Farm-level GHG emissions and the CAP. An econometric assessment on an Italian panel.
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1 Farm-level GHG emissions and the CAP. An econometric assessment on an Italian panel. Silvia Coderoni 1 and Roberto Esposti 1 1 Department of Economics and Social Sciences, Università Politecnica delle Marche, Ancona, Italy 2015 AIEAA Conference, "Innovation, productivity and growth: towards sustainable agrifood production" Ancona, June 2015
2 Policy framework Eu policy Europe 2020 Europe 2030 Europe 2050 Emission reduction target (baseline) Sectors (baseline) -20% (1990) ETS-Emission Trading System: -21% (2005) Non ETS (including Agriculture) : 10% (2005). Italy -13% -40% (1990) ETS (-43% ) Non ETS (including Agriculture): -30% (2005) LULUCF-Land Use Land Use Change and Forestry [Dec. n.529/2013/ue] -80/95% (1990) Agri GHG emissions -42/49% HOW Incentives: Common Agricultural Policy climate change challenge both in pillar I and II Ancona, June th AIEAA Conference 3
3 Research question Agricultural GHG emission in Italy (and in Europe) have declined from 1990 to 2012 (EEA, 2014). Do we observe a decline at the farm level? Why? Define a methodology for the reconstruction of agricultural GHG emissions (i.e. Carbon Footprint-CF) at farm level and of its evolution over time. Interpretations of the differences observed across farm typologies and territories and, above all, of the farm-level CF evolution over time with specific reference to the possible role of the CAP: the 2003 Reform of its First Pillar those Second Pillar s measures targeted to activities and practices that have a direct impact on the CF Ancona, June th AIEAA Conference 4
4 The FADN sample Constant sample of farms yearly observed over the pre and post-2005 period: balanced panel extracted from the FADN- Farm Accountancy Data Network (RICA) database: 6,542 farms observed over years with all the needed information to compute the respective Carbon Footprint (CF) Capture the effect of policy measure on production choices and consequently (joint badput) on GHG emissions. Ancona, June th AIEAA Conference 5
5 GHG emissions at farm level What: emissions on which the farmer has a direct control/makes choices (no Supply Chain/Life Cycle Assessment) How: using Intergovernmental Panel on Climate Change (1997, 2000, 2006) methodolody adapted at farm level (Coderoni et al., 2013), FADN activity data & ISPRA/IPCC default emission factors one synthetic farm-level emission measure: the farm CF IPCC CATEGORY SOURCE GHG 4A Enteric Fermentation CH 4 4B Manure Management N 2 O, CH 4 4C Rice CH 4 4D Agricultural Soils N 2 O, CH 4 1A Energy CO 2 5A Forest land CO 2 5B Cropland CO 2 Ancona, 5C June 2015 Grassland 4th AIEAA Conference CO 6 2
6 5 Carbon Footprints categories GHG emission sources and the respective FADN activity data Emission sources CF category FADN data N 2 O manure management CF livestock Animal numbers CH 4 manure management CF livestock Animal numbers CH 4 enteric fermentation CF livestock Animal numbers CH 4 rice crops CF crops Rice area (UAA) N 2 O agricultural soils Various Direct emissions Use of synthetic fertilisers CF fertilizer Fertilisers expenditure Biological N fixation CF crops N-fixing crop area Crop residues CF crops Crop area (UAA) Indirect emissions Atmospheric deposition CF fertilizer/ CF crops Fertil. expe. & animal numbers Leaching and run-off CF fertilizer/ CF crops Fertil. expe. & animal numbers CO 2 Energy CF Fuel Fuel expenditure CO 2 Forest land CF Land use (A, B) UAA CO 2 Cropland CF Land use (A, B) UAA CO 2 Grasslands CF Land use (A, B) UAA Ancona, June th AIEAA Conference 7
7 evolution of the 5 CF categories CF category Var (%) CF Fuel 25,9 27,1 29,8 31,4 32,6 88,6 CF Crops 14,1 14,3 14,3 14,8 14,9 53,9 CF Fertilizers 45,0 57,6 58,8 58,3 64,8 2,1 CF Livestock 99,2 100,4 101,0 101,6 100,0 0,07 CF Land Use A -3,3E-03-3,3E-03-3,1E-03-3,1E-03-3,1E-03 0,02 CF Land Use B 5,8E-03 6,0E-03 6,0E-03 6,0E-03 6,0E-03 0,04 CF Total A 184,1 199,3 203,9 206,1 212,4 14,3 (563,7) (584,5) (623,7) (642,9) (644,3) CF Total B 184,1 199,3 203,9 206,1 212,4 14,3 (563,7) (584,5) (623,7) (642,9) (644,3) ton CO 2e per farm avg.; st. dev. in parenthesis. A: ISPRA IEF; B: JRC based IEF. Source: own elaborations (-;emissions) Large heterogeneity, but some robust evidence emerges Land Use does not matter much (at least with IPCC approach) Only CF livestock (and LU) is almost stable
8 Total farm-level CF evolution: farm size Farm typology: Var (%) Economic Size: ES ,2 29,3 30,0 32,1 32,6 33,7 ES ,0 132,7 134,3 120,3 124,8 0,8 ES>=7 887,4 932,8 992,1 965,4 989,2 0,7 UAA: UAA < 10 ha 46,6 53,3 54,1 53,7 53,9 29,2 UAA ha 145,2 157,8 157,3 158,1 166,9 0,8 UAA >50 ha 719,0 762,7 784,0 791,7 804,9 0,5 Correlation coefficient UAA- CF 0.2 ton CO 2e per farm avg. Source: own elaborations Ancona, June th AIEAA Conference 10
9 Total farm-level CF evolution: farm typologies Farm typology: Var (%) Crops 149,2 168,4 174,4 175,4 185,1 1,0 Permanent Crops 44,4 59,8 62,1 64,2 70,0 1,3 Livestock 425,2 434,5 439,8 442,8 442,8 0,1 Mixed crops and livestock 196,2 211,6 216,4 219,9 229,2 83,3 ton CO 2e per farm avg. Source: own elaborations Ancona, June th AIEAA Conference 11
10 The potential role of the CAP first pillar payments (FPP/GPV) second pillar payments (i.e Programming Period): low impact, organic, afforestation Multinomial logit model: 4 factor categories Geographical information Size (UUA, ES) Production mix Policy variables Ancona, June th AIEAA Conference 12
11 Estimation results 3 groups of farms (threshold 5%): Marginal effects p 1 - GHG decrease (no. 1801) 1 nd pillar payments * 2 nd pillar payments * 2 - GHG stable (no. 516) 1nd pillar payments nd pillar payments GHG increase (no. 4225) 1nd pillar payments -7,00E-05 * 2nd pillar payments * Ancona, June th AIEAA Conference 13
12 Next steps Ordered logit model: as estimation show «coherence» between choices, seems usefull to test the probability associated with «ranked» choices Two steps choice tree (nested logit): Farmj Decrease stable increase Input mix output Input mix output Ancona, June th AIEAA Conference 14
13 Some concluding remarks Novelty of the paper: farm-level GHG emission calculation A methodology based on international standards applied to an Italian FADN balanced panel This allows assessing: Whether/How emission performance evolves over time at the farm-level What drives this evolution: what is the impact of the CAP on production choices and so on GHG level Still several data and methodological issues to be fixed (e.g. land use changes) but some evidence emerges: Apparently farms more supported by 1 st pillar payments show better emission performances Ancona, June th AIEAA Conference 15
14 Thank you Silvia Coderoni Ancona, June th AIEAA Conference 16
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