IIASA Integration Assessment via Downscaling of Population, GDP,

and Energy Use

Urbanization, Development Pathways and Carbon Implications

NIES, Tsukuba, JapanMarch 28-30, 2007

gruebler@iiasa.ac.at

Why Downscaling?• Need for spatially explicit scenario drivers, e.g.

for land-use change and forestry models• Description of spatial heterogeneity (adds to

scenario uncertainty, even if illustrative)• Necessary input to impact and vulnerability

assessments (e.g. people and cities at risk of sea level rise)

• Can help to identify additional constraints: spatial energy and pollution densities, infrastructure needs,…

• Input to country-level policy analysis

Core research question: Where are key drivers of change and of vulnerabilities?

Downscaling Philosophy• Focus first on main drivers for land availability and

economics of agriculture and forestry (population and GDP)

• Capture scenario uncertainty (3 IIASA-GGI scenarios: A2r, B2, B1)

• Avoid proportional scaling techniques if possible• Occam’s Razor: In absence of data/models apply

simplest assumption/algorithm possible• Calibrate with global data sets as they become available

(G-ECON, GRUMP,…ongoing activity)• Complement “top-down” with “bottom-up” assessments

(plausibility, missing scenario uncertainty,…)

Downscaling Approach

• Interdisciplinary team incl. demographers, economists, geographers, land-use modelers,…

• 2-step approach: Global/regional→national→grid-cell levelreflecting distinctly different user needs

• Combination of constrained optimization and simulation techniques

• Reflects data/methods available 2004/5

Thanks to:

Erik

Anne

Keywan

Peter

Brian

Vadim

Serguei

IIASA Integrated Assessment & Scenario AnalysisScenario Storyline•Economic development•Demographic change•Technological change•Policies

Population Economy

DIMAForest

ManagementModel

AEZ-BLSAgriculturalModeling

Framework

Downscaling ToolsSpatially explicit and national scenarios

MESSAGE-MACROSystems Engineering / Macro-Economic Modeling Framework (all GHGs and all

sectors)

Endogenous Climate Model

National, regional & spatially explicit socio-economic drivers

Spatially explicit socio-economic drivers

Consistency of land-cover changes (spatially explicit maps of

agricultural, urban, and forest land)

Potential and costs of forest bioenergy and

sinks

Carbon and biomass price

Feedbacks

Agricultural bioenergypotentials and costs

Drivers for land-use related non-CO2 emissions

Feedbacks

Global and Regional Scenarios

CLIMATE andACIDIFICATIONIMPACTMODELS

NATIONALPOLICY MODELS(GAINS)

Emissions Emissions & Abatement Costs

Scenario Taxonomy

Scenario Overview (World by 2100)

-1.7-1.2-0.6Efficiency, %/yr

9193811GtC-e total7909801390370ppmv (CO2-equiv)

-1-2<11GtC forests3

715

44035

62000

480-670520-670670-1090Stabil. Levels (ppm-equiv)

4510GtC-e all others

61627GtC energy614736Zero-C, % share

105013001750PE, EJ330240190GDP, 1012$

71012Population, 109

B1B2A2r

Downscaling Flow Chart

GDPurban/rural

NAM

…

WEU

World

BelgiumAustria

Rural

Urban

SAS

…UK

Urban POP

Regional11 regions

Sub-National

National185

countries

CellsGRID

7.5’ x 7.5’

Rural POP

NAM

…

WEU

BelgiumAustria

SAS

…UK

Regional11 regions

National185

countriesWorld

Sub-National

CellsGRID

7.5’ x 7.5’

Rural

Urban

Urban GDP

Rural GDP

POPULATION GDP

PEOPLEper square km

GDP at MERper ha

Per capita urban GDP

Per capita rural GDP

Optimization

urbanshare

nationalprojections

Spatialdatasets

gravitytype

models

Approach

Population GDPexisting methodology: global and world regional scenarios

• National populationprojections (constrained downscaling using UN)

• Estimation of futureurban population (UN scenario extensions H/M/L)

• Depicting urbanized areas• Distributing of rural/urban

population (downscaling)• Projections (based on

gravity-type models)

• National GDP projections(constrained optimization)

• Urban and rural per capita GDP estimates for base year

• Projections of urban and rural per capita GDP disparities

• Distributing per capita GDP over rural/urban population

National POP Scenarios• Input: 3 SRES scenarios incl. one substantial

revision (A2r, developed at IIASA)• Based on UN long-range (300yr) scenarios• Regional population scenario downscaled to

national level using UN scenario with closest match in demographic characteristics

• Improved over previous efforts CIESIN, MEA• Remaining problem: some discontinuities after

2050 (halt of migration in UN scenarios)

Comparison of population downscaling

for China and Afghanistan

Comparison of 2 Downscaling Methodsfor a Low Population Scenario (B1)

National GDP Scenarios

• 186 National GDP scenarios downscaled from 11 world regional level for 3 scenarios

• Optimization algorithm with constraints:– sum of national GDPs = regional GDP– GDP growth = f(GDP/capita)– different pathways for clusters of countries within

region– upper and lower bounds of income disparities

(B1 only)

1: Topological Relationship Between GDP Growthad GDP/Capita Levels (scenario dependent)

• Western Europe – A2:“the rich slow down”

f(x) = a * log(x) + bx … GDP/CAP

• South Asia – B1: “the poor catch up”

f(x) = a * x / (x2+b) + cx … GDP/CAPa … 2 * xmax * ymaxb … xmax2

ymax … max growth ratexmax … GDP/CAP@ ymax

0

2

4

6

8

10

12

14

0 10000 20000 30000 40000 50000

Per capita income (US$)

GD

P gr

owth

(per

cent

)Region: SouthAsia (B1)

Modelapproximation

0

1

2

3

10000 20000 30000 40000 50000 60000

Per capita income (US$)

GD

P gr

owth

(per

cent

)

Region: WesternEurope (a2)

Model approximation

2: Model Application for all Countries in Region, Constrained by Regional Total GDP scenario

GDP Growth - LAM - B1

0

2

4

6

8

10

12

0 10000 20000 30000 40000 50000 60000

GDP/CAP

Gro

wth

GDP Growth - LAM - A2

0

2

4

6

8

10

12

0 10000 20000 30000 40000 50000 60000

GDP/CAP

Gro

wth

GDP Growth - FSU - B1

-4

-2

0

2

4

6

8

10

0 10000 20000 30000 40000 50000 60000

GDP/CAP

Gro

wth

GDP Growth - FSU - A2

-4

-2

0

2

4

6

8

10

0 10000 20000 30000 40000 50000 60000

GDP/CAP

Gro

wth

Result – GDP/CAP

GDP per Capita - B1OECD90 versus ALM

100

1000

10000

100000

1990 2010 2030 2050 2070 2090

OECD90

ALM

GDP per Capita - A2OECD90 versus ALM

100

1000

10000

100000

1990 2010 2030 2050 2070 2090

OECD90

ALM

Disparities in Projected Country GDPs

Lorenz Curves based on 185 Countries

00.10.20.30.40.50.60.70.80.9

1

0 0.2 0.4 0.6 0.8 1

Fraction of Population

Frac

tion

of G

DP

1990

2100 (A2)

2100 (B1)

Equality

0.741

0.528

0.133

Urbanization Scenarios

• Combination of country level projection (to 2030) and 3 scenarios (to 2100)

• Based on UN urbanization projections (2003)

• Extension of UN Projection by 3 scenarios: High (A2r), Medium (B2), and Low (B1)urbanization

Urbanization Trends

UN data and projection

IIASA scenarios:High/Medium/Low

Sub-National Scenarios 1 (POP)

• Estimation of base-year sub-national rural/urban population/area allocation (constrained by UN urbanization statistics)

• Spatially explicit allocationfor 3 scenarios:-urban: based on gravity model (with

density saturation) w. limited range-rural: proportional scaling (weak)

Population Density, A2 and B1

Sub-National Scenarios 2 (GDP)

• Estimation of base year sub-national rural/urban GDP per capita

• 3 scenarios of rural/urban income convergence: High (B1), Medium (B2),Low (A2r)

• Constrained by national total GDP scenarios

• Spatial allocation: based on population density and rural/urban income differential scenarios (weak)

Base Year GDP comparison (1): National Statistics

Sub-National Shares of GDP (Brazil)

y = 1.0261x - 0.0968R2 = 0.9647

0

10

20

30

40

0 10 20 30 40

Statistics, %, 1998

Mod

el, %

, 199

0

Sub-National Shares of GDP (USA)

y = 1.0734x - 0.1439R2 = 0.9693

0

5

10

15

0 5 10 15

Statistics, %, 1995

Mod

el, %

, 199

0

Sub-National Shares of GDP (India)

y = 0.9917x + 0.5064R2 = 0.6982

0

5

10

15

0 5 10 15

Statistics, %, 1994

Mod

el, %

, 199

0

Sub-National Shares of GDP (China)

y = 0.724x + 1.0006R2 = 0.5802

0

2

4

6

8

10

0 2 4 6 8 10

Statistics, %, 1994

Mod

el, %

, 199

0

Base Year GDP comparison (2):With G-ECON Data Set (W. Nordhaus)

USA GCP comparisony = 1.1292x - 1.0044

R2 = 0.9182

0

2

4

6

8

0 2 4 6 8Nordhaus (LOG)

TNT

(LO

G)

sample: 1027 (out of 1156) cells 5729.24 (5753.25) billion US$1990 (Nordhaus)5657.62 (5657.62) billion US$1990 (TNT)

Dem. Rep. of Congo. 1995 GCP comparison

y = 1.2427x - 2.0374R2 = 0.6151

4

6

8

10

4 6 8 10

Nordhaus, US$95, 10̂

TNT,

US$

90, 1

0^

Urban/Rural per capita GDP in A2 and B1(Pacific Asia)

100

1000

10000

100000

1980 2020 2060 2100

GD

P M

ER p

er c

apita

, US$

1990

PAS total PAS rural PAS urban

PAS, B1

100

1000

10000

100000

1980 2020 2060 2100

GD

P M

ER p

er c

apita

, US$

1990

PAS total PAS rural PAS urban

PAS, A2

GDP Density with urban/rural residenceand income differences

Spatial Resolution

• Base year (1990): 2.5 x 2.5 arc seconds• Scenarios (2000-2100): 7.5 x 7.5 arc sec.• Public Data Base (web access): 0.5 x 0.5

degrees• http://www.iiiasa.ac.at/Research/GGI/DB

Use of Downscaled Scenarios

• Land price scenarios for determining biomass and forest C-sequestration potentials, and deployment in stabilization scenarios (iterated results, consistent C-prices)

• Impact and vulnerability assessments (people and GDP at risk)

• Energy access and energy density

Biomass PotentialsDynamic GDP maps (to 2100) Dynamic population density (to 2100)

Development of bioenergy potentials & use “bottom-up” assessment

Consistency of land-price, urban areas, net primaryproductivity, biomass potentials/use (spatially explicit)

“Top-down”Downscaling

Biomass Potentials and Use:Significant reduction (compared to SRES/TAR) due to inter-

sectorial linkages and consistent land and C-prices

0

100

200

300

400

500

B1 B2 A2r

EJ

pot_oldpot_newuse_olduse_new

EJ

Downscaling – Does it Matter?

• Yes for biomass and land-based forest C-sequestration(esp. in B1 –low POP high income– world)

• Main determinant: GDP distribution and to lesser extent rural population allocation(urbanization exerts indirect influence only)

• Wrong research question: bioenergy and sinks in C-controlled world less constrained by land availability, but rather how agricultural production and forest ecosystem and amenity services will be affected by energy and C prices (much larger economic leverage of biomass/bioenergy and sinks)

• Main influence of urbanization - Energy Densities: Transport infrastructure needs and costs underestimated (esp. for BECCS), urban energy demand determines fuel mix and quality (electricity and liquids rather than biomass)

Tokyo: Electricity Demand and Supply Densitiesvs. Solar Energy Supply

1

10

100

1000

10000

100000

0 1000 2000 3000km2

kWh

Solar radiation converted to electricity

Solar radiation

Electricity demand

Source: TEPCO & NIES, 2002

Europe: Power Density of Demand (W/m2): Grey areas indicate where biomass or wind

can satisfy local energy demand (< 0.5 W/m2)

England:Energy demand footprintlarger than country area

Ongoing & Future Work

• Improved base-year calibration• Experimental scenarios of spatially

heterogeneous rural growth• Mapping energy access and spatially

explicit scenarios of final energy use• Extensions to GHG and air pollutant

(aerosols) emissions

Population Density

Population Density vs Final Energy per Capita

Data Available Online

• Full scenario data for 11 world regions, 3 scenarios to 2100• Population and GDP data plus urban/rural split for 185 countries for 3

scenarios• Dynamic population and GDP maps, 3 scenarios

http://www.iiasa.ac.at/webapps/ggi/GgiDb/dsd?Action=htmlpage&page=series

• Documentation: Special Issue Technological Forecasting & Social Change74(8–9), October–November 2007. Electronically already available via ScienceDirect