Introduction: biomass for energy

Transcription

1 A relevant LCA methodology adapted to biomass-based products : GHG emission reduction and energy production in agriculture, forestry, aquaculture and mariculture: potentials and impact Anthony Benoist Dominique Dron Assaad Zoughaib

2 Introduction: biomass for energy Promotion of biomass for energy purposes Two main potential advantages Mitigating climate change Reducing dependence on fossil materials Need to assess the biomass uses Life Cycle Assessment (LCA) 2

3 Performances of biomass uses Source: Quirin et al., 2004 Wide ranges Sugar beet ethanol: GHG savings from 3 to 11 tco 2-eq /ha/yr Rapeseed biodiesel: GHG savings from 0.5 to 3 tco 2-eq /ha/yr Then Why such a discrepancy? How to reduce it? 3

4 LCA methodology Life Cycle Assessment Born in the early 1970s Fundamental principle: «From cradle to grave» Standardization Late 1990s Reviewed in 2006 ISO 14040:2006 and ISO 14044:2006 Four execution phases I - Goal and scope definition II - Inventory analysis III - Impact assessment IV - Interpretation 4

5 Phase 1: Goal and scope definition Compilation of assumptions and choices among which: System boundaries Functional unit Biofuel production: energy content (MJ, BTU,..) Wheat production: weight? protein amount? 5

6 Phase 2: Inventory analysis Providing the information needed of the system flows Main concern: Data quality: geographical and time coverage Main issue: Allocation: based on an indicator, or system expansion Inputs System A System B Outputs Equivalence rule P 0 P 1 P 1 ' 6

7 Phase 3: Impact assessment 7

8 Phase 4: Interpretation Giving an answer to the issue defined in the goal and scope phase LCA results Results reliability and accuracy Assumptions and data consistency Sensitivity and uncertainty analysis 8

9 How does this help? Case studies: wheat EtOH and rapeseed ME Amount and transparency of results Important production in Europe Indicators: primary energy consumption and GHG emissions 9

10 LCA parameters influence: Objectives and methodology Sensitivity analysis for each LCA parameter Input data Assumption Studies: CCPCS 91, Ademe 02, GM 02, JRC 03 and JRC 07. Categories of input data influence Local specificity Approximations Uncertainties (N 2 O) 10

11 LCA parameters influence: Results Allocation rule: 48 to 82 % System boundaries (energy consumption) Local conditions: 24 to 118 % N 2 O uncertainties 11

12 LCA parameters influence: additional shortcomings Approximations underestimation Due to the analysis methodology Common assumptions: agricultural buildings and machinery (Audsley et al., 2003) Soil integration Substitution of grasslands by cultivation area, France: soil carbon loss of 92 ± 26 tco 2 /ha (Arrouays et al., 2002) Wheat EtOH and rapeseed ME GHG savings: 0.5 to 4 tco 2 /ha (Quirin et al., 2004) 12

13 LCA for biomass-based products: perspectives Objective: reducing biomass-based products LCA results discrepancies Generic difficulties: methodological issues Influence up to 80 % Certification, European directive 23 rd January 2008 Specific difficulties: Limiting geographical coverages (influence beyond 100 %) Integrating the most accurate data available on N 2 O fluxes (influence from 30 to 70 %) Improvement of the LCA methodology Soil quality integration 13

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