Economic modeling and the ex-ante measurement of sustainability · Economic modeling and the ex-ante measurement of sustainability TRIER-PISA SUMMER SCHOOL ON MEASUREMENT OF WELFARE

Economic modeling and the ex-ante measurement of sustainability

TRIER-PISA SUMMER SCHOOL ON MEASUREMENT OF WELFARE AND SOCIAL PROGRESS

Fabio Eboli

FEEM, CMCC

PISA, 11th September 2013

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OUTLINE

MOTIVATION AND PURPOSE

METHODOLOGY

CURRENT AND FUTURE SUSTAINABILITY (SCENARIOS ANALYSIS)

CONCLUSIONS

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OUTLINE


METHODOLOGY


CONCLUSIONS

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SUSTAINABLE DEVELOPMENT IN SHORT

• Well known concept

Economic growth crucial but no good proxy for wellbeing

Economic growth may negatively affect other components of wellbeing (however, not only environment ….)

Inter‐generational Equity

• Stockholm 1972, Our Common Future 1987, Rio 1992 and 2012

qualitative debate (we are going in the wrong direction, we need to do something) => Paradigm shift

sustainable development => green growth, green economy (Inclusive Growth, GGKP – Green Growth Knowledge Platform, Europe 2020)

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• Not only environmental concerns …. but everlasting trade‐offs between economic growth and environmental deterioration

Kenneth Boulding => The Economics of the ComingSpaceship Earth (1966)“I am tempted to call the open economy the "cowboy economy," the cowboy being symbolic of the illimitable plains and also associated with reckless, exploitative, romantic, and violent behavior, which is characteristic of open societies. The closed economy of the future might similarly be called the "spaceman economy", in which the earth has become a single spaceship, without unlimited reservoirs of anything, either for extraction or for pollution, and in which, therefore, man must find his place in a cyclical ecologicalsystem which is capable of continuous reproduction of material form eventhough it cannot escape having inputs of energy.”

ECONOMIC GROWTH vs ENVIRONMENT

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=> long‐term sustainable trends?

• Many concerns: Coase (1960), Hardin (1968), Limits to Growth (1972), Wackernagel and Rees (1994, 1996)

MARKET FAILURES: EXTERNALITIES

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• Environment and Natural Resources => Inputs/constraints for economic systems?

Upstream => Extraction of natural resources (extraction/harvestrate higher than growth/regeneration rate)

Downstream => Pollution of environment receptors (pollutionquality and quantity worse/higher than assimilative/carryingcapacity)

• An alternative Production Function

MARKET FAILURES: EXTERNALITIES

Q = f (L,K) Q = f (L,K,R,-E)

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• 250 “RIO + 20” principle => We recognize that progress towards the achievement of the goals needs to be assessed and accompanied by targets and indicators, while taking into account different national circumstances, capacities and levels of development.

• RIO 2012 Issues Briefs n. 6 => Sustainable development indicators and composite indicators are considered to be a good vehicle in helping to measure sustainable development and progress achieved in it … They can help to share policy on the basis of information which is transparent and evidence‐based.

• Sustainable development indicators: A statistical measure that gives an indication on the sustainability of social, environmental and economic development.

• Composite indicators: the compilation of individual indicators into a single index, on the basis of an underlying model of the multi‐dimensional concept that is being measured.

RIO + 20 STATEMENTS

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SUSTAINABLE DEVELOPMENT GOALS

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• 2012 = RIO + 20 => 2 main objectives

integration of sustainable development at all levels of institutional governance

green economy (make more concrete the concept of sustainable development?)

RIO 2012

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HOW TO MEASURE SUSTAINABILITY?

• From theory to practice

Policy‐makers ask for quantitative assessment (distance‐to‐target)

Immediate focus on collection of single indicators – dashboards mainly from institutional bodies:

UN CSD (Sustainable Development Goals)

OECD (Green Growth indicators)

EUROSTAT (Sustainable Development indicators)

ISTAT (Benessere Equo e Sostenibile)

• Main limitation: overall sustainability (at various level of governance) difficult to evaluate and compare

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COMPOSITE INDEXES

• OECD Handbook on constructing composite indicators

• Sen‐Stiglitz‐Fitoussi Commission’s concerns:

heterogeneity among indicators

the arbitrary character of the procedures used to weight their various components

• Aggregate Indexes

Human Development Index (economic + social)

Ecological Footprint (economic pressure on environment)

Index of Sustainable Economic Welfare, Genuine Savings ‐ Adjusted Net Savings, Genuine Progress Indicator (adjusted GDP)

• Both indicators and composite indexes only look at historical and current trend

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OUTLINE


METHODOLOGY


CONCLUSIONS

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FEEM SI: SCOPE, METHODOLOGY, RESULTS

• FEEM SI tries to fill this main gap => how sustainability may evolve in the future

under different assumptions on economic drivers

considering a set of sustainability policies

interdisciplinary approach (all dimensions considered at once)

• CGE + aggregation procedure

• Main results:

decoupling between World Domestic Product and World Sustainability in the reference scenario

policies addressing one specific field of sustainability may imply relevant trade‐offs among spheres

a comprehensive policy for sustainability may lead to a win‐win solution (increase in sustainability and no emerging trade‐offs)

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FRAMEWORK

• New approach to consider:

All dimensions simultaneously involved

Common framework for comparison

Future projections and scenario analysis

• Quantitative assessment of sustainability at country/macroregion scale (worldwide coverage) over time

• New (and quite complex) methodology:

Indicators computation => macroeconomic model (recursive‐dynamic computable/applied general equilibrium model)

Aggregate Index => normalisation + aggregation

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OVERALL STRUCTURE AND MAIN STEPS

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INDICATORS’ SELECTION

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Criteria for selection:

Recognized/used by sustainable developmentcommunity

Historical record available for all the world

Consistent with the macro‐economic model to compute them over time

Targets/benchmarks available for normalizationpurposes

INDICATOR SET ORGANIZATION TYPE

EU Sustainable Development Strategy (EU

SDS)

European Commission Theme‐based indicator set

UN Commission on Sustainable Development

(UN CDS)

United Nations

Three‐pillar indicator set (2001)

Theme‐based indicator set (2006)

World Development Indicators (WDI) World Bank

Theme‐based indicator set

EEA core set of indicators Eurostat, European

Environmental Agency

Environmental indicators

International Energy Outlook (IEO) International Energy

Agency

Environmental indicators

World Economic Outlook Databases (WEO) International Monetary

Fund

Economic Indicators

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INDICATORS’ TREE

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INDICATORS’ DESCRIPTION

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SD Dimension INDICATOR LONG DESCRIPTION

Economic

R&D R&D expenditure / GDP (%)

Investment Net Investment / Capital Stock (%)

GDP p.c. GDP (PPP) / Population

Relative Trade Balance Trade Balance / Market Openness (exp + imp)

Public Debt Government Debt / GDP (%)

Environmental

GHG per capita Kyoto GHGs Emissions / Population

CO2 Intensity CO2 Emissions / Total Primary Energy Cons.

Energy Intensity Total Primary Energy Supply / GDP PPP

Renewables Renewable Cons. / Total Primary Energy Cons. (%)

Water Water Use / Total Available Water (%)

Plants Endangered Species / Total Species (%)

Animals Endangered Species / Total Species (%)

Social

Population Density Population / Country Inhabitable Surface

Education Education Exp. / GDP (%)

Health Health Exp. / GDP (%)

Food Relevance Food Cons. / Households’ Exp. (%)

Private Health Private Health Exp. / Total Health Exp. (%)

Energy Imported Energy Imported / Energy Cons. (%)

Energy Access Population with Access to Electricity / Total Population (%)

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MODELLING FRAMEWORK

• ICES‐SI framework

Recursive‐Dynamic Computable General Equilibrium model (ICES)

GTAP 7 database

=> Both extended for FEEM SI purpose

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MODELLING FRAMEWORK: DATABASE

• GTAP7 database (Narayanan and Walmsley, 2008)

Content: all economic (and energy) flows in Input‐Output (SAM) matrix format

Baseyear: 2004

Geographic coverage: world (113 countries/regions)

Sector coverage: the whole economic system(split in 57 sectors)

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MODELLING FRAMEWORK: DB EXTENSIONS

• Split of several sectors

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Original GTAP7 sector New involved sector

“Other Business Services” R&D

“Other Generative Services” Private Health/Public HealthEducation

“Electricity” Renewables

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• Enriched with following data for 2004:

GDP (PPP)

Public Debt

CO2 and other GHGs

Water consumption and available water stock

Animals and Plants species

Inhabitable surface

Energy Access population

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MODELLING FRAMEWORK: DB EXTENSIONS

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MODELLING FRAMEWORK: CGE MODELS

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• Main scope: assessing higher‐order (general) effects on the whole economic system assuming localized shocks within it

• First applications: International trade, taxation, agriculturalpolicy => recent development on environmental economics(mainly climate change and other transboundary issues)

• Main results: impacts on GDP, sectoral output and prices, international trade when considering market‐driven(autonomous) adaptation of economic agents (vs bottom‐up approaches) => scenario analysis

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GTAP (Hertel, 1997)

GTAP-E (Burniaux and Troung, 2002)

ICES (Eboli et al., 2010)

ICES-SI (Carraro et al., 2011)

MODELLING FRAMEWORK: CGE MODELS

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NORMALIZATION: RESCALING

• Indicators are normally expressed in different measure units => make them comparable and allow aggregation requires a normalization procedure (such that all of them will be defined in the [0,1] interval).

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NORMALIZATION: BENCHMARKING

• Next steps is to define classes of sustainability

• Benchmarks are taken by literature or policy targets => find intermediate levels of sustainabilty is very hard for many indicators, even for experts

• We are planning to define only two levels for next issue: fully sustainability and fully unsustainability (sustainability threesholds), in between => linear behaviour => Less precise but less speculative!

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AGGREGATION: PREFERENCES’ ELICITATION

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Economic Social Environmental Weights

Worst Worst Worst 0

Best Worst Worst 20

Worst Best Worst 50

Worst Worst Best 30

Best Best Worst X ≥ 50

Best Worst Best X ≥ 30

Worst Best Best X ≥ 50

Best Best Best 100

Experts/stakeholders/decision makers?

Monotonici

ty

• The preference among sustainability indicators is obtained with an “ad hoc” questionnaire that elicits individual preferences on the specific performance of each sustainability indicator and their coalitions.

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AGGREGATION: CONSENSUS AND CHOQUET

• A consensusmeasure among experts’ valuations is considered in order to derive a ‘representative’ weight assigned to each sustainability indicator (andness/orness). For this purpose, the metric distance measure is used to assign weights to the responses of each expert at each node in the decision tree.

• This method optimizes the trade off between simplicity and effectiveness in representing preferences by focusing specifically on the interrelations across indicators (non additive measure, since allows considering redundancy and synergy). Therefore, a suitable algorithm based on the Choquet integralaggregates all criteria into a single outcome, taking into account all the coalition weights.

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AGGREGATION: SHAPLEY INDEX

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Indicator’s contribution to overall index

Relative importance of each indicator at a given node

Indicator Contribution to

overall index

GDP per capita 0.1128

Population Density 0.0790

Education 0.0644

Health 0.0639

GHG per capita 0.0637

R&D 0.0635

Water 0.0635

Renewables 0.0618

CO2 Intensity 0.0616

Investment 0.0600

Energy Intensity 0.0564

Relative Trade Balance 0.0487

Food relevance 0.0416

National Debt 0.0410

Private Health 0.0362

Animals 0.0258

Plants 0.0253

Energy Imported 0.0154

Energy Access 0.0154

Node Criterion Shapley value

FEEMSIEconomic 0.326Social 0.316Environmental 0.358

EconomicGrowth drivers 0.379

GDP per capita 0.346

Exposure 0.275

SocialPopulation Density 0.250

Well Being 0.406

Vulnerability 0.344

EnvironmentAir pollution 0.350

Energy 0.330

Natural Endowment 0.320

Growth DriversR&D 0.514

Investment 0.486

ExposureRelative Trade Balance 0.543

National Debt 0.457

Well BeingEducation 0.502

Health 0.498

Vulnerability

Food relevance 0.383

Energy Security 0.283

Private Health 0.333

Energy SecurityEnergy Imported 0.500

Energy Access 0.500

Air pollutionGHG per capita 0.508

CO2 Intensity 0.492

EnergyEnergy Intensity 0.477

Renewables 0.523

Natural EndowmentBiodiversity 0.446

Water 0.554

BiodiversityAnimals 0.504

Plants 0.496

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OUTLINE


METHODOLOGY


CONCLUSIONS

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APPLICATION: REGIONAL DETAIL

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No. Country/Region

1 Australia2 NewZealand3 Japan4 Korea5 China6 India7 Indonesia8 SEastAsia9 RoAsia10 USA11 Canada12 Mexico13 Brazil14 RoLA15 Austria16 Benelux17 Denmark18 Finland19 France20 Germany

No. Country/Region

21 Greece22 Ireland23 Italy24 Poland25 Portugal26 Spain27 Sweden28 UK29 RoEU30 Switzerland31 Norway32 RoEurope33 Russia34 RoFSU35 Turkey36 MiddleEast37 NorthAfrica38 RoAfrica39 SouthAfrica40 RoWorld

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APPLICATION: SECTOR DETAIL

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No. Sector

1 Food 2 Forestry3 Fishing4 Coal5 Oil6 Gas7 Petroleum Products8 Other Electricity9 Renewables10 Nuclear11 Biofuels12 Energy Intensive Industries13 Other Industries14 Water15 Market Services16 Public Services17 R&D18 Education19 Private Health20 Public Health

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WORLD SUSTAINABILITY RANKING IN 2011

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FEEM SI vs … GDP!!!

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GDP p.c.

Ranking Country

FEEM SI

Ranking GDP p.c.

Ranking Country

FEEM SI

Ranking

1 Norway 1 21 Portugal 17

2 USA 11 22 Poland 29

3 Switzerland 3 23 MiddleEast 28

4 Australia 12 24 RoEU 22

5 Austria 4 25 Russia 21

6 Ireland 9 26 Mexico 23

7 Denmark 6 27 RoEurope 15

8 Benelux 20 28 RoLA 18

9 Sweden 2 29 SouthAfrica 30

10 Canada 7 30 Brazil 13

11 Germany 16 31 Turkey 27

12 Finland 5 32 RoFSU 35

13 UK 14 33 RoWorld 33

14 Japan 26 34 SEastAsia 34

15 France 8 35 NorthAfrica 36

16 Spain 19 36 Indonesia 38

17 Italy 25 37 China 39

18 Korea 24 38 India 40

19 NewZealand 10 39 RoAsia 37

20 Greece 31 40 RoAfrica 32

Correlation coefficients between GDP p.c. & FEEM SI, economic, social and environmental pillars

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WORLD SUSTAINABILITY MAPS IN 2011

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CURRENT SUSTAINABILITY DRIVERS

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SENSITIVITY/ROBUSTNESS

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Distribution of FEEM SI value by country according to 500 artificial decision makers

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THE BASELINE SCENARIO

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Variable Reference source

PopulationUN World Population Prospect (2010 revision) –medium fertility variant

Fossil fuel prices Eurelectric (2010)

GDP

2005‐2009 = World Bank (WDI 2010)2010‐2020 = MMC_G10 scenario Med Pop ‐Medium Growth ‐ Fast Convergence (Conv) developed within the RoSE project + World Economic Outlook 2010 (IMF, 2010) for downscaling at country level

Energy intensity2005‐2009 = IEA (2010)2010‐2020 = endogenous

CO2 emissions2005‐2009 = IEA (2010)2010‐2020 = endogenous

Public debt IMF (2010)

Main variables and reference sources in the baseline scenario

THE BASELINE SCENARIO

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GDP GROWTH 2005-2020

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SUSTAINABILITY PICTURE: 2020 vs 2011

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SUSTAINABILITY TRENDS: TOP TEN

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SUSTAINABILITY TRENDS: BOTTOM TEN

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SUSTAINABILITY TRENDS: AGGREGATES

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COUNTERFACTUALS => POLICY SET

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Overall Policy Design

Climate Policy (Cancún high pledges and coordinated action)

Increased water use efficiency in agriculture and industrial sectors in all countries

Subsidies on Education in LDC (MDG targets)

Subsidies on Public Health in LDC (MDG targets)

Subsidies on R&D in Advanced Economies and technological transfer in agriculture and industrial sectors in LDC

Climate Policy: Cancun Agreements - High‐pledges

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POLICY EFFECTS: WORLD

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-8

-6

-4

-2

0

2

4

6

8

%

SD POLICY vs. BASELINE

Economic Social Environmental FEEM SI GDP pc

-8

-6

-4

-2

0

2

4

6

8

%

SOCIAL POLICY vs. BASELINE


-8

-6

-4

-2

0

2

4

6

8

%

ENV POLICY vs. BASELINE


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POLICY EFFECTS: REGIONAL AGGREGATES

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-8

-6

-4

-2

0

2

4

6

8

10

SD Policy Social Policy Environmental Policy

% V

aria

tion

-15

-10

-5

0

5

10

15

20

25


% V

aria

tion

-15

-10

-5

0

5

10

15

20

25


% V

aria

tion

EU27

DC LDC

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POLICY EFFECTS: REGIONAL AGGREGATES

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FOCUS ON CLIMATE POLICY

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OUTLINE


METHODOLOGY


CONCLUSIONS

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CONCLUSIONS• A nex composite index to assess future sustainability worldwide is proposed

• The approach allows considering higher order effects deriving from changes in economic system (also due to policies for sustainability)

• In the next decade, sustainability at world level is expected to decrease, mainly due to the social component deterioration (decoupling between GDP and sustainability)

• Ad hoc sectoral policies are expected to increase sustainability at world level (higher benefits than costs in terms of sustainability) => but trade‐offs among pillars and world GDP reduction

• An integrated policy for SD implies the best outcomes at world level in terms of both GDP and Sustainability (win‐win solution)

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MAIN OUTCOMES

• Projects

Two‐years based updated releases of sustainability assessment (methodology improvement, new experts and stakeholders surveys, ….) => next: 12 November 2013

In‐Stream (INtegrating MainSTREAM Economic Indicators with Sustainable Development Objectives) project

• Scientific outcomes

1 website, 2 brochures, 2 methodological report, 4 working papers, 2 on line articles on RE3 journal, 2 submitted articles to scientific journals, 2 articles on “Equilibri”, 1 book chapter, 6 conferences, 2 summer school

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NEXT STEPS

• Introduction of new indicators (corruption, ICT, wasteproduction, material intensity)

• Database updated to 2007

• Baseline extension to 2030

• New elicitation method (from nest to scenario evaluation)

(https://qtrial.qualtrics.com/SE/?SID=SV_3CwRDaHAnkJvG9D)

• Event of presentation: 12/11/2013

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TEAM

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Project CoordinatorCarlo Carraro

Team LeaderFabio Eboli

Research Team

Francesco Bosello

Lorenza Campagnolo

Luca Farnia

Silvio Giove

Ramiro Parrado

Roberta Pierfederici

Web Master

Paolo Gittoi

Irene Bellin

Graphic Project

Renato Dalla Venezia

Management

Monica Eberle

Communication

Jacopo Crimi

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Thank you for your attention!

[email protected]

www.feemsi.org

Economic modeling and the ex-ante measurement of sustainability · Economic modeling and the ex-ante measurement of sustainability TRIER-PISA SUMMER SCHOOL ON MEASUREMENT OF WELFARE

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