Targeting Multiple Management Objectives in Sustainable Fisheries

Transcription

1 Journal of Management and Sutainability; Vol. 4, No. 3; 2014 ISSN E-ISSN Publihed by Canadian Center of Science and Education Targeting Multiple Management Objective in Sutainable Fiherie Eric A. L. Li 1 1 Department of Statitic and Actuarial Science, Univerity of Hong Kong, Hong Kong Correpondence: Eric A. L. Li, Department of Statitic and Actuarial Science, Univerity of Hong Kong, Pokfulam Road, Hong Kong. Tel: ericli@aa.hku.hk Received: May 30, 2014 Accepted: July 2, 2014 Online Publihed: Augut 29, 2014 doi: /jm.v4n3p54 URL: Abtract Modern fiherie management mut balance between return (economic value) and rik (harvet uncertainty and tock collape). Management tool hould target multiple imultaneou management objective. Thee include output, number of eaon, total tock value and it uncertain fluctuation. I provide a tylized fihery model for imulating the outcome of thee competing management objective under variou regulatory and market environment. Reult how that thee objective need not be mutually excluive. They can be traded off gradually, quantitatively and tranparently. The trade-off involve profit and output veru job proviion, employment ecurity, tock conervation and le-riky harveting. The puruit of higher return mut balance againt the rik of tock collape and harvet fluctuation. Keyword: marine reerve, fiherie management, imulation, trade-off. 1. Fiherie Management Objective Traditional fiherie management typically concern the health of the fih tock, in particular, it ize. For example, the Ecological Quality Objective (EcoQO) for the North Sea demeral fih community (Greentreet et al., 2011) i the tate-of-the-art development in thi regard. It identifie ize-baed metric a the mot effective indicator of the tate of the ecological community. Such metric are alo enitive to environmental influence. The large fih indicator (LFI) i one uch indicator that i enitive to fihing-induced change. Thi complex relationhip undercore the need for operational theoretical ize-reolved multipecie fih community model to upport management toward broader ecoytem objective. At the moment, quantitative fih tock aement technique are far in advance of ucceful fiherie management application. For example, technique uch a tock ynthei (SS) (Methot Jr. & Wetzel, 2013) adopt a tatitical age-tructured population modeling framework. It i highly calable from data-weak ituation where it operate a an age-tructured production model, to complex ituation where it can flexibly incorporate multiple data ource and biological/environmental procee. Depite the advance in thee technique, there have been relatively few ucceful application in management practice. Hilborn (2007) hold a more optimitic view. Beide uing marine-protected area a the central tool of a new approach to rebuilding the marine ecoytem of the world, he argue that much can alo be learned from example of uccefully managed fiherie: Thee leon are topping the competitive race to fih by appropriate incentive for fihing fleet and good governance. The major tool of reetting incentive i granting variou form of dedicated acce, including community-baed fihing right, allocation to cooperative, and individual fihing quota. Many of the failed fiherie of the world occur in juridiction where central government are not functional, and local control of fiherie i an eential part of the olution. (Hilborn, 2007, p. 296) The eential element of thee uccefully managed fiherie are economic utainability and the cooperative nature of the fihery indutrie. For example, BenDor et al. (2009) argue that thi i particularly important in rural coatal area where the fihing indutry i often a dominant employer. They analyze the interaction between economic and ecological dynamic ytem uing a multi-agent dynamic model of fihery management. Multiple agent (fiher) harvet multiple fih pecie and adapt the amount and allocation of their effort to their value function. Thee are given a net profit of the fih harvet old for a market price. Their model ugget that competitive fiher behavior lead to a decline of all fih tock, a well a their profit. On the other hand, cooperative fiherie that jointly et utainable limit for total harvet and effort can tabilize the fih tock and profit at ignificantly higher level. Thee then lead to a continuou accumulation of capital for all fiher. 54

2 Journal of Management and Sutainability Vol. 4, No. 3; 2014 I argue that, for modern fiherie manager, their firt-and-foremot objective mut be to enure that (1) the tock i utainably harveted in a biological ene; (2) the tock i optimally harveted in an economic ene; and (3) all takeholder are to be treated equally and fairly. For (1) and (2), the neceary hurdle mut include a utainable tock ize, the total derivable economic value from the tock, and the periodic harvet output, effort and profit. For (3), the hurdle are le well-defined. However, it i reaonable to demand that they include the chance or of achieving the total derivable economic value; the number of harvet eaon required; and the variability of thi random total derivable economic value. All of them affect to a large extent the livelihood of the partie employed by or involved in the fihery: fihermen, manager, cientit and coatal communitie. Achieving thee objective often have the added bonu of ameliorating the economic conequence of a tock aement error. The economic value of the tock upport the fihermen, the crew, the community, the manager and the cience. The variability or uncertainty of economic return affect thoe employed by the fihery. Worker have to be laid off in a low eaon, only to be hired back in a high eaon. Variability affect the tability of livelihood. Furthermore, the notion of maintaining a certain tock ize i popular with variou legilation (Lauck et al., 1998, p. S76). Thu, finding the that thi could be achieved i important. In economic, we might imilarly wih to maximize the of achieving certain level of fihery economic value and the number of eaon required to achieve them. Thi i becaue they affect the long-term planning of the invetment and contruction of amenitie and facilitie in the community. There are other non-economic but tock-related objective. A complete lit of thee ha been compiled by Novaczek (1995), Hockey and Branch (1997, p. 377) and Lauck et al. (1998, p. S77). Thi paper attempt to target ome common fiherie management objective: (1) achieving utained and optimal level of periodic harvet effort, output and profit; (2) maximizing the total expected fih tock value; the of achieving thi or certain pre-pecified level of total tock value; and the utainable tock ize; (3) minimizing the fluctuation in the total expected tock value; and the number of harvet period to achieve the maximum total expected tock value. I lay out the ret of the article a follow. The next ection explain the model that run imulation on the above management objective. Section 3 then conduct a erie of imulation experiment over thee competing management objective. I will highlight the inherent conflict and trade-off among thee objective. Section 4 conclude with policy implication. 2. The Simulation Model My imulation are baed on an ertwhile model (Li, 2000). There are two aumption: (1) The fihery i cooperative, profit-maximizing, licene-retricted, poaching-free, maintain a biologically utained tock, and i mutually agreed to a legally binding diviion of harvet profit through a uccefully negotiated internal tranfer payment ytem (Munro 1996); and (2) Harvet cot and demand for the harvet output are governed by determinitic market condition and there exit no uncertainty regarding tock ize, harvetability, and market demand. The unit harvet effort cot and the perfectly competitive harvet output price are both contant. Finally, the model alo aume a table production function. The optimal harvet olution for the fleet i obtained by maximizing profit over harvet effort, ubject to the harvet production equation and a tock utainability requirement that link output to effort. Next, a certain percentage of total harvetable area i deignated a marine reerve or no-take zone where all commercial harvet activitie are banned. Suppoe the marine reerve i trictly monitored and enforced and that the tock ditribution over the total harvetable area i perfectly homogeneou. A perfectly homogeneou tock ditribution aumption implie that the percentage of bioma inide the reerve and the percentage of bioma in the harvetable area are to be maintained at all time. Thu the bioma denity (kg/m 3 ) throughout the reerve and harvetable area i contant. Therefore, when the harvetable area i being exploited, there will be a net migration of individual (bioma) out of the reerve into the harvetable area. Alo, the percentage of the reerve tock foraging outide the reerve area i the ame a that of non-reerve tock foraging inide the reerve area and exchange between the two over time i conitent. Conequently, harvet will be a function of non-reerve tock and the intenity of fihing effort only. Suppoe that a fih tock i never going to collape, the total value that can be derived from the fihery i the um 55

3 Journal of Management and Sutainability Vol. 4, No. 3; 2014 of all utained future periodic harvet profit dicounted to the preent. When the future propect of a tock collape poe a relevant threat, the aumption of a long time frame i no longer valid. Moreover, the fihery value that can be derived from harvet profit will alo depend on when the collape i likely to occur, and the timing of the collape hould alo involve the interaction of effort and population dynamic. Once the tock experience an unexpected collape, it can never recover to an economically viable level for utainable harvet. The model aume that a marine reerve can mitigate uch a collape. The mathematic how that the of tock collape decreae with the proportion of the total harvetable area deignated a a marine reerve. My imulation model, baed on Li (2000), i a multi-period, marine-reerve-adjuted, tochatic-tock-ize Schaefer model. It examine the relationhip between the rik of tock collape and the return of fihery economic value by uing optimum harveting and a marine reerve a the twin tool of management control. Next, I run imulation on thi model uing a tylized fihery. My objective i to how that the reerve ize and harvet effort can target ome common management objective, in addition to maximizing fihery economic value. The main equation of the model are ummarized in Appendix A (Li, 2000). My imulation are conducted by treating the model a an application package with the input parameter, control variable and output value detailed in the following Table 1. Table 1. Input parameter, control variable and output value for the imulation model Input parameter: Parameter name: Symbol: Unit: 1. intrinic growth rate r % 2. per kg fih price p AUD$/mmt 3. tock carrying capacity k mmt (million metric ton) 4. harveting cot c AUD$/worker boat hour 5. catchability coefficient q mmt per worker boat hour per unit bioma 6. dicount rate ρ % 7. tock collape rate λ Control variable: Variable name: Symbol: Unit: 1. reerve feaibility (<1-c/pqk) 1-c/pqk % 2. reerve ize % 3. optimal reerve ize * 3c c 8cpqk 2pqk % 4. utainable harvet effort Output value/ Management objective: E 2q 1 r c 1 1 pqk Objective name: Symbol Unit: worker boat hour (mbh) 1.utainable periodic harvet profit 2.total expected fih tock value 3. utainable harvet output rpk c 1 4 pqk1 E V 1 E(V )* i the maximum E(V ) achieved at * H rk 1 4 pqk 2 2 c 1 AUD$ million AUD$ million mmt 56

4 Journal of Management and Sutainability Vol. 4, No. 3; utainable tock ize X k c pqk 1 5.time (no. of eaon) to E(V ) 1 1 t ln 1 mmt harvet period t 6.prob. of time to E(V ) 7.prob. achieving 50% E(V )* 8.prob. achieving 75% E(V )* 9.prob. achieving 100% E(V )* 10.prob. achieving 125% E(V )* 11.prob. achieving 150% E(V )* t* i the t that reache E(V )* P t P P % E V 50% E V * * 75% E V 75% E V * * * E V P t P E V P P * * 125% E V 125% E V 1 * * 150% E V 150% E V * * The model input parameter include the intrinic growth rate r, per kg price p, tock carrying capacity k, per worker-boat-hour harvet cot c and the fleet catchability coefficient q. Thee are all parameter in the original Schaefer model (1954). The market dicount rate ρ and the Poion tock dicount rate λ are from Li model. The firt control variable i the marine reerve ize that i et by the regulator. There i a feaibility requirement on the o that the model i implementable. That i, mut produce enible (non-negative and finite) olution for harvet output, effort and profit. The econd control variable i harvet effort E, although it level i alway et to maximize total harvet profit and the fihery' economic value. Thi i becaue fiher will fully exploit the regulation once they are laid down, whether the regulation are on quota, fihing area, limited eaon or gear ued. In thi paper we aume no irregular or illegal activitie uch a poaching. Conequently, and E a control variable for targeting management objective. The model output repreent variou management objective. Traditionally, they include the utainable periodic harvet profit, harvet output, and the utainable tock ize. In thi paper, I add the total expected tock value; the expected time (number of harvet period) and the of achieving the total expected tock value; and the repective probabilitie of achieving 50%, 75%, 100%, 125% or 150% of total expected tock value. 3. Simulation Reult and Dicuion 3.1 Simulating Maximum Stock Value by Comparative Static Simulation reult of 8 different cenario for a tylized fihery are ummarized in Table 2. The 8 cenario include the bae cenario and 7 other cenario. In each cenario exactly one parameter ha a different value from the bae cenario. In economic analyi, the technique i known a comparative tatic. It meaure the change in optimum value of the objective function and of the control variable when exactly one parameter of the model experience a change. In the bae cenario, the tock intrinic growth rate r i 1% p.a., per kg fih price p i AUD$10 ($10 Autralian dollar), the tock carrying capacity k i 1 million metric ton (or 1 mmt), the harveting cot c i AUD$50 per worker per boat per hour (including all food, fuel, bait and tackle, equipment hire, ervice and maintenance), the fleet harveting productivity (the catchability coefficient q) i mmt per worker boat hour per mmt bioma. Thi implie that, for example, given a bioma k of 1 mmt, for each veel or boat that operate for one hour, a crew of 10 fihermen can catch mmt or 1 metric tonne of fih. Finally, the financial-market dicount rate ρ i 5% p.a. and the natural rate of tock collape λ (without a marine reerve) i 0.02 (i.e., every year the tock ha a of 0.02 of collaping). The reerve ize i defined a the percentage of bioma not ubject to exploitation. Thi i equivalent to being the percentage of marine water fenced off under the homogeneou tock ditribution aumption. Any feaible or workable reerve ize 57

5 Journal of Management and Sutainability Vol. 4, No. 3; 2014 mut be under 95% (1-c/pqk) and the calculated optimal reerve ize * (that maximize the total expected tock value) i 41.94%. Thi will achieve a eaonal harvet profit Π of AUD$20.88 million (out of a utained harvet output H of mmt and utilizing harveting effort E of 78,702 worker-boat-hour each eaon). The upercript * and imply the optimal and the utainable, repectively. The maximum total expected tock value i E(V )* (the maximum E(V ) value) = AUD$ million. Following thi harveting program, an expected harvet eaon (t*) will pa before E(V )* i realized, with a of ucce, P(E(V )*), of The probabilitie of achieving 50% and 75% of E(V )*, i.e., P(0.5E(V )*) and P(0.75E(V )*), are and repectively. It i not poible to achieve 125% and 150% of E(V )* (P(1.25E(V )*) = P(1.5E(V )*) = 0). Finally, the reulting utained bioma X 2 i mmt (the lower utained bioma X 1 = mmt will not be purued by manager). Table 2. Comparative tatic on maximum total expected fih tock value Bae cenario Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Scenario 8 Management objective: Output value: p = $15/kg k = 2 mmt q = mmt /mbh/mmt bioma r = 5% p.a. c = $100/mbh ρ= 10% p.a. λ = c/pqk 95.00% 96.67% 97.50% 98.33% 95.00% 90.00% 95.00% 95.00% * 41.94% 53.87% 60.70% 68.52% 41.94% 12.72% 21.39% 68.92% Π $ $ $ $ $ $ $ $ E(V )* $ $ $ $ $1, $ $ $ H E 78, , ,123 50, ,512 50,721 59, , t* P(t*) P(0.5E(V )*) P(0.75E(V )*) P(E(V )*) P(1.25E(V )*) n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a In cenario 2, p i raied to AUD$15 per kg (keeping all other input parameter unchanged). The harveting fleet till aim to maximize periodic harvet profit and total expected tock value by trading off thi maximum periodic profit with the rik of udden tock collape. Compared to the bae cenario, * i increaed to 53.87% but till within the feaible limit of 96.67%. Thi will achieve a higher Π of AUD$32.28 million (out of a higher H of mmt and utilizing higher E of 100,557 worker-boat-hour each eaon). E(V )*i higher a AUD$ million. In thi cenario, more eaon (t* = 37.19) are required to achieve E(V )* but the of ucce i alo higher (0.710). The probabilitie of achieving 50% and 75% of E(V )* are alo higher at and repectively, compared to thoe in the baed cenario. However, X 2 i lower at mmt depite (or indeed becaue of) the more alluring market price. Again it i impoible to achieve 125% or 150% of E(V ). In cenario 3, k i raied to 2 mmt. * i increaed to 60.70% but within the new feaibility limit of 97.50%. Thi will achieve a higher Π of AUD$43.84 million (out of a higher H of mmt and utilizing higher E of 119,123 worker-boat-hour each eaon). E(V )* i higher at AUD$ million. Still more eaon (t* = 39.92) are required to achieve E(V )* but the of ucce i alo increaed to The probabilitie of achieving 50% and 75% of E(V )* are alo correpondingly higher at and repectively. With a higher 58

6 Journal of Management and Sutainability Vol. 4, No. 3; 2014 bioma k to tart with, the reulting utainable tock ize of X 2 i alo higher at mmt. In cenario 4, q i raied to All other input parameter remain unchanged from the bae cenario, to which all comparion are again made. * i increaed to 68.52% and i within the now more tolerable feaibility limit of 98.33%. Thi will achieve a higher Π of AUD$22.42 million (out of a higher H of mmt and utilizing lower E of 50, worker-boat-hour each eaon). Again, E(V )* i higher at AUD$ million. The of ucce at achieving E(V )* i higher at On the other hand, more eaon (t* = 43.82) are alo required to achieve thi. Correpondingly, the probabilitie of uccefully achieving 50% and 75% of E(V )* are higher at and repectively. With a higher catch efficiency, the utainably harveted tock ize of X 2 i not urpriingly lower at mmt. In the 5 th cenario, the tock growth rate r i increaed to 5%. The optimal reerve ize * tay at 41.94% and within the ame feaibility limit of 95%. Thi will achieve a ignificantly higher eaonal harvet profit Π of AUD$ million (out of a much higher H of mmt and utilizing an equally impreive higher E of 393, worker-boat-hour each eaon). The total expected tock value E(V )* ha increaed to AUD$1, million. Thi ugget that a higher tock growth rate bring in ubtantially more economic benefit than a rie in market price, bioma or catch efficiency. What more, all relevant probabilitie of ucce, number of eaon required and the utainably harveted tock ize are teady. In the 6 th cenario, c i more cotly at AUD$100 per worker per boat per hour. * i dramatically lowered to 12.72% but till within the now lower feaibility limit of 90%. Π, H, E, E(V )* alo dropped ignificantly to AUD$19.60 million, mmt, 50, worker-boat-hour, and AUD$ million repectively. Thi ugget a higher harveting cot (perhap due to hortage of killed fihermen, higher wage and/or expenive capital equipment) could be devatating to the fihing community. The probabilitie of uccefully achieving E(V )* or 50%, 75% or 125% of it are alo lower to 0.624, 0.851, and repectively. The only bright pot i that fewer eaon (t* = 27.04) are required to achieve E(V )* and the utained tock ize X 2 i higher at mmt. In the 7 th cenario, ρ i raied to 10% p.a. (perhap during a receion). The effect of raiing interet rate (or dicount rate) i to low down the economy, reducing invetment, employment and conumption. The reaon for the economy lowing down i that future earning are dicounted more (meaning le valuable compared to current earning). Current borrowing cot are alo higher and thee ultimately reduce economic activity, including fihery. To make up for the lower future harvet profit, the tock i harveted more intenively. Thi lead to a lower optimal reerve ize * at 21.39% (within the ame feaible boundary of 95%). A a reult of the more intenive ue of the reource, Π and H are higher at AUD$21.92 million and mmt repectively. However, harvet effort E i lower at 59, worker-boat-hour each eaon, reflecting a lowing economy. High interet rate eat into the total expected tock value E(V )*, which dropped to AUD$ million. The lower E(V )* require fewer eaon (t* = 19.96) to achieve. The probabilitie of ucce at achieving 50%, 75% and 100% of E(V )* are repectively higher at 0.915, and Not urpriingly, the more intenive ue of the tock lowered the utainably harveted tock ize X 2 to mmt. In the lat cenario 8, λ i raied to 0.1. Thi mean catatrophic tock collape (making all future harvet non-profitable) i now five time more likely than the bae cenario. It i not urpriing that a marine reerve i now more appealing a an inurance againt tock collape and * i increaed to 68.92% (within the ame feaibility limit of 95%). A higher reerve ize eat into the harvet profit Π and output H, which are both lower at AUD$17.60 million and mmt repectively. A a reult, fihermen need to work harder at a higher E of 134, worker-boat-hour each eaon. The reult i a lower E(V )* of AUD$ million. They now require only t* = eaon to achieve but with a lower of ucce at The probabilitie of 59

7 Journal of Management and Sutainability Vol. 4, No. 3; 2014 ucce at achieving 50%, 75%, 125% and 150% of E(V )* are all lower at 0.795, 0.680, and repectively. The latter two are only poible ( > 0) with a lower E(V ) to begin with. Stronger protection meaure (higher *) mean that i alo now higher at mmt, vi-à-vi the bae cenario. 3.2 Simulating Competing Management Objective In the next et of imulation, the previou eight cenario are retained. However, I do not maximize the total expected tock value by electing the optimal marine reerve ize and harvet effort. Intead, I imulate the output value of competing management objective by running the reerve ize over it entire feaible range in each cenario (Table 3 to 10). Unlike the reerve ize, harvet effort cannot be manipulated in thi manner. Thi i becaue fiher alway fully exploit the regulatory environment. Therefore any le-than-optimal harvet effort will not be realitic. Table 3. Simulating competing management objective for bae cenario Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 95.00% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $6.250 $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 47,500 52,469 58,594 66,327 76,389 90, , , , , t P(t) P(0.5E(V )*) n/a n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Table 4. Simulating competing management objective for cenario 2 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 96.67% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $ $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 48,333 53,498 59,896 68,027 78,704 93, , , , ,333 (0) (0.000) T P(t) P(0.5E(V )*) n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 60

8 Journal of Management and Sutainability Vol. 4, No. 3; 2014 Table 5. Simulating competing management objective for cenario 3 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 97.50% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $ $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 48,750 54,012 60,547 68,878 79,861 95, , , , , T P(t) P(0.5E(V )*) n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Table 6. Simulating competing management objective for cenario 4 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 98.33% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $ $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 16,389 18,176 20,399 23,243 27,006 32,222 39,931 52,469 76, , t P(t) P(0.5E(V )*) n/a P(0.75E(V )*) n/a P(E(V )*) n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Table 7. Simulating competing management objective for cenario 5 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 95.00% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $ $0.000 E(V )* $1, $1, $1, $1, $1, $1, $1, $1, $1, $ $0.000 H E 237, , , , , , , , ,500 1,250, t P(t) P(0.5E(V )*) n/a n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 61

9 Journal of Management and Sutainability Vol. 4, No. 3; 2014 Table 8. Simulating competing management objective for cenario 6 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $6.250 $0.000 E(V )* $ $ $ $ $ $ $ $ $ $0.000 H E 45,000 49,383 54,688 61,224 69,444 80,000 93, , , t P(t) P(0.5E(V )*) n/a n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Table 9. Simulating competing management objective for cenario 7 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 95.00% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $6.250 $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 47,500 52,469 58,594 66,327 76,389 90, , , , , t P(t) P(0.5E(V )*) n/a n/a P(0.75E(V )*) n/a n/a n/a P(E(V )*) n/a n/a n/a n/a P(1.25E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a P(1.5E(V )*) n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a Table 10. Simulating competing management objective for cenario 8 Management 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 95.00% objective/ Reerve ize Π $ $ $ $ $ $ $ $ $ $6.250 $0.000 E(V )* $ $ $ $ $ $ $ $ $ $ $0.000 H E 47,500 52,469 58,594 66,327 76,389 90, , , , , t P(t) P(0.5E(V )*) n/a P(0.75E(V )*) n/a n/a P(E(V )*) n/a n/a P(1.25E(V )*) n/a n/a P(1.5E(V )*) n/a n/a n/a 62

10 Journal of Management and Sutainability Vol. 4, No. 3; 2014 The feaible limit (the lat column of each table) for the reerve ize i the larget acceptable percentage of the bioma (or urface area of the fihing ground if the tock i ufficiently mobile) beyond which eaonal harvet output, effort and profit will become negative. In all eight cenario, the maller the reerve ize (going from right to left along the table), the higher the eaonal profit Π and eaonal output H. On the contrary, the following are lower: eaonal effort E ; utainably harveted tock ize X 2 ; number of eaon required (t, not t*) to achieved the expected total tock value E(V ) (not the maximum E(V )* achievable at the optimal *); and the probabilitie of ucce at achieving the total expected tock value or any portion of it. In other word, under any regulatory environment (repreented here by ), profit (Π ) and output (H ) are in conflict with and mut be traded off for job proviion and long-term employment ecurity (E and t), tock conervation (X 2 ) and le riky harveting (P(E(V )). More importantly, the trade-off i not a nuclear option (all or nothing). It can be done incrementally and evaluated quantitatively. The probabilitie of ucce at achieving 50%, 75%, 100%, 125% and 150% of the maximum total expected tock value E(V )* (not the E(V ) under each imulated ) appear to drop a little after reaching their maximum before becoming impoible to achieve (beyond the feaibility limit for ). The reaon i that a the reerve ize increae and reache it feaibility limit (1-c/pqk), the eaonal harvet effort, output and profit and the total expected tock value drop abruptly. The rate of decline i much teeper than the rate of increae when the reerve ize rie from zero. Thi decline in E(V ) i ufficiently large to overcome the reduction in the rik of tock collape at high, cauing the of ucce at achieving E(V ) to drop lightly. Notice that the of achieving the maximum total expected tock value E(V )* at the optimal (mot efficient) reerve ize * i P(E(V )*) or P(t*). It i not the ame a the of achieving the total expected tock value E(V ) at any other reerve ize, i.e., P(E(V )) or P(t). The reaon i that the mot efficient eaonal output, effort and profit under the optimum-harveting regime are different for different reerve ize. Thi will lead to a different total expected tock value, i.e., all dicounted future harvet profit calculated at preent. Finally, at the reerve ize feaibility limit, all eaonal harvet effort, output and profit go to zero o there will be no total expected tock value to peak of. The of ucce become meaningle. Without harveting, the bioma reache it biological maximum carrying capacity (X 2 ). Notice that X 1 and X 2 are both achievable and utainable level of the bioma. Since X 2 >X 1, manager will alway trive to achieve X Policy Implication Modern fiherie management hould be objective-oriented and follow a partnerhip approach. Any committee of fiher, manager and community member mut not ee their differing and competing objective a conflicting goal that can only be either one or the other (mutually excluive). Intead, their deire or agenda mut be laid bare to all concerned and ubject to benign negotiation and agreeable trade-off. Furthermore, the trade-off hould be done gradually (i.e., no objective hould be 0% or 100% atified) and be quantitatively tranparent. In thi paper, I adopted a multi-period, marine-reerve-adjuted, and random-tock-collape-prone modification of the original Schaefer model (Li, 2000). I then ran imulation on variou common management objective. The imulation were run on the principle of optimum harveting (i.e., retricted entry and profit maximization). Thi ue the reerve ize and fihing effort to balance the return (total derivable tock value) againt rik (of random tock collape and uncertainty in total derivable tock value). The imulation reult are two-fold. Firt, by maximizing total tock value, change in the model parameter can dratically affect the outcome of the common objective. For example, a higher tock growth rate bring in ubtantially more economic benefit than a rie in market price, bioma or catch efficiency; wherea a higher harveting cot could be devatating to the fihing community. Riing interet rate force the harvet fleet to work harder by both cutting harvet effort and fihing more intenively into the tock. Higher collape rik bring about the exact oppoite. Second, by altering the regulatory environment (repreented by the reerve ize), I have hown that profit and output are in conflict with and mut be traded off the following: job proviion and long-term employment ecurity, tock conervation and le riky harveting. More importantly, the trade-off can be achieved gradually and 63

11 Journal of Management and Sutainability Vol. 4, No. 3; 2014 incrementally. It i not merely either one or the other. It can alo be quantitatively evaluated. An economically viable harvet return mut balance againt the rik of tock collape and harvet uncertainty. Acknowledgement I am grateful for the financial upport from the Department of Statitic and Actuarial Science at the Univerity of Hong Kong. Reference BenDor, T., Scheffran J., & Hannond, B. (2009). Ecological and economic utainability in fihery management: A multi-agent model for undertanding competition and cooperation. Ecological Economic, 68, Greentreet, S. P. R., Roger, S. I., Rice, J. C., Piet, G. J., Guirey, E. J., Fraer, H. M., & Fryer, R. J. (2011). Development of the EcoQO for the North Sea fih community. ICES Journal of Marine Science, 68(1), Hilborn, R. (2007). Moving to utainability by learning from ucceful fiherie. Ambio, 36(4), Hockey, P. A. R., & Branch, G. M. (1997). Criteria, objective and methodology for evaluating marine protected area in South Africa. South African Journal of Marine Science, 18, Lauck, T., Clark, C. W., Mangel, M., & Munro, G. R. (1998). Implementing the precautionary principle in fiherie management through marine reerve. Ecological Application, 8(1), S72 S78. Li, E. A. L. (2000). Optimum Harveting with Marine Reerve. North American Journal of Fiherie Management, 20(6), Methot, Jr. R. D., & Wetzel, C. R. (2013). Stock ynthei: A biological and tatitical framework for fih tock aement and fihery management. Fiherie Reearch, 142, Munro, G. R. (1996). Approache to the economic of the management of high ea fihery reource: a ummary. Canadian Journal of Economic, 29(April Special Iue), Novaczek, I. (1995). Poible role for marine protected area in etablihing utainable fiherie in Canada. In N. L. Shackell & J. H. M. Willion (Ed.), Marine protected area and utainable fiherie (pp ). Centre for Wildlife and Conervation Biology, Acadia Univerity, Wolfville, Nova Scotia, Canada. Schaefer, M. B. (1954). Some apect of the dynamic of population important to the management of commercial marine fiherie. Bulletin of the Inter-American Tropical Tuna Commiion, 1, Copyright Copyright for thi article i retained by the author(), with firt publication right granted to the journal. Thi i an open-acce article ditributed under the term and condition of the Creative Common Attribution licene ( 64