iiasa integration assessment via downscaling of population
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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
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