Birdsview of state-of-the-art scientific understanding on sustainable biomass sourcing.

Transcription

1 Birdsview of state-of-the-art scientific understanding on sustainable biomass sourcing. BIO-BIOTA_PFPMCG-SCOPE Joint Workshop on Biofuels & Sustainability, 26 th February 2013, Sao Paulo Brazil André Faaij Scientific Director, Utrecht University; Head of Unit, Energy & Resources

2 Utrecht University NL Shanghai Ranking 2011: Utrecht University rated as: 12th best university in Europe best in the Netherlands 5th year in a row 48 th best university worldwide Of 56 leading universities in Europe, Utrecht University, with ETH Zurich, Imperial College London and Cambridge University make up the top 4 of the CHE Ranking.

3 - Faculty of Geosciences Connecting knowledge for sustainability Some 130 scientific staff Interdisciplinary; Key areas: Energy & Resources (Energy Supply, Energy Demand, Integrating activities (e.g. modelling) Innovation sciences & governance. Environmental sciences Range of bachelor & master programs Diverse funding of research: govt. scientific, EC, industry, international bodies, NGO s, etc.

4 Energy & resources: Interlinked concepts for understanding, exploring, monitoring and evaluation. Technologies: Engineering Technology Assessment Technological Learning Impacts: wide portfolio of methods (environmental / ecological / socio-economic ); how to measure sustainability? Potential supply: options, limitations, opportunities. Modeling & uncertainty analysis: wide range of tools. Implementation: policy options, energy transition & innovation policies, roadmaps, scenario s

5 Energy demand, GHG emissions and climate change

6 Potential emissions from remaining fossil resources could result in GHG concentration levels far above 600ppm.

7 Energy system transformation [GEA/van Vuuren et al CoSust, 2012]

8 Biobased economy; friend or foe? Food vs. Fuel Biofuels a crime against humanity Threats for biodiversity, water, farmers LUC & iluc, Carbon Payback result in poor GHG balances Large number of external damages.

9 Driving forces, dimensions, scales [IPCC-SRREN, 2011]

10 Global Primary Energy Supply, EJ/y 2050 Bioenergy Potentials & Deployment Levels 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 Potential 2050 Global Biomass AR4, Land Use 3 and 5 million km 2 Past Literature Range of Technical Potentials EJ Technical Potential Based on 2008 Model and Literature Assessment Projections Chapter 2 Possible Deployment Levels 2011 IPCC Review* Chapter 10 Modelled Deployment Levels for CO2 Concentration Targets ppm <440 ppm 300 Maximum Percentile 75 th median th Minimum [IPCC-SRREN, 2011]

11 Key factors biomass potentials Issue/effect Importance Impact po supply a rece Supply potential of biomass 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 * Demand potential of biomass Bio-energy demand versus supply ** Cost of biomass supply ** Learning in energy conversion ** Market mechanism food-feed-fuel ** demand a rece Dornburg et al., Energy & Environmental Science 2010

12 [Dornburg et al., 2010 in: IPCC-SRREN, 2011]

13 Global biodiesel & fuel ethanol production EU USA Brazil Argentina Others Biodiesel Ethanol (Source: Lamers et al., RSER, 15 (2011) )

14 Global wood pellet production (Source: Lamers et al. RSER, 16(2012)

15 Global wood pellet trade 2010 (Source: Lamers et al. 2012) Source: Lamers et al., RSER, 16(2012)

16 Policy context Europe: Renewable Energy Directive (RED): 20% Renewable Energy in the EU in 2020 (+ specific for member states; e.g. 14% for the Netherlands). Subtarget of at least 10% renewable energy transport (every member state). European Fuel Quality Directive: Fuel suppliers at least 6% less GHG in 2020 via: biofuels, efficiency refineries, or other energy carriers (gas, electricity, hydrogen, etc.). 20% less GHG in 2020 (possibly 30%) -> national targets + Emission Trading. Biomass in e.g. Refining, steel, etc. can contribute. June 2010 National Action Plans for implementation RED. Intervention in RED ( ): CAP on 1 st generation biofuels; iluc discussion frozen. Evaluation in Now: Horizon 2020 activities (JTI s/ppp s, outlook to 2030; heavy emphasis on biobased economy

17 Simulated Biomass trade flows (pellets) Low Import High Import Low Import High Import Total trade (Mtoe) Total trade (Mt wood pellet eq.)* Of which Intra-EU 55% 38% Of which Inter-EU 45% 62% *) Mt eq. = million metric tonne pellet equivalent (18 MJ/kg) Low Import scenario 14 32% 68% 29 52% 48% 40 32% 68% High Import scenario Import non-eu Import Import non-eu non-eu FI NO FI NO SE SE RU RU EE EE LV LV DK DK LT LT UK UK BY IE NL NL DE BE AT SK AT MD HR FR SI MC IT ME AL KS RO RS IT BG ME MK AL GR PT ES KS BG MK GR TR Import non-eu MT HR BA TR Import Import non-eu non-eu UA MD HU CH RO RS BA ES CZ UA HU CH SI PT DE LU SK MC PL BE CZ LU FR BY IE PL CY MT CY Year: [Hoefnagels et al, UU/Task 40, 2011]

18 Advancing markets pushed by technological progress and pulled by high oil prices 2 nd generation biofuels Biorefining, biochemicals, biomaterials Aviation and shipping Likely to compete for the same resources Should meet the same sustainability criteria (but that is not the case today!) Competition or synergy?

19 Final energy (EJ) Biofuels; they are not going away. Large-scale deployment of advanced biofuels vital to meet the roadmap targets Advanced Copernicus biofuels Institute reach cost parity around 2030 in an optimistic case [IEA Biofuels Roadmap]

20 [See e.g: van Dam et al., RSER, 2010]

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22 Confrontation bottom-up vs. top down Key steps iluc modelling efforts: iluc modelling CGE; historic data basis Model shock, short term, BAU, current technology. Quantify LUC Quantify GHG implications (carbon stocks) Bottom-up insights: Coverage of BBE options, advancements in agriculture, verification of changes (land, production) Gradual, sustainability driven, longer term, technological change (BBE, Agriculture LUC depends on zoning, productivity, socio-economic drivers Governing of forest, agriculture, identification of best lands. [IEA Bioenergy 38/40/43, 2011]

23 Example: Corn ethanol Results 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] Lywood et al. [25] [Wicke et al., Biofuels, 2012]

24 But we need the aggregated modeling frameworks World is far too complex E.g. consequential LCA becomes unmanageable. Many interactions come from global level: trade determining factor, food & energy prices, competing (energy & mitigation) technologies, etc. Showing BAU IS important: markets and governments are imperfect

25 iluc mitigation options Controlling extent of LUC Increasing efficiency in agriculture, livestock and bioenergy production Integrating food, feed and fuel production Increasing chain efficiencies Minimizing degradation 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

26 Redesigning modelling frameworks & scenario s IMAGE / IMAGE TIMER PBL 2. Better integrate existing models MAGNET LEI 1. Improve existing models, knowledge and data Bottom-up analysis of technological learning in bioenergy & agriculture UU

27 Bioenergy Tech change & learning / integration Bottom up IMAGE MAGNET - Current status and future prospects of technologies; - Sustainable residue removal (student project, superv. by Vassilis) - Learning in bioenergy chains - Updating input data TIMER based on bottom-up - Dynamic modeling of residues sustainable residue potential - Updating crop yields & learning rates; - Introduction of 2 nd generation fuels; - Inclusion residues as feedstock for biobased value chains - Endogenize tech learning for bioenergy chains, shifts to new technologies

28 Agriculture Tech change & learning / integration Bottom up IMAGE MAGNET Agriculture & livestock (yields, inputs for different mgmt systems & regions effects of shifting systems) Updating & reinforcing scenarios for agricultural crop yields - link to mgmt system Endogenize tech change, efficiency improvements, shift to new mgmt systems / technologies; Price effect of tech change

29 Challenges for science, business and policy Land & natural resources (local global) Integral land use strategies (agriculture, BBE, nature, rural development) Full impact analyses and optimization; Include macro -themes; iluc, food security, rural development, water/biodiversity. Governance Drive down the learning curves Technologies (fuels, biomaterials, power, carbon management (CCS) Cropping systems Logistics, markets, CoC Business models & investment.

30 Work in Brazil BION/BE-BASIC Detailed regional analysis on land-use (potentials, dynamics). LCA/EIA/economics/optimisation over time of advanced biobased supply chains. Methodology development & demonstration regional impacts water & biodiversity. Socio-economic impacts on regional level. Combined with (senior researcher capacity): Macro-economic analysis methods (LEI) Remote sensing (TUD) Stakeholder perspectives (TUD, univ A dam) In Brazil with CTBE and partners (ICONE, Embrapa, ESALQ, etc.); joint BIOEN project for Copernicus-UU is developing related efforts in: Southern Africa, Indonesia, Eastern Europe, Colombia, Argentina, SE-US and on sustainable forest management strategies.

31 Thanks for your attention For more information, see: - Sciencedirect/Scopus

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33 Land availability for biomass production under different scenario s in Mozambique F. van der Hilst, J.A. Verstegen, D. Karssenberg, A.P.C. Faaij, Spatio-temporal land use modelling for the assessment of land availability for energy crops illustrated for Mozambique, Global Change Biology Bioenergy, Volume 4, Issue 6, November 2012, Pages Floor van der Hilst, André Faaij, Spatio-temporal cost supply curves for bioenergy production in Mozambique. Biofuels, Bioproducts and Biorefining (BioFPR), Volume 6, Issue 4, July 2012, Pages

34 Land use developments Land use developments can not be predicted But future land use developments can be explored by means of a scenario approach. Economic Low technological change Low Business environmental as concern Low agricultural productivity usual Land Use Mozambiqu e Global High technological change High environmental Progressive concern High agricultural productivity and sustainable Regional Environmental

35 % of yield level Yield increase BAU Prograssive Weighted average yield 2000 = 100% weighted average based on share % in total production in hectares

36 x1000 ha Land requirements for crop production Business as usual scenario Progressive scenario Additional land required due to low Cropping Intensity (Area harvested/area cropland) other industrial fruit&veg roots&tubers cereals total BAU 2000 BAU 2006 BAU 2015 BAU 2030 P 2000 P 2006 P 2015 P 2030 The area required for food production increases due to higher intake per capita and population growth. The land requirements are smaller in the progressive scenario, due to higher yields and higher cropping intensity.

37 Land use allocation Land is allocated to a land use function when it is most suitable for that specific faction based on several land use change determinants Priority grid Current land use Soil suitability Population density Distance to cities Distance to water Distance to roads Nr of neighboring cells

38 Excluded areas For energy crops All of the excluded land areas Previous slide Land required for crops Land required for pasture Deforested areas Farm areas DUAT Community areas Excluded areas Community areas and DUAT Farm areas Deforested area grazing cropland Excluded areas general

39 2009 BAU Progressive BAU Progressive

40 2015 BAU Progressive BAU Progressive

41 2020 BAU Progressive BAU Progressive

42 2025 BAU Progressive BAU Progressive

43 2030 BAU Progressive BAU Progressive

44 Mha Land availability BAU scenario Progressive scenario very suitable suitable moderatly suitabble marginally suitable not suitable Development of land availability over time differentiated for suitability classes for the BAU scenario (left) and the Progressive scenario (right).

45 Cost breakdown of eucalyptus related to soil suitability.

46 Spatial distribution of feedstock potentials and costs (2015) BAU Progressive

47 Cost Supply curves for eucalyptus /GJ

48 Global (fuel) ethanol trade streams of minimum 1 PJ in (Source: Lamers et al., RSER, 15 (2011) )

49 Global (fuel) ethanol trade streams of minimum 1 PJ in [Lamers et al., RSER, 2012]

50 Global biodiesel trade streams of minimum 1 PJ in (Source: Lamers et al., RSER, 15 (2011) )

51 Global biodiesel trade streams of minimum 1 PJ in Sustainable (Source: Development and Lamers Innovation Management et al., in Faaij & Junginger (eds), forthcoming in 2013)