Introduction
Modelling approach
Results
Conclusions and perspectives
References
Marginal abatement costs of greenhouse gas emissions from EU-24 agriculture Greenhouse Gas Emission Abatement Workshop, Dublin
Stéphane De Cara
Pierre-Alain Jayet
INRA, UMR-210 Économie Publique (INRA/AgroParisTech), Grignon, France
April 2nd 2009
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MACs in the literature
Agricultural MACs in the literature 400
Abatement rate at 20 EUR/tCO2eq
200
Various modelling approaches: bottom-up, supply-side LP, partial and general equilibrium Various resolutions, scales and coverage
0
100
Frequency
300
Results from 15 studies:
0.0
0.2
0.4
Abatement rate MACs of agricultural GHG emissions in the UE24
0.6
0.8 AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MACs in the literature
Agricultural MACs in the literature 400
Abatement rate at 50 EUR/tCO2eq
200
Various modelling approaches: bottom-up, supply-side LP, partial and general equilibrium Various resolutions, scales and coverage
0
100
Frequency
300
Results from 15 studies:
0.0
0.2
0.4
Abatement rate MACs of agricultural GHG emissions in the UE24
0.6
0.8 AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MACs in the literature
Agricultural MACs in the literature 400
Abatement rate at 100 EUR/tCO2eq
200
Various modelling approaches: bottom-up, supply-side LP, partial and general equilibrium Various resolutions, scales and coverage
0
100
Frequency
300
Results from 15 studies:
0.0
0.2
0.4
Abatement rate MACs of agricultural GHG emissions in the UE24
0.6
0.8 AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MACs in the literature
Agricultural MACs in the literature 400
Abatement rate at 20 EUR/tCO2eq
200 0
100
Frequency
300
Variability is a key feature, both between studies and across space 0.0
0.2
0.4
0.6
0.8
Abatement rate
200
Modelling assumptions matter (equilibrium vs supply side models, mitigation options, negative-cost abatements,..)
0
100
Frequency
300
400
Abatement rate at 50 EUR/tCO2eq
0.0
0.2
0.4
0.6
0.8
Abatement rate
200 0
100
Frequency
300
400
Abatement rate at 100 EUR/tCO2eq
0.0
0.2
0.4
0.6
0.8
Abatement rate
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MACs in the literature
Agricultural MACs in the literature 400
Abatement rate at 20 EUR/tCO2eq
200 0
100
Frequency
300
Variability is a key feature, both between studies and across space 0.0
0.2
0.4
0.6
0.8
Abatement rate
200 0
100
Frequency
300
400
Abatement rate at 50 EUR/tCO2eq
0.0
0.2
0.4
0.6
0.8
Abatement rate
200
⇒ Focus on the variability of MACs across countries, regions, and (typical) farms. ⇒ Explore the implications for the effort sharing among Member States ⇒ Summarize the variability of MACs through response functions fitted on simulation results
0
100
Frequency
300
400
Abatement rate at 100 EUR/tCO2eq
Modelling assumptions matter (equilibrium vs supply side models, mitigation options, negative-cost abatements,..)
0.0
0.2
0.4
0.6
0.8
Abatement rate
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
The model
Modelling framework Updated and expanded version of the model described in De Cara et al. (2005) Input data: FADN (119 regions in the EU-24): accountancy data, yields, area, type of farming, altitude zone Typology: 1,307 typical farms, representative at the FADN region level (119), covering annual crop and livestock farmers Exogenous variables: Total area, prices, yields, baseline livestock numbers, variable costs, CAP-related parameters, technical coefficients (agronomic, livestock feeding, emission coefficients, etc.) 1,307 independent models: MILP, maximization of total gross margin subject to crop area, CAP, livestock feeding, etc. constraints Calibration: Based on EU-FADN 2004 data Output: Crop area mix, livestock numbers, animal feeding Emissions: IPCC-based relationships MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
The model
Emission coverage
Enteric fermentation (CH4 ): linked to animal feeding (for cattle) and animal numbers Manure management (CH4 and N2 O): linked to animal numbers Agricultural soil (N2 O): linked to fertilizer use, animal numbers (N from manure inputs), crop area (residues and N-fixing crops) Rice cultivation (CH4 ): rice area
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
The model
Key assumptions Area constraints: total area constraint, maximal area shares, balance between crops, between cereals and oilseeds, etc. Livestock demography (cattle): Demographic equilibrium between age classes, stable places constraints (±15% of initial livestock numbers). Livestock feeding: Protein and energy requirements by animal categories, maximum ingested matter Manure management: Constant nitrogen excretion rates by animal categories, fixed shares of each management system as in the NCs to the UNFCCC Fertilizer use: Total fertilizer expenditures from FADN, split by crop for each farm type, assumption on a composite fertilizer price by crop and by country. Fixed per-hectare N input by crop and by farm-type.
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MAC variability
Marginal abatement costs Base run: 2004 FADN data and corresponding CAP measures Introduction of a tax on (CO2 eq) emissions Abatement results solely from changes in crop area allocation, animal feeding, and animal numbers: No adoption of alternative management practices. No “cleaning” technology. Constant nitrogen application by crop and farm-type.
No price impact, no leakage (price-taker assumption). No structural changes (constant population of farmers). No change in the macroeconomic and policy environment. No monitoring/control costs
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MAC variability
Variability of marginal abatement costs Variability in abatement costs comes from differences in : Factors’ productivity Importance/type of livestock activities Degree of specialization, possibilities of substitutions Set of binding technical constraints (in particular animal feeding) Specific CAP provisions
We summarize variability into Maximum feasible abatement rate Price-response of abatements
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
MAC variability
Variability of marginal abatement costs Abatement rate 1 α1
α2
0
E(t) = E(0)(1 − r(t)) ( α1 (1 − e −τ1 t ) r(t) = (1+γ) α2 1 − e −τ2 t ⇒ These two models are estimated for each country, region, and typical farms
Tax (EUR/tCO2 eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
EU-24 abatement supply
0.6
Abatement supply (EU−24)
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Abatement rate
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6000
tax (EUR/tCO2eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
EU-24 abatement supply
0.6
Abatement supply (EU−24)
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α2 τ2 RSE
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0.5
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0.4
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0.3
Abatement rate
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Model 1: 0.6103∗∗∗ 0.0037∗∗∗ 0.0127
0.0
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2000
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6000
tax (EUR/tCO2eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
EU-24 abatement supply
0.6
Abatement supply (EU−24)
●
●
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α2 τ2 RSE
●
0.5
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0.4
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Abatement rate
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Model 1: 0.6103∗∗∗ 0.0037∗∗∗ 0.0127
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0
2000
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Model 2: α2 0.6196∗∗∗ τ2 0.0066∗∗∗ γ -0.1180∗∗∗ RSE 0.0059 ⇒ Price elasticity of emissions: -0.088 (40 EUR/tCO2 eq)
tax (EUR/tCO2eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
EU-24 abatement supply 0.4
Abatement supply (EU−24)
●
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0.3
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0.2
Abatement rate
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tax (EUR/tCO2eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
EU-24 abatement supply 0.4
Abatement supply (EU−24)
●
●
0.3
● ● ●
For a 10% reduction target, the corresponding price is about 41 EUR/tCO2 eq
0.2
Abatement rate
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0.1
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tax (EUR/tCO2eq)
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
Cost-effective effort sharing for a 10% target Cost−effective effort sharing, 41 EUR/tCO2eq (fitted) 0.25
Abatement rate
0.20
0.15
0.10
0.05
bel cyp cze dan deu ell esp est fra gbr hun irl ita ltu lux lva ned ost pol por suo sve svk svn UE1
0.00
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
Burden sharing agreement (2008) Effort sharing agreement (excl. ETS)
0.2
Abatement rate
0.1
0.0
−0.1
bel cyp cze dan deu ell esp est fra gbr hun irl ita ltu lux lva ned ost pol por suo sve svk svn UE1
−0.2
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
EU-24 results
MACs corresponding to national targets Effort sharing MACs (fitted)
200
MAC (EUR/tCO2eq)
150
100
50
bel cyp cze dan deu ell esp est fra gbr hun irl ita ltu lux lva ned ost pol por suo sve svk svn UE1
0
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
Variability of MACs at the typical farm level
Variability of MACs at the typical farm level
0.0
1.2
1.0 −0.1 0.8
−0.2
0.6
0.4 −0.3 0.2
bel cyp cze dan deu ell esp est fra gbr hun irl ita ltu lux lva ned ost pol por suo sve svk svn UE1
Max. abatement rate MACs of agricultural GHG emissions in the UE24
bel cyp cze dan deu ell esp est fra gbr hun irl ita ltu lux lva ned ost pol por suo sve svk svn UE1
−0.4
0.0
Price elasticity of emissions AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
Conclusions and perspectives
Conclusions and perspectives Lower marginal abatement costs than in previous similar studies A collection of response-functions that can be helpful in other analyses Variability of MACS
Econometric relationships between the parameters of MAC curves and typical farms’ characteristics Uncertainty analysis MACs are not the end of the story: we need implementable economic intruments Linkage between GHG abatements and LULUCF-related emissions/sinks
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
Introduction
Modelling approach
Results
Conclusions and perspectives
References
Conclusions and perspectives
References
De Cara, S., Houzé, M., and Jayet, P.-A. (2005). Methane and nitrous oxide emissions from agriculture in the EU: A spatial assessment of sources and abatement costs. Environmental and Resource Economics, 32(4):551–583. De Cara, S. and Thomas, A., editors (2008). Projections d’émissions/absorptions de gaz à effet de serre dans les secteurs forêt et agriculture aux horizons 2010 et 2020. Rapport final pour le Ministère de l’Agriculture et de la Pêche. INRA.
MACs of agricultural GHG emissions in the UE24
AES Workshop on Ag GHG emissions
