Environmentally-Adjusted Total Factor Productivity: the Case of Carbon Footprint. An application to Italian FADN farms

Transcription

1 Environmentally-Adjusted Total Factor Productivity: the Case of Carbon Footprint. An application to Italian FADN farms Silvia Coderoni, Roberto Esposti and Edoardo Baldoni Department of Economics and Social Sciences Università Politecnica delle Marche OECD EXPERT WORKSHOP. Measuring environmentally adjusted agricultural total factor productivity (EATFP) and its determinants December 2015 OECD Headquarters, Paris

2 Outline of the presentation Research objectives FADN sample Why GHGs? GHG estimation at farm level using FADN Some preliminary results Next steps 2

3 Research objectives Challenge faced by European agriculture: how to increase production in order to respond to growth in global food demand while preserving environment?. EU Innovation Partnership for Agricultural productivity and Sustainability (2012) Unique indicator of these joint performances: Environmentally-Adjusted TFP (EATFP). Explore and illustrate the feasibility of EATFP calculation in the specific case of Italian agriculture Micro level with Italian FADN farms 3

4 The FADN sample The Farm Accountancy Data Network (FADN) is an instrument for evaluating the income of agricultural holdings. The FADN is the only source of microeconomic data that is harmonised. Rete di Informazione Contabile Agricola (RICA) is the Italian FADN Dataset with very detailed accounts on: Structure, assets and location of sampled holdings; Quantities and Value of output; Quantities, Value and technical features of input used. Balanced panel extracted from RICA: constant sample of 6,542 farms observed over years

5 MFP measurement Multifactor productivity relative levels as disembodied technological change in the form of capital-labour-energy-materials (KLEMS) MFP Relative levels of Regional and Provincial MPF computed using the Fisher Index Space-time index obtained using a temporally-fixed graph (Hill, 2003), applying an EKS transform in the first year of the panel Econometric estimation of a profit function with stochastic frontier and explicit spatial correlation among efficiency/error components Inspection of the impact of spatial aggregation in MFP measurement Measurement of KLEMS MFP for all the EU FADN regions using farm-level FADN data 5

6 Why Greenhouse gases? Relevance of the climate change mitigation objectives in international and EU arena (both for climate and agricultural policy) Europe 2020: non ETS sectors (including Agriculture): -10% compared with 2005 levels. Europe 2030 targets non ETS -30% (1990); Europe 2050 agri GHG emissions -42/49%. The role of agriculture in climate change mitigation agenda is going to increase up to 2050 Well-established international criteria and protocols to achieve a proper-farm level indicator of this environmental performance, the farm-level Carbon Footprint. Embodies different environmental pressures in a unique indicator of a pure public bad (global warming) 6

7 WHAT and HOW to measure What Emissions on which the farmer has a direct control No Life Cycle Assessment How Intergovernmental Panel on Climate Change (1997, 2000, 2006) methodology adapted at farm level (Ref. Coderoni et al. 2013), Cross cutting approach One synthetic farm-level emission measure: the farm Carbon Footprint (CF) IPCC SOURCE GHG category 4A Enteric CH 4 Fermentation 4B Manure N 2 O, CH 4 Management 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 5C Grassland CO 2 7

8 GHGs emissions at farm level Generally speaking IPCC approach is based on a linear relationship between activity data (AD) and emission factors (EF): e= emissions, K th farm, l th emission source AD: FADN e EFl AD k k, l l 1 EF: tier from 1 to 3 (less uncertainty). We use national EF (ISPRA) or IPCC default emission factors; 5 categories of carbon footprint: livestock, fuel, cultivation, land use, fertilizers 8

9 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 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 Leaching and run-off CF fertilizer/ CF crops Fertil. expe. & animal CO 2 Energy CF Fuel Fuel expenditure CO 2 Forest land CF Land use UAA CO 2 Cropland CF Land use UAA CO 2 Grasslands CF Land use UAA 9

10 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 (LU: -,emissions; +, remuvals) 10

11 Farm-level CF evolution: farm size Var (%) Economic Size: ESU 4 25,2 29,3 30,0 32,1 32,6 33,7 ESU ,0 132,7 134,3 120,3 124,8 0,8 ESU>=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 ton CO 2e per farm avg. Source: own elaborations 11

12 Strengths & Weaknesses Strengths Only direct emissions at farm gate on which farmer has a direct control Evaluate policies implemented at farm level EU level dataset and comparable to UNFCCC estimates Weaknesses National average EF Few mitigation measures (scale and composition effect). Lack of information to estimate GHGs within FADN Solution: data from FADN sample Toward a farm level (t2) EF (very important for-also international-comparisons): Available data: Data on manure managment system, energy, milk produced, N applied to soils, Data needed: grazing, aeration for rice, milk fat content, portion of cow giving birth, feeding practices, soil type*, hystosoils*, timing of conversion (*obtainable) 12

13 Next steps Unique indicator: EATFP 3 main issues: 1. Badput 2. Price (negative) 3. Joint-output associated with some productions: not eliminable Parametric and non-parametric techniques Analise the spatial dependence both in TFP and EATFP calculations 13

14 Thank you Silvia Coderoni 14