -0.74 -1.87 -2.24 -1.62

TE -6.12 -7.47 -9.73 -7.77

MENA -10.62 -11.26 -13.13 -11.67

SSA -9.89 -7.17 -8.81 -8.62

SASIA -2.96 -4.89 -6.61 -4.82

CHINA 0.93 0.50 -1.42 0.005

EASIA 2.45 0.34 -1.15 0.54

LACA -6.69 -6.61 -8.25 -7.18

Land productivity

Changes in agricultural productivity, without adaptation for 1.5°C increase and 600 ppm in 2050 r.t. 1980-1999 average


How to introduce these impacts into a CGE

Sketching the structure of ICES

Database (xx.HAR)

Key parameters (xx.PAR)

The model equations (xx.TAB)

Command File (xx.CMF)

Output in % change (xx.SOL)

Output in Levels (xx.UPD)

Instructions + which variables are exogenous

and which endogenous (“closure”)


How to introduce these impacts into a CGE

The “nature” of the impact

Supply side impacts on “stocks” or productivity (e.g. health labour productivity, agriculture land productivity, sea level rise land stock)

They affects variables which are typically exhogenous, easy to accommodate direct inputs to the “command file”

Demand side impacts changes in preferences (e.g. health health care demand, energy energy demand, tourism recreational services demand)

They affect variables which are typically endogenous, this is a tricky issue


Explaining demand-side shock modeling

Equation PRIVDMNDS# private consumption demands for composite commodities (HT 46) #(all,i,TRAD_COMM)(all,r,REG) qp(i,r) - pop(r) = sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)];

Equation PRIVDEGYCOM# private consumption demands for energy commodities (HT 46) #(all,i,EGYCOM)(all,r,REG) qp(i,r) - pop(r) = adsp(i,r)+ sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)];

Equation PRIVDNEGYCOM# private consumption demands for non-energy commodities (HT 46) #(all,i,NEGYCOM)(all,r,REG) qp(i,r) - pop(r) = adsnec(r) + sum{k,TRAD_COMM, EP(i,k,r)*pp(k,r)} + EY(i,r)*[yp(r) - pop(r)];

Equation NEWBUDGET# eplicit budget costraint #(all,r,REG) INCOME(r)*y(r) = sum(i,TRAD_COMM, VPA(i,r)*(pp(i,r)+qp(i,r)) + VGA(i,r)*(pg(i,r)+qg(i,r))) + SAVE(r)*(psave(r)+qsave(r));


Explaining demand-side shock modelingVariable (all,i,MASER_COMM)(all,s,REG)apd(i,s) # private cons. dem. shock parameter for market services in reg. r #;Equation PHLDDMAS# private consumption demand for market services. (HT 48) #(all,i,MASER_COMM)(all,s,REG) qpd(i,s) = apd(i,s)+qp(i,s) + ESUBD(i) * [pp(i,s) - ppd(i,s)];

Variable (all,s,REG)apdC(s) # private cons. dem. shock parameter for all non market in reg. r #;

Equation PHLDDNMAS# priv. cons. demand for for all trad comm but market services. (HT 48) #(all,i,NOMASER_COMM)(all,s,REG) qpd(i,s) = apdC(s) + qp(i,s) + ESUBD(i) * [pp(i,s) - ppd(i,s)];

Equation NEWBUDGET# eplicit budget costraint #(all,r,REG)

sum(i,TRAD_COMM, VPA(i,r)*(pp(i,r)+qp(i,r))) = sum(i,TRAD_COMM, VIPA(i,r)*(ppm(i,r)+qpm(i,r)))+ sum(i,TRAD_COMM, VDPA(i,r)*(ppd(i,r)+qpd(i,r)));


An IIA exercise example

The model static & recursive dyn CGE

12 Regions:USA: United StatesNEWEURO: Eastern EU OLDEURO: EU 15 KOSAU: Korea, S. AfricaCAJANZ: Canada, Japan, New

ZealandTE: Transitional EconomiesMENA: Middle East and North AfricaSSA: Sub Saharan AfricaSASIA: India and South AsiaCHINA: ChinaEASIA: East AsiaLACA: Latin and Central America

17 Sectors:RiceWheatCereal CropsVegetable FruitsAnimalsForestryFishingCoalOilGasOil ProductsElectricityWaterEnergy Intensive industriesOther industriesMarket ServicesNon-Market Services

Used for

investi-gations

on transi-tional dyna-mics



The baseline asumptions: % changes 2001-2050

AgricultureEnergy Sectors

ElectricityOth. Ind.

And Services

USA 197.0 21.16 107.4 0.0 62.8 146.7 97.1

OLDEURO 179.4 -12.12 124.8 8.4 76.5 148.7 46.9

NEWEURO 574.3 -15.10 191.6 40.6 128.9 224.4 205.6

KOSAU 351.0 64.12 118.5 5.4 71.5 137.8 178.2

CAJANZ 243.1 -9.58 118.5 5.4 71.5 137.8 178.2

TE 496.0 -8.03 191.6 40.6 128.9 224.4 205.6

MENA 638.1 87.51 191.6 40.6 128.9 229.1 272.9

SSA 773.0 122.34 223.4 55.9 153.9 270.1 272.9

SASIA 1440.0 50.29 223.4 55.9 153.9 267.7 249.9

CHINA 1066.1 14.50 223.4 55.9 153.9 267.7 249.9

EASIA 1043.0 46.75 223.4 55.9 153.9 270.1 272.9

LACA 428.6 50.02 223.4 55.9 153.9 270.1 272.9

Sectoral Labour ProductivityLabour Force

Land productivity

Capital Stock (static model)



Real GDP % growth (2001 - 2050)

0

200

400

600

800

1000

1200

1400

1600

1800

USA

OLD

EURO

NEWEURO

KOSAU

CAJANZ TE

MENA

SSA

SASIA

CHINA

EASIA

LACA

static ICES

dynamic ICES

IPCC B2

GDP trend 2001-2050 (% change) - ICES

0

200

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800

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2004

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2026

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2034

2036

2038

2040

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2044

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2048

2050

USA

OLDEURO

NEWEURO

KOSAU

CAJAZ

TE

MENA

SSA

SASIA

CHINA

EASIA

LACA

The baseline results


CC impacts

Land StockLand loss

to SLRWheat Rice

CerCrops

Other Food Productions

USA -0.026 -5.66 -6.19 -8.18 -6.68

OLDEURO -0.014 -5.31 -5.18 -7.17 -5.88

NEWEURO -0.031 -1.13 -2.64 -4.60 -2.79

KOSAU -0.005 -7.78 -2.90 -3.11 -4.60

CAJANZ -0.004 -0.74 -1.87 -2.24 -1.62

TE -0.007 -6.12 -7.47 -9.73 -7.77

MENA -0.011 -10.62 -11.26 -13.13 -11.67

SSA -0.067 -9.89 -7.17 -8.81 -8.62

SASIA -0.204 -2.96 -4.89 -6.61 -4.82

CHINA -0.045 0.93 0.50 -1.42 0.005

EASIA -0.316 2.45 0.34 -1.15 0.54

LACA -0.025 -6.69 -6.61 -8.25 -7.18

Land productivity

Natural Gas

OilProducts

ElectricityLabourProd.

Public Health Care

Exp.

Private Health Care

Exp.

Mserv Demand

Income Transfers*

USA -13.67 -18.52 0.76 -0.04 -0.18 -0.017 -0.49 -30.6

OLDEURO -13.43 -15.63 -1.21 0.05 -0.30 -0.011 1.74 71.1

NEWEURO -12.93 -17.39 0.76 0.10 -0.45 -0.009 -1.81 -6.4

KOSAU ns => end -13.03 12.31 -0.30 0.80 0.048 -0.94 -6.8

CAJANZ -5.047 -12.63 -4.80 0.09 0.00 -0.001 5.84 161.3

TE -13.12 -17.39 0.74 0.11 -0.23 -0.007 -2.44 -15.1

MENA -6.559 -8.69 11.05 -0.30 1.72 0.120 -3.60 -41.9

SSA ns => end -6.51 16.35 -0.35 0.46 0.063 -3.19 -6.2

SASIA ns => end ns => end 20.38 -0.10 0.33 0.084 -0.87 -6.9

CHINA ns => end ns => end 20.38 0.13 0.60 0.055 -3.59 -39.5

EASIA ns => end ns => end 20.38 -0.11 1.39 0.092 -3.37 -33.4

LACA ns => end ns => end 21.37 -0.13 0.73 0.071 -1.93 -45.6

Households' Energy Demand Tourism DemandLabour Productivity and Health Care

1.5º C temperature increase in 2050 wrt 1980-1999 average

(% change wrt baseline)


ResultsCC vs Baseline: Real GDP (% change)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

USA

OLDEURO

NEWEURO

KOSAU

CAJANZ

TE

MENA

SSA

SASIA

CHINA

EASIA

LACA

CC vs Baseline: World Prices (% change)

-5

-4

-3

-2

-1

0

1

2

3

4

5Rice

Wheat

CerCrops

VegFruits

Animals

Forestry

Fishing

Coal

Oil

Gas

Oil_Pcts

Electricity

Water

En_Int_ind

Oth_ind

MServ

NMServ

CC vs Baseline: Terms of Trade (% change)

-6

-5

-4

-3

-2

-1

0

1

2

3

4

USA

OLDEURO

NEWEURO

KOSAU

CAJANZ

TE

MENA

SSA

SASIA

CHINA

EASIA

LACA


Comparison with the existing literature

Source: IPCC, 2007 FAR

In 2050 Damage = 0.3% of world

(2050) GDP ~

352 billions US $ 2001


Comparison with the existing literature

-200

-100

0

100

200

300

400

$/tC

93 tutti

50 pr

51 prtp1%

16 prtp 3%

314 Stern261 prtp<1%

Intervalli di confidenza al 67%

Survey di 108 stime (Tol, 2005)

0

10

20

30

40

50

60

70

80

90

<00-

25

25-5

0

50-7

5

75-1

00

100-

125

125-

150

150-

175

175-

200

200-

225

225-

250

>250

$/tC

% S

tud

i Tutti gli studi

3% PRTP

1% PRTP

<1% PRTP


Results, static vs dynamic

Real GDP 2050 : % change CC vs Base

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

agric

ultur

e

ener

gy d

eman

d

heal

th

sea

level

rise

tour

ism

agric

ultur

e

ener

gy d

eman

d

heal

th

sea

level

rise

tour

ism

all im

pact

s sta

tic

all im

pact

s dyn

amic

USA

OLDEURO

NEWEURO

KOSAU

CAJANZ

TE

MENA

SSA

SASIA

CHINA

EASIA

LACA

DynamicStatic

Real GDP 2050 excluding SASIA: % change CC vs Base

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

USA

OLDEURO

NEWEURO

KOSAU

CAJANZ

TE

MENA

SSA

CHINA

EASIA

LACA

Dynamic Static


The sectoral picture

USA OLDEURO NEWEURO KOSAU CAJANZ TE MENA SSA SASIA CHINA EASIA LACALand -0.02 -0.01 -0.03 0.00 0.00 -0.01 -0.01 -0.06 -0.16 -0.04 -0.27 -0.02Capital -0.17 0.48 -0.05 -0.48 0.58 0.06 -1.46 -0.94 -2.11 -0.28 -1.22 -0.84Rice -1.99 -1.13 2.45 -1.11 -3.87 1.25 0.37 -0.76 -2.05 0.35 0.09 1.07Wheat -4.27 -1.57 1.07 -0.64 2.51 1.29 1.18 -0.51 -1.09 0.22 0.19 -0.02CerCrops -2.46 -1.27 1.40 -0.34 -1.39 1.40 2.52 0.25 -1.58 1.03 0.37 0.28Other Food -3.03 -1.33 1.23 -1.13 -3.05 1.43 0.65 -0.21 -2.02 0.83 0.60 0.21Forestry 0.21 -1.43 0.99 -0.26 -7.77 1.48 0.73 -0.39 -1.94 -0.11 -0.27 0.99Fishing 0.67 -1.32 1.18 0.23 -7.35 2.12 0.43 -0.77 -2.27 0.24 0.09 1.04Coal 0.03 -0.13 0.08 0.18 -0.47 0.27 0.66 0.64 0.31 0.35 0.45 0.34Oil -0.19 -0.21 -0.47 -0.18 -0.36 -0.29 -0.08 -0.12 -0.13 -0.14 -0.13 -0.11Gas -1.91 -3.04 -9.72 -0.34 -1.88 -1.39 0.26 0.21 0.13 1.53 0.42 1.04Oil_Pcts -1.59 -1.03 -1.08 -0.88 -0.71 -1.25 -0.99 -1.76 -0.48 1.03 1.40 1.36Electricity -0.05 -1.72 0.28 1.24 -5.35 1.17 3.93 4.59 2.85 2.26 4.76 5.71Water 0.24 -0.84 0.87 0.25 -4.94 0.95 0.07 -0.60 -0.68 0.22 0.15 0.90En_Int_ind 0.21 -0.91 0.75 0.37 -5.67 1.39 1.97 0.72 -0.82 0.02 -0.44 1.16Oth_ind 0.39 -1.08 1.24 -0.80 -6.74 1.83 2.43 -0.77 -2.24 0.47 0.07 1.12MServ -0.19 1.24 -0.43 -0.63 3.01 -0.06 -2.92 -1.57 -1.91 -1.14 -2.17 -1.77NMServ 0.02 -0.03 -0.35 0.54 -0.05 -0.80 -0.20 -0.30 0.72 0.52 0.63 0.17Investment -0.30 0.70 -0.09 -0.78 0.93 0.03 -1.97 -1.35 -2.59 -0.49 -1.74 -1.30

Climate change impacts on production in 2050 (% change wrt base)

-0.12 0.36 -0.06 -0.43 0.42 0.12 -0.89 -0.63 -1.80 -0.18 -0.91 -0.57GDP


Caveats in interpreting the results

The “climate scenario issue” (uncertainty on the possible temperature increase)

The “Impact scenario issue” (no irreversibility and or catastrophic events)

The “economic scenario issue”: the geographical scale, transitional dynamics and frictions in substitution.

The economic variable represented: stock vs flows (GDP as a welfare measure)


Stock vs flows, the case of sea level rise

Source: Tol (2001)


Stock vs flows, the case of sea-level rise

The implicit value of land ($ per km2)

GTAP TOL ann. (2001) TOL (2005)ANZ 5730 21684 302589CHIN 120353 35423JPK 357800 3898261 217495SEA 114920 220937 445591SAS 65173 295282CAN 4985 28855 296888USA 59258 277200 24211CAM 50262 66556SAM 18295 182711 88035WEU 167156 3544343 275392EEU 63931 95764FSU 2689 33162 168630MDE 10952 180658NAF 1853 461452SSA 9286 54858 271173ROW 37093 160458


Building damage functions

A standard approach

In a more or less sophisticated way, parameters of a given damage function, whose functional form is chosen with some ad hoc properties, are calibrated such that in a given time with a given temperature the total damage reaches a given level expressed as (%) loss of potential GDP.

This amounts to:

Assume exogenously the link between damage and temperature (linear, quadratic, cubic)

A more or less additive procedure in the estimation of total damage


Examples of damage functions

Nordhaus and Yang (1996)

Nordhaus and Boyer (1999 -)

Manne and Richels (1996 -)

Peck and Teisberg (1992 -)


An example: CCDF calibration in RICE 2007

Source: Nordhaus (2007), lab notes on RICE 2007


An alternative methodology. Tol

Source: Tol, (2002)


Using (static) CGE to calibrate the damage function

Quantify “all” impacts for different ΔTs

Plug them together into the CGE

Estimate the parameters of the “implicit” regional damage functions

The main advantage of this procedure is to consider autonomous adaptations and thus impact interactions.


Damages and damage coefficients


A new calibration

Recall the RICE 99 (and subsequent) damage function


“New damages” by temperature


“New damages” by region


New emission path…


Open questions:

Is it legitimate to use a static model to calibrate a CC damage function?

Is it legitimate to use a “flow-based” model to calibrate a CC damage function?

Is it legitimate to use a “market-based” model to calibrate a CC damage function?

CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI 1 Dottorato Climate Change and Policy Modelling...

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