Preconference Water Economics, 24 June, 2009
17th Annual Conference of the EAERE, Amsderdam
AN INTEGRATED SIMULATION MODEL
TO EVALUATE NATIONAL MEASURES
FOR THE ABATEMENT OF AGRICULTURAL
NUTRIENTS IN THE BALTIC SEA
Kari Hyytiäinen, Heini Ahtiainen, Jaakko Heikkilä, Janne Helin, Anni Huhtala, Antti
Iho, Kauko Koikkalainen, Antti Miettinen, Eija Pouta and Janne Vesterinen
MTT Agrifood Research Finland
1. INTRODUCTION
• The Baltic Sea is probably the most polluted sea in the world
• Eutrophication is one of the most severe problems causing damage to
human well-being and poses a serious threat to the functioning of the
ecosystem
• Eutrophication is caused by excess amount of nutrients (nitrogen N and
phosphorus P) in the water
• Common property problem - the 14 countries use the Baltic Sea as a
rent-free sink of nutrients
• International collaboration: e.g. Helcom Baltic Sea Action Plan (2007)
includes country targets for nutrient abatement
• However, at present there are no international agreements that are
binding - each country still acts at its self-interest
• => environmental investments in nutrient abatement are national
projects
Objectives:
1. Develop a model for evaluating the profitability of
national investments in sea water quality
2. Evaluate the effect of weather-induced variation in land
load of nutrients on profitability of investment
3. Demonstrate the model’s potential with preliminary
parameters and computations
The model was initially developed in a pre-study assessing the
feasibility of the cost-benefit analysis of protecting the Baltic Sea
ecosystem
Stern review of climate change (2007)
2. STOCHASTIC SIMULATION MODEL
Model components:
(1) nutrient dynamics in the
sea basins adjoining
Finnish coast
(2) nutrient loads from land
and other sources
(3) benefits from nutrient
abatement
(4) costs of agricultural
abatement
=> cost-benefit analysis for
environmental investment
in water quality
Bothnian
Bay
Bothnian
Sea
Gulf of
Finland
Baltic
proper
2.1 Dynamics of nutrient concentrations
State variables: the amount of nitrogen QN and phosphorus QP in each sea basin
Exchange of nutrients
between basins
Riverine loads of N
Amount of N in basin i
Atmospheric deposition of N
ni
QiN, t
LiN, j ,t
AiN
j 1
1
N fixation by
cyanobacteria
QiP,t
Wi ,ink ckN,t
Di
Bi
Fi
4
LPi , j ,t
j 1
N
Wi ,out
c
k
i ,t
k 1
ni
QiP, t
Denitrification
4
QiN,t
1
sedimentation
AiP
P
Wi ,out
c
k
i ,t
Wi ,ink ckP,t
Bi
Ii
k 1
Internal loading
of P
Assume: - stochastic riverine loads of N and P
- all other sources and sinks of nutrient remain the same
2.2 Predicting the riverine loads of N and P
• strong annual variation (weather conditions, rainfall)
• loads are spatially correlated
• interannual loads are not dependent
• future development of agricultural sector and agricultural policies
largerly determine the future trend of nutrient loads
Annual N and P loads for 7 watershed groups
and over a 200 year period
Nutrient abatement
L
Mean load –
baseline scenario
D
SAZ
Matrix of normally
distributed random variables
st. dev. Of
past loads
Cholesky decomposition of
covariance-variance matrix
Riverine loads of N and P to the sea basins in the Northern Baltic Sea
Watersheds grouped by countries and sea basins
Nitrogen
Phosphorus
Bothnian Bay
Bothnian Sea
Sw eden Finland
Finland
Gulf of Finland
Sw eden Finland
Bothnian Bay
Russia
Estonia
y =6
y =7
Bothnian Sea
Gulf of Finland
Sw eden Finland Finland Sw edenFinland Russia Estonia
y =1
y =2
y =3
y =4
y =5
1986
17610
28865
27463
31297
13229
104135
29414
1106
1672
1668
1255
703
4301
507
1987
18514
28683
20274
33908
14331
109897
31345
1142
2073
1417
1540
658
2824
753
1988
16764
27771
28776
26351
15556
84847
17273
1060
1676
1870
1253
679
5007
984
1989
17106
31830
23656
27147
14931
54565
13730
1416
2185
1402
1264
646
3414
812
1990
15219
19399
29847
27065
15149
69524
19326
822
1250
1675
1134
571
3893
801
1991
17652
29807
24378
25645
13592
77610
18479
990
1830
1496
1183
607
4239
697
1992
19325
38644
28222
29412
15408
82906
19110
1157
2336
1490
1132
664
4282
696
1993
19808
28727
19333
34830
10653
71516
16325
1227
2091
1137
1510
529
4971
614
1994
15212
22428
19188
23382
11261
74242
13692
908
1592
1208
962
606
3976
979
1995
19463
26029
22463
33686
12519
80358
15490
1154
1642
1330
1335
567
4239
843
1996
17644
23488
19937
21539
11566
63932
11556
641
1221
1223
580
582
4073
480
1997
18733
25655
20590
26460
8968
63752
13200
1458
1541
1107
1107
428
4140
647
1998
27049
39461
26790
43643
13296
69860
22260
1232
2210
1479
1206
648
4353
891
1999
21636
26374
24451
27771
12021
75924
18227
924
1551
1599
1380
562
4640
1324
2000
27366
42726
35375
42042
13885
67931
13720
1328
2199
3144
1637
621
4261
662
Aver.
19273
29326
24716
30278
13091
76733
18210
1104
1805
1550
1232
605
4174
779
st.dev.
3632
6504
4676
6359
1920
14646
5722
222
357
490
255
69
545
213
Source: Baltic Nest Institute 2008
y =8
y =9
y =10
y =11
y =12
y =13
y =14
Statistics and predictions for the Finnish agricultural sector
Indicator
Subsurface draining (1000 ha/year)
Clearing of arable land (1000 ha/year)
Afforestation of arable land (1000 ha/year)
Total area of agricultural land (1000 ha)
Meadows (1000 ha)
Yield of barley (kg/ha)
Fallows and cultivated arable land (1000 ha)
Artificial fertilization of N (kg/ha)
Artificial fertilization of P (kg/ha)
Silage/hay (1000 ha)
No. of cows (1000)
No. of farms (1000)
Average farm area (ha)
No. of dairy farms (1000)
No. of estates on grain cultivation (1000)
Lime for soil improvement (kg/ha)
Use of pesticides (g/ha)
No. of tractors (1000)
No. of horses (1000)
No. of pigs (1000)
a
1950s 1960s 1970s 1980s 1990s
23
34
38
33
8
10
4
7
7
7
4
10
2462
1650
1200
8
243
122
300
2669
153
1980
249
69
31
1050
1000
297
10
210
80
150
2589
146
2570
290
83
28
943
730
229
12
98
112
193
92
600
234
32
1000
2453
138
3150
401
111
30
682
490
129
17
48
47
488
850
208
42
1500
2222
25
2700
720
84
10
664
370
88
26
24
41
376
500
170
56
1300
2006/7 2020s 2050s
5
5
5
7
12
9
2
6
5
2295
34
3500
230a
74
8
654
309
69
33
15
41
303
650
175
66
1400
2410
35
4000
390
86
5
650
230
48
50
6
36
400
700
150
82
1200
2525
35
4500
290
74
5
700
240
25
100
2
15
450
700
100
82
1200
set-aside fields that are not entitled to agricultural support (about 100,000-150,000 ha) are not included
Sources: Yearbook of Farm Statistics (several years); predictions: outcomes of the sector model for Finnish agriculture
The expected loads of N and P now, and after 20 and 50 years, in the
Northern Baltic sea by countries and sea basins (baseline scenario)
Nutrient source
Rivers from Sweden to Bothnian bay
Rivers from Finland to Bothnian bay
Rivers from Finland to Bothnian sea
Rivers from Sweden to Bothnian sea
Rivers from Finland to Gulf of Finland
Rivers from Russia to Gulf of Finland
Rivers from Estonia to Gulf of Finland
Total P (tons/year)
2008
2028
1 104
950
1 805
1 600
1 550
1 500
1 232
900
605
600
4 174
5 500
779
1 000
2058
900
1 400
1 800
880
450
7 000
1 150
Total N( ton/year)
2008
2028
19 273
20 000
29 326
33 000
24 716
35 000
30 278
23 500
13 091
12 000
76 733
85 000
18 210
20 000
2058
19 000
30 000
33 000
23 000
11 500
90 000
21 000
(a) three projections of N loads by countries
(b) three projections of P loads by countries
140000
10000
Russia
120000
Russia
8000
tons /year
80000
60000
6000
4000
Estonia
40000
Estonia
2000
20000
Finland
0
Finland
0
2010
2020
2030
2040
2050
2060
2070
2010
2020
2030
2040
2050
2060
2070
(d) five developments of P concentration
(c) five developments of N concentration
26
480
460
24
440
420
22
mikrograms /ltr
mikrograms /ltr
tons /year
100000
400
380
360
20
18
340
320
16
300
14
280
2010
2020
2030
2040
2050
2060
2070
2010
2020
2030
2040
2050
2060
2070
Sample projections of loads for the Gulf of Finland, baseline scenario
2.3 Benefit (damage) from improved (reduced) water quality
Indicator of water quality: Secchi depth (Vesterinen et al. 2009, data from the
Finnish Environment Institute)
schi ,h,t
i
1,i
ln ciP,h,t
2 ,i
ln ciN,h,t
ciN,h,t ciP,h,t
3,i
4 ,i
1000
3.5
Bothnian Bay
Water clarity, m
3.0
2.5
2.0
Bothnian Sea
1.5
Gulf of Finland
1.0
2020
2040
2060
2080
2100
Sample developments of water clarity –
baseline scenario, no additional nutrient abatement
temp
5 ,i
depth, i 1,2,3, h, t
Two approaches to describe the benefits of nutrient abatement
(1) Travel cost method: recreational value of Baltic Sea basins (boating, fishing,
swimming), Vesterinen et al. (2009)
2 ,i
vali ,h ,t
1,i
3, i
schi ,h ,t
schi ,h ,t
,
i 1,2,3,
h, t
1,..., 200
(2) Meta-analysis: summary of the previous valuation studies on the Baltic Sea
=> estimate for marine-related amenities, Ahtiainen (2009)
vali ,h ,t
2 ,i
1,i
schiht
1 e
, i 1,2,3,
3i
4i
h, t
Change in the value of near-home coastal
recreation, million €/year
(a) on water recreation (travel-cost method)
100
Gulf of Finland
50
0
Bothnian Bay
-50
Bothnian Sea
-100
-150
0
1
2
3
4
Change in use and non-use value, million €/year
Water clarity, m
(b) on use and non-use value (meta-data)
100
Gulf of Finland
Bothnian Sea
50
0
Bothnian Bay
-50
-100
-150
0
1
2
Water clarity, m
3
4
2.4 Costs of nutrient abatement
Cost-efficient combination of abatement measures and the resulting unit cost
(€/kg) are obtained from a static farm-level optimization model (Helin 2008)
Profit-maximizing solutions for representative dairy farm and cereal farm (nutrient
load is set as a constraint)
Alternative abatement measures include:
- reduction of nutrient fertilization
- changes in cultivated crops and cultivation methods,
- reductions in dairy cattle
- changes in cattle diet
- allocation of set-aside fields
Evaluated levels of nutrient abatement
(1) Baseline scenario
(2) N16 (16% reduction in N and 3.5% reduction in P)
(3) N30 (30 % reduction in N and 3.5% reduction in P)
(4) P16 (16 % reduction in P and 2% reduction in N)
(5) P30 (30 % reduction in P and 2% reduction in N)
2.5 Cost-benefit analysis
200
Bh
vali ,h ,t
vali ,1,t e
rt
vali ,h , 200
r
i 1
Ch
c _ abath
NPV h
BC h
1 D
r
Bh
Ch , h
Bh
, h
Ch
2,..., 5
vali ,1, 200
2,..., 5
e
200t
h
2,..., 5
Statistics: annual
variation in land
loads, covariances
Sector
model
outcomes
Future development
of mean loads
Nutrient
abatement
measures
Annual loads
of N and P
Dynamics of N
and P in sea
basin
Costs
Benefits
Cost-benefit analysis
3. RESULTS
International
involvement
Finland only
valuation
Expected NPV, million €
approach
r
N16
P30
P16
Travel cost
0.1 % -24365 -12915
-4116
costal population 2.6 %
-942
-503
-162
5.1 %
-482
-261
-85
Expected B/C-ratio
N16
P30
P16
0.04 0.17 0.26
0.03 0.16 0.24
0.03 0.14 0.22
Metadata
0.1 %
costal population 2.6 %
5.1 %
-23799
-882
-448
-9767
-275
-139
-2582
-44
-21
0.06
0.09
0.10
0.38
0.54
0.54
0.55
0.79
0.80
Metadata
total population
0.1 %
2.6 %
5.1 %
-22319
-795
-402
-4040
29
17
360
116
61
0.12
0.18
0.19
0.75
1.05
1.06
1.08
1.55
1.56
Finland, Sweden Travel cost
0.1 %
Russia, Estonia costal population 2.6 %
5.1 %
-22201
-874
-452
-7648
-339
-189
-1223
-72
-46
0.12
0.10
0.09
0.51
0.43
0.38
0.78
0.66
0.58
Metadata
0.1 %
costal population 2.6 %
5.1 %
-21421
-705
-345
-1796
203
124
1329
203
117
0.15
0.28
0.31
0.88
1.34
1.41
1.23
1.96
2.08
Metadata
total population
-17643
-450
-200
11487
963
531
7974
599
331
0.30
0.54
0.60
1.73
2.63
2.76
2.42
3.84
4.08
0.1 %
2.6 %
5.1 %
Analysis by basins:
International
involvement
Finland only
valuation
approach
Travel cost
Finland only
Metadata
Basin
BB
BS
GoF
Expected NPV, million €benefit-cost ratio
N16
P30
P16
N16
P30
P16
-212 -101
-30
0.03
0.24
0.36
-188 -112
-38
0.03
0.06
0.10
-82
-48
-16
0.03
0.08
0.13
BB
BS
GoF
-194
-178
-75
-5
-95
-37
19
-29
-10
0.11
0.08
0.10
0.95
0.20
0.29
1.37
0.31
0.46
Finland, Sweden Travel cost
Russia, Estonia
BB
BS
GoF
-208
-184
-59
-81
-107
-1
-20
-36
10
0.05
0.05
0.29
0.39
0.10
0.98
0.59
0.17
1.52
Finland, Sweden Metadata
Russia, Estonia
BB
BS
GoF
-179
-165
0
80
-79
127
62
-20
77
0.18
0.15
0.99
1.59
0.34
3.41
2.28
0.53
5.10
BB = Bothnian Bay
BS = Bothnian Sea
GoF = Gulf of Finland
4. Conclusions
• The model structure is appropriate for evaluating the efficiency of
national investments in water quality
• All model components require further development
example 1: valuation of benefits
example 2: spatial and temporal resolution of the model
• Preliminary results are logical:
(1) it becomes profitable for Finland to invest on water quality only if
the neighbouring countries are committed to similar reductions
(2) environmental investment gives the highest return for:
- Gulf of Finland (high population, small impact on water quality)
- Bothnian Bay (small population, high impact on water quality)