Precondi)ons for sustainable biomass sourcing, improved models, monitoring & governance ExCo 74 workshop Land use and Mi6ga6on of iluc,

Transcription

1 - > Precondi)ons for sustainable biomass sourcing, improved models, monitoring & governance ExCo 74 workshop Land use and Mi6ga6on of iluc, Brussels, 22nd October Prof. Dr. André P.C. Faaij Academic Director - Energy Academy Europe Dis6nguished Professor Energy System Analysis University of Groningen

2 Biomass resources; poten)als <- > precondi)ons

3 Poten6als Different types (technical, economic, implementa6on, sustainable ) Development over 6me Spa6al levels. Different biomass categories and produc6on systems

4 Key factors biomass poten)als Issue/effect Importance Impact po supply a Supply potential of biomass rece Improvement agricultural management *** Choice of crops *** Food demands and human diet *** Use of degraded land *** Competition for water *** Use of agricultural/forestry by-products ** Protected area expansion ** Water use efficiency ** Climate change ** Alternative protein chains ** Demand for biomaterials * [Dornburg et al., Energy & demand a Demand potential of biomass Environmental Science 2010] rece Bio-energy demand versus supply ** Cost of biomass supply ** Learning in energy conversion ** Market mechanism food-feed-fuel **

5 Bioenergy poten)als (ranges based on expert opinion). (IPCC AR5 WGIII, 2014)

6 2050 Bioenergy Potentials & Deployment Levels Global Primary Energy Supply, EJ/y 2008 Global Energy Total 2000 Total Biomass Harvest for Food/Fodder/Fiber as Energy Content 2008 Global Biomass Energy 2050 Global Energy AR4, 2007 Technical Poten6al 2050 Global Biomass AR4, 2007 Land Use 3 and 5 million km 2 Past Literature Range of Technical Poten4als EJ Technical Poten)al Based on 2008 Model and Literature Assessment Chapter 2 Possible Deployment Levels 2011 IPCC Review* Chapter 10 Modelled Deployment Levels for CO2 Concentra)on Targets ppm <440 ppm 300 Maximum Percen6le 75 th median th Minimum 2050 Projec6ons [IPCC- SRREN, 2011]

7 Yield projec6ons Europe Observed yield CEEC and WEC Linear extrapola6on of historic trends Widening yield gap Applied scenarios Yield [ton/ha] Low, baseline and high 0 Observed historic yields Projections Source FAOSTAT [Wit & Faaij, Biomass & bioenergy, 2010]

8 Source: FAOSTAT and own calculations Developments in yields and inputs [De Wit et al, RSER 2011]

9 Results - spa6al produc6on poten6al Arable land available for dedicated bio- energy crops divided by the total land Potential Low potential < 6,5% Countries NL, BE, LU, AT, CH, NO, SE and FI Moderate potential 6,5% - 17% FR, ES, PT, GE, UK, DK, IE, IT and GR High potential > 17% PL, LT, LV, HU, SL, SK, CZ, EST, RO, BU and UKR [Wit & Faaij, Biomass & Bioenergy, 2010]

10 Results - spa6al cost distribu6on Produc6on cost ( GJ - 1 ) for Grassy crops Potential Low Cost Moderate Cost < 2,00 2,00 3,20 Countries PL, PT, CZ, LT, LV, UK, RO, BU, HU, SL, SK, EST, UKR FR, ES, GE, IT, SE, FI, NO, IE High Cost > 3,20 NL, BE, LU, UK, GR, DK, CH, AT [Wit & Faaij, Biomass & Bioenergy, 2010]

11 Crop specific supply curves Feedstock poten6als Produced on 65 Mha arable and 24 Mha on pastures (grass and wood) Significant difference between 1st and 2nd genera6on crops Supply poten6als high compared to demand 2010 (0,78 EJ/yr) and 2020 (1,48 EJ/yr) Production Costs ( /GJ) Summary baseline 2030 Starch Oil Sugar 1 st generation 2 nd generation Grass Grass Wood Wood Supply (EJ/year) 1 EJ (ExaJoule) = 24 Mtoe [Wit & Faaij, Biomass & Bioenergy, 2010]

12 Example: GHG balance of combined agricultural intensification + bioenergy production in Europe + Ukraine [Wit et al., BioFPR, 2014]

13 Mozambique

14 Drivers Mozambique Demand for food is expected to increase - Increased popula6on - Increased dietary intake (Kcal + nutri6ous) Produc6vity in agricultural sector - BAU con6nuous historic trends - PROG steep increase in produc6vity Total food 1000 MT meat, milk, eggs industrial crops fruits & veg roots & tubers cereals % of yield level Annual yield increase BAU 0.7% Progressive 4.2% Example: Maize ton/ha BAU Prog BAU Prograssiv Total [Hilst et al., GCB- Bioenergy, 2012]

15 Land use allocation Land is allocated to a land use func6on when it is most suitable for that specific func6on based on several land suitability factors Example: suitability for cropland Priority grid [Hilst et al., GCB- Bioenergy, 2012] Current land use Soil suitability Popula6on density Distance to ci6es Distance to water Distance to roads Nr of neighboring cells Scenarios Land requirements Results Discussion Conclusion

16 2010 BAU Progressive BAU Progressive

17 2030 BAU Progressive BAU Progressive

18 Land availability Mozambique BAU scenario Progressive scenario Mha very suitable suitable moderatly suitabble marginally suitable not suitable Development of land availability over 6me differen6ated for suitability classes for the BAU scenario (lep) and the Progressive scenario (right). [Hilst et al., GCB- Bioenergy, 2012] Scenarios Land requirements Results Discussion Conclusion

19 Spatial distribution of feedstock potentials and costs (2015) BAU Progressive [Hilst et al., BioFPR, 2012]

20 Cost Supply curves for eucalyptus /GJ [Hilst et al., BioFPR, 2012]

21 Setting selection BAU 2020 PROG

22 Overall results Environmental Impacts Environmental Impacts Gaza- Inhambane Nampula BAU PROG BAU PROG Impact Unit EU SG EU SG EU SG EU SG GHG Emission b Life cycle Kg CO 2 - eq /GJ biomass LUC related emissions Total emissions Kg CO 2 - eq /GJ biomass Kg CO 2 - eq /GJ biomass Total avoided emissions Kg CO 2 - eq /GJ EtOH Soil c Soil Organic Carbon kg C /GJ biomass Wind Erosion Qualita6ve Water d Water use efficiency Odt biomass / l water Water deple6on mm/season Biodiversity e MSA MSA x100 /GJ biomass

23 Overall Results Socioeconomic Impacts Socio- economic Impacts Gaza- Inhambane Nampula BAU PROG BAU PROG Impact Unit EU SG EU SG EU SG EU SG Legality f No ex- ante analysis possible, recommenda4ons to comply with na4onal law are provided see Land rights g Land right risk Qualita6ve Food security i Food security Qualita6ve +/- +/ Economic viability j Feedstock $/GJ biomass End product $/GJ EtOH Local Prosperity k Total jobs X 1000 jobs Local labour % Total investment M$ Total wages M$ Social well- being l Total no of people affected X 1000 people Labour condi)ons m No ex- ante analysis possible, recommenda4ons to comply with (inter- ) na4onal law and best prac4ce are provided, see

24 [IPCC- SRREN, 2011]

25 Global Assessments and Guidelines for Sustainable Liquid Biofuel Produc)on in Developing Countries FINAL REPORT, March 2013 hhp:// publica)ons/execu)ve%20summary_final.pdf

26 iluc mi6ga6on

27 Uncertain!!! deprecia6on Carbon losses over 20 years; aper that iluc = zero. Carbon intensity fossil ref excludes upstream Emissions. These will increase (>200 g/mj possible)

28 Confronta)on bohom- up vs. top down iluc modelling Key steps iluc modelling efforts: CGE; historic data basis Model shock, short term, BAU, current technology. Quan6fy LUC Quan6fy GHG implica6ons (carbon stocks) Bohom- up insights: Coverage of BBE op6ons, advancements in agriculture, verifica6on of changes (land, produc6on) Gradual, sustainability driven, longer term, technological change (BBE, Agriculture LUC depends on zoning, produc6vity, socio- economic drivers Governing of forest, agriculture, iden6fica6on of best lands. [IEA & other workshops, ; Wicke et al, GCB- B, 2014, a.o.]

29 Example: Corn ethanol esults from PE & CGE models B: Ethanol LUC- related GHG emissions (g CO2e/MJ) Corn Searchinger et al. [3] CARB [13] EPA [18] Hertel et al. [14] Tyner et al. [15] Group 1 Tyner et al. [15] Group 2 Tyner et al. [15] Group 3 Al- Riffai et al. [16] Laborde [17] [Wicke et al., Biofuels, 2012]

30 iluc mitigation options Controlling (i)luc Increasing efficiency in agriculture, livestock and bioenergy produc6on Integra6ng food, feed and fuel produc6on Increasing chain efficiencies Minimizing degrada6on and abandonment of agricultural land Controlling type of LUC Sustainable land use planning (incl. monitoring) Excluding high carbon stock and biodiversity areas Using set- aside, idle or abandoned agricultural land Using degraded and marginal land

31 Contrast: Modeling for iluc factors is only half the science we need; reac)ve instead of pro- ac)ve concept. Biofuel policies also half the policy we need; mandates without proper precondi6ons, resul6ng in CONFLICTS Versus Interlinked agricultural& biobased economy policies (agri, clima, energy ). Inves6gate (and implement) Integral land use strategies (agriculture, BBE, nature, rural development) to achieve SYNERGIES

32 Further inves6ga6ons yield gaps (KIS project) 12.0 Maize 12.0 Rice, paddy Yield (t/(ha.yr)) Yield (t/(ha.yr)) USA China Wheat Sugar cane Brazil India Yield (t/(ha.yr)) Yield (t/(ha.yr)) Zambia & Zimbabwe Australia Soybeans Australia Brazil Legend: Countries assessed in this study Countries assessed by De Wit et al. [1] Maize Rice Soybean Wheat Sugarcane Beef and milk Yield (t/(ha.yr)) China India United States of America 0.5 Zambia Zimbabwe [Gerssen- Gondelach, et al. (in progress)]

33 General approach iluc mi)ga)on From economic models Baseline: developments in food, feed and fibres Biomass target: the amount required to meet targets such as RED. [Brinkman, et al. (in progress)] 33

34 General approach [Brinkman, et al. (in progress)] 34

35 Regional assessment Each region has its own target. If all reach the target without displacing production ILUC can be avoided. Region 1 Region 2 Region 3 Region 35

36 Thanks for your ahen)on For more informa)on, see: - Sciencedirect/Scopus (scien)fic) - Google scholar cita)ons (personal) - hhp://srren.ipcc- wg3.de/report (IPCC) A.P.C.Faaij@rug.nl ; Andre.Faaij@energyacademy.org