EP, 25 January 2012

1. Existing methodologies and best practices on assessing ILUC Luisa Marelli European Commission – DG Joint Research Centre (JRC) Institute for Energy and Transport EP, 25 January 2012

2. Outlines 1. How is ILUC measured and how do models work? 2. GHG emissions from different models - uncertainties - impact on food consumption 3. US legislation 4. Other environmental effects 5. Conclusions

3. How to measure ILUC? 2020 “Policy” Scenario with extra biofuels • There is only one reality • So you cannot know what would have happened without biofuels Compared with 2020 “Baseline” scenario without extra biofuels Models do not compare differences between NOW and 2020 ILUC cannot be measured directly….. Agro-economic models are used

4. Crops for biofuels come from 3 sources Economic models: Increased crop demand due to biofuels crop price increases less consumption for food higher crop yields crop area expansion Land emissions models emissions from land use change

5. Where does ILUC occur? Results from different models (JRC model comparison) Outside the EU Within the EU Biodiesel Ethanol For EU biofuels most land use change is outside EU

6. ILUC GHG emissions differ across models but are significant in all cases (can negate GHG savings from biofuels) Commission’s reference results

7. JRC vs. IFPRI land emissions models • IFPRI 2011 economic results with IFPRI land emissions model: • IFPRI 2011 economic results with JRC land emissions model: • similar total results: • Peat-drainage emissionsaccount for about 50% of total EU biofuels ILUC emissions

8. ILUC depends on feedstock: Oilseeds GHG > cereals > sugar crops

9. Uncertainty, but certainly above zero Frequencies distribution on ILUC emissions • US Corn ethanol [Plevin et al, 2010] • IFPRI, 2011

10. Impact of preventingforestconversion • Even if we prevent forest conversion globally, GHG emissions are still significant! No new cropland on forest

11. Fixing food demand increases LUC How economic models work: By products replace feed crops LUC Land area Reduced food demand LUC increase with fixed food demand land requirement to meet increased feedstock demand due to biofuels lower productivity of new cropland Increased yields Net LUC with reduced food demand

12. Big ILUC credits from less food • All economic models incorporate iLUC savings from reduced food demand. • Without reduction in food consumption, crop production would increase from 10% to 220%, according to feedstock type/model (see figure in backup slide) • IFPRI: 29% increase, but stopping cereals replacing fruit and vegetables (reduction in food quality) would increase by 300-400%) • So...... • Either remove this “iLUC credit” from a reduction in food demand, • OR accept that part of the biofuels “benefit” is people eating less

13. US legislation – ILUC Emissions Soy Sugar Cane Corn Palm Oil

14. Other environmental effects of ILUC Impacts on biodiversity 2. JRC roughly estimated change in species abundance (an indicator for biodiversity): 1. IFPRI says ILUC happens on these land types: • Results • On the land converted by ILUC, • on average there may be up to ~85% loss of biodiversity

15. Conclusions • There is no scientific support for believing ILUC = 0 • Even with uncertainties, ILUC is above zero for all biofuel feedstocks: from ~10 to ~90 gCO2eq/MJ, according to feedstock type (even more, according to US studies ) • For EU biofuels most ILUC occurs outside the EU Without savings in food consumption, models would give higher ILUC emissions • ILUC is not only GHG emissions: the impact on biodiversity could be potentially high. California and US already account for ILUC

16. THANK YOU FOR YOUR ATTENTION All JRC studies available at: http://re.jrc.ec.europa.eu/bf-tp/index.htm

17. ADDITIONAL SLIDES • Backup/supporting material

18. JRC and IFPRI land use models Main differences in GHG calculations IFPRI-JRC • Land use factor for soil emissions • IFPRI: all crops are considered as ‘annual crop’ • JRC: oil palm and sugar cane considered as ‘perennial’ and ‘semi-perennial’  perennial crops bring less disturbance to the soil than annual crops • Peatland emissions • IFPRI: emission factor of 55 tCO2ha-1yr-1 • JRC: updated emission factor 86 tCO2ha-1yr-1 (following JRC expert consultation on ILUC – Nov. 2010, recent literature publications and experimental studies)

19. ILUC increases if food consumption is constant – (results of JRC model comparison) Total feedstock with food constant Feedstock requirement reported by models Cereals replacing fruits/vegetables +87% +15% +59% +87% +76% +20% +220% +48% +101% +92% +37% +12% +21% +78% +19% +29%

20. Biodiversity impact Estimation of potential Impacts on biodiversity 1. Additional cropland from IFPRI “central” scenario (~17,000 km2): • 42% from pasture • 39% from managed forest • 3% from primary forest • 16% from savannah and grassland 2. Indicator for biodiversity: Mean Species Abundances (MSA), from Global Biodiversity Model (GLOBIO3) Mean abundance of original species in undisturbed ecosystems

21. Biodiversity impact 3. IFPRI land use classes adapted to GLOBIO3 classes:

22. Results – Biodiversity 4. Estimation of total biodiversity loss: Where: MSAi = Mean Species Abundance of land use type i %i = % of land conversion according to IFPRI scenario MSAca = Mean Species Abundance of cultivated area RESULTS: This rough estimation shows that the land use change foreseen by IFPRI may lead to a decrease in MSA index of ~85% on the converted land N.B. this is a preliminary estimation of the potential risks for biodiversity. More work foreseen for 2012.

23. “Historical” approach is oversimplified but verifiable Consequential approaches are very subjective Models Already shown

24. Alternative ILUC approaches: historical

25. Problems with E4tech’s EU-wheat scenario • 1. In the most “ILUC negative” scenario, E4tech assume that EU wheat will come from abandoned land in EU. But all economic models show that most crop area expansion caused by EU ethanol demand would be outside EU + it’s unclear how E4tech concluded that EU cropland would be abandoned in the baseline 2. That land would otherwise sequester carbon as it reverts to nature. But E4tech underestimate the lost carbon sequestration on this land because of a reporting error by Winrock International 3. . Furthermore E4tech worked out too small an area of EU abandoned land by assuming it has EU-average wheat yield Historical data shows yields on abandoned EU cropland are much less than average EU yield

26. 4. E4tech assume that most of the extra wheat in EU will come from yield increase and not from area increase. The ratio of extra yield to extra area is fixed (by historical precedence). But they set no limit to how much yield can increase: if they would double the wheat demand, they would automatically almost double the wheat yield. For EU wheat scenario E4tech get a 12% higher average wheat yield in EU, compared to baseline in the same year. That would require an incredible price increase due to biofuels, according to all published estimates of yield elasticity. Looked at another way….E4tech roughly doubles the annual rate of yield increase in the EU ethanol scenario. This would mean at least double the rate of investment in farm improvements and research. That would only follow if the expected financial return would also more-than-double. That financial return is proportional to crop price, so the wheat price would have to more than double (due to EU ethanol) to make this possible.

27. 3rd E4tech problem: Marginal yields are much less than average in EU • the countries which lost most crop area 1997-2007 averaged ~65% of EU-average yield [according to EUROSTAT data] • National data (UK 2004 farm survey) shows cereals yield on marginal UK farms is < 64% of UK average wheat yield. • The worst field on a farm has on average 63% of the average farm yield. (English farm survey 2004) • 0.65 x 0.64 x 0.63 = 0.18 • Including any 2 of these 3 factors would more than double the amount of “abandoned land” required, and reverse the E4tech conclusion for EU-wheat -ethanol.