Future Land Cover Change and Forests - Global Challenges - Bioenergy versus Deforestation Florian Kraxner E.-M. Nordström, P. Havlík, M. Obersteiner, et al. +>30 collaborators Ecosystems Services and Management Program International Institute for Applied Systems Analysis (IIASA), Austria The 3 rd Global Forest Carbon Working Group Meeting “Future of Global Forests” May 2013 IIASA, Austria

Sustainable bioenergy feedstock - global scenarios and outlook Florian Kraxner, E.-M. Nordström, P. Havlík, M. Obersteiner, et al. Ecosystems Services and Management Program, IIASA Bio-energy and CCS (BECCS): Options for Brazil, June 2013, Sao Paulo, Brazil

ESM’s Organizational Structure Earth Observation Systems (EOS) (Fritz/See) Environmental Resources and Development (ERD) (Havlik/ Mosnier/Valin) Methods for Economic Decision–Making under Uncertainty (MEDU) (Khabarov/Fuss) Policy and Science Interface (PSI) (Kraxner/Boettcher) ESM Lead / Management (MGT) (Obersteiner/Kraxner) Agro- Environmental Systems (AES) (v.d.Velde/Balkovic) Forest Ecosystems Management (FEM) (Forsell)

ESM’s Integrated Modeling Cluster

Modeling Biomass Supply at Global Scale – An Integrated Modeling Approach Source: IIASA (2011) 1 3 2

G4M

Biophysical forest model G4M 7, date 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

The Global Forest Model estimates the impact of forestry activities (afforestation, deforestation and forest management) on biomass and carbon stocks. By comparing the income of managed forest (difference of wood price and harvesting costs, income by storing carbon in forests) with income by alternative land use on the same place, a decision of afforestation or deforestation is made. As G4M is spatially explicit (currently on a 0.5° x 0.5° resolution which is brought down to 30"x30" for Europe already). The model can use external information (like wood prices, prescribed land-use change from GLOBIOM) from other models or data bases, which guarantee food security and land for urban development or account for disturbances As outputs, G4M produces estimates forest area change, carbon sequestration and emissions in forests, impacts of carbon incentives (e.g. avoided deforestation) and supply of biomass for bio-energy and timber. NPPForest Cover TemperaturePrecipitation Estimated NPP Soil The Global Forestry Model G4M - from NPP to C Sequestration

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)

Forest Area Development A2r (2000 – 2035) Source: IIASA, G4M (2008)

Deforestation 2050 under BAU Losses under BAU by 2050 will be mio ha Tropical deforestation is considered the second largest source of anthropogenic greenhouse gas emissions (IPCC, 2007) and is expected to remain a major emission source for the foreseeable future (MEA, 2005) the net effect of all deforestation is basically almost an increase of 20 per cent additional emissions from human activity going into the atmosphere and feeding into climate change. deforestation is to blame for about one and a half billion tons of carbon dioxide being released into the atmosphere every year for the past 15 years (GCP). To the left we see the picture of tropical Africa now and in 2100 under BAU (the more red the less tropical forest, Source: Kindermann et al. 2006

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 - EPIC The Biophysical Agriculture Model EPIC Source: Schmid (2008)

SOC increase SOC 0.18 t/ha/year Crop Yield DM Crop Yield t/ha, or -7.9% Source: INSEA, Schmid (2006) EPIC – Management Change (conventional  minimum tillage)

Source: Data: Tyndall, Afi Scenario, simulation model: EPIC (2011) EPIC - Relative Difference in Means (2050/2100) in Wheat Yields

GLOBIOM

Global Biosphere Management Model Demand Wood products Food Bioenergy G4M Exogenous drivers Population growth, economic growth Primary wood products SUPPLY PROCESS Biophysical models 50 regions EPIC RUMINANT Crops OPTIMIZATIO N Partial equilibrium model Max. CSPS

Model general structure 19 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

GLOBIOM - Supply chain Natural Forests Managed Forests Short Rotation Tree Plantations Plantations Cropland Grassland Other natural land Bioenergy Bioethanol Biodiesel Methanol Heat Electricity Biogas Wood products Sawn wood Pulp Livestock products Beef Lamb Pork Poultry Eggs Milk Crops Corn Wheat Cassava Potatoes Rapeseed etc… LAND USE CHANGE Wood Processing Bioenergy- Processing Livestock Feeding

21 World partitioned in 52 regions 28 regions represented on the map + Sub-saharan Africa split in Western Africa, Eastern Africa and Southern Africa (Congo Basin and South Africa already separated)

Main input driversModel output  Population  GDP  Technological change  Bio-energy demand (POLES team)  Diet patterns (FAO, 2006)  Production Q  Consumption Q  Prices  Trade flows  Land use change  Water use  GHG emissions  Other environmental parameters (nutrient cycle, biodiversity,…) Modelling framework 22

GLOBIOM Products AGRICULTUREFORESTRYBIOENERGY Wheat Rice Maize Soybean Barley Sorghum Millet Cotton Dry beans Rapeseed Groundnut Sugarcane Potatoes Cassava Sunflower Chickpeas Oil Palm Sweet potatoes Buffalo Cattle Sheep Goat Pig Poultry Beef Lamb Pork Poultry Eggs Milk Biomass for log production Fuel wood Other wood Pulp wood Logs Ethanol FAME Methanol Heat Electricity Biogas

GLOBIOM - Livestock Global production system map FAO/ILRI 14 livestock production systems 6 animal types: –Buffalo –Cattle –Sheep –Goat –Pig –Poultry

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 Direct and indirect Water demand of feedstock/livestock production systems

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 Trade and trade-off assessments Direct and indirect water demand of feedstock/livestock production systems

Globally Consistent Assessment of Forest Development and Bioenergy…

Background Global Future Energy Portfolios, 2000 – 2100 Source: modified after Azar et al., 2010

Cumulative biomass production (EJ/grid) for bioenergy between 2000 and 2100 at the energy price supplied by MESSAGE based on the revised IPCC SRES A2r scenario (country investment risk excluded). Source: Rokityanskiy et al. 2006

Forest Area Development A2r (2000 – 2035) Source: IIASA, G4M (2008)

Source: compiled from FAO 2005, 2001; CIESIN 2007, ATFS 2008; FSC 2008; PEFC Kraxner et al., 2008 Certified area relative to managed forest area by countries Forest Management Certification (Potentials)

Global BE Feedstock Scenarios – Definitions & Objectives WWF, 2011 Objectives: a)to achieve a global perspective using an integrated modeling approach; b)to frame the boundaries for lower scale assessments; and c)to identify potential trade-offs to be considered in future research. Zero Net Deforestation and Degradation (ZNDD) means no net forest loss through deforestation and no net decline in forest quality through degradation.

Cumulative deforestation caused by land-use change according to the different scenarios. Global Deforestation Trends BEPlus similar to BAU BE2010 on same high level because of unrestricted deforestation RED keeps deforestation at present level

Cumulative land-use change and net forest cover change (managed + unmanaged forest area) caused by additional bioenergy production under the BiodivRED scenario (compared to the 2010 level of bioenergy production) Land Use Change – Effect of Adding BE, Biodiv & RED – rel to BAU Net gain of total forest area due to restriction of deforestation Protection of biodiversity within pristine and other types at the costs of grassland and savannah (which is mostly located in the southern hemisphere)

Total Land Cover Change Do nothing Target Nature+ Diet Shift

most of the loss of unmanaged forest takes place in the tropical areas of South America, Africa and Asia Loss of pristine (unmanaged) forest as a proxy for BE production on Biodiversity Cumulative loss of area of unmanaged forest in different regions under the BAU scenario Cumulative loss of area of unmanaged forest in different regions under the BEPlus RED scenario the loss of unmanaged forest is not only considerably smaller but also more evenly distributed from a global perspective Regional Effects by Adding BE, Biodiv, RED - Unmanaged Forest rel to BAU

GHG emissions from total land use under the different scenarios GHG Emissions by Scenarios Under the BE2010 scenario, the bioenergy use is small compared to the other scenarios, and the GHG emissions are the highest, 8,091 Mt CO2/year. The GHG emissions are lower under the BAU and BEPlus scenarios, where the bioenergy use is more extensive. Lowest GHG emissions can be achieved under the RED scenarios

Water consumption for agriculture under the different scenarios Agricultural Water Demand by Scenarios All scenarios show increased demand Lowest restriction on forest and biodiversity conservation show less water need Higher restriction implies less land available for eg food production = intensification

The demand for bioenergy will be high and will increase competition for land Bioenergy production is a significant but not the major driver of forest loss Avoiding large-scale deforestation is possible, even under expanded bioenergy production. Unmanaged forest will be lost under all scenarios but under the RED scenarios the loss is only half of the loss under the BAU scenario GHG emissions may be substantially reduced by minimizing deforestation Minimization of deforestation may have negative impacts on other natural ecosystems The more forest and biodiversity one would like to be conserved, the less land will be available for food production The more conservation and protection, the higher the need for optimization and intensification Various policy areas must be coordinated to ensure sustainable use of resources Future studies need to go into the details identified here Summary & Discussion & Conclusions

REGIONAL CASE STUDY AFRICA - CONGO BASIN

REDD in the Congo Basin CONGOBIOM 1550 simulation units Internal transportation costs Spatial representation of fuel wood demand Cocoa and coffee included Delineation of forest concessions and protected areas

Deforestation Impact/Driver Analysis Deforested area in Congo Basin in 2030 (Mio. Ha) Meat

REDD in the Congo Basin Transport time with existing infrastructures (Circa 2000) Transport time with new infrastructures Source: National Ministries, World Bank

High hopes…

Contact Florian Kraxner Ecosystem Services and Management Program International Institute for Applied Systems Analysis, IIASA Laxenburg, Austria Paper contribuion: Florian Kraxner; Eva-Maria Nordström; et al. (2013). Global Bioenergy Scenarios - Future Forest Development, Land-Use Implications, and Trade-Offs. Biomass and Bioenergy (in press)