Skip to main content

Windows of Opportunity for Sustainable Fisheries Management: The Case of Eastern Baltic Cod

Abstract

We study under which conditions a ‘window of opportunity’ for a change from an overfishing situation, with high fishing effort, but low stocks and catches, towards sustainable fishery management arises. Studying the Eastern Baltic cod fishery we show that at very low stock sizes (as they prevailed in the early 2000s) all interest groups involved in the fishery unanimously prefer maximum-sustainable-yield management (as prescribed by the management plan in place since 2007) over the previous overfishing situation. With increasing stock sizes, the present value of fishermen surplus would be higher when switching back to overfishing again, while other interest groups maintain their preference for sustainable fishery management.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. Nieminen et al. (2016) use game theory to study shared multispecies management with an age-structured Eastern Baltic Cod stock.

  2. We also ignore environmental stochasticity. For the case of Eastern Baltic Cod, Kapaun and Quaas (2013) show that environmental variability has a small effect on optimal catch quantities. On these grounds, we feel that the distributive effects of alternative fishery management scenarios can be well understood in a deterministic setting.

  3. As for consumers, we are considering Marshallian consumer surplus. For a quasi-linear utility function, a change in Marshallian consumer surplus is equivalent to the Hicksian measures of compensating or equivalent variation (Just et al. 2004). As we do not have information on consumption of goods other than fish, and as expenditures for fish are only a small fraction of income for the consumers of Baltic cod, we use Marshallian consumer surplus as the welfare measure for users of fish.

  4. In the Gordon-Schaefer specification, fishing effort is proportional to the exploitation rate \(F_t\).

  5. Under open-access conditions, US is proportional to FS. This can be verified by plugging (16) into (6) to derive US and into (9) with (10) to derive FS. Hence, it is obvious that the two curves should move parallel in the BAU scenario.

References

  • Andersen S, Harrison GW, Lau MI, Rutström EE (2008) Eliciting risk and time preferences. Econometrica 76:583–618

    Article  Google Scholar 

  • Anderson LG (1980) Necessary components of economic surplus in fisheries economics. Can J Fish Aquat Sci 37:858–870

    Article  Google Scholar 

  • Bailey M, Rashid Sumaila U, Lindroos M (2010) Application of game theory to fisheries over three decades. Fish Res 102(1–2):1–8

    Article  Google Scholar 

  • Baland J, Bjorvatn K (2013) Conservation and employment creation: can privatizing natural resources benefit traditional users? Environ Dev Econ 18:309–325

    Article  Google Scholar 

  • Blenckner T, Llope M, Mollmann C, Voss R, Quaas MF, Casini M, Lindegren M, Folke C, Chr Stenseth N (2015) Climate and fishing steer ecosystem regeneration to uncertain economic futures. Proc Biol Sci 282(1803):20142809

    Article  Google Scholar 

  • Boyce JR (2004) Instrument choice in a fishery. J Environ Econ Manag 47:183–206

    Article  Google Scholar 

  • Clark CW (1990) Mathematical bioeconomics: the optimal management of renewable resources, 2nd edn. Wiley, New York

    Google Scholar 

  • Clark CW, Clarke FH, Munro GR (1979) The optimal exploitation of renewable resource stocks: problems of irreversible investment. Econometrica 47:25

    Article  Google Scholar 

  • Copes P (1972) Factor rents, sole ownership and the optimum level of fisheries exploitation. Manch Sch Econ Soc Stud 40:145–163

    Article  Google Scholar 

  • Costello C, Grainger C (2015) Property rights, regulatory capture, and exploitation of natural resources. National Bureau of Economic Research, Cambridge

    Book  Google Scholar 

  • Dickson RR, Brander KM (1993) Effects of a changing windfield on cod stocks of the North Atlantic. Fish Oceanogr 2(3–4):124–153

    Article  Google Scholar 

  • EC (2007) Council Regulation No 1098/2007 of 18 September 2007 establishing a multiannual plan for the cod stocks in the Baltic Sea and the fisheries exploiting those stocks, amending Regulation (EEC) No 2847/93 and repealing Regulation (EC) No 779/97. Off J Eur Union (L 248):1–10

  • Faber M, Frank K, Klauer B, Manstetten R, Schiller J, Wissel C (2005) On the foundation of a general theory of stocks. Ecol Econ 55:155–172

    Article  Google Scholar 

  • FAO (2012) The state of world fisheries and aquaculture 2012. Food and Agriculture Organization of the United Nations, Rome, London

    Google Scholar 

  • Froese R, Proelß A (2010) Rebuilding fish stocks no later than 2015: will Europe meet the deadline? Fish Fish 11:194–202

    Article  Google Scholar 

  • Froese R, Quaas M (2011) Three options for rebuilding the cod stock in the eastern Baltic Sea. Mar Ecol Prog Ser 434:197–200

    Article  Google Scholar 

  • Grafton RQ, Kompas T, Hilborn RW (2007) Economics of overexploitation revisited. Science 318(5856):1601

    Article  Google Scholar 

  • Grainger CA, Costello C (2016) Distributional effects of the transition to property rights for a common-pool resource. Mar Resour Econ 31:1–26

    Article  Google Scholar 

  • Grainger CA, Parker DP (2013) The political economy of fishery reform. Annu Rev Resour Econ 5:369–386

    Article  Google Scholar 

  • Hannesson R (1983) Bioeconomic production function in fisheries: theoretical and empirical analysis. Can J Fish Aquat Sci 40:968–982

    Article  Google Scholar 

  • Hannesson R (2011) Game theory and fisheries. Annu Rev Resour Econ 3:181–202

    Article  Google Scholar 

  • Hoffmann J, Quaas MF (2016) Common pool politics and inefficient fishery management. Environ Resour Econ 63:79–93

    Article  Google Scholar 

  • Homans FR, Wilen JE (1997) A model of regulated open access resource use. J Environ Econ Manag 32:1–21

    Article  Google Scholar 

  • ICES (2012) Report of the Baltic Fisheries Assessment Working Group 2012 (WGBFAS), 12–19 April 2012, ICES Headquarters, Copenhagen. ICES CM 2012/ACOM:10

  • Johnson R, Libecap GD (1982) Contracting problems and regulation: the case of fishery. Am Econ Rev 72:1005–1022

    Google Scholar 

  • Just RE, Hueth DL, Schmitz AL (2004) The welfare economics of public policy: a practical approach to project and policy evaluation. Elgar, Cheltenham

    Google Scholar 

  • Kapaun U, Quaas MF (2013) Does the Optimal size of a fish stock increase with environmental uncertainties? Environ Resour Econ 54:293–310

    Article  Google Scholar 

  • Karpoff JM (1987) Suboptimal controls in common resource management: the case of the fishery. J Polit Econ 95:179–194

    Article  Google Scholar 

  • Klauer B, Manstetten R, Petersen T, Schiller J (2013a) Die Kunst langfristig zu denken: Wege zur Nachhaltigkeit, 1st edn. Nomos, Baden-Baden

    Book  Google Scholar 

  • Klauer B, Manstetten R, Petersen T, Schiller J (2013b) The art of long-term thinking: a bridge between sustainability science and politics. Ecol Econ 93:79–84

    Article  Google Scholar 

  • Kronbak LG (2004) The dynamics of an open-access fishery: Baltic Sea cod. Mar Resour Econ 19:459–479

    Article  Google Scholar 

  • Liu X, Lindroos M, Sandal L (2016) Sharing a fish stock when distribution and harvest costs are density dependent. Environ Resour Econ 63:665–686

    Article  Google Scholar 

  • Nielsen M (2006) Trade liberalisation, resource sustainability and welfare: the case of East Baltic cod. Ecol Econ 58:650–664

    Article  Google Scholar 

  • Nieminen E, Kronbak LG, Lindroos M (2016) International agreements in the multispecies Baltic Sea fisheries. Environ Resource Econ 65:109–134

    Article  Google Scholar 

  • Pauly D, Froese R (2012) Comments on FAO’s State of Fisheries and Aquaculture, or ‘SOFIA 2010’. Mar Policy 36:746–752

    Article  Google Scholar 

  • Péreau J, Doyen L, Little LR, Thébaud O (2012) The triple bottom line: meeting ecological, economic and social goals with individual transferable quotas. J Environ Econ Manag 63:419–434

    Article  Google Scholar 

  • Pintassilgo P, Kronbak LG, Lindroos M (2015) International fisheries agreements: a game theoretical approach. Environ Resour Econ 62:689–709

    Article  Google Scholar 

  • Quaas MF, Froese R, Herwartz H, Requate T, Schmidt JO, Voss R (2012) Fishing industry borrows from natural capital at high shadow interest rates. Ecol Econ 82:45–52

    Article  Google Scholar 

  • Regulation (EU) No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC.: EU

  • Ricker WE (1954) Stock and recruitment. J Fish Res Bd Can 11:559–623. doi:10.1139/f54-039

    Article  Google Scholar 

  • Samuelson PA (1974) Is the rent-collector worthy of his full hire? East Econ J 1:7–10

    Google Scholar 

  • Statistics Denmark (2016) REGNFI01: fishery, financial results and balance by account items. Accessed 10 June 2016

  • Stoeven MT, Quaas MF (2012) Privatizing renewable resources: who gains, who loses? Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics, 2012-02. Univ. Dep. of Economics, Kiel

  • Tahvonen O (2009) Economics of harvesting age-structured fish populations. J Environ Econ Manag 58:281–299

    Article  Google Scholar 

  • Tahvonen O, Quaas MF, Schmidt JO, Voss R (2013) Optimal harvesting of an age-structured schooling fishery. Environ Resour Econ 54:21–39

    Article  Google Scholar 

  • Turvey R (1964) Optimization and suboptimization in fishery regulation. Am Econ Rev 54(2,1):64–76

    Google Scholar 

  • Voss R, Quaas MF, Schmidt JO, Hoffmann J (2014) Regional trade-offs from multi-species maximum sustainable yield (MMSY) management options. Mar Ecol Prog Ser 498:1–12

    Article  Google Scholar 

  • Weitzman ML (1974) Free access vs private ownership as alternative systems for managing common property. J Econ Theory 8:225–234

    Article  Google Scholar 

  • Wilen JE (2000) Renewable resource economists and policy: what differences have we made? J Environ Econ Manag 39:306–327

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martin F. Quaas.

Appendix

Appendix

Conversion of an Age-Structured Model into a Biomass Model

To set up the age-structured model, we use a similar approach and notation as Tahvonen (2009) and Tahvonen et al. (2013). We use \(x_{st} \) to denote the number of fish in age group \(s=1,\ldots ,\, S\) at the beginning of period \(t=0,\, 1,\ldots \), where S is the oldest age group considered in the model. Age-specific survival rates \(\alpha _{s} >0\), age-specific proportions of mature individuals \(\gamma _{s} >0\), and mean weights of fish (in kilograms) \(w_{s} \), in age groups \(s=1,\ldots ,\, S\), all are assumed to be constant as in the standard biological stock assessments, such as ICES (2012) for the Eastern Baltic cod.

Harvesting takes place at the beginning of each period but after recruitment. The fisheries literature typically uses the instantaneous fishing mortality \(f_{t}\). Assuming that the instantaneous fishing mortality \(f_{t }\) is constant throughout the fishing season, we can use the Baranov catch equation that gives the fraction of the stock harvested during the entire season, i.e. the exploitation rate, as \(1-\mathrm{exp}\left( -f_{t} \right) \). Note that the instantaneous fishing mortality, i.e. the instantaneous exploitation rate, can be above one. Only in the limit \(f_{t} \rightarrow \infty \) the number of fish harvested over the entire fishing season would equal the stock size. The age-structured population model with harvesting activity is described by the following equations

$$\begin{aligned} x_{0t}&=\sum \limits _{s=1}^{S} \gamma _{s} \, w_{s} \, x_{st} \nonumber \\ x_{1,\, t+1}&=\varphi (x_{0t} ) \nonumber \\ x_{s+1,\, t+1}&= \alpha _{s} \, \left( x_{st} -(1-\exp (-q_{s}^{} f_{t} ))\, x_{st} \right) \, \text { for }s=1,\ldots ,\, S-2 \\ x_{S,\, t+1}&= \alpha _{S-1} \, \left( x_{S-1,\, t} -(1-\exp (-q_{S-1}^{} f_{t}^{} ))\, x_{S-1,\, t} \right) \nonumber \\&\quad +\alpha _{S} \, \left( x_{St} -(1-\exp (-q_{S}^{} f_{t} ))\, x_{St} \right) .\nonumber \end{aligned}$$
(23)

The spawning stock biomass \(x_{0t} \) is given by the sum of biomasses (age-specific weight \(w_{s}\) times number of fish in that age group, \(x_{st}\)) of the fraction of fish that is mature in the respective age classes, \(\gamma _{s} \). The number of recruits, \(x_{1t}\), is given by the stock-recruitment function \(\varphi (x_{0t} )\). Of all fish of age group s that remain in the sea after fishing, a fraction \(\alpha _{s} \) survives natural mortality (second last of the above equations). The equation describing the dynamics for the last age group differs from the equations for the younger age groups, as \(x_{St} \)captures all individuals that are of age S and older in year t.

For the Eastern Baltic cod stock, we consider eight age classes (\(S=8\)), as in the standard stock assessment by the International Council for the Exploration of the Sea (ICES 2012). We parameterize the age-structured fish population using data from the ICES (2012) assessment report. For age-specific weights in stock \(w_{s} \) we use the values for 2011; also age-specific maturities \(\gamma _{s} \) are directly taken from the assessment report. The survival rates \(\alpha _{s} \)are computed from natural mortalities. To account for age-dependent vulnerability to fishing gear, we multiply the fishing mortality rate with a catchability factor for each age class. For the Eastern Baltic Cod stock, these catchability factors are computed from the age-specific fishing mortalities averaged over the 5 years from 2002 to 2006. The age-specific parameter values are summarized in Table 1.

Table 1 Parameter values for age-structured fish population model from ICES (2012)

We assume a stock-recruitment function of the Ricker (1954) type,

$$\begin{aligned} \phi (x_{0} )=\beta _{1} \, x_{0} \, e^{-x_{0} /\beta _{2} }. \end{aligned}$$
(24)

This stock-recruitment function has a peak at \(x_{0} =\beta _{2} \), and is decreasing for spawning stocks larger than \(\beta _{2} \). Such a type of stock-recruitment relationship is an appropriate description of recruitment biology of Baltic cod, capturing in particular the cannibalism of older cod on juveniles. For the parameters of the stock-recruitment function, we use the estimates \(\beta _{1} =1.70\) and \(\beta _{2} =1/0.00182=549,000\) tons from Voss et al. (2014).

In order to transform the eight-dimensional population model into a biomass model, we compute the steady-state stock biomass and harvest biomass for different fishing mortality rates. We then use the equilibrium harvest and biomass from the age-structured population model to fit the surplus production function (Blenckner et al. 2015)

$$\begin{aligned} g(B_{t}^{} )=\frac{r}{\alpha } B_{t}^{} \left( 1-\left( \frac{B_{t}^{} }{K} \right) ^{\alpha } \right) \end{aligned}$$

We obtain estimates \(r=0.736\), \(K=1158\) thousand tons, and \(\alpha =1.441\). The graph of the resulting surplus-production function is shown in Fig. 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Quaas, M.F., Stoeven, M.T., Klauer, B. et al. Windows of Opportunity for Sustainable Fisheries Management: The Case of Eastern Baltic Cod. Environ Resource Econ 70, 323–341 (2018). https://doi.org/10.1007/s10640-017-0122-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10640-017-0122-y

Keywords

  • Sustainable resource use
  • Fisheries economics
  • Resource rent
  • Consumer surplus
  • Worker surplus

JEL Classification

  • Q22
  • Q28