IIASA Integration Assessment via Downscaling of Population, GDP, and Energy Use

Transcription

1 IIASA Integration Assessment via Downscaling of Population, GDP, and Energy Use Urbanization, Development Pathways and Carbon Implications NIES, Tsukuba, Japan March 28-30, 2007

2 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?

3 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, )

4 Downscaling Approach Interdisciplinary team incl. demographers, economists, geographers, land-use modelers, 2-step approach: Global/regional national grid-cell level reflecting distinctly different user needs Combination of constrained optimization and simulation techniques Reflects data/methods available 2004/5

5 Thanks to: Anne Brian Erik Keywan Peter Serguei Vadim

6 IIASA Integrated Assessment & Scenario Analysis Feedbacks Scenario Storyline Economic development Demographic change Technological change Policies Feedbacks Global and Regional Scenarios Population Economy Downscaling Tools Spatially explicit and national scenarios Spatially explicit socio-economic drivers National, regional & spatially explicit socio-economic drivers DIMA Forest Management Model Consistency of land-cover changes (spatially explicit maps of agricultural, urban, and forest land) AEZ-BLS Agricultural Modeling Framework CLIMATE and ACIDIFICATION IMPACT MODELS Carbon and biomass price Potential and costs of forest bioenergy and sinks MESSAGE-MACRO Systems Engineering / Macro-Economic Modeling Framework (all GHGs and all sectors) Endogenous Climate Model Agricultural bioenergy potentials and costs Drivers for land-use related non-co2 emissions NATIONAL POLICY MODELS (GAINS) Emissions Emissions & Abatement Costs

7 Scenario Taxonomy

8 Scenario Overview (World by 2100) 2000 A2r B2 B1 Population, GDP, $ PE, EJ Efficiency, %/yr Zero-C, % share GtC energy GtC forests 1 < GtC-e all others GtC-e total ppmv (CO 2 -equiv) Stabil. Levels (ppm-equiv)

9 Downscaling Flow Chart POPULATION GDP Regional 11 regions National 185 countries Sub- National Cells GRID 7.5 x 7.5 Cells GRID 7.5 x 7.5 Sub- National National 185 countries Regional 11 regions World NAM Per capita urban GDP NAM World WEU Austria Belgium Urban POP Spatial datasets Urban GDP Austria Belgium WEU national projections urban share UK Urban Rural gravity type models Rural POP PEOPLE per square km GDP at MER per ha Rural GDP Urban Rural GDP urban/rural UK Optimization SAS Per capita rural GDP SAS

10 Approach Population GDP existing methodology: global and world regional scenarios National population projections (constrained downscaling using UN) Estimation of future urban 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

11 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)

12 Comparison of population downscaling for China and Afghanistan

13 Comparison of 2 Downscaling Methods for a Low Population Scenario (B1)

14 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)

15 1: Topological Relationship Between GDP Growth ad GDP/Capita Levels (scenario dependent) 3 Western Europe A2: the rich slow down f(x) = a * log(x) + b x GDP/CAP GDP growth (percent) 2 1 Region: Western Europe (a2) Model approximation Per capita income (US$) 14 South Asia B1: the poor catch up f(x) = a * x / (x 2 +b) + c x GDP/CAP a 2 * xmax * ymax b xmax 2 ymax max growth rate xmax GDP/CAP@ ymax GDP growth (percent) Region: South Asia (B1) Model approximation Per capita income (US$)

16 2: Model Application for all Countries in Region, Constrained by Regional Total GDP scenario GDP Growth - LAM - A2 GDP Growth - LAM - B Growth 6 Growth GDP/CAP GDP Growth - FSU - A GDP/CAP 10 GDP Growth - FSU - B Growth 4 2 Growth GDP/CAP GDP/CAP

17 Result GDP/CAP GDP per Capita - B1 OECD90 versus ALM GDP per Capita - A2 OECD90 versus ALM OECD90 OECD ALM ALM

18 Disparities in Projected Country GDPs Lorenz Curves based on 185 Countries Fraction of GDP Fraction of Population (A2) 2100 (B1) Equality

19 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

20 Urbanization Trends IIASA scenarios: High/Medium/Low UN data and projection

21 Sub-National Scenarios 1 (POP) Estimation of base-year sub-national rural/urban population/area allocation (constrained by UN urbanization statistics) Spatially explicit allocation for 3 scenarios: -urban: based on gravity model (with density saturation) w. limited range -rural: proportional scaling (weak)

22 Population Density, A2 and B1

23 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)

24 Base Year GDP comparison (1): National Statistics Sub-National Shares of GDP (USA) 40 Sub-National Shares of GDP (Brazil) 15 y = x R 2 = y = x R 2 = Model, %, Model, %, Sub-National Statistics, Shares of %, 1995 GDP (India) 15 y = x R 2 = Sub-National Shares Statistics, of %, GDP 1998 (China) 10 y = 0.724x R 2 = Model, %, Model, %, Statistics, %, 1994 Statistics, %, 1994

25 Base Year GDP comparison (2): With G-ECON Data Set (W. Nordhaus) Dem. Rep. of Congo GCP comparison 8 y = x R 2 = USA GCP comparison 10 TNT (LOG) 6 4 TNT, US$90, 10^ 8 6 y = x R 2 = sample: 1027 (out of 1156) cells ( ) billion US$1990 (Nordhaus) ( ) billion US$1990 (TNT) Nordhaus (LOG) Nordhaus, US$95, 10^

26 Urban/Rural per capita GDP in A2 and B1 (Pacific Asia) PAS, B PAS, A2 GDP MER per capita, US$ GDP MER per capita, US$ PAS total PAS rural PAS urban PAS total PAS rural PAS urban

27 GDP Density with urban/rural residence and income differences

28 Spatial Resolution Base year (1990): 2.5 x 2.5 arc seconds Scenarios ( ): 7.5 x 7.5 arc sec. Public Data Base (web access): 0.5 x 0.5 degrees

29 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

30 Biomass Potentials Dynamic GDP maps (to 2100) Dynamic population density (to 2100) Top-down Downscaling Development of bioenergy potentials & use bottom-up assessment Consistency of land-price, urban areas, net primary productivity, biomass potentials/use (spatially explicit)

31 Biomass Potentials and Use: Significant reduction (compared to SRES/TAR) due to intersectorial linkages and consistent land and C-prices EJ pot_old pot_new use_old use_new B1 B2 A2r

32 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)

33 Tokyo: Electricity Demand and Supply Densities kwh vs. Solar Energy Supply Electricity demand Solar radiation Solar radiation converted to electricity km 2 Source: TEPCO & NIES, 2002

34 Europe: Power Density of Demand (W/m 2 ): Grey areas indicate where biomass or wind can satisfy local energy demand (< 0.5 W/m 2 ) England: Energy demand footprint larger than country area

35 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

36 Population Density

37 Population Density vs Final Energy per Capita

38 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 Documentation: Special Issue Technological Forecasting & Social Change 74(8 9), October November Electronically already available via ScienceDirect