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COBECOS mid-term meeting, 2-3 September 2008, San Sebastian Italian case study: GSA 9 bottom trawling fishery Status of model estimation at September 2008 COBECOS Costs and Benefits of Control Strategies Paolo Accadia (accadia@irepa.org)accadia@irepa.org

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2 Layout Brief description of the case study Brief description of the case study Management and enforcement system Management and enforcement system Data available (to be available) Data available (to be available) Penalty probability function Penalty probability function Private cost of violation Private cost of violation Enforcement cost function Enforcement cost function Private benefit function Private benefit function Social benefit function Social benefit function

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3 The case study area: GSA 9 GSA 9

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4 Fleet and species The fishery under analysis is multi-species (>45) and multi-gear (trawling is predominant but many vessels are authorised to fish with more than 1 gear). The fleet is divided in three fleet segments: The fishery under analysis is multi-species (>45) and multi-gear (trawling is predominant but many vessels are authorised to fish with more than 1 gear). The fleet is divided in three fleet segments: Trawls Trawls Small scale Small scale Polyvalent Polyvalent The polyvalent and small scale fleets use a combination of fishing gears. Polyvalent are vessels over 12 m in length, while small scale vessels are under 12 m in length. The polyvalent and small scale fleets use a combination of fishing gears. Polyvalent are vessels over 12 m in length, while small scale vessels are under 12 m in length. Target species are: hake, mullet, octopus, shrimp and lobster. These are medium migratory species. Hake is a long live species (up to 20 years) while the others are medium live species (not >4 years). In the bio-economic model simulations are performed for the following species: Target species are: hake, mullet, octopus, shrimp and lobster. These are medium migratory species. Hake is a long live species (up to 20 years) while the others are medium live species (not >4 years). In the bio-economic model simulations are performed for the following species: European hake European hake Striped mullet Striped mullet Shrimp Shrimp Other species Other species

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5 Management system Measures regulating bottom trawling in GSA 9 are the ones applied at national level. In Italy, the trawling activity is managed trough a combination of input control and technical measures, consisting in: 1. Input control measures: fishing activity regulated by a closed license scheme; fishing activity regulated by a closed license scheme; seasonal withdrawal of the fishing activity during certain period, generally in the summer months. seasonal withdrawal of the fishing activity during certain period, generally in the summer months. technical stop of the fishing activity on Saturday and Sunday. technical stop of the fishing activity on Saturday and Sunday. 2. Technical measures: minimum distance from the coast; minimum distance from the coast; minimum mesh size; minimum mesh size; minimum landing size for some target species. minimum landing size for some target species.

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6 Enforcement system The main body responsible for the control on the fishery sector is the Guardia Costiera. The main body responsible for the control on the fishery sector is the Guardia Costiera. Other military corps (Carabinieri, Guardi di Finanza, Polizia, etc..) have a subsidiary responsibility in the fishery control. Other military corps (Carabinieri, Guardi di Finanza, Polizia, etc..) have a subsidiary responsibility in the fishery control. Three enforcement tools are considered in this case study: Three enforcement tools are considered in this case study: Landing inspections; Landing inspections; Inspections at sea; Inspections at sea; Inspections at sea with aircraft support. Inspections at sea with aircraft support. The inspection reports do not describe in very detail the type of elementary control made. It is assumed that each of the enforcement tools can investigate on all types of violation. The inspection reports do not describe in very detail the type of elementary control made. It is assumed that each of the enforcement tools can investigate on all types of violation. At the moment, based on the data available, violations are classified as follow: At the moment, based on the data available, violations are classified as follow: Fishing without holding a fishing licence Fishing without holding a fishing licence Using or keeping on board prohibited fishing gears Using or keeping on board prohibited fishing gears Unauthorized fishing. Unauthorized fishing.

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7 Data for the enforcement functions Data available: Data available: Infringements and fines by type of behaviour; Infringements and fines by type of behaviour; Inspections and sanctions by enforcement tool (not for the entire area GSA9 and not for inspections at sea with aircraft support). Inspections and sanctions by enforcement tool (not for the entire area GSA9 and not for inspections at sea with aircraft support). Data to be available: Data to be available: Inspections by enforcement tool; Inspections by enforcement tool; Infringements, sanctions and fines by type of behaviour and enforcement tool; Infringements, sanctions and fines by type of behaviour and enforcement tool; Average cost per hour of people employed by enforcement tool, and number of hours; Average cost per hour of people employed by enforcement tool, and number of hours; Average cost per hour of the aircrafts employed in fishery control, and number of hours; Average cost per hour of the aircrafts employed in fishery control, and number of hours; Average cost per nautical mile of the ships employed in fishery control, and number of miles. Average cost per nautical mile of the ships employed in fishery control, and number of miles.

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8 Penalty Probability Function (1) Enforcement effort, e, is measured in terms of number of inspections. Enforcement effort, e, is measured in terms of number of inspections. Under the assumption that a vessel is not inspected twice in a day, the maximum number of inspections in a year equals the total number of days at sea for a fleet. Under the assumption that a vessel is not inspected twice in a day, the maximum number of inspections in a year equals the total number of days at sea for a fleet. As fishing effort E is estimated in terms of number of days at sea: max(e) = E. As fishing effort E is estimated in terms of number of days at sea: max(e) = E. Assuming that an inspection of a violating vessel always produces a sanction: Assuming that an inspection of a violating vessel always produces a sanction: When all units of fishing effort are inspected, the probability to be sanctioned when violating is 1: When all units of fishing effort are inspected, the probability to be sanctioned when violating is 1: (E) = 1 (E) = 1

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9 Penalty Probability Function (2) The penalty probability function can be estimated as the ratio between sanctions and violations: The penalty probability function can be estimated as the ratio between sanctions and violations: (e) = p(S|V) = S/VV = ? (e) = p(S|V) = S/VV = ? Assuming that for an enforcement tool, vessels to be inspected (or a sample of them) are randomly selected. By using the number of inspections and related sanctions, the total number of violations V can be estimated as follow: Assuming that for an enforcement tool, vessels to be inspected (or a sample of them) are randomly selected. By using the number of inspections and related sanctions, the total number of violations V can be estimated as follow:

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10 Penalty Probability Function (3) When vessels to be inspected are not randomly selected, the penalty probability function can be estimated as follow: When vessels to be inspected are not randomly selected, the penalty probability function can be estimated as follow: Assuming a quadratic function for the probability of penalty: Assuming a quadratic function for the probability of penalty: Coefficients can be estimated on the data as follow: Coefficients can be estimated on the data as follow:

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11 The private cost of violation for a fleet by violation i and enforcement tool j is estimated as follow: Under the assumption: Private cost of violation

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12 Enforcement cost function As in this case study enforcement is measured in terms of number of inspections, the enforcement effort can be obtained as a production function where production factors are: As in this case study enforcement is measured in terms of number of inspections, the enforcement effort can be obtained as a production function where production factors are: Man-hours employed in landing inspections; Man-hours employed in landing inspections; Man-hours employed in inspections at sea; Man-hours employed in inspections at sea; Flight-hours in activity of fishery control; Flight-hours in activity of fishery control; Nautical miles for activity of fishery control; Nautical miles for activity of fishery control; The cost of enforcement can be estimated as a linear function of the production factors: The cost of enforcement can be estimated as a linear function of the production factors: Landing inspections; Landing inspections; Inspections at sea; Inspections at sea; Inspections at sea with aircraft support. Inspections at sea with aircraft support. where,, and represent the unit costs of the production factors. where,, and represent the unit costs of the production factors.

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13 Private Benefit Function Short run bio-economic simulation model. Short run bio-economic simulation model. Multi-fleet and multi-species model. Multi-fleet and multi-species model. Full-compliance model Economic box State VariationBiological box t = t+1 Management Tax Subsidies Activity Capacity Activity Capacity Selectivity CatchProfit Activity Capacity

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14 Fishing mortality Population of age a+1 at time t+1 is calculated as follow: Population of age a+1 at time t+1 is calculated as follow: The total mortality for the age-class a at time t is obtained by the fishing mortality and the natural mortality rates: The total mortality for the age-class a at time t is obtained by the fishing mortality and the natural mortality rates: Z a,t = F a,t + M a,t. The fishing mortality for the age-class a at time t, F a,t, is obtained by the fishing mortality rates associated to each fishing gear-fleet operating in the area: The fishing mortality for the age-class a at time t, F a,t, is obtained by the fishing mortality rates associated to each fishing gear-fleet operating in the area: F a,t = F a,t,g The fishing mortality for the age-class a at time t associated to the fishing gear-fleet g is a function of selectivity, catchability and effort: The fishing mortality for the age-class a at time t associated to the fishing gear-fleet g is a function of selectivity, catchability and effort: F a,t,g = S a,t,g q t,g E t,g Catches per fishing gear are estimated by the following equation: Catches per fishing gear are estimated by the following equation:

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15 Effects of violation on the fishing mortality Different types of infractions can affect the components of the fishing mortality: Different types of infractions can affect the components of the fishing mortality: Fishing without holding a fishing licence is supposed to affect fishing effort: Fishing without holding a fishing licence is supposed to affect fishing effort: F a,t,g = S a,t,g q t,g (E t,g + V t,g ) Using or keeping on board prohibited fishing gears is supposed to affect selectivity: Using or keeping on board prohibited fishing gears is supposed to affect selectivity: F a,t,g = S a,t,g q t,g E t,g + S a,t,g q t,g V t,g Unauthorized fishing is supposed to affect catchability: Unauthorized fishing is supposed to affect catchability: F a,t,g = S a,t,g q t,g E t,g + S a,t,g q t,g V t,g The same unit of effort can violate more than 1 fishery rule. So, fishing mortality should be estimated as a combination of the above equations. The same unit of effort can violate more than 1 fishery rule. So, fishing mortality should be estimated as a combination of the above equations.

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16 Social Benefit Function How can the shadow value of biomass be estimated by a simulation bio-economic model?

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17 Thank you for the attention! Paolo Accadia (accadia@irepa.org)

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