Transcript A two-model comparison of GHG abatement costs in Baden-Württemberg INRA

A two-model comparison of GHG
abatement costs in Baden-Württemberg
INSEA Meeting – 20-21 Sept 2004
UMR Economie Publique
INRA-INAPG
Stéphane De Cara
Daniel Blank
Florence Carré-Bonsch
Pierre-Alain Jayet
INRA
UHOH
ENVICARE
INRA
Why a model comparison?
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Two models based on similar principles but different
in their modeling choices, scales, etc.
No observation data for GHG abatement costs:
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Cross-validation of the models
What comes from the complexity of the emission processes
and from the modeling choices?
Both models provide emissions and abatement costs
estimates (so far restricted to agricultural emissions)
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Running both could help define acceptable ranges of
estimates
Prepare the next step: including carbon offsets through
agro-forestry, energy crops, and carbon-sequestering
agricultural practices
Why Baden-Württemberg?
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BW is interesting as a case-study region
beyond the fact that it lies at the intersection
between the two models:
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GHG emissions from agriculture about 5 Mt CO2eq
(65 Mt for Germany)
Diversity of farming systems, natural conditions,
crop and livestock activities makes it a good test
for aggregation and typology
Previous calculations by INRA show that marginal
abatement costs in BW are lower that the EUaverage
AROPA-GHG estimates of
marginal abatement costs
800,000
350
14% 325
700,000
300
Abatement (ktCO2)
BW
500,000
Cumulative initial emissions
12% 275
600,000
250
10% 225
Germany
400,000
Tax on emissions = 55.84 EUR/tCO2eq; EU abatement rate= 8%
200
FADN regions
Farm-types
8% 175
150
6% 125
300,000
EU-15
200,000
4%
100
75
50
100,000
2%
0,000
0%
25
0
0
10
20
30
40
50
Tax (EUR/tCO2)
60
70
80
90
100
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
Relative abatement rate
60%
65%
70%
75%
80%
85%
90%
A fundamental difference…
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Scale: EU-15 (AROPA-GHG) vs BadenWürttemberg (EFEM)
As a result:
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EFEM provides a more detailed description
of the BW agriculture:
AROPA-GHG tends to favor a ‘generic’
modeling approach
Consequences for abatement costs
estimates?
Scale and resolution
AROPA-GHG
EFEM
Common principles
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Mathematical programming based on a farm-type
approach
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The results share the standard LP properties
Similar modeling of the economic behavior (microeconomic approach, rationality, price taker agents)
Supply-side, short/medium run models:
Fixed input/output prices (diff from CGE or PE models)
 Fixed capital endowments
 Fixed structural determinants (number of farms, distribution
of farming systems, technical constraints, etc.)
=> Facilitate the comparison and the interpretation of the
results
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Major differences
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AROPA-GHG:
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Mostly relies on EU FADN data
Generic-oriented approach (adaptable to 101
FADN regions)
less FT for BW (aggregation), but more activities
per FT (more flexibility within a farm-type)
EFEM:
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Sub-regional information
Crop and livestock activities combined with a set
different input intensities or performance levels
(more flexibility with respect to input use and
productivity)
Key-items for the comparison
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‘Technical’ items
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Agricultural practice modeling
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General modeling approach
Data sources and parameters estimations
Farm-type characterization
Aggregation method and extrapolation factors
Fertilization
Manure management
Animal feeding
Emission accounting and emission coverage
General modeling approach
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The general modeling approach is similar:
max  k (x k )  g k  x k
xk
s.t. A k  x k  z k ; x k  0
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Activities are more detailed in EFEM (combination of
activities and performance levels)
BW-specific policy instruments in place in EFEM
No explicit account of labor requirement in AROPAGHG
Data sources and parameters
estimation
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AROPA-GHG
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For the most part, the EU FADN
Econometric estimations in order to get variable costs by
crop
Some alternative sources (technical workbooks for animal
feeding, IPCC GPG)
Calibration of parameters coming from expert knowledge
EFEM
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A wider range of data sources, some of them BW-specific
Estimation procedures not documented
Calibration?
Farm-types characterization
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AROPA-GHG
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11 farm-types aggregated from the FADN sample
Additional information from FADN:
Altitude (1: plain, 2: hills > 300m, 3: mountains
>600m)
Farming systems (FADN Classification)
Only selects annual crop or livestock farms (no
permanent crops)
EFEM
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5 farm-types in 8 sub-regions (27 FT)
Rather specialized farms (less flexibility)
Aggregation and extrapolation
factor
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Very important for dealing w/
aggregation bias
AROPA-GHG
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FADN extrapolation factors (assumes FADN
sample is representative)
EFEM
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Re-computation of the extrapolation factors
Extrapolation factors in EFEM
Total BW
Reg1
Reg2
Reg3
Reg4
Reg5
Reg6
Reg7
Reg8
Crop Farm
4289
(2567)
2862
(1143)
235
(91)
2428
(1159)
12
(-)
2338
(1009)
1703
(1030)
2473
(1942)
16340
(8942)
Forage
Growing
1695
(1073)
2057
(1128)
4473
(1837)
4649
(2122)
2149
(1185)
4968
(3172)
4588
(2393)
1807
(1494)
26386
(14404)
Intensive
Livestoc
k
309
(405)
135
(-)
76
(42)
355
(311)
30
(-)
1084
(835)
301
(343)
1222
(823)
3512
(2759)
Permanent
Crop
5917
(2636)
9624
(3197)
454
(175)
83
(-)
36
(34)
201
(222)
332
(159)
589
(102)
17236
(6526)
Farm Type
Fertilization
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AROPA-GHG
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For each crop
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Constant yield
A composite fertilizer (combination of 2 exogenously
chosen types of fertilizers)
N input is derived from FADN expenditure and
econometric estimations
EFEM
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Combination of cropping activities and inputintensity (N use)
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A discrete set of input-use (akin to Schneider’s approach)
More flexible
Manure management
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AROPA-GHG
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Uses IPCC / NC values for quantities of
manure produced and managed
EFEM
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Specific constraints on manure spreading
and storage
Management / storage is endogenous (?)
Animal feeding
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AROPA-GHG
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Energy and protein requirements taken from
technical sources
Energy and protein contents of purchased
feedstuffs and on-farm consumption
Fixed milk- and meat-yields
Cattle demography
EFEM
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Same as for fertilization (discrete choice of yields
and input use)
Cattle demography?
Emission accounting
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Emissions beyond the agricultural sector
in EFEM
Emission factors differ for some
categories (methane from enteric
fermentation)
Baseline emissions by sources
Baseline emissions by source (GWP: CH4=23, N2O=296)
10000
9000
8000
7000
CO2 animal production
CO2 energy animals
CO2 purchase feedstuff
CO2 Pflanze
UHOH: 5092 ktCO2eq
INRA: 5115 ktCO2eq
CO2 plant dryining
CO2 energy plants
CO2 fertilizer prod.
N2O fertilizer production
6000
ktCO2
other N2O
5000
4000
3000
N2O Agr. soils Indir. Emiss.
Subtot
N2O Agr. soils Animal
production
N2O Agr. soils Dir. Emiss.
Subtot
N2O Manure management
Total
CH4 Manure
management
Leaching and run-off
Atm. deposition
Crop Residue
Anim. wastes applied to soils
Synth fertilizers
Total
Swine
2000
1000
Dairy
Non-dairy
CH4 Enteric ferment. Total
Dairy
0
UHOH Emissions
INRA (1) Emissions
INRA (2) Emissions
Common
emission
coverage
Abatement supply
18%
16%
14%
Abatement (ktCO2)
12%
10%
8%
6%
4%
2%
0%
0
10
20
30
40
50
60
70
80
90
100
Tax (EUR/tCO2)
EFEM
AROPA-GHG
Cost-effective abatements
Abatements
600
ktCO2eq
500
400
300
200
100
0
AROPA-GHG
10
EFEM
AROPA-GHG
EFEM
20
AROPA-GHG
50
tax (EUR/tCO2eq)
CH4 Enteric ferment. Total
N2O Manure management Total
N2O Agr. soils Indir. Emiss. Subtot
CH4 Manure management Total
N2O Agr. soils Dir. Emiss. Subtot
N2O Agr. soils Anim. Prod.
EFEM
Comparing the results at a
compatible resolution
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CLC information (cropland, grassland,
and non-agricultural land)
Altitude information (%age of the
number of farms by altitude)
=> CLC resolution
Aggregate at the 8 EFEM sub-region
Altitude
GHG emissions from AROPA-GHG
at CLC resolution (250mx250m)
GHG emissions from AROPA-GHG
at CLC resolution (250mx250m)
Total emissions of CH4
(tCO2eq/ha)
Total emissions of N2O
(tCO2eq/ha)
GHG emissions from AROPAGHG at NUTS3 resolution
Total emissions of CH4
(tCO2eq/ha)
Total emissions of N2O
(tCO2eq/ha)
Abatement of GHG emissions
AROPA-GHG (20 €/tCO2e)
Abatement of GHG emissions
AROPA-GHG (50 €/tCO2e)
AROPA-GHG/EFEM: Discussion
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Differences in areas
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Aggregate baseline emissions:
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Close but somewhat different in terms of sources repartition
and per-ha emissions
Abatement supply
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Different farm-type characterization (permanent crops)
Different for 0 to 30 EUR/tCO2eq
Larger range on prices (by construction)
Convexity for the lower taxes in EFEM
Spatial distribution of emissions
What’s next?
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Should we further refine of regional
disaggregation?
Extend the spatial disaggregation at the
EU-15
Carbon offsets:
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What can we include in both models
(forestry, energy crops, management
practices,…)?
AROPA-GHG/EFEM Comparison:
Propositions for further work
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Common basis for comparing abatement costs and emission
sources:
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Common list of sources
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Common GWPs
Estimating MAC by source?
Estimating cross-derivatives?
Focusing on abatements for 10€, 20€, 50€/tCO2eq
Spatial downscaling of INRA results using UHOH sub-regions?
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shouldn’t be in the computation of MAC
We should however keep track of this valuable information
Impacts of yields response to N on MAC
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Restricted to agriculture: e.g. emissions from fertilizer production
Matching farm-types: Altitude, crop areas, farming systems,…
Draft for a UHOH/INRA joint paper
Informations fluxes
(INRA / INSEA)
GENEDEC
(new MS UE-25 ?)
INSEA
JRC
FADN
STICS
Grignon database
ARTIX
AROPAj
runs
INRA station
Results
Requested data
(INRA / INSEA)
« Not localised
»
Farm Types
/ Weights / …
E
N
V
I
C
A
R
E
Practice
Costs
Rotation
Yields
now
Climate
Change
tomorrow
Split FT / W ?
EPIC
STICS
new species / new cultivars