Reconciling Top-down versus Bottom- up Modeling The IIASA ...

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

5

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

27

Spatial distribution of feedstock resources

28

Transport Network

29

Sweden: Process Integration2 TWh/y 4 TWh/y 6 TWh/y

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Sweden Ethanol Production Cost (€/GJ)

31

Example of Results - EU

32

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

35

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

40

JECAMI

http://www.jecami.eu/RG_v2/

41

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

54

Disagreement Mapping with the help of Geo-Wiki.orgwww.geo-wiki.org

Am example of global land cover datasets disagreement

56

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

[email protected]

http://www.iiasa.ac.at