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)