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:

Adaptation in (CGE) Modelling

Francesco Bosello

2CENTRO EURO-MEDITERRANEO PER I CAMBIAMENTI CLIMATICI

Defining adaptation (1)

“Adjustment in ecological, social, or economic systems in response to actual or expected climatic stimuli, and their effects or impacts. […] refers to changes in processes, practices or structures to moderate or offset potential damages or to take advantages of opportunities associated with changes in climate” (IPCC TAR, 2001)

“Process by which strategies to moderate, cope with and take advantage of the consequences of climate events are enhanced, developed and implemented” (UNDP, APF, 2005).

“Policies, practices, and projects with the effect of moderating damages and/or realising opportunities associated with climate change” (EEA, 2005)

“Institutional definitions”



“Process through which people reduce the adverse effects of climate on their health and well-being, and take advantage of the opportunities that their climatic environment provides” (Burton, 1992)

“Any adjustment, whether passive, reactive or anticipatory, that is proposed as a means for ameliorating the anticipated adverse consequences associated with climate change (Stakhiv, 1993)

“Changes in a system in response to some force or perturbation, in our case related to climate” (Smithers and Smit, 1997)

“Definitions from the science”



There are some key concepts

Systemic process or transformation

Through space and time

Reducing(increasing) negative(positive) impacts if successful viceversa if unsuccessful (maladaptation)

Triggered by a climatic pressure


Classifying adaptation (1)

The subject of adaptation: Who or what adapts Ecological, social and/or economic systems. These changes can be the result of natural responses and in this case they usually involve organisms or species, or of socio-economic or institutional reactions in which case they are undertaken by individual or collective actors, private or public agents.

The object of adaptation: What they adapt to Adaptive responses can be induced either by changes in average conditions or by changes in variability of extreme events. While in the first case the change is slow and usually falls within the “coping range” of systems, in the second case changes are abrupt and outside this coping range (Smit and Pilifosova, 2001).



How Adaptation Occurs Several criteria can be used to identify the different adaptation processes (see e.g. Smit et al. 1999; Klein and Tol, 1997; Fankhauser et al., 1999; IPCC, 2001).

Concept or Attribute

Purposefulness Autonomous → Planned

Timing Anticipatory → Responsive

Temporal Scope Short term → Long term

Spatial Scope Localised → Widespread

Function/Effects Retreat – accommodate – protect – prevent

Form Structural – legal – institutional

Valuation of performance Effectiveness-efficiency-equity

Source: adapted from Smit et al. 1999



Autonomous Adaptation: natural automatic response to a “shock”. Also socio-economic systems react partly autonomously. There are substitution possibilities triggered by price (scarcity) signals.

Planned Adaptation: strategies apt to alleviating the damage once it is (or will be) materialized via proper modifications of the impacted socio-economic-environmental system. Undertaken by public agencies – agents.

Objectives of planned adaptation (Klein and Tol, 1997): increasing the robustness of infrastructural design and long term

investment, increasing the flexibility of vulnerable managed system, enhancing the adaptability of vulnerable natural systems, reversing trends that increase vulnerability (“maladaptation”), improving societal awareness and preparedness.


A short history of adaptation (1)

Art. 2 objectives "...achieve [stabilization of greenhouse gas concentrations] within a time frame to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and to enable economic development to proceed in a sustainable manner.” (UN, 1992).

Art. 3.3: “The Parties should take precautionary measures to anticipate, prevent or minimise the causes of climate change and mitigate its adverse effects”.

UNFCCC (Rio Earth Summit 1992)

not a new issue indeed…



Article 4.1(b): parties are committed to “formulate, implement, publish and regularly update national and, where appropriate, regional programmes containing […] measures to facilitate adequate adaptation to climate change”.

Article 4.1(e): all parties should “cooperate in preparing for adaptation to the impacts of climate change” and to “develop and elaborate appropriate and integrated plans for coastal zone management, water resources and agriculture, and for the protection and rehabilitation of areas, particularly in Africa, affected by drought and desertification, as well as floods”.



Kyoto Protocol (1997)

Art. 10: “All parties […] shall […] formulate, implement, publish and regularly update national and, where appropriate, regional programmes containing measures to […] facilitate adequate adaptation to climate change”

Article 12.8: “The Conference of the Parties […] shall ensure that a share of the proceeds from certified project activities is used to […] assist developing country Parties that are particularly vulnerable to the adverse effect of climate change to meet the cost of adaptation”



However until the end of ’90s main policy & research focus was on mitigation (e.g.

see 1995 IPCC SAR)

Prudential and “politically correct”

Mitigation “easy” to investigate

Then

Awareness of climate inertias

Difficulty of effective implementation of mitigation policies (Kyoto)

Interest on adaptation (2001 TAR, 2007 FAR)

Standard tools for economic policy (command and control and/or market based mech.s) & standard analysis could be applied to mitigation

More of an issue for “developing countries”


Where do we stand now (1)

IPCC TAR (2001) [p. 779] “…Adaptation to climate change has the potential to substantively reduce many of the adverse impacts of climate

change and enhance beneficial impacts - though neither without cost nor without leaving residual damages…”

An increasing number of researches, but still far from conclusive, highlights a strong potential for adaptation to reduce climate change adverse effects see e.g. the vast literature on coastal protection or on agriculture


Where do we stand now (2)

“…Current knowledge of adaptation and adaptive capacity is insufficient for reliable predictions of adaptations; it also is insufficient for rigorous evaluation of planned adaptation options, measures and policies of governments” (IPCC 2001, TAR)

Nevertheless it is recognised that:

“…more quantitative information on the costs and benefits of economy-wide adaptation is required…” (the Stern review, 2006)

“Only a few credible estimates are now available of the cost of adaptation (in the Developing countries) …and… highly speculative” (the Stern Review 2006)

“At a global or international level, defining a socially, economically and environmentally justifiable mix of mitigation, adaptation and development remains difficult and a research need” (IPCC 2007, FAR)


Adaptation in modelling

Exhogenous adaptation: the Hope’s PAGE (2002) model

Select a mitigation and adaptation policySelect a mitigation and adaptation policy

Global and regional

temperature

Global and regional

temperature

Costs ofmitigationCosts of

mitigationCosts of

adaptationCosts of

adaptation

Impacts Impacts CostsCosts

Source: Hope (2006)



0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2000 2020 2040 2060 2080 2100

ATL AD GRT ATL1

Tolerability Plateau

Slope

Damage Reduction

Source: Hope (2006)

Adaptation can achieve an increase in the tolerable t increase (plateau + slope effect) and a reduction in damage

°C



251.2520000non-econLDC

251.2520000non-econOECD

502.520000econLDC

904.5200020.12000econOECD

%%/yeardegCdegC/yearSectorRegion

PlateauSlopeStart datePlateauSlopeStart date

Reduction in impactsTolerable temperature rise

251.2520000non-econLDC

251.2520000non-econOECD

502.520000econLDC

904.5200020.12000econOECD

%%/yeardegCdegC/yearSectorRegion

PlateauSlopeStart datePlateauSlopeStart date

Reduction in impactsTolerable temperature rise

Source: Hope (2006)

Default adaptation strategy in PAGE2002



Source: Hope (2006)

Costs of default adaptation strategy in economic sector, PAGE 2002

min mode max

1 °C rise in

tolerable temp

3 12 25

10% reduction in

impact

0.4 1.6 3.2

$US (2000) billion/year2000 - 2200



Endogenous adaptation as a flow, DeBruin et al. (2007)

D = CC damageRD = residual damageGD = gross damagePC = adaptation costP = adaptation rateY = GDPTE = temperature



Restrictions:

0.3 < P(t=2130) < 0.8

0.1% of GDP < PC(t=2130) < 0.5% of GDP

and

7% of TD < PC(t=2130) < 25% of TD

To be estimated



Endogenous adaptation as a stock. Bosello (2006)

N

n

T

t

t

tnC tnL

tnCtnLn

1 1

)1(

),( ),(

),(log),()1()(max

]),(),([),(),(),( )1( tnLtnKtnKtnAtnQ R

),(),()1()1,( tnItnKtnK K “Productive” capitalaccumulation

Gross production

Obj. Funct.

),(),()1()1,( tnIAtnADStnADS SAD

),(&),()1()1,( tnDRtnKtnK RKR R “Knowledge stock”

accumulation

“Adaptation” capitalaccumulation

),()),(1]()),()(exp(

)(),([),( tnQtn

tnKn

ntntnE

R

Emissions

The “double” role of knowledge: productivity increasing and emission intensity decreasing



),(&),(),(),(),( tnDRtnIAtnItnCtnY Ususal output allocation function

“Link” gross – net output

Net (of CC damage and environmentalPolicies) output

2

2

1,

1,

1 ( , )( , )

11 ( ) / 2,5

exp( ( , ))

bn

n

b n tn t

T tSAD n t

),(),(),( tnQtntnY

Abatement costs (as forgone GDP) depending (+) on abatement rates

Climate change damages (as forgone GDP) depending (+) on temperature and (-) on adaptation “capital”


Adaptation: compendium of results. (Hope, 2006)

At given assumptions aggressive adaptation policies could be easily justified as well: for instance, at a 5% discount rate, the mean cost of US $ 0.5 trillion implied by an ambitious adaptation strategy implemented between 1990 and 2100, were outweighed by the US $ 17.5 trillion benefits from the reduction in economic impacts.


Adaptation: compendium of results. (De Bruin et al., 2006)

Climate change damages


Adaptation: compendium of results. (Bosello, 2006)

Abatement Rates

0

2

4

6

8

10

12

142000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Ab

ate

men

t in

%

M

M+A

M+A+R

CO2 Emissions

0

50

100

150

200

250

300

350

400

1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Millio

n T

on

s C

O2

M

M+A

M+A+R

Cumulated Disc. Environmental Damage

0

200

400

600

800

1000

1200

1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Billio

ns U

S $

M

M+A

M+A+R


Adaptation: compendium of results. (Bosello, 2006)

0

1

2

3

4

5

6

7

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

% o

f to

tal c

apit

al s

tock

0

2

4

6

8

10

12

14

% o

f ab

ated

em

issi

on

s

Abatement Adaptation stock Knowedge stock

Mitigation and R&D investment start at the beginning, adaptation later

Mitigation, R&D investment and adaptation (M+A+R)


Autonomous adaptation: CGE modelling

It can “intrinsically” highlight the role of autonomous adaptation (i.e. economic substitution possibilities) as long as the difference between direct and indirect costs can be shown.

Extreme care in the interpretation (and communication) of results.

The big issues here:

The geographical scale effect: large aggregations conceal “hot spots”

Stock vs flows (do not compare pears with apples)!



Sometimes it is “embedded” in the model. It is nothing else than the very functioning of the general equilibrium structure of the model with all its interconnected adjustments (see SLR example)

In some other cases also autonomous adaptation need to be modelled.

This is the case of climate change impacts on health or tourism flows (but is this really adaptation or impact?) when endogenous (in this case demand) variables have to be modified



Example: Bosello et al. (2007), “Economy wide estimations of climate change impacts Sea level rise”


Autonomous adaptation: CGE modelling, SLR

Inputs to the CGE model Outputs from the CGE model

ml $as % of

GDP Land Labour CapitalUSA -0.052 121 0.0002 -0.0013 -0.016 0.015 0.684 -0.034 -0.034

CAN -0.002 72 0.0017 -0.0004 0.029 0.032 0.822 -0.013 -0.009

WEU -0.029 298 0.0005 -0.0019 -0.005 0.016 0.608 -0.035 -0.036

JPK -0.141 146 0.0004 -0.0018 0.006 0.011 1.132 -0.034 -0.035

ANZ -0.010 237 0.0075 -0.0008 0.081 0.010 0.967 -0.022 -0.028

EEU -0.041 45 0.0016 -0.0047 -0.001 -0.037 0.629 -0.074 -0.079

FSU 0.000 0 0.0000 -0.0007 -0.005 0.010 0.613 -0.038 -0.040

MDE -0.007 75 0.0011 -0.0045 0.000 -0.001 0.998 -0.035 -0.044

CAM -0.120 182 0.0049 -0.0098 0.052 -0.023 0.806 -0.053 -0.059

SAM -0.041 647 0.0043 -0.0007 0.102 0.020 0.742 0.005 0.007

SAS -0.396 6000 0.1180 -0.0649 0.078 -0.212 1.420 -0.285 -0.292

SEA -0.839 14913 0.1475 -0.1092 -0.032 -0.357 2.372 -0.468 -0.504

CHI -0.091 579 0.0063 -0.0303 -0.060 -0.236 0.521 -0.235 -0.260

NAF -0.039 1120 0.0548 -0.0036 0.012 0.039 0.795 -0.002 0.016

SSA -0.130 8869 0.2359 -0.0094 0.100 -0.029 1.034 -0.055 -0.062

SIS -0.167 188 0.0220 -0.0189 -0.020 -0.086 0.885 -0.162 -0.169

Terms of Trade

Invest. flows

Factor PricesLand loss

Direct costs: value of land lost

GDP

Reference Year 2050: % changes wrt baselineland loss ranking

direct cost

ranking

gdp loss ranking

USA 8 15 12CAN 15 10 16WEU 12 13 10JPK 4 14 11ANZ 13 6 13EEU 9 11 7FSU 16 16 14MDE 14 12 8CAM 6 8 5SAM 10 9 15SAS 2 3 2SEA 1 2 1CHI 7 7 3NAF 11 4 9SSA 5 1 6SIS 3 5 4


Autonomous adaptation: CGE modelling, SLR

0.1475

0.0063

0.0548

0.2359

0.0220

0.0649

0.1092

0.0303

0.0036 0.00940.0189

0.1180

0

2000

4000

6000

8000

10000

12000

14000

16000

SAS SEA CHI NAF SSA SIS

Mill

ion

s $

0.00

0.05

0.10

0.15

0.20

0.25

2050

GD

P %

DC abs GEC abs DC %GDP GEC % GDP

0.00

02 0.00

17

0.00

05

0.00

04

0.00

75

0.00

16

0.00

00 0.00

11

0.00

49

0.00

43

0

100

200

300

400

500

600

700

800

USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM

Mill

ion

s $

0.000

0.002

0.004

0.006

0.008

0.010

0.012

2050

GD

P %

DC abs GEC abs DC %GDP GEC % GDP


Planned adaptation and CGE modelling.

The typical approach is the “if then” one.

That is: a scenario with climate change impacts and without adaptation is compared with a scenario with adaptation. Adaptation is one (or more) specific planned adaptation strategy ad hoc modelled and a lower climate change impact is imposed.

Thus we return to the shortcoming of not having an endogenously defined optimal level of adaptation

In addition, when stock and flow variables at a time are concerned by climate change and adaptation, results are difficult to interpret.


Planned adaptation and CGE modelling.

Bosello, F., Roson, R. and Tol, R.S.J. (2007), “Economy wide estimates of the implications of climate change: sea level rise”, Environmental and Resource Economics, 37.

Example


Modelling coastal protection

The amount of savings by regional households adjusts endogenously. In each region regional savings increase uniformly to meet the increased investment demand.

This additional investment increases (exogenously) regional investment. (Cost of coastal protections from Nicholls)

Given that income is allocated between savings, private and public expenditure, more savings => less consumption. The sum of the three “shares” cannot exceed 1.

The idea: coastal protection implies additional investment need.


CGE modelling and planned adaptation: SLR

Equation(linear)RORGLOBAL# global supply of cap. goods, or global rental rate on investment (HT 59)# (all,r,REG)

qcgds(r)= investment(r);

(8) New exhogenous variables(8) Endogenous variables deleted (equations for cgds)


CGE modelling and planned adaptation: SLREQUATION (Linear) DEVSAVE1 (all,r,USA1) (all,i,EU1) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE2 (all,r,EU1) (all,i,JPN1) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE3 (all,r,JPN1) (all,i,EEFSU1) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE4 (all,r,EEFSU1) (all,i,CHIND1) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE5 (all,r,CHIND1) (all,i,EEx1) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE6 (all,r,EEX1) (all,i,RoA11) qsave(r)=qsave(i);

EQUATION (Linear) DEVSAVE7 (all,r,RoA11) (all,i,RoW1)qsave(r)=qsave(i);

(7 ??) New endogenous variables



Equation DISTPARSUM #sum of the distribution parameters# (all,r,REG)

DPARSUM(r)*dpsum(r)=DPARPRIV(r)*dppriv(r)+DPARGOV(r)*dpgov(r)+ DPARSAVE(r)*dpsave(r);

From exogenous to endogenous

From endogenous to endogenous



Full Protection: Selected Results

Inputs Outputs

USA 0.01 0.151 0.001 -0.189 -0.206 -0.069EU 0.025 0.302 -0.022 -0.262 -0.296 -0.16EEFSU 0.332 3.179 0.049 -0.143 0.033 -0.133JPN 0.032 0.242 -0.009 -0.569 -0.605 -0.344RoA1 0.799 9.422 0.103 -0.315 -0.009 -0.13EEx 0.185 2.235 0.015 -0.274 -0.223 -0.069CHIND 0.106 1.254 0.003 -0.961 -0.889 -0.116RoW 0.148 1.817 0.009 -0.355 -0.31 -0.115

Household utility

index (% change

w.r.t. baseline)

CO2

Emissions (% change

w.r.t. baseline)

Coastal protection

expenditure (% of GDP)

Private expenditure (% change

w.r.t. baseline)

Investment induced by

coastal protection (% change

w.r.t. baseline)

GDP (% change

w.r.t. baseline)

= highest (absolute) values



GDP: No Protection vs Full Protection (2050)

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

USA EU EEFSU JPN RoA1 EEX CHIND RoW

% o

f G

DP

No Protection

Full Protection



Utility: No Protection vs Full Protection

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

USA EU EEFSU JPN RoA1 EEx CHIND RoW

% c

han

ge

in u

tili

ty

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

% c

han

ge

in u

tili

ty

No Protection

Full Protection


SLR, optimal adaptation (the PESETA project 2007)

0

20

40

60

80

100

120

Austri

a

Belgiu

m

Denm

ark

Finlan

d

Franc

e

Ger

man

y

UKingd

om

Gre

ece

Irelan

dIta

ly

Luxe

mbo

urg

Nethe

rland

s

Portu

gal

Spain

Sweden

Cypru

s

Czech

Rep

Eston

ia

Hunga

ry

Latv

ia

Lith

uani

aM

alta

Polan

d

Slova

kia

Slove

nia

0

2

4

6

8

10

12

14

16

% of land at risk protected % of protected land

A2 2085 high SLR scenario: optimal protection levels for the EU from the DIVA model


SLR, optimal adaptation (the PESETA project 2007)

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Austria

Belgiu

m

Denm

ark

Finla

nd

France

Ger

man

y

UKingd

om

Gre

ece

Irela

nd Italy

Luxem

bourg

Nether

lands

Portugal

Spain

Swed

en

Cypru

s

Czech

Rep

Estonia

Hungar

y

Latvi

a

Lithuan

ia

Mal

ta

Poland

Slova

kia

Slove

nia

-0.11

-0.06

-0.01

0.04

0.09

0.14

GDP Opt AD

GDP No AD

A2 2085 high SLR scenario: GDP no adaptation vs optimal adaptaton







Date post:	26-Mar-2015
Category:	Documents
Upload:	michael-cunningham
View:	212 times
Download:	0 times

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

Documents