Skip to main content


Front. Mar. Sci., 07 April 2022
Sec. Marine Pollution
Volume 9 - 2022 |

Fishing for Litter: Creating an Economic Market for Marine Plastics in a Sustainable Fisheries Model

Linh Nguyen1,2 Roy Brouwer3,1*
  • 1Department of Environmental Economics, Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  • 2Tinbergen Institute, Amsterdam, Netherlands
  • 3Department of Economics and the Water Institute, University of Waterloo, Waterloo, ON, Canada

This paper studies an economy specialized in fisheries facing a rising marine litter problem. We present a dynamic optimization model to explain the mechanism through which marine litter causes inefficiencies in the fishery sector. We do so by investigating the properties of the model when the marine litter externality is internalized through the price of fish. We find that if the marine litter externality is neglected, fish harvest increases, and ocean quality deteriorates. We subsequently explore the possibility of introducing an incentive scheme where marine litter can be traded in a hypothetical market. The introduction of a so-called fishing-for-litter market removes the inefficiencies caused by fishermen neglecting marine litter and provides a direct incentive for them to maximize overall welfare through resource recovery, i.e. by converting plastic waste into a new valuable resource.


Oceans are essential to global well-being and economic development (Bennett et al., 2019). They provide the necessary resources for sea fisheries in coastal communities. Especially in areas where other economic opportunities are scarce, sea fisheries provide a vital source of employment and income (Pascoe et al., 2019). However, our oceans are unfortunately heavily overfished and contaminated with marine litter (Campbell et al., 2016; Nash et al., 2017). Marine litter is present in every ocean (Cheshire et al., 2009; Eriksen et al., 2014; Pham et al., 2014) and 61-87% of it is made up of plastics (Barnes et al., 2009; Worm et al., 2017; Barboza et al., 2019). Plastics are extremely resistant to biodegradation and difficult to remove. Over time, large plastic items do not dissolve, but rather break down into tiny particles that can travel vast distances and spread throughout the marine environment (Law, 2017). Lighter pieces float at the sea surface, accumulate in ocean gyres or get washed ashore, while heavier pieces sink to the ocean floor where they gradually decompose (Maes et al., 2018; Kane et al., 2020). Plastics enter the marine food web through ingestion by marine organisms, and there is growing concern also for the potential consequences for human food safety and public health (Vethaak and Leslie, 2016; Leslie and Depledge, 2020). Marine litter originates from numerous economic sectors and activities. Two key contributing sectors are fisheries (e.g., accidental loss or deliberate dumping of buoys, nets, ropes, and other waste from fishing crew) and retail (e.g., plastic bags, bottles, packaging, cosmetics and personal care products) (Watkins et al., 2015; Löhr et al., 2017). Although marine litter has attracted increasing attention in recent years, relatively few studies have investigated their social costs (Brouwer et al., 2017).

The objective of this study is to examine how marine litter affects the fishing industry, one of the key industries that rely on the marine environment. Marine litter causes negative impacts on the fishery sector in a variety of ways, all of which result in either reduced revenues or increased costs (European Parliament, 2019). In order to mitigate the impacts of marine litter on the fishery sector, the environmental organization KIMO initiated the “Fishing for Litter” program in 2000 in co-operation with the Dutch Fisheries Association with the aim to clear the North Sea of litter. The program has since its initiation also been implemented in Belgium, Germany, the U.K., Ireland, Sweden, Norway, Denmark, Italy, Croatia and Spain. During their fishing activities, fishermen usually catch litter as a by-product in their nets. By providing them with hardwearing bags, the initiative encourages fishermen to collect and deposit litter onshore at designated waste disposal sites. The program has been shown to reduce the volume of litter accumulating in the oceans (OSPAR, 2007), thereby allowing fishermen to spend more time on their regular fish catching activities by reducing the amount of time they have to spend untangling litter from nets. The fishery sector is believed to be able to play a significant role in removing marine litter from oceans and seas (Ronchi et al., 2019).

This paper aims to incorporate the impacts of marine litter on marine fisheries in a dynamic economic optimization model to explain how marine litter leads to inefficiencies, and how these inefficiencies can be alleviated through the introduction of different incentive schemes. The main research question is twofold: what challenges does the fishery sector face when the externalities of marine litter are not accounted for, and how can these negative externalities be mitigated to benefit the fishery sector? To this end, we first compare the fishermen’s utility when marine litter externalities are internalized through the price of fish to a situation where the externalities are not accounted for in the fishermen’s decision-making. Here, we assume that increasing societal demand for cleaner oceans is reflected in the price for fish and we use a utility instead of a profit function to account for the fishery sector’s sense of stewardship for marine resources, which is typically omitted from conventional profit functions. Secondly, we explore the implications of creating a new economic market based on the existing “Fishing for Litter” scheme, where we add a novel element, namely that the litter caught by participating fishermen can be sold as part of national and international resource recovery programs.

The paper is organized as follows. The next section reviews the literature on the economic costs of marine litter. The third section summarizes the fishery economics literature and is followed in the fourth and fifth sections by a description of an optimal control model related to the economics of fishery with and without the litter externality. This model is extended in the one but last section, focusing on the creation of an economic market for fishing for litter. Finally, the last section concludes.

The Economic Cost of Marine Litter

The fishery sector faces various direct and indirect economic costs from marine litter. The direct costs typically consist of the labor and material costs spent on repairing and replacing damaged or lost gear including entangled propellers, fouled anchors and rudders, or blocked intake pipes and valves (European Parliament, 2019). Wallace (1990) reports that in the Eastern US, over 45% of the fishing vessels had their propellers disabled, over 30% had their gear fouled, and almost 40% had their engine cooling system clogged by plastic debris at some point during the time they are out to catch fish. In Irian Jaya, Indonesia, Nash (1992) finds that more than half of the gill net fish expeditions of a small fishery community had debris fouling their nets and as a result the community changed its fishing activities. Mcllgorm et al. (2011) report that floating debris becomes entangled in the propellers and affects the engine cooling system which may pose navigational hazards or immobility of vessels. In a survey among Scottish fishermen carried out by Mouat et al. (2010), 95% of the vessels reported to damage their nets on debris on the ocean floor, among others due to old wires on the seabed. On average, marine litter costs the Scottish fishing industry an estimated 12 to 13 million euros per year. This is equivalent to approximately 5% of the total annual revenues from fisheries (Mouat et al., 2010).

In addition, the sector also experiences indirect losses of earnings, for example due to reductions in the quality of fish. Micro-plastics can be taken up by small organisms such as zooplankton and shellfish at the bottom of the trophic chain from where they can move to the next level in the food web (Wright et al., 2013). Ingestion may block the digestive tract, alter the metabolism system and fat storage, which in turn can cause reduced feeding capacity and malnutrition. A stomach filled with plastic can create a false sense of satiation and ultimately lead to starvation. Toxic chemicals absorbed from plastics can furthermore lead to hormone disorders and reduce a fish’s reproductive capacity (Gregory, 2009; Lozano and Mouat, 2009). There is a growing body of evidence that shows that marine plastics pose human health risks through the food chain (Galloway, 2015; Lusher, 2015; Rochman, 2015; Barboza et al., 2019). Although it is difficult to establish and quantify the full extent of potential human health problems, plastics can carry toxic compounds which include persistent organic pollutants such as PCBs, DDT and bisphenol-A. These chemicals may disrupt the human endocrine system and give rise to various diseases if ingested in significant amounts (Thompson et al., 2009; Gallo et al., 2018). Once consumers become aware of these health impacts, the fishery sector may suffer significant losses if this leads to a drastic fall in demand for fish (Brouwer et al., 2015). For example, Van der Meulen et al. (2014) show that the release of information that mussels and oysters become smaller in size and absorb poisonous chemical substances in micro-plastics caused a loss of up to 0.7% of annual income in the UK aquaculture sector.

Another factor contributing to the indirect costs for fisheries is a phenomenon called “ghost fishing”. According to the United States’ National Oceanic and Atmospheric Administration (NOAA), ghost fishing occurs when derelict fishing gear, i.e. any fishing gear such as trawl nets, gill nets, traps, cages and pots discarded, lost or abandoned in the marine environment, continues to fish. The durable nature of materials used in fishing equipment means that they can continue to indeterminately trap and kill marine wildlife for decades. Ghost fishing has been identified as a key damage factor in commercial fisheries (Macfadyen et al., 2009), undermining the conservation efforts of vulnerable fish stocks (Sheavly and Register, 2007).

Finally, fisheries incur losses in revenue due to a reduction in their potential harvestable catch and the sustainability of their catch in general (Butler et al., 2013; Arthur et al., 2014; Bilkovic et al., 2014).

Methodological Approach and Novelty

Traditional economic fishery models, often called bio-economic models, can be used to study the effects of modifications in environmental quality on the commercial harvesting of fish stocks (Seijo et al., 1998; Prellezo et al., 2012). In this section, we specify how our model resembles and where it deviates from the standard bio-economic fishery model. Bio-economic models usually rely on some key assumptions, which we also utilize in our modelling framework here. We base our model on the common assumption that fishermen have the maximization of the discounted net present value of resource rents as their main objective (Arnason, 2009). They seek a long-term sustainable fish stock when deciding on their catch level (Clark et al., 2005).

Our theoretical framework is an extension of the Schaefer-Gordon model (Gordon, 1954; Schaefer, 1991) by using a dynamic specification. The dynamic approach allows us to analyze the adjustment process in which an optimal harvest level is attained, taking account of a discount rate. Compared to other dynamic modelling studies, we include the environmental quality component as an argument in the utility function rather than as an exogenous factor. This is in line with Freeman (1993), who extends the basic dynamic bio-economic model by including an additional explanatory variable to represent an environmental influence.

Contrary to previous modelling frameworks where environmental quality is not included as a control variable, changes in environmental quality occur endogenously in the model presented in this study, affecting both the objective function and the imposed constraint. Another important deviation from the standard fishery model is that the Schaefer-Gordon model specifies a profit function, while we express the optimization problem using a utility function. In this way, we are able to account for the fishery sector’s strong sense of stewardship for marine resources, as for example expressed by fishermen’s participation in voluntary initiatives such as Fishing for Litter, which is typically omitted from a conventional profit function.

The mathematical structure of our model is an application of optimal control theory to the management of fisheries. We adapt the standard optimal control model to analyze in a stylized fashion the externalities associated with marine litter and add in a “Fishing for Litter” market as an innovative solution. In the model extension, the paper is heading towards a solution for an environmentally sustainable fishery sector, which we define as the sustainable harvest of fish that can be realized based on fishermen’s efforts of litter recovery. Hence, an environmentally sustainable optimum is the maximum fish harvest, which maximizes utility of fishermen that can be sustained in the steady state subject to a constraint on ocean quality with litter catch effort. Given the objective of our study, we put special emphasis on sustaining a certain level of ocean quality while studying the economics of the fishery sector. To this end, we specify a logistic litter growth function instead of a logistic fish stock growth function usually described in the standard Schaefer-Gordon model. This constraint describes how (fast) litter in the ocean reaches its maximum capacity.

Model Building Blocks

Public Preferences for Ocean Quality

We consider an economy that supplies fish to the market which presumably has full information and no transaction costs. Market clearing conditions dictate that the equilibrium price reflects the marginal cost and utility attached to the last unit of fish traded on the market. We denote Ot as a general index for the current stock of ocean quality, which includes a wide range of factors including the quality of water and marine habitats, fish stocks, and the sustainability of ocean health; and Ht as the aggregate harvest of marine resources (fish). We assume that at any time t, the preferences of fish consumers are positively influenced by Ot. Public preferences are presented by their willingness to pay (WTP) for fish as:


where ϕ ∈ [0,1) is a measure of public preferences for ocean clean-up, and pHO>0,2pHO2<0.

Equation (1) can be interpreted as the inverse demand function for fish. Let us suppose for a moment that we have a price for ocean quality po. Then, at the optimal level of demand for fish we have:


where MRS is the marginal rate of substitution between ocean quality and fish harvest. If we assume that the price of ocean quality is 1 then equation (2) tells us that at the optimal level of demand for fish harvest, the price of fish equals marginal WTP, which measures how much a consumer is willing to sacrifice of ocean quality for a marginal amount of extra fish harvest. The trade-off between fish harvest and ocean quality plays out in our model as follows: when the level of fish harvest is small (and ocean quality is high), a consumer is willing to give up more of ocean quality to gain a little bit more of fish (pHO>0). Conversely, when the level of fish harvest is large (and ocean quality is low), a consumer is willing to give up less of ocean quality for a marginal gain in fish. In short, marginal WTP reflects the general public’s diminishing marginal utility (2pHO2<0).

Fisherman’s Utility

For simplicity, we assume that the revenues of a representative fisherman are a function of fish harvest and the price of fish, without any significant capital or labor costs. As a result, the total revenue is equal to the profit that the fishery owner obtains:


Equation (3) represents a production function of the fishery sector where Ht and Ot enter as input factors.

At the same time, the fisherman’s utility at time t is determined by his consumption level Ct and ocean quality Ot. The lifetime utility of the representative fisherman is given by an infinitely discounted sum of (logarithmic) instantaneous utility:

Ut=tu(Ct,Ot)eρtdt=t(ln Ct+β ln Ot)eρtdt(4)

where ρ ∈ (0,1) is the discount rate. The concavity of the logarithmic utility function indicates diminishing marginal utility as a result of an increase in consumption level aggregated across all fishermen. The explicit functional form of the utility function is needed to obtain a close-formed solution.

The Evolution of Ocean Quality

We follow the common approach in the economic growth and environment literature, which describes environmental quality as an accumulated stock of renewable resources (Bovenberg and Smulders, 1995; Smulders and Gradus, 1996). In our case, the ocean quality is a stock variable of renewable marine resources. We adhere to another convention in the literature (Becker, 1982; Cerina, 2007), that is, we define the stock of ocean quality as the difference between the maximum tolerable level of litter P¯ and the current litter amount Pt where 0<Pt<P¯:


Differentiating both sides with respect to time we obtain the law of evolution of ocean quality:


The fishery sector accumulates litter via the accidental or deliberate act of dumping fishing gear into the seas. Hence the more fishing activities take place, the more litter there is. We assume that litter increases proportionally with harvest level at a rate α. Several studies in the environmental-economics growth modelling literature adopt the assumption that the stock of pollution is absorbed and processed at a natural rate because nature has a regenerative capacity to compensate for the adverse pollution effects. We slightly deviate from this notion in our study and assume that litter is removed by existing man-made efforts at an exogenous rate 0 < m0 < 1 due to the non-decomposable nature of most marine litter (Kershaw, 2015). Combining the two assumptions, we arrive at the change in litter in time:


Combining (6) and (7) we obtain the motion equation of ocean quality:


Optimal Solution When Internalizing Marine Litter Impacts

In this section, the fishery owner is informed about public preferences for ocean quality when solving his utility maximization problem. In this case, we assume that the fisherman is aware that the unit price of fish pH is influenced by ocean quality Ot, as described in equation (1). Consumers’ WTP for fish will fall if they become, for example, concerned about the health consequences of having micro-plastics in the food chain, including fish products. Conversely, consumers’ WTP for fish will rise if they are assured about the clean ocean environment. The fisherman solves in this case the following optimization problem:

max{Ht}0{(HtOtϕ)+β ln (Ot)}eρtdt(9)
s.t. O˙t=m0(P¯Ot)αHt(10)

The externality of marine litter is internalized by substituting the explicit form of the fish price in equation (1). This is an optimal control problem with one state variable Ot and one control variable Ht. The first order condition and Euler equation are:


where μt is a period-t utility value of having one more unit of ocean quality in period t.

The resulting optimal dynamic system is described as:


The complete mathematical characterization of the system can be found in the Appendix to this paper.

Steady State

In equilibrium, there is no change in ocean quality, i.e., O˙=0. From (13) and (14), we can see that O˙=0 implies H˙=0.


The existence of a unique steady state can be proven geometrically. The two loci are two straight lines with H1(O) having a positive slope and H2(O) having a negative slope and a positive vertical intercept. Therefore, the two loci intersect in the positive orthant (O,H) plane. The unique steady state is given by:


The system exhibits instability. The equilibrium (Oso,Hso) is a local saddle point for the system (13) and (14) (see the Appendix for the complete proof). All the steady state values of the relevant variables (the control variable H and the state variable O) are now expressed as functions of the parameters of the model.

Comparative Analysis

The steady state level of ocean quality increases with the general public’s care for ocean quality (Osoϕ>0), with the fishermen’s care for ocean quality (Osoβ>0), when litter is removed at a faster pace (Osom0>0), and with the maximum level of litter in the oceans (OSOP¯>0). However, it decreases when fishermen care less about future utility (Osoρ>0).

Similarly, the steady state level of fish harvest decreases with the general public’s care for ocean quality (Hsoϕ<0) and fishermen’s care for ocean quality (Hsoβ<0). It increases when litter is removed at a faster pace (Hsomo>0), ocean quality is more capable to carry a higher maximum level of litter (Hsop>0) and when fishermen care less about future utility (HsoP¯>0).

It is interesting to analyze how the fisherman’s revenues react with regards to his preference for ocean quality β. As β grows, its total effect on revenue is ambiguous: an increasing β leads to a higher steady-state value of ocean quality O, which increases the WTP and hence the revenue. Yet an increasing β causes a lower steady-state value of fish harvest H which reduces revenue. At a low β, an increase in the fisherman’s care for ocean quality eventually leads to an increase in his revenue. This is because a low level of β means a low level of O. When O is low, the general public attaches a high marginal value to even a slight improvement in ocean quality. Therefore, their WTP grows significantly when O increases, enough to override the lower fish harvest and result in a higher revenue. By the same token, at a high enough β, the marginal value and WTP that the general public attaches to an improvement in ocean quality is no longer high enough to compensate for the reduction in fish harvest due to too much care for ocean quality from fishermen, so that the revenue inevitably decreases in this case. These findings might be particularly interesting for policymakers. When fishermen become aware of the general public’s concerns for marine litter, policies that raise fishermen’s awareness and stimulate them to participate in litter recovery initiatives might help to mitigate the issue and enhance the fishery sector’s economic development.

Solution Without Litter Externality

Unlike in the optimal case, often fishermen are not fully informed about public preferences for ocean quality. They may not realize that the marine litter externality can affect their revenues through consumers’ WTP. Hence, it is appropriate here to assume that the fisherman takes the price of fish as given. He solves the following optimal control problem with one state variable Ot and one control variable Ht:

max{Ht}0{ln (HtpH)+β ln (Ot)}eρtdt(19)
s.t. O˙t=m0(P¯Ot)αHt(20)

The externality of marine litter is neglected in the fisherman’s revenue function by keeping the price of fish constant, rather than substituting its explicit form as in equation (1). The first order condition and Euler equation are:


where μt is a period-t utility value of having one more unit of ocean quality in period t. The resulting optimal dynamic system is characterized as follows:


The mathematical characterization of the system can be found in the Appendix to this paper.

Steady State

We are interested in a sustainable equilibrium in which there is no change in ocean quality (O˙=0). From (23) and (24), we can see that O˙=0 implies H˙=0.


The existence of a unique steady state can be proven geometrically as before. The unique steady state is given by:


This system exhibits instability. The equilibrium (Oe,He) is a local saddle point for the system (23) and (24) (see the Appendix for the complete proof). The steady state values differ from (Oso,Hso) in that the term ϕ is no longer present because the fisherman takes public preferences as given.

Compared with the optimal case in the previous section, we will always have Hso > He and Oso > Oe. When the litter externality is not accounted for in the price of fish, fishermen focus only on harvesting more fish and do not care for ocean quality. The reason lies in the absence of the elasticity of public WTP with respect to ocean quality ϕ. Without understanding the public’s care and concerns for marine litter, fishermen are not incentivized to care either.

In this case, the steady state value for O is not a function of public preferences for ocean quality (ϕ). Consequently, when β = 0 (fishermen do not care for ocean quality), ocean quality drops to 0. This is of course at the extreme, but it still shows how imperative it can be for fishermen to know that marine litter concerns can affect consumers’ WTP and in turn affect their revenues.

Creating a Market for Marine Plastics Recovery

Public Preferences for Marine Plastics Recovery

The initiative “Fishing for Litter” aims to mitigate the impacts of plastics pollution in oceans and seas by giving fishermen bags to separate litter from their fish catch. We go one step further by imagining there is societal demand and hence a WTP for this litter (e.g. for resource recovery purposes), such that the litter caught and brought ashore can be traded on a hypothetical market. In this way, fishermen are provided a direct financial incentive to start fishing for litter besides, or instead of, fishing for fish. This could be made possible through introducing a new resource recovery sector that demands the litter caught from the fishery sector (Dijkstra et al., 2021).

Fisherman’s Utility

A fisherman dedicates part of his time to catching litter which he could have spent on catching fish. This implies that the current revenue is reduced to:


where l represents the fraction of a fisherman’s revenue devoted to catch litter or the fisherman’s effort in catching litter. However, since the fisherman can trade the litter recovered in the market at a price, he can gain back some, all, or more than what he has invested (γ). The total revenue that the fisherman obtains is therefore:


The lifetime utility of a representative fisherman is given by the infinitely discounted sum of instantaneous utilities as specified in the equation below:

Ut=t(HtpHlHtpH+γlHtpH)+βln (Ot)]eρtdt(31)

where ρ ∈ (0,1) is the discount rate.

The Effect of Fishing for Litter

The effort that fishermen put into catching litter is introduced such that the rate of litter removal is increased as the amount of effort in catching litter increases. Let m(m0,l) be the function of litter recovery where m0 is the exogenous removal rate (due to external factors) and l is the fishing-for-litter effort. We further assume that m(m0,l) has the following characteristics:

m(m0,l)l>0   ·l>0(34)

The first assumption (eq. 32) means that when fishermen exert no effort to catch litter, litter is removed at exogenous rate m0. The second assumption (eq. 33) tells us that if fishermen exert all effort into catching litter, the whole stock of current litter is removed. The third assumption (eq. 34) tells us that the recovery function is increasing in effort l.

We assign the following explicit form to m(m0,l) that satisfies these three assumptions:


The motion equation of ocean quality now becomes:


Fishing for Litter

We showed in the previous section that a deviation from the optimal solution occurs when the externality of marine litter is not captured in the production function. What we will see in this section is that an appropriate policy design such as a market for trading marine litter creates incentives which induce fishermen to replicate the optimal solution. The fisherman’s optimization problem now becomes:

max{Ht}0{ln (1l+γl)HtpH+β ln (Ot)}eρtdt(37)
s.t. O˙t=[m0+(1m0)l](P¯Ot)αHt(38)

Similar to problem (19), a fisherman maximizes his lifetime utility taking public preferences as given. This is again an optimal control problem with one state variable Ot and one control variable Ht.

The first order condition and Euler equation are:


The resulting optimal dynamic system is:


Steady State

We are interested in an equilibrium which implies sustainability for ocean quality, i.e. O˙=0. This implies that H˙=0:


The existence of a unique steady state can be proven geometrically as before. The steady state is given by:


The equilibrium (Ofl,Hfl) is a local saddle point for the system described in equations (41) and (42) (see the Appendix for the proof). Similar to the steady state values in (27) and (28), (Ofl,Hfl) do not contain the term ϕ because the fisherman takes public preferences as given. Nevertheless, (Ofl,Hfl) contains the term l which measures fishermen’s efforts for litter recovery. This was not the case in (27) and (28).

Comparative Analysis

Comparing the fish harvest to the second model, we have:

Hfl=He  when l=0Hfl>Oe    l>0

This shows that an effort in fishing-for-litter by fishermen always increases the fish harvest compared to the case when marine litter is not accounted for. Additionally, the more they invest in fishing for litter, the more fish harvest fishermen have Hl>0.

The same results applied to ocean quality. We have:

Ofl=Oe  when l=0Ofl>Oe   l>0

This indicates that as long as fishermen put effort into catching litter, ocean quality will always be better compared to the case where litter is not accounted for. Additionally, the more effort fishermen put into catching litter, the better ocean quality will get (Ol>0). An increase in fishermen’s fishing-for-litter efforts gives rise to an increase in fish harvest. Similarly, when O is low, the marginal economic value that fishermen assign to a slightly improved ocean quality is high, so they are expected to invest more effort in catching litter. As a consequence, an increase in ocean quality allows fishermen to catch more fish.

Clearly with a market for plastics recovery, fishermen are better off in terms of both fish harvest and ocean quality compared to a situation where plastic litter is not accounted for. The difference lies in the effort fishermen put into fishing for litter. Although fishermen are not informed about public preferences for ocean quality, the marine litter externality is directly accounted for in fishermen’s behavior. In essence, a market for litter recovery has transferred public preferences for less marine litter to fishermen and created an active response action within the sector. More specifically, public WTP for plastic litter recovery provides fishermen with a direct incentive to catch more litter. This policy has proven to tackle plastic litter issues as well as enhance the development of the fishery sector, even if fishermen do not have full information about public preferences.


At the heart of the global plastics problem is a linear economy. Most of the time, producers provide, and consumers buy single-use items that are disposable or have planned obsolescence. This externality of global consumption patterns has brought about tremendous costs to the oceans and seas from which we derive many benefits. Our results chart a way forwards towards a circular economy in which marine plastics are recovered and repurposed for alternative use or recycled into a new product (Dijkstra et al., 2020). The shift towards a sustainable plastics economy, i.e. an economy with plastics that have a more durable or even sustainable life cycle, can bring about better ocean quality and ultimately more marine resources for the fishery sector.

This paper analyses a solution to the marine plastics problem by presenting a dynamic economic optimization model in which the fishery sector maximizes its life-time utility as a steward of our oceans and seas. We analyze the dynamic properties of the model in two distinct versions. We find that when the plastic litter externality is not internalized through the price of fish, the fish harvest is higher and ocean quality deteriorates compared to a situation where marine litter is internalized through the price of fish. We then analyze a possible solution, namely an incentive scheme based on the existing “Fishing for Litter” initiative, where the public’s WTP for plastic litter recovery encourages fishermen to catch and remove the plastics from our oceans and seas. We conclude that a market for marine plastics recovery provides fishermen with a direct incentive to catch litter. As a result, the policy effectively tackles the global marine litter problem and contributes at the same time to the development of a more efficient and sustainable fishery sector.

We acknowledge that a possible limitation of our model is that the results might depend on the specific choice of the functional form of the utility function. This particular representation is chosen because it enables us to develop a tractable model of dynamic optimization that provides insights into the fishing for litter decision-making process over time. A more generic form would not have teased out this distinction. However, a more general constant elasticity of substitution (CES) function might allow for a simpler and perhaps different solution, which is worth exploring in the future. Another extension of the model would be the inclusion of the capital, labor and material costs in the profit function, including the damage costs of marine litter to catch rates.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author Contributions

LN: conceptualization, methodology, formal analysis, and writing–original draft. RB: conceptualization, methodology, writing–review and editing, and supervision. All authors contributed to the article and approved the submitted version.


This work was funded under the European Horizon 2020 project CLAIM: Cleaning Litter by developing and Applying Innovative Methods in European Seas (Grant agreement 774586).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Arnason R. (2009). Fisheries Management and Operations Research. Eur. J. Oper. Res. 193 (3), 741–751. doi: 10.1016/j.ejor.2007.07.028

CrossRef Full Text | Google Scholar

Arthur C., Sutton-Grier A. E., Murphy P., Bamford H. (2014). Out of Sight But Not Out of Mind: Harmful Effects of Derelict Traps in Selected US Coastal Waters. Mar. Pollut. Bull. 86 (1-2), 19–28. doi: 10.1016/j.marpolbul.2014.06.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Barboza L. G. A., Cózar A., Gimenez B. C. G., Barros T. L., Kershaw P. J., Guilhermino L. (2019). Macroplastics Pollution in the Marine Environment, in World Seas: An Environmental Evaluation. 2nd Edn. Ed. Sheppard C. (Cambridge, MA: Academic Press), 305–328.

Google Scholar

Barnes D. K., Galgani F., Thompson R. C., Barlaz M. (2009). Accumulation and Fragmentation of Plastic Debris in Global Environments. Philos. Trans. R. Soc. B: Biol. Sci. 364 (1526), 1985–1998. doi: 10.1098/rstb.2008.0205

CrossRef Full Text | Google Scholar

Becker R. A. (1982). Intergenerational Equity: The Capital-Environment Trade-Off. J. Environ. Econ. Manage. 9 (2), 165–185. doi: 10.1016/0095-0696(82)90020-1

CrossRef Full Text | Google Scholar

Bennett N. J., Cisneros-Montemayor A. M., Blythe J., Silver J. J., Singh G., Andrews N., et al. (2019). Towards a Sustainable and Equitable Blue Economy. Nat. Sustainability 2 (11), 991–993. doi: 10.1038/s41893-019-0404-1

CrossRef Full Text | Google Scholar

Bilkovic D. M., Havens K., Stanhope D., Angstadt K. (2014). Derelict Fishing Gear in Chesapeake Bay, Virginia: Spatial Patterns and Implications for Marine Fauna. Mar. Pollut. Bull. 80 (1-2), 114–123. doi: 10.1016/j.marpolbul.2014.01.03

PubMed Abstract | CrossRef Full Text | Google Scholar

Bovenberg A. L., Smulders S. (1995). Environmental Quality and Pollution-Augmenting Technological Change in a Two-Sector Endogenous Growth Model. J. Public Econ. 57 (3), 369–391. doi: 10.1016/0047-2727(95)80002-Q

CrossRef Full Text | Google Scholar

Brouwer R., Galantucci M., Hadzhiyska D., Ioakeimidis C., Leermakers A., Ouderdorp H., et al. (2015). Socio-Economic Assessment of the Costs of Marine Litter, D 4.11. Developed Under CleanSea Project Co-Funded by the European Union Seventh Framework Programme Under Grant Agreement, (308370) (Amsterdam, Netherlands: Vrije Universiteit Amsterdam).

Google Scholar

Brouwer R., Hadzhiyska D., Ioakeimidis C., Ouderdorp H. (2017). The Social Costs of Marine Litter Along European Coasts. Ocean Coastal Manage. 138, 38–49. doi: 10.1016/j.ocecoaman.2017.01.011

CrossRef Full Text | Google Scholar

Butler J. R. A., Gunn R., Berry H. L., Wagey G. A., Hardesty B. D., Wilcox C. (2013). A Value Chain Analysis of Ghost Nets in the Arafura Sea: Identifying Trans-Boundary Stakeholders, Intervention Points and Livelihood Trade-Offs. J. Environ. Manage. 123, 14–25. doi: 10.1016/j.jenvman.2013.03.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Campbell L. M., Gray N. J., Fairbanks L., Silver J. J., Gruby R. L., Dubik B. A., et al. (2016). Global Oceans Governance: New and Emerging Issues. Annu. Rev. Environ. Resour. 41 (1), 517–543. doi: 10.1146/annurev-environ-102014-021121

CrossRef Full Text | Google Scholar

Cerina F. (2007). Tourism Specialization and Environmental Sustainability in a Dynamic Economy. Tourism Econ. 13 (4), 553–582. doi: 10.5367/000000007782696032

CrossRef Full Text | Google Scholar

Cheshire A. C., Adler E., Barbière J., Cohen Y., Evans S., Jarayabhand S., et al. (2009). UNEP/IOC Guidelines on Survey and Monitoring of Marine Litter. UNEP Regional Seas Reports and Studies, 18. IOC Technical Serious No. 83 (Nairobi: UNEP).

Google Scholar

Clark C. W., Munro G. R., Sumaila U. R. (2005). Subsidies, Buybacks, and Sustainable Fisheries. J. Environ. Econ. Manage. 50 (1), 47–58. doi: 10.1016/j.jeem.2004.11.002

CrossRef Full Text | Google Scholar

Dijkstra H., van Beukering P., Brouwer R. (2020). Business Models and Sustainable Plastic Management: A Systematic Review of the Literature. J. Cleaner Prod. 258, 120967. doi: 10.1016/j.jclepro.2020.120967

CrossRef Full Text | Google Scholar

Dijkstra H., van Beukering P., Brouwer R. (2021). In the Business of Dirty Oceans: Overview of Startups and Entrepreneurs Managing Marine Plastic. Mar. Pollut. Bull. 162, 111880. doi: 10.1016/j.marpolbul.2020.111880

PubMed Abstract | CrossRef Full Text | Google Scholar

Eriksen M., Lebreton L. C. M., Carson H. S., Thiel M., Moore C. J., Borerro J. C., et al. (2014). Plastic Pollution in the World’s Oceans: More Than 5 Trillion Plastic Pieces Weighing Over 250,000 Tons Afloat at Sea. PloS One 9 (12), 111913. doi: 10.1371/journal.pone.0111913

CrossRef Full Text | Google Scholar

European Parliament (2019). “Report on the Impact on Fisheries of Marine Litte/2160(INI)),” in Committee on Fisheries Rapporteur: Catherine Chabaud. RR\1225949EN.Docx, Brussels. Available at:

Google Scholar

Freeman A. M. (1993). The Measurement of Environmental and Resource Values: Theory and Methods (Washington: Resources for the Future).

Google Scholar

Gallo F., Fossi C., Weber R., Santillo D., Sousa J., Ingram I., et al. (2018). Marine Litter Plastics and Microplastics and Their Toxic Chemicals Components: The Need for Urgent Preventive Measures. Environ. Sci. Eur. 30 (1), 1–14. doi: 10.1186/s12302-018-0139-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Galloway T. S. (2015). “Micro- and Nano-Plastics and Human Health,” in Marine Anthropogenic Litter. Eds. Bergmann M., Gutow L., Klages M. (Cham: Springer), 343–366.

Google Scholar

Gordon H. S. (1954). “The Economic Theory of a Common Property Resource: The Fishery,” in Classic Papers in Natural Resource Economics. Ed. Gopalakrishnan C. (London: Palgrave Macmillan), 178–203.

Google Scholar

Gregory M. R. (2009). Environmental Implications of Plastic Debris in Marine Settings – Entanglement, Ingestion, Smothering, Hanger’s on, Hitch-Hiking and Alien Invasions. Philos. Trans. R. Soc. B: Biol. Sci. 364 (1526), 2013–2025. doi: 10.1098/rstb.2008.0265

CrossRef Full Text | Google Scholar

Kane I. A., Clare M. A., Miramontes E., Wogelius R., Rothwell J. J., Garreau P., et al. (2020). Seafloor Microplastic Hotspots Controlled by Deep-Sea Circulation. Science 368 (6495), 1140–1145. doi: 10.1126/science.aba5899

PubMed Abstract | CrossRef Full Text | Google Scholar

Kershaw P. J. (2015). Biodegradable Plastics and Marine Litter. Misconceptions, Concerns and Impacts on Marine Environments (Nairobi: Technical Report, United Nations Environment Programme (UNEP).

Google Scholar

Law K. L. (2017). Plastics in the Marine Environment. Annu. Rev. Mar. Sci. 9 (1), 205–229. doi: 10.1146/annurev-marine-010816-060409

CrossRef Full Text | Google Scholar

Leslie H. A., Depledge M. H. (2020). Where is the Evidence That Human Exposure to Microplastics Is Safe? Environ. Int. 142, 105807. doi: 10.1016/j.envint.2020.105807

PubMed Abstract | CrossRef Full Text | Google Scholar

Löhr A., Savelli H., Beunen R., Kalz M., Ragas A., Van Belleghem F. (2017). Solutions for Global Marine Litter Pollution. Curr. Opin. Environ. Sustainability 28, 90–99. doi: 10.1016/j.cosust.2017.08.009

CrossRef Full Text | Google Scholar

Lozano R. L., Mouat J. (2009). Marine Litter in the North-East Atlantic Region: Assessment and Priorities for Response (London: KIMO International Report).

Google Scholar

Lusher A. (2015). “Microplastics in the Marine Environment: Distribution, Interactions and Effects,” in Marine Anthropogenic Litter. Eds. Bergmann M., Gutow L., Klages M. (Cham, Heidelberg, New York, Dordrecht, London: Springer), 245–307.

Google Scholar

Macfadyen G., Huntington T., Cappell R. (2009). Abandoned, Lost or Otherwise Discarded Fishing Gear. UNEP Regional Seas Reports and Studies, 185. FAO Fisheries and Aquaculture Technical Paper No. 523 (Rome: UNEP/FAO).

Google Scholar

Maes T., Barry J., Leslie H. A., Vethaak A. D., Nicolaus E. E. M., Law R. J., et al. (2018). Below the Surface: Twenty-Five Years of Seafloor Litter Monitoring in Coastal Seas of North West Europ–2017). Sci. Total Environ. 630, 790–798. doi: 10.1016/j.scitotenv.2018.02.245

PubMed Abstract | CrossRef Full Text | Google Scholar

Mcllgorm A., Campbell H. F., Rule M. J. (2011). The Economic Cost and Control of Marine Debris Damage in the Asia-Pacific Region. Ocean Coastal Manage. 54 (9), 643–651. doi: 10.1016/j.ocecoaman.2011.05.007

CrossRef Full Text | Google Scholar

Mouat J., Lozano R. L., Bateson H. (2010). Economic Impacts of Marine Litter (Lerwick, Shetland, UK: Kommunenes Internasjonale Miljøorganisasjon. Report).

Google Scholar

Nash A. D. (1992). Impacts of Marine Debris on Subsistence Fishermen An Exploratory Study. Mar. Pollut. Bull. 24 (3), 150–156. doi: 10.1016/0025-326X(92)90243-Y

CrossRef Full Text | Google Scholar

Nash K. L., Cvitanovic C., Fulton E. A., Halpern B. S., Milner-Gulland E. J., Watson R. A., et al. (2017). Planetary Boundaries for a Blue Planet. Nat. Ecol. Evol. 1 (11), 1625–1634. doi: 10.1038/s41559-017-0319-z

PubMed Abstract | CrossRef Full Text | Google Scholar

OSPAR Commission (2007). Guidelines on How to Develop a Fishing-for-Litter Project. Reference Number: 2007–10. (London, UK).

Google Scholar

Pascoe S., Cannard T., Dowling N. A., Dichmont C. M., Breen S., Roberts T., et al. (2019). Developing Harvest Strategies to Achieve Ecological, Economic and Social Sustainability in Multi-Sector Fisheries. Sustainability 11 (3), 644. doi: 10.3390/su11030644

CrossRef Full Text | Google Scholar

Pham C. K., Ramirez-Llodra E., Alt C. H. S., Amaro T., Bergmann M., Canals M., et al. (2014). Marine Litter Distribution and Density in European Seas, From the Shelves to Deep Basins. PloS One 9 (4), e95839. doi: 10.1371/journal.pone.0095839

PubMed Abstract | CrossRef Full Text | Google Scholar

Prellezo R., Accadia P., Andersen J. L., Andersen B. S., Buisman E., Little A., et al. (2012). A Review of EU Bio-Economic Models for Fisheries: The Value of a Diversity of Models. Mar. Policy 36 (2), 423–431. doi: 10.1016/j.marpol.2011.08.003

CrossRef Full Text | Google Scholar

Rochman C. M. (2015). “The Complex Mixture, Fate and Toxicity of Chemicals Associated With Plastic Debris in the Marine Environment,” in Marine Anthropogenic Litter. Eds. Bergmann M., Gutow L., Klages M. (Cham: Springer), 117–140.

Google Scholar

Ronchi F., Galgani F., Binda F., Mandić M., Peterlin M., Tutman P., et al. (2019). Fishing for Litter in the Adriatic-Ionian Macroregion (Mediterranean Sea): Strengths, Weaknesses, Opportunities and Threats. Mar. Policy 100, 226–237. doi: 10.1016/j.marpol.2018.11.041

CrossRef Full Text | Google Scholar

Schaefer M. (1991). Some Aspects of the Dynamics of Populations Important to the Management of Commercial Fisheries. Bull. Math. Biol. 53 (1-2), 253–279. doi: 10.1016/S0092-8240(05)80049-7

CrossRef Full Text | Google Scholar

Seijo J. C., Defeo O., Salas S. (1998). Fisheries Bioeconomics: Theory, Modelling and Management (Rome: Food and Agriculture Organization of the United Nations, Technical Paper 368), 108p.

Google Scholar

Sheavly S. B., Register K. M. (2007). Marine Debris and Plastics: Environmental Concerns, Sources, Impacts and Solutions. J. Polym. Environ. 15 (4), 301–305. doi: 10.1007/s10924-007-0074-3

CrossRef Full Text | Google Scholar

Smulders S., Gradus R. (1996). Pollution Abatement and Long-Term Growth. Eur. J. Political Economy 12 (3), 505–532. doi: 10.1016/S0176-2680(96)00013-4

CrossRef Full Text | Google Scholar

Thompson R. C., Moore C. J., vom Saal F. S., Swan S. H. (2009). Plastics, the Environment and Human Health: Current Consensus and Future Trends. Philos. Trans. R. Soc. London B: Biol. Sci. 364 (1526), 2153–2166. doi: 10.1098/rstb.2009.0053

CrossRef Full Text | Google Scholar

Van der Meulen M. D., De Vriese L., Lee J., Maes T., Van Dalfsen J. A., Huvet A., et al. (2014). Socio-Economic Impact of Microplastics in the 2 Seas, Channel and France Manche Region: An Initial Risk Assessment (Merelbeke, Belgium: MICRO Interreg project Iva). Available at:

Google Scholar

Vethaak A. D., Leslie H. A. (2016). Plastic Debris Is a Human Health Issue. Environ. Sci. Technol. 50, 6825–6826. doi: 10.1021/acs.est.6b02569

PubMed Abstract | CrossRef Full Text | Google Scholar

Wallace B. (1990). “How Much do Commercial and Recreational Fishermen Know About Marine Debris and Entanglement? Part 1,” in Proceedings of the Second International Conference on Marine Debris April 2–7, 1989, Honolulu, Hawaii. NOAA-TM-NMFS-SWFSC-154 (Washington, DC: Dept of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service), 1140–1148.

Google Scholar

Watkins E., ten Brink P., Withana S., Mutafoglu K., Schweitzer J.-P., Russi D., et al. (2015). “Marine Litter: Socio-Economic Study,” (London, Brussels: Institute for European Environmental Policy).

Google Scholar

Worm B., Lotze H. K., Jubinville I., Wilcox C., Jambeck J. (2017). Plastic as a Persistent Marine Pollutant. Annu. Rev. Environ. Resour. 42 (1), 1–26. doi: 10.1146/annurev-environ-102016-060700

CrossRef Full Text | Google Scholar

Wright S. L., Thompson R. C., Galloway T. S. (2013). The Physical Impacts of Microplastics on Marine Organisms: A Review. Environ. Pollut. 178, 483–492. doi: 10.1016/j.envpol.2013.02.031

PubMed Abstract | CrossRef Full Text | Google Scholar


Mathematical Characterization of the Optimal Solution Model

The Pontryagin maximum principle states that we can solve the optimization problem using a standard Hamiltonian function. We use the Hamiltonian to directly arrive at the time evolution of the system so that we can predict what state the system will evolve into after an infinitesimal interval of time elapses. In the optimal solution model, the fisherman’s problem is:

max{Ht}0{(HtOtϕ)+β ln (Ot)}eρtdt
s.t. O˙t=m0(P¯Ot)αHt

Using the present-value multiplier λt, we define the present-value Hamiltonian:


First order conditions:


Note that H explicitly depends on time. Using the current-value multiplier μt, we define the current-value Hamiltonian, which does not explicitly depend on time as follows:


where μt = eρt γt which implies that μ˙t=ρμt+λ˙teρt.

The new first order conditions become:

HHt=0   HAHt=0

From the new first order conditions, we obtain the first order condition and Euler equation:


Differentiating the first order conditions and equating it to the Euler equation, we obtain the optimal dynamic system:


Proof That the Steady State (Oso, Hso) Is a Local Saddle Point:

Rewrite system (13) and (14) as:


Linearizing the system around the steady state (Oso,Hso) we have:




So that detJ=(m0+ρ)m0(m0+ρ)2ϕ+β<0

So the unique steady state is a local saddle point. ■

Proof That the Steady State (Oe, He) Is a Local Saddle Point:

Rewrite system (23) and (24) as:


Linearizing the system around the steady state (Oe,He) we have:




So that detJ=(m0+ρ)m0(m0+ρ)2β<0

So the unique steady state is a local saddle point. ■

Proof That the Steady State Ofl, Hfl Is a Local Saddle Point:

Rewrite system (41) and (42) as:


Linearizing the system (41) and (42) around the steady state (Ofl,Hfl), we have:




So that

det J=[ρ+m0+(1m0)l][m0+(1m0)l][ρ+m0+(1m0)l]2β< 0

So the unique steady state is a local saddle point. ■

Keywords: marine litter, fishing for litter, plastics pollution, sustainable fishery, resource recovery

Citation: Nguyen L and Brouwer R (2022) Fishing for Litter: Creating an Economic Market for Marine Plastics in a Sustainable Fisheries Model. Front. Mar. Sci. 9:722815. doi: 10.3389/fmars.2022.722815

Received: 09 June 2021; Accepted: 09 March 2022;
Published: 07 April 2022.

Edited by:

Christian Joshua Sanders, Southern Cross University, Australia

Reviewed by:

Leandra Regina Gonçalves, Universidade de São Paulo, Brazil
Hans Uwe Dahms, Kaohsiung Medical University, Taiwan

Copyright © 2022 Nguyen and Brouwer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Roy Brouwer,