COBECOS Case study on Icelandic cod. Overview Common types of violations Modeling approach –Using...
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Transcript of COBECOS Case study on Icelandic cod. Overview Common types of violations Modeling approach –Using...
COBECOSCase study on Icelandic cod
Overview
• Common types of violations
• Modeling approach– Using COBECOS code– Using our own code
• Results
• Conclusions
Types of violations
• Quota and fishing permit violations• Landing violations• Gear violations, e.g. mesh size• Area closure violations• Utilization factors exaggeration• Ice percentage exaggeration• Discarding
Basic model
Private benefits
Social benefits
2
1
2
( , , ; , *, *) ( , ) ( ) ( *)
( ) ( ( , ) *)
I
i i ii
q q
PB x q B x f s s
f Q x q
s e f s s e
e s
( , , ) ( , ) ( ) ( ( , ) ( , ))SB x B x C G x Q x s e s e s s
The COBECOS code
• Set up for one quantity violation – What violation to include?– Should other violations be transformed into
quantity violations?
• What about possible interdependence between different types of violations and different types of effort?
Our own model
• No limit to the number of management tools or enforcement measures
• Current version includes the following types of violations
1. Landing/quota violations2. Mesh size violations3. Utilization factor/ice percentage exagg.4. Discarding
Private benefits
• Pure private benefits
– where is a mesh size index and is relative discards
• and where– mesh size affects costs:– discarding affects price:
, , ;
qB q x p q c
x
20 1 2( ) ( )c a a a
20 0 1 2p p b b b
Private benefits
• Full private benefit function
where is the relative exaggeration of utilization (or ice percentage)
and were is the function relating the enforcement effort and the probability of getting fined
1 2, 3 4 1 2 3 4
2 21 1 1 2 2 2
2 23 3 3 4 4 4
( , , , ; , , ; , , , , , , , , ) ( , , ; )
( ) ( ) ( ) ( )
( ) ( ) ( ) ( )
PB q e e e e x f f f f q B q x
f e q q f e
f e f e
( )( ) 1 exp i iei ie
Social benefits
where
1 2 3 4 1 2 3 4( , , , ; , , , , ) ( , , ; ) ( , , , )
( ( ; ) )
SB e e e e q x B q x C e e e e
G x q
22
1( ; )
1 ( )G x a x b x
24 4
1 2 3 4 0 1 21 1
( , , , ) i ii i
C e e e e c c e c e
Parameter estimatesParameters Value Estimation
method Source
Landings price, p0 220 ISK/kg. Estimation Agnarsson et al 2007 Discard function, b0 -5 Fitted Discard function, b1 11 Fitted Discard function, b2 5 Fitted
Kristofersson and Rickertsen (2009) and authors
Fishing costs, a0 100 Estimation +adjustment
Agnarsson et al 2007 Authors
Fishing costs, a1 60 Estimation +adjustment
Agnarsson et al (2007) Authors
Fishing cost, a2 0.5 Set Authors Fishing cost, 1.1 Estimation Agnarsson et al 2007
Biomass growth, a 0.6699 Estimation Agnarsson et al 2007 Biomass growth, b 0.3353 Estimation Agnarsson et al 2007
Biomass growth, 1 Set Authors Base year biomass 0.715 million
mt Biological estimate
Marine research Institute 2007
Shadow value of biomass,
150 ISK/kg Bio-economic estimate
Agnarsson et al 2007 Arnason et al. 2007.
Parameter estimatesParameters Value Estimation
method Source
Management target 1, q 0.215 million mt Set Authors
Management target 2, 1 Set Authors
Management target 3, 1 Set Authors
Management target 4, 1 Set Authors
Probability 1, 1 3.3 Fitted Authors Probability 2, 2 5.3 Fitted Probability 3, 3 6.6 Fitted Probability 4, 4 2.5 Fitted
Authors
Fine 1, f1 10233 Fitted Authors Fine 2, f2 506 Fitted Authors Fine 3, f3 1014 Fitted Authors Fine 4, f4 1521 Fitted Authors
Enforcement cost function, c0 0.493 Estimation Authors Enforcement cost function, c1 0.586 Estimation Authors Enforcement cost function, c2 1.233 Estimation
+adjustment Authors
Optimization• Two-tiered maximization procedure
– enforcement agency selects values for enforcement effort– fishermen respond by choosing profit maximizing
harvest, mesh size, reported utilization factor and discard rate.
– enforcement agency selects new enforcement efforts…– continues iteratively until the optimal enforcement effort
has been located.
• Uses standard numerical search routine in MATLAB
User interface
• Runs in a free runtime environment• Allows changes to key parameters
Results
Enforcement situation
Harvest2 Mesh- size
Utilization factor
Discards
e 1 e 2 e 3 e 4 q
0.715 0.5 1.24 1.032(233%) (-50%) (24%) (3.2%)0.225 0.913 1.013 1.011(5%) (-9%) (1.3%) (1.1%)0.231 0.907 1.028 1.012(7%) (-9%) (2.8%) (1.2%)0.215 1 1 1(0%) (0%) (0%) (0%)
Table 3Enforcement of the Icelandic cod fishery: Key results
18.74
Private benefits (b.ISK)
Social benefits (b.ISK)
No enforcement 0.00 0.00 61.42 -17.51
0.13
Optimal enforcement
0.22 0.03
29.89
30.08
Voluntary compliance
0.00 0.00 17.65 31.60
0.13 0.16
0.05
0.00 0.00
Numbers in parentheses indicate deviations from the management measures q=0.215, =1, =1 and =1
Enforcement effort1
0.00 0.00
At current effort levels
0.13 0.03 19.03
Private benefits and mgt targetsFigure 4 Private benefits and management targets
Social benefits and enf. effortsFigure 5 Social benefits and enforcement effort
Sensitivity analysise 1 e 2 e 3 e 4 q
30.08 18.74 0.22 0.03 0.13 0.16 0.225 0.91 1.01 1.0131.60 17.65 0.00 0.00 0.00 0.00 0.215 1.00 1.00 1.00
Parameters
ValueFine, f 1 11000 10233 30.09 18.84 0.14 0.03 0.10 0.13 0.224 0.91 1.01 1.01
Fine, f 1 9000 10233 30.02 18.81 0.23 0.03 0.13 0.14 0.223 0.91 1.01 1.01
Biomass x 1000 715 42.34 27.49 0.20 0.04 0.15 0.17 0.231 0.95 1.02 1.02Biomass x 600 715 20.56 13.10 0.30 0.03 0.24 0.25 0.220 0.89 1.01 1.01S. value, 170 150 33.87 18.59 0.37 0.04 0.12 0.14 0.223 0.92 1.01 1.01S. value, 130 150 26.56 19.35 0.16 0.02 0.05 0.09 0.230 0.86 1.03 1.02F. cost, a 0 120 100 24.94 13.11 0.15 0.06 0.17 0.06 0.222 0.95 1.01 1.01
F. cost, a 0 80 100 35.35 24.51 0.21 0.03 0.09 0.10 0.230 0.90 1.02 1.02
Price, p0 240 220 34.00 23.23 0.29 0.03 0.30 0.19 0.225 0.91 1.01 1.01
Price, p0 200 220 25.92 14.30 0.19 0.03 0.10 0.11 0.223 0.91 1.01 1.01
Reference pointsOptimal enforcement
Voluntary compliance
Base value
Sensitivity analysis
Social benefits (b ISK)
Private benefits (b ISK)
Enforcement effort1 Management Measures
Conclusions
• The benefits from enforcement are much larger than the costs
• Enforcement effort should be increased to optimize social benefits– specifically for landing and utilization factor
• Optimal effort depends on the parameters of the model in complex ways
Conclusions
• It is feasible to model a relationship with multiple management measures and types of enforcement
• The biggest obstacle to building complex models of fisheries enforcement is the lack of data
Atlantic cod (Gadus morhua)