reconciling top-down versus bottom- up modeling the iiasa
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Reconciling Top-down versus Bottom-up Modeling
The IIASA integrated model cluster and multiple-scale case studies
Florian Kraxner and the ESM team
Deputy DirectorEcosystem Services and Management (ESM) Program
International Institute for Applied Systems Analysis (IIASA)
IIASA Workshop with INEGI and CONACYT30-31 October 2015
Aguascalientes, Mexico
IIASA approach to joining top-down and bottom-up approaches
Top-down assessment
- Amount needed, identify sources of uncertainty/largest sensitivities/need for bottom-up analysis, system effects -
Bottom-up analysis- Technical potential, costing, LCA,
stakeholder involvement, mainstreaming in existing policies, prioritization of goals -
IIASA Integrated Assessment Framework
air pollution emission coefficients & abatement costs
Population Economy G4Mspatially explicit
forest management model
GLOBIOMintegrated
agricultural, bioenergy and forestry model
MESSAGEsystems engineering model (all
energy sectors, all GHGs, pollutants and water)
socio-economic drivers
consistency of land-cover changes (spatially explicit
maps of agricultural, urban, and forest land)
carbon and biomass price
agricultural and forest bioenergy potentials,
land-use emissions and mitigation
potential
National level ProjectionsMAGICC
simple climate model
GAINSGHG and air
pollution mitigation
model
GHG emissions
demandresponse
iteration
MACROAggregated
macro-economic model
energy service prices
socio-economic drivers EPIC
agricultural crop model
AccessFuel choice model
for cooking
Transport Module Modal split,
cost and value of time
BeWhereSpatially explicit
Techno-economic energy system
optimization model
Since 2014
Since 2015
Since 20153
Modeling Biomass Supply at Global Scale – An Integrated Modeling Approach
Source: IIASA (2015)
Distinguishing features• Bottom-up approach
– Biophysical “feasibility” of policies– Fine-grained management – Interactions given land constraints
• Consistent scaling– Globally consistent national policy impact
assessments• Modularity
– Upstream (geo-wiki)– Downstream (GEM)– Lateral
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G4M
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Age / Max Age
Tota
l Cab
on P
rodu
ctio
n / M
axim
um C
arbo
n P
r
-0.1-0.3-0.5-1-3-10
Biophysical forest model G4M• Forest parameters from G4M
– Provides annual harvestable wood (for sawn wood and other wood)
– Afforestation/Deforestation (NPV)– Forest management (rot/spec)– Forest Carbon stock
• Downscaling FAO country level information on above ground carbon in forests (FRA 2005) to 30 min grid (Kinderman et al., 2008)
– Harvesting costs– Forest area change– Spatially explicit
7, date
• NPP• Population Density• Land cover• Agricultural suitability• Forest Biomass• Price level• Discount rate• Corruption• Product use
Source: Kindermann (2010)
Input Data Sets for the Global Forestry Model (G4M)
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Forest Area Development A2r (2000 – 2035)
Source: IIASA, G4M (2008)
Source: GEO-BENE, Kindermann (2010)
The Global Forestry Model G4M - Avoiding Deforestation under different Policies
EPIC
• Weather• Hydrology• Erosion• Carbon sequestration• Crop growth• Crop rotations• Fertilization• Tillage• Irrigation• Drainage• Pesticide• Grazing• Manure
Processes
Major outputs:Crop yields, Environmental effects (e.g. soil carbon, )
20 crops (>75% of harvested area)4 management systems: High input, Low input, Irrigated, Subsistence
Cropland - EPICThe Biophysical Agriculture Model EPIC
Source: Schmid (2008)
SOC
increase SOC0.18 t/ha/year
Crop Yield
DM Crop Yield-0.30 t/ha, or -7.9%
Source: INSEA, Schmid (2006)
EPIC – Management Change (conventional minimum tillage)
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Source: Data: Tyndall, Afi Scenario, simulation model: EPIC (2011)
EPIC - Relative Difference in Means (2050/2100) in Wheat Yields
GLOBIOMGLOBAL/REGIONAL APPROACH
Model general structure• Partial equilibrium model on land use at global scale
(endogenous prices balance supply and demand)– Agriculture: major agricultural crops and livestock products– Forestry: managed forests for sawnwood, and pulp and paper
production– Bioenergy: conventional crops and dedicated forest plantations
• Optimization of the social welfare (producer + consumer surplus)
• Base year 2000, recursively dynamic (10 year periods)• Supply defined at the grid cell resolution• Demand defined at the level of 52 world regions• Main data source: FAOSTAT, complemented with
bottom-up sectoral models for production parameters
GLOBIOM
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GLOBIOM - Supply chain
Natural Forests
Managed Forests
Short Rotation TreePlantations
Cropland
Grassland
Other natural land
BioenergyBioethanolBiodiesel MethanolHeatElectricityBiogas
Wood productsSawn woodPulp
Livestock productsBeefLambPorkPoultryEggsMilk
CropsCornWheatCassavaPotatoesRapeseedetc…
LAND
USE
CHA
NGE
Wood Processing
Bioenergy-Processing
Livestock Feeding
World partitioned in 52 regions
52 regions represented on the map+ Sub-saharan Africa split in Western Africa, Eastern Africa and SouthernAfrica (Congo Basin and South Africa already separated)
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GLOBIOM: Typical applications• Agricultural prospective
– Schneider et al. (2011) Impacts of population growth, economic development, and technical change on global food production and consumption. Agricultural Systems
– Smith et al. (2010) Competition for land, Philosophical transactions– Applied scenarios such as Eastern Africa with CCAFS
• Deforestation– Mosnier et al. (2010) Modeling impacts of development trajectories on forest cover in the Congo Basin– Living Forest Report – WWF (2011)
• Climate change mitigation– Valin et al. (2010) Climate change mitigation and food consumption patterns
• Biofuels– Fuss et al. (2011) A stochastic analysis of biofuel policies– Havlik et al. (2010) Global land-use implications of first and second generation biofuel targets. Energy Policy– Mosnier et al. (2010) Direct and indirect trade effects of EU biofuel targets on global GHG emissions
• BioenergyKraxner et al (2013) Global Bioenergy Scenarios Biomass and Bioenergy
• Trade and trade-off assessments• Direct and indirect water demand of feedstock/livestock production systems• Water Exploitation Index (Water Stress)
– Palazzo et al (2014) – ongoing work with YSSPs, based on ISI-MIP water results…
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Emissions from LUC and Forestry sectors
-1000
-500
0
500
1000
1500
2000
BAU FC FC+
BAU FC FC+
BAU FC FC+
BAU FC FC+
BAU FC FC+
2010 2020 2030 2040 2050
DeforestationReforestationOther LUCNet LUCF
GHG emissions Brazil for 2000-20302000 and 2010 emissions data: SEEG
Energy, Industry GHG emissions projection: 2.2% growth/yearLULUCF GHG emissions projections: GLOBIOM-Brazil
2020: 37% decrease from BAU set in COP-152020 onwards: decrease in LUCF offset by growth in Energy and Industry
0
500
1000
1500
2000
2500
20002010
20202030
1460
599360
240
296
366
456568
327
406436 463
76
95119 148
38
4963 81
Residues
Industry
Agriculture
Energy
LUC
BEWHEREREGIONAL/NATIONAL ESS APPROACH
Reference systemDemandNew bioenergy
plants
Existing industries
Biomass
Heat & power
Transport fuel
Fossil fuel
Forest industries
Biomass import
Sawmillresiduals
Domestic biomass
BeWhere Model
Biofuel Import
CHP
Optional flows
Existing flows
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BeWhere Model
• Techno-economical model, geographic explicit
• Mixed integer linear program (GAMS)
• Spatially explicit - 0.2 ˚ to 0.5˚grid cell
• Static - yearly basis, with fluctuation of heat demand over the year
• Minimize the total cost of the whole supply chain for the region’s welfare
min [ Cost + Emissions * (Carbon Tax) ]
• Does not maximize the profit of a plant25
The BeWhere Umbrella
/Forest resources
Crop residuals
Algae
MSW
Solar
Wind
Hydro
Biofuel
Heat
Power
Power to liquid/gas
Biogas
Fertilizers
Biochar
Co-firing
Ecosystem services
BECCS 26
BeWhere Applications
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Spatial distribution of feedstock resources
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Transport Network
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Sweden: Process Integration2 TWh/y 4 TWh/y 6 TWh/y
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Sweden Ethanol Production Cost (€/GJ)
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Example of Results - EU
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Share of biomass potentials per technology
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Size and location of existing sugarcane mills in the state of Sao Paulo in Brazil(60% of the total cane production)
• Feedstock availability• Size and location sugarcane mills• Costs and emissions of biomass production• Annualized investment and O&M costs• Conversion efficiencies• Costs and emissions during biomass/biofuel
transportation• Emission factors of avoided transport fuel
and/or power• Prices of fuel and power
Data source/model inputs
The BeWhere model for Brazil
Optimizing biorefinery for energy production
Existing power Station
Power, heatdemand
Fossil fuelbased power
New power Station
Environmentalconstraint
Cost minimization forthe welfare of the region
COST = � 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑡𝑡𝑐𝑐𝑡𝑡 +𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑒𝑒𝑒𝑒𝑒𝑒𝑐𝑐𝑒𝑒𝑡𝑡𝑒𝑒 ∗ 𝑐𝑐𝑡𝑡𝑐𝑐𝑐𝑐𝑡𝑡𝑒𝑒 𝑐𝑐𝑡𝑡𝑐𝑐𝑡𝑡
Resources(Solar, wind, hydro,
biomass)
Environmental constraint(biomass use)
Optimize location, capacity and technology
of renewable power generation sites
Latest BeWhere Version
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IUCN Categories
Ia – Strict Nature ReserveIb – Wilderness AreaII – National ParkIII – Natural Monument or FeatureIV – Habitat/Species Management AreaV – Protected LandscapeVI – Protected Area with Sustainable Use of Natural Resources
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Harmonized Protected Areas
Scenario 1 –General protection levelProduction restrictions
High protectionMedium protectionLow protection
Low protection
High protection
Hydro power modelingBusiness as Usual High Carbon tax
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May, 21, 2015 Sonthofen, Germany42
May, 21, 2015 Sonthofen, Germany43
May, 21, 2015 Sonthofen, Germany44
The BeWhere Network• Sweden (LTU, MDH, KTH)• Italy (UNIUD, EURAC)• Finland (UEF)• The Netherlands (WUR)• Austria (BOKU, RSA)• Norway (SINTEF)• Japan (TITECH)• Indonesia (MEMR, TIB, ICRAF, CIFOR)
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BeWhere and YSSP20
0820
0920
1020
112012
20132014
2014
2015 2015 2015 46
BeWhere ThesisLeduc, S. (2009)Development of an optimization model for the location of biofuel production plants.
Schmidt, J. (2009)Cost-effective CO2 emission reduction and fossil fuel substitution through bioenergy production in Austria: a spatially explicit modeling approach.
Wetterlund, E. (2012)System studies of forest-based biomass gasification.
Khatiwada, D. (2013)Assessing the sustainability of bioethanol production in different development contexts –a systems approach.
Slegers, PM (2014) Scenario studies for algae production.
Campana, PE (2015) PV water pumping systems for agricultural applications.
Karthikeyan, K (2016) Potential of forest based bioenergy in Finland.
Patrizio, P (2016) Biogas production in Italy.
2016
2016
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The Journey of BeWhere
Technology
Economy
Environment
Social
www.iiasa.ac.at/bewhere
EARTH OBSERVATION SYSTEMS FOR DATA IMPROVEMENT
GLC-2000JRC Ispra
MODIS 2000Boston University
Land availability uncertainty
GLC agricultural land
790 M ha available
MODIS agricultural land
1 215 M ha available
+/-50% ???disagreement
Land availability uncertainty is a USD >350 billion Question in the scenario
Global Land Cover and Cropland Mapping
• Land Cover uncertainties• Geo-wiki.org / humanimpact.geo-wiki.org• Global cropland mapping initiative
Where are we?Disagreement between MODIS and GlobCover (and
GLC2000)
Another way to improve knowledge of land cover: http://Geo-Wiki.org
• Geo-wiki makes GEO data easy to visualize and analyze.
• Volunteers from around the globe can classify Google Earth imagery, input their agreement/ disagreement with the existing data
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Disagreement Mapping with the help of Geo-Wiki.orgwww.geo-wiki.org
Am example of global land cover datasets disagreement
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www.geo-wiki.orgwww.geo-wiki.org
Geo-Wiki mobile
Fritz et al, 2013, Environmental Science and Technology
Cai et al., 20111107 mil. hectares
Fritz et al., 2013375 mil. hectares
Geo-Wiki Output: Downgrading recent estimates of land availability for biofuel production
Geo-Wiki Output: Global Map of Human Impact / Wilderness
Ambition: WUDAPT (World Urban Database and Access Portal Tools)
• Mapping the physical geography of cities• Fine grid urban canopy parameters (UCP) and
morphological material data (MMD) needed for high resolution weather and climate models, e.g. WRF and CLM-U
• Scope: All major urban centers in the world• Engage urban modeling and climate communities
collaborating in staged data collection to map Local Climate Zones
www.wudapt.org
Cities Geo-Wiki
Geo-Wiki
Visualization of Global Land Cover, Biomass, Photos, etc.
Crowdsourcing ofLand Cover
(Google Earth, Bing Maps)
Creation of Hybrid Land Cover Maps
Validation of LandCover Maps
In-situ Data viaGeo-Wiki
Pictures app
Serious Games(Cropland Capture)
Geo-Wiki Family of Crowdsourcing Tools
Contributes toOpen Data
Geo-wiki mobile
Contact
Florian Kraxner
Deputy DirectorEcosystem Services and Management Program, ESMInternational Institute for Applied Systems Analysis, IIASA Laxenburg, Austria
http://www.iiasa.ac.at