Carbon accounting of forest bioenergy: from model calibrations to policy options

Transcription

1 Carbon accounting of forest bioenergy: from model calibrations to policy options The Transatlantic Trade in Wood for Energy: A Dialogue on Sustainability Standards and GHG Emissions October 23-24, 2013, Savannah Georgia Patrick Lamers 1

2 Outline Carbon debt Concept Modeling options Example: South-Eastern US studies Options to deal with carbon debt Policy options Industry options Conclusions 2

3 Carbon debt Concept Modeling options 3

4 The question Time lag and volume of (pulse) emissions from biogenic C release and thus the overall climate mitigation potential of bioenergy Not whether biogenic C released during combustion is taken up again via forest growth sustainable forestry is a prerequisite! 4

5 Modeling options Framework choices 1.Reference point 2.Spatial boundary: stand vs. landscape (LS) 3.Empirical vs. theoretical forest inventory data: dynamic vs. fixed LS 4.Forest C vs. whole LCA 5.Displacement effects 6.C accumulation vs. climate dynamics 7.Albedo effect Scenario assumptions and parameterization 5

6 Framework choices 1. Reference point Defines a timelag Definition important Terminology varies Currently most common: Payback: the time until the site reaches its pre-harvest carbon level Parity: the time until the site reaches the same carbon volume as the reference scenario (e.g. protection) 2. Stand vs. Landscape Single plot Only one scenario at any given time The baseline remains static Multi plot Bundle of possible harvest plots (fixed or dynamic) Scenario applied to one patch; other plots continue growing or releasing carbon (decay) 6

7 Stand level: single plot Source: Eliasson et al

8 Landscape level Multiple similar plots of varying (here) or same age Average over all single plots Source: Eliasson et al

9 Typical stand level C balance Carbon density (Mg C ha -1 ) Harvest scenario 1. Harvest 2. Harvest Protection scenario (Baseline) Carbon debt1 t be1 x Cdebt2 t be2 t py Time (years) Carbon break-even points Carbon parity point Carbon payback time: t be1 + t be2 - x 9

10 Typical landscape C balance Carbon density (Mg C ha -1 ) Harvest scenario Protection scenario (Baseline) Carbon debt t be t py Time (years) Carbon break-even point Carbon parity point Compare e.g. to : Mitchell et al

11 Modeling options Framework choices 1.Reference point 2.Spatial boundary: stand vs. landscape (LS) 3.Empirical vs. theoretical forest inventory data: dynamic vs. fixed LS 4.Forest C vs. whole LCA 5.Displacement effects 6.C accumulation vs. climate dynamics 7.Albedo effect Scenario assumptions and parameterization 11

12 5. Displacement effects Market mediated effect (demandsupply interrelation) Regional, supraregional, global Competition for fiber may result in competition for land (and thus land use change) Graph: Pöyry in Dehue

13 6. C accumulation vs. climate dyn. Typically: Cumulative CO2 emissions C fluxes without climate responses Impulse response functions (IRF) Desribe the atmospheric decay of a pulse emission Important to understand the climate response to pulse emissions Can be used to describe temperature responses Additional measures / indicators: GWPbio: Global Warming Potential (biomass) (I)GTP: (Integrated) Global Temperature Change Potential Literature: Cherubini et al. 2011, 2012, 2013; Joos et al. 2012; Peters et al

14 6. C accumulation vs. climate dyn. Typically: Cumulative CO2 emissions C fluxes without climate responses Impulse response functions (IRF) Desribe the atmospheric decay of a pulse emission Important to understand the climate response to pulse emissions Can be used to describe temperature responses Additional measures / indicators: GWPbio: Global Warming Potential (biomass) (I)GTP: (Integrated) Global Temperature Change Potential Literature: Cherubini et al. 2011, 2012, 2013; Joos et al. 2012; Peters et al

15 7. Albedo effect Surface albedo: reflection coefficient describing the diffuse reflectivity of a surface Source: Cherubini et al

16 Modeling options Framework choices 1.Reference point 2.Spatial boundary: stand vs. landscape (LS) 3.Empirical vs. theoretical forest inventory data: dynamic vs. fixed LS 4.Forest C vs. whole LCA 5.Displacement effects 6.C assumulation vs. climate dynamics 7.Albedo effect Scenario assumptions and parameterization 1.Energy counterfactual 2.Forest baseline 3.Forest assortment 4.Forest biome 16

17 Parameterization I 1. Energy counterfactual GHG counterfactual! Direct replacement vs. grid mix? (Supra-) National vs. Regional? 2. Forest baseline No harvest? Protection? In-/excluding wildfire? Pulp and paper harvest? Other? Depends on (regional) economics at harvest Varies over time! Not static! 17

18 Parameterization II 3. Forest assortment Large variety of sourcing areas and feedstock US SE: pine thinnings, residues (e.g. tops) BC: residues, MPB Russia: commercial forestry residues, e.g. aspen 4. Forest biome Natural biogenic productivity Management adaptation (e.g. fertilization) Site sensitivity, e.g. regarding slash removal 18

19 Example South-Eastern US analyses 19

20 The case study area Loblolly pine plantations Planted post 1950 to generate fiber for timber, pulp & paper Rotation time: yrs Timber & housing market low since 2008 Increased thinning to save timber value Thinnings for P&P and wood pellet production (~25% of total harvest volume)

21 Three studies three outcomes Study Galik & Abt 2012 BERC 2013 Jonker et al Region VA, USA South-East USA GA, USA Model FORCARB Combination GORCAM Biome: tree species Temperate southern: pure Loblolly pine Temperate southern: 8 different forest type groups Temperate southern: pure Loblolly pine Assumed age at initial harvest point 22 Inventory data (0- ~130) 20 Rotation [years] 22 dynamic 20 Initial land conversion Managed to managed forest Managed to managed forest Natural to managed forest Managed to managed forest Harvest share for bioenergy Whole-trees Whole-trees Whole-trees Methodology/Framework Dynamic landscape Dynamic landscape Stand- and fixed landscape Forest data Geospatially explicit Geospatially explicit Representative, theoretical plots Applicability/Biomass use Real case, local use Real case, local use and export Real case, export only Post-harvest carbon cycling Yes Yes Yes Full LCA No Yes Yes Forest baseline Protection: no management / use BAU (timber only harvest) Protection: no harvest, natural regrowth Energy counterfactual - Fossil electricity (several) Fossil electricity (hard coal) Displacement modeling Yes: e.g. shift in forest area, type, management intensity Yes: dynamics regarding pulpwood use e.g. Indirect: shift in management insensity Carbon payback (reference: pre-harvest C level) Managed forest: 36 years Protection reference: 71 years (stand) (extended stand), < 2 years (landscape) Carbon Parity (reference: counterfactual) years years 21

22 Direct comparison 22

23 Options to deal with carbon debt Policy options Industry options Conclusions 23

24 Policy options to deal with C debt Panic option: black list (feedstock, region, etc.) Proper option: regional indicators (biome & forest assortment specific) Fundamental: requirement for sustainable forest management (SFM), i.e. replanting, site monitoring, etc. 24

25 More policy and industry options Policy level Incentivize the use of marginal and unused land Increase forest productivity (output) Increase supply chain efficiency (avoid losses) Integrate fiber production for energy and material (industrial ecology): cascading, residue utilization Industry / project level Establish new plantations on degraded/c-poor land carbon credit Increase forest productivity (e.g. fertilizer, weed control) within SFM limits Increase initial number of seedlings, early stand density, and the use of precommercial thinnings 25

26 Conclusions Biogenic carbon cycle vs. fossil emissions Sustainable forest management (for multiple purposes) is fundamental No single correct C debt accouting method Current C debate pays little attention to forestry economics & climate dynamics Policy options should be carefully weighed and regarded in the wider climate mitigation (policy) context 26

27 Results of a model comparison Source: Lamers & Junginger

28 C debt influencing factors Source: Lamers & Junginger

29 References BERC (2012) Biomass supply and carbon accounting for southeastern forests. (eds Colnes A, Doshi K, Emick H, Evans A, Perschel R, Robards T, Saah D, Sherman A), Montpelier, VT, USA, Biomass Energy Resource Center, Forest Guild, Spatial Informatics Group. Cherubini F, Peters GP, Berntsen T, Stromman AH, Hertwich E. CO2 emissions from biomass combustion for bioenergy: atmospheric decay and contribution to global warming. GCB Bioenergy 3(5): (2011). Cherubini F, Bright RM, Stromman AH. Site-specific global warming potentials of biogenic CO2 for bioenergy: contributions from carbon fluxes and albedo dynamics. Environmental Research Letters 7(4): (2012). Cherubini F, Guest G, Strømman AH. Application of probability distributions to the modeling of biogenic CO2 fluxes in life cycle assessment. GCB Bioenergy 4(6): (2012). Cherubini F., Bright R. M., Strømman A. (2013) Global climate impacts of forest bioenergy: what, when and how to measure? Environmental Research Letters, 8. Dehue, B. (2013). GHG accounting of forest bioenergy. JRC Workshop, Arona, Italy. Eliasson P., Svensson M., Olsson M., Ågren G. I. (2013) Forest carbon balances at the landscape scale investigated with the Q model and the CoupModel Responses to intensified harvests. Forest Ecology and Management, 290, Galik C. S., Abt R. C. (2012) The effect of assessment scale and metric selection on the greenhouse gas benefits of woody biomass. Biomass and Bioenergy, 44, 1-7. Harper, R. & Turner, J. (2013). Wood Supply Where s the Pulpwood? (Potential Diameter-class Imbalance in Southern Pines). Forest Bioenergy Conference, USDA Forest Service. 29

30 References Jonker G.-J., Junginger M., Faaij A. (2013) Carbon payback period and carbon offset parity point of wood pellet production in the Southeastern USA. GCB Bioenergy, in press. Joos F, Roth R, Fuglestvedt JS, Peters GP, Enting IG, von Bloh W, et al. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. Atmospheric Chemistry & Physics Discussions 12(8): (2012). Lamers P., Marchal D., Heinimö J., Steierer F. (forthcoming) Woody biomass trade for energy. In: International Bioenergy Trade: History, status & outlook on securing sustainable bioenergy supply, demand and markets. (eds Junginger M, Goh CS, Faaij A) pp Page. Berlin, Springer. Lamers P., Junginger M. (2013) The debt is in the detail: a synthesis of recent temporal forest carbon analyses on woody biomass for energy. Biofuels, Bioproducts and Biorefining, 7, Mitchell S. R., Harmon M. E., O'Connell K. E. B. (2012) Carbon debt and carbon sequestration parity in forest bioenergy production. GCB Bioenergy, 4, Peters GP, Aamaas B, T. Lund M, Solli C, Fuglestvedt JS. Alternative Global Warming Metrics in Life Cycle Assessment: A Case Study with Existing Transportation Data. Environmental Science & Technology 45(20): (2011). 30

31 Thank you! +1 (303)