Working Group members were notified in advance about the second survey and agreed to a short turnaround time. The survey was administered during a fishery closure, during which time all commercial fishers were onshore. The survey was only open for one week (the last week of the closure), during which 28 responses were received. From these, a total of 18 useable responses were received, with the other 10 respondents only providing details of their involvement with the fishery and no assessments of the harvest strategy options. Four respondents answered most, but not all of the harvest strategy related questions. These were included in the set of usable responses, with the average response of the rest of the group used to replace the missing values in the subsequent analyses. Two-thirds (12) of the usable responses were from Working Group members, with the remainder from individuals not involved in determining the harvest strategies. The distribution of the responses by stakeholder group is shown in Figure 5.

**Figure 5.** Impact survey response by stakeholder group.

#### 4.2.1. Harvest Strategy Impacts

The expected (perceived) impact of each harvest strategy option against the objectives is shown in Figure 6. The charter sector allocation option is generally expected to provide benefits against nearly all objectives, with the possible exception of two objectives: discard mortality (Ecol1.2.2) (possibly due to a perception of some respondents that the quota will encourage high-grading and discarding as in commercial fisheries, although the current recreational trip limits are also essentially a quota) and costs of management (Econ2.5) (possibly due to a perception of higher monitoring, surveillance and control costs). The addition of environmental overrides is generally considered

to have ecological sustainability benefits, but potentially negative consequences for the economic objectives. Social outcomes are expected to be mixed, with improvement against some objectives (e.g., Social4.2.1 and Social4.2.3, both relating to improved social perceptions) and losses in other areas (Social4.3.1—access to local seafood). The addition of spatially explicit control rules and environmental overrides is expected to produce a similar pattern with respect to ecological and economic outcomes as the previous option, although the magnitude and uncertainty around some of these impacts is greater (e.g., Ecol1.1.2, Ecol1.2.1, Ecol1.2.3, Econ2.2).

**Figure 6.** Distribution of the impacts against the management objectives. The thick bar represents the median weight; the box represents the range of weights between the 75th and 25th percentile (i.e., 50% of the observations); the "whiskers" represent the upper and lower bounds of the distribution.

Separating out some of the OS species and applying TACs to these is expected to potentially have more substantial ecological benefits, with a mixed result in terms of economic objectives. With a large number of species included in the OS quota, separating out key species is likely to improve their ecological sustainability. In contrast, separating out the coral trout species into separate TACs is expected to have little or no ecological benefit (as there are only a small number of species covered by this quota), but would entail economic costs.

Willingness to comply with management regulations (Manage3) is also likely to vary with the different potential harvest strategies. With the exception of the separate charter allocation, and to a lesser extent the OS species quotas, willingness to comply is expected to decrease, with the greatest decreases being expected with the two environmental override options. These options potentially impose additional restrictions on the fishery (in response to environmental events), and hence may attract additional opposition by fishers.

4.2.2. Overall Performance Measures of Each of the Harvest Strategies

The derived impact matrix was multiplied by the matrix of objective weights (i.e., *W'I*) to produce a matrix of potential weighted scores for each of the harvest strategy options, with each score ranging from −3 to 3. The distributions of these scores are illustrated in Figure 7. For each of the 18 expert assessments of the impact of the strategy on the objectives, there are 110 weighted scores reflecting heterogeneity in the importance of these objectives; or for each of the 110 individuals providing the objective importance weights, there are 18 different weighted scores associated with each strategy representing the uncertainty in the effects of the strategy on the objectives. In total, 1980 subjectively weighted scores are estimated for each harvest strategy reflecting both heterogeneity in the stakeholder groups and uncertainty in the expert assessments. All experts and stakeholders are assumed to have equal weight.

**Figure 7.** Weighted impact score distributions and their cumulative subjective probabilities.

The derived cumulative subjective probabilities in Figure 7 represent the likelihood that a harvest strategy alternative would perform better than the baseline harvest strategy alone. From these, it can be seen that no single alternative management option was considered an improvement in all cases, with both positive and negative outcomes expected given the variability in expected impact and objective weightings. The cumulative subjective probability associated with the point at which the score equals zero provides a measure of the relative likelihood of a positive or negative outcome. For example, based on the relative frequency of each score in the outcome set [59], the addition of a separate allocation to the charter sector is expected to have a 60% chance of a positive outcome and a 40% chance of a zero or negative outcome.

The results were also considered by stakeholder group to determine if there were substantial differences of opinion in terms of the relative merit of particular harvest strategy additions. The use of mean scores has been found to provide a more reliable indicator than individual scores in other studies where expert opinion has been elicited [61]. Given the common set of expert assessments of the impacts of each harvest strategy on the different objectives, differences between groups reflects the differences in the importance given to these objectives.

The average score for the charter allocation option was small but positive for all of the stakeholder groups (Table 3). Similarly, the average score for the split OS quota was also positive across all of the stakeholder groups. This suggests that, on average, these options would be beneficial from the perspective of all groups. In contrast, the split CT quota was negative for all groups, suggesting it is not a desirable option.

**Table 3.** Average weighted "score" by stakeholder group.

The scores for environmental overrides were generally negative on average for the commercial and recreational sectors, but positive for the manager, scientist and other stakeholder groups (Table 3). Adding spatially explicit control rules improved the average score for all of the stakeholder groups, although the overall average was still marginally negative for quota holders and commercial fishers.

An alternative to considering the average score is to consider the proportion of outcomes with a negative score (Table 4). Generally, the charter allocation and split OS quota produced negative outcomes on fewer than 35% of cases, whereas the split CT option resulted in negative scores more than 57% of cases.

**Table 4.** Proportion of estimates with a negative value by stakeholder group.

#### **5. Discussion and Conclusions**

The aim of the study was to identify the impact of different harvest strategies for the CRFFF in achieving ecological, economic and social objectives, as identified by the various stakeholder groups. A fourth objective was also considered, namely, the implications of the management option for fishery governance, following the proposal by Stephenson, et al. [3]. The process was undertaken with considerable stakeholder engagement, with the CRFFF Working Group developing the operational fishery objectives and also the baseline and potential alternative harvest strategies.

While the Working Group agreed on a set of objectives that the harvest strategy should address, not surprisingly the importance weight given to these objectives varied between stakeholder groups. All groups tended to rank the ecological sustainability objectives the highest. The relative weight of the economic objectives varied between stakeholder groups, with the commercially oriented groups (fishers, quota owners and buyers) generally weighting economic objectives similar to the ecological sustainability objectives, while the other groups tended to weight the economics objectives much lower. Social objectives were generally given a low weight by all stakeholder groups, with the exception of recreational fishers and the "other" group, which consisted predominantly of conservation group and industry association representatives.

These different objective weightings had an impact on how the different harvest strategy options were potentially perceived by the different stakeholder groups. For example, the use of environmental overrides was considered to result in benefits in terms of expected ecological outcomes but at a higher economic cost. For the commercially oriented groups with similar economic and ecological sustainability objective weights, these effects largely cancelled out, resulting in a slight negative expected benefit. For the other groups with the lower economic weighting, these options were seen to result in a positive outcome.

The apparent trade-offs between ecological and economic outcomes also reflects a tendency for most respondents to discount future economic and ecological impacts (both positive and negative) differently. For example, harvest strategies that performed well against the objective of achieving MEY-level stocks should also result in longer term economic benefits. However, the impacts estimated by the stakeholders were much more short term in respect to economic outcomes than ecological outcomes. This may reflect the state of economic resilience in the fishery, where a reduction in economic performance due to higher operating costs involved in achieving higher ecological performance may result in greater short-term economic stress. To some extent this is also reflected in the relative weighting by the groups, with those sectors with a financial stake in the industry placing greater importance on economic outcomes, potentially not so much to only improve economic performance, but to also ensure it does not deteriorate.

While we did not test every combination, the analyses demonstrate how triple bottom line outcomes can be affected by relatively small changes in the harvest strategy and through combining approaches targeted at particular aspects. For example, the addition of spatial consideration to the environmental override option was introduced to improve both ecological outcomes and social outcomes, with spatial management allowing inter-regional equity considerations to be addressed. Although social objectives had a low weighting for many groups, the additional social benefits from the introduction of spatial considerations were sufficient to change the sign on the average net score from negative to positive for some groups, and reduce the magnitude of the negative sign in the others.

The outcomes from the process are not necessarily an endpoint in terms of determining an optimal harvest strategy, since managers will need to consider these findings in light of the agreed strategic direction, their legislative mandate and feasibility. The perceived relative benefits of alternative harvest strategy options were opinion-based: harvest strategy evaluation against triple bottom line objectives, acknowledging alternative sector weightings, can also be undertaken quantitatively using simulation modelling. While acceptability to all groups may not be required given the constraints under which managers must operate and also the diversity of views across and within stakeholder groups, the process identified which areas of a potential option may need further consideration to be more acceptable to a greater proportion of stakeholders. For example, reducing the costs (or the perception of costs) associated with the environmental overrides and spatial management options would result in benefits being realized by all groups. Similarly, further investigation as to why separate OS quota was seen as desirable, while separate CT quota was not, may lead to improvements in the management of both sets of species. As a consequence, these results provide valuable insights which can inform the subsequent consultation to refine the harvest strategies. While considered separately, the options are not mutually exclusive. For example, a charter sector allocation could be implemented in addition to the other options.

A key feature of the process used in this study is that it allows managers to integrate all dimensions of sustainability into the harvest strategy development process and also provides an explicit role for stakeholder engagement. Industry and other groups were directly involved in identifying the objectives, weighting the objectives, identifying the potential harvest strategies and providing input (based on expert knowledge) as to how these strategies are likely to perform. The process provides a formal framework in which this consultation can take place. As such, it directly addresses the key impediments to developing effective EBFM [3] by providing a suite of stakeholder-developed ecological, economic, and social objectives as well as a general process to integrate these into harvest strategies that consider the three dimensions of sustainability.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2071-1050/11/3/644/s1: Detailed description of the alternative harvest strategies.

**Author Contributions:** Conceptualization, S.P., N.D., C.D. and R.P.; Formal analysis, S.P. and T.C.; Funding acquisition, N.D.; Investigation, S.P., T.C., N.D., C.D., S.B., T.R., R.P. and G.L.; Methodology, S.P., N.D. and C.D.; Project administration, N.D.; Resources, S.B. and T.R.; Software, S.P.; Supervision, N.D.; Writing – original draft, S.P.; Writing – review & editing, S.P., T.C., N.D., C.D., S.B., T.R., R.P. and G.L.

**Funding:** This research was funded by the Fisheries Research and Development Corporation (FRDC), grant number 2015-013. In-kind contributions were made by CSIRO, Queensland Department of Agriculture and Fisheries, and the Great Barrier Reef Marine Park Authority (GBRMPA).

**Acknowledgments:** The authors would like to thank the CRFFF Working Group members who contributed to the development of the fisheries objectives and harvest strategies, and the survey participants who provided the necessary data to undertake the analyses.

**Conflicts of Interest:** S.B. and T.R. are managers of the fishery. All other authors declare no conflict of interest. The funder (FRDC) had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Determinants of Catch Sales in Ghanaian Artisanal Fisheries**

#### **Kwamena K. Quagrainie 1,\* and Jingjie Chu 2**

Received: 20 November 2018; Accepted: 4 January 2019; Published: 9 January 2019

**Abstract:** The study examined the determinants of catch sales of artisanal fishers through wealthy middle women in fishing communities of Ghana, often known as *fish mothers* or "fish mongers". The effects of selected variables were examined with a double hurdle model. Self-financing was found to negatively affect the fishers' sale of fish catch through *fish mothers*. The fishers were 19% less likely to sell to *fish mothers* if they self-finance, and that self-financing will result in a 10% downward unconditional change on the percentage of fish sold to the *fish mothers*. Factors that positively influenced the sale of fish catch through the *fish mothers* were price, percentage of high value fishes, size of boat, fishing experience, and number of fishing trips conducted in a year. The estimated average partial effects of boat size had the strongest effect with about 146% and 91% change, respectively on conditional and unconditional effect on the percentage of catch sales sold through the *fish mothers*. Overall, the study shows that long-term consistent economic and investment considerations such as investing in larger boats are important drivers for fishers' choice of selling catches through fish mothers. The main implication of the results is that fishers need some economic leverage such as access to formal capital and financial resources to incentivize them to exercise control over their marketing activities so that they can receive a higher profit from their fishing operations. This is important for the sustainability of coastal fisheries communities and the sector as a whole. Artisanal fishers need resources such as low interest loans and market information systems that will enable them to negotiate prices for their fish catch with fish mothers.

**Keywords:** artisanal fishers; double-hurdle; fish marketing; *fish mothers*

#### **1. Introduction**

Ghana is an advanced fishing country relative to other African countries with very experienced fishers, which has been attributed to long traditions from the colonial era. Marine fishing in Ghana was developed mainly with the arrival of European traders when the Europeans hired fishers as canoe men to transport people and goods from their ships to the beaches, and vice-versa [1]. The marine capture fishery in Ghana is the major source of local fish production, accounting for over 80% of the total fish product supply [2]. The sector consists of four subsectors: small scale or artisanal, semi-industrial or inshore, industrial or deep sea, and tuna fleets. The fishing sector plays a major role in poverty reduction and livelihoods for coastal communities, which is estimated to contribute about 1.2 percent of Ghana's gross domestic product (GDP) and 5.6 percent of the Agriculture GDP [2,3]. The low national GDP contribution from the fisheries sector is due to declining fisheries stocks [3]. The small scale or artisanal subsector accounts for about half of the total marine catch in Ghana [2,4], which makes it important for the sustainability of the fisheries sector.

The artisanal fishery involves the use of canoes or dug-out wooden boats with inboard or outboard engines. The fishing gears are diverse, including beach seine nets, purse seine nets, set nets, drift gillnets, and hook and line. The major commercial species landed are small pelagic low value fishes such as anchovies (*Engraulis* spp.), sardinellas (*Sardinella* spp.), and mackerels (*Scomber* spp.). Large motorized canoes or boats operate in deep waters and have storage facilities or compartments on board for storing ice to preserve catches. The main fishes landed by artisanal fishers include sardinellas (mainly *Sardinella aurita*), tuna (*Auxis thazard*), mackerels (mainly *Scomber japonicas*), burrito (*Brachydeuterus auritus*), and Atlantic bumper (*Chloroscombrus chrysurus*). Some of the minor fishes landed include red fish (mainly *Sparus caeruleostictus* and *Dentex angolensis*), anchovies (*Engraulis encrasicolus*), flat fish species, and the white grouper (*Epinephelus aeneus*), some of which are considered high value fishes. Most of the fishers target high value species such as white grouper, tuna, and red fish because of the high demand and premium prices, however the percentage of these fishes landed are low due to the fishing methods used and unhealthy fish stocks [5,6].

The artisanal fishery plays an important role in coastal communities by providing employment, income, and a cheap source of protein. The major challenges they face are seasonality, small margins and low returns [5,7,8]. The sector's performance is critical for the growth, economic development, and sustainability of the coastal communities. The returns accruing to artisanal fisheries are affected by several factors including limited value addition and consequent post-harvest losses, weak backward-forward market linkages, poor infrastructure, low bargaining power, as well as low and lack of variety of catch. Expenses associated with fishing activities include fuel, food, labor, taxes, and other variable overheads.

Women are important players in the small-scale fisheries subsector in developing countries. Their participation rate in pre- and post-harvesting activities is estimated at about 48%, and in Ghana, it is around 40% [9]. Women participation in the subsector is higher if only post-harvest is considered. In particular, women in Ghana have a vital role in informal financing of fishing operations [7,10], partly due to lack of access to financial support from other channels like bank loans.

The marketing system and remuneration for the artisanal fishery take a number of forms. A proportion of the catch goes to wealthy middle-women, often known as "*fish mothers*" or "*fish mongers*" to cover any pre-financing arrangements; a portion is sold or given to the boat owner to cover fixed costs (boat and fishing gear); hired laborers may receive a portion of the catch as wages; and smaller portions are shared [10]. Thus, fish may be sold through *fish mothers* as well as other female fish value chain agents including fish processors, fish retailers, food vendors, relatives, and spouses. Some spouses may also be *fish mothers*. Fishing is a capital-intensive industry, and an advantage to fishers for selling their catch to *fish mothers* is the reciprocal leverage they have to secure relatively low cost capital to finance their business like fuel cost. Selling fish catch to *fish mothers* has less risk as it is a cash market, and usually *fish mothers* can buy high volumes of fish landed. Fishers can obtain higher prices especially from *fish mothers* who primarily serve distant regional, or national, and in some cases international markets [7]. However, there are some disadvantages for selling to *fish mothers*. *Fish mothers* have often used their financing leverage to acquire and own fishing equipment used by the fishers and therefore control the production chain [10].

The traditional roles for women in the fishing industry in Ghana, which were structured through responsibilities and customary roles for accessing resources are gradually changing [10]. Traditionally, a fisher is required to provide fresh fish from his catch to his spouse for retailing either as fresh or processed products to generate income to maintain the household as well as to provide income for further investments in the fishing business. However, there are increasing capital costs associated with artisanal fisheries due to the use of larger canoes, high powered motors, and expensive fishing equipment, and limited access to formal bank credit because of a low loan repayment rate arising from poor loan management, low catches, high risks of industry participation, and relatively high indebtedness of fishers [11]. Therefore, it appears fishers are increasingly taking control of their earnings to ensure capital is available to invest in the fishing business, thus taking away the traditional fish marketing role from their spouses [7,10].

Artisanal fishers carry out their transactions through various marketing channels including middle-women, spouses, processors, small traders, food vendors, and direct to end users. Each channel provides some level of benefits and returns. *Fish mothers* tend to pre-finance fishing trips of fishers who need credit with fuel and food in exchange for secure access to the catch from the fishers. In the artisanal fishery value chain in Ghana, it is commonly perceived that *fish mothers* and processors accrue a greater percentage of the profit margins generated along the fisheries value chain, followed by retail traders, and then the fishers. The literature on food and product markets in developing countries has overwhelmingly reported increasing power of middlemen, intermediaries and retailers, and their tendency to negotiate lower prices from producers [12–14]. *Fish mothers* usually trade diverse food products, which gives them more access to capital, providing them some leverage with both fishers, processors and retail traders [10]. *Fish mothers* can pre-finance several fishers and consequently have access to ample supply of fish. Such arrangements tend to place some power in the hands of the *fish mothers* at various landing sites [7,10].

The motivation for and determinants of fishers' choices and decisions regarding who to sell fish have not been empirically tested, though commonly, *fish mothers* are the preferred choice for selling fish catch. This study seeks to fill that information gap by examining the relationship between the fishers' decisions to sell fish catch and the percentage of catch sold through *fish mothers* and a number of economic and socio-demographic factors. Since *fish mothers* commonly pre-finance fishing trips of some fishers in the form of fuel and food, which in turn force the fishers to sell through *fish mothers*, the main hypothesis examined in this study is whether fishers' self-financing affects the decision to sell through *fish mothers* and the actual percentage of catch sales through them. Other factors examined include input characteristics such as boat size, number of fishing trips per year, as well as some demographic characteristics.

#### **2. Modeling Framework and Empirical Specifications**

The Double Hurdle (DH) model [15–17] is adopted to examine the fisher's marketing decision to sell fish catch through *fish mothers*. The decision process is assumed to involve two hurdles: the first hurdle relates to whether or not the fisher will sell their catch to *fish mothers* (*y*∗ 1*i* ), and the second relates to the percentage of fish catch sold through *fish mothers* (*y*∗ 2*i* ). The hurdles involve zeros because some fishers do not sell fish catch through *fish mothers* at all. The DH model addresses the zero outcomes arising from the fishers' deliberate decisions, and it is a more general and flexible model than other models such as the Tobit model [18] by allowing separate stochastic processes for the incidence and intensity of sales [19]. The DH model is commonly applied to market participation studies [17,20–25].

The DH model allows the decision to sell to *fish mothers* and the percentage of sales to be determined by two different stochastic processes. Thus, the model permits the possibility of estimating the first and the second decision processes using a different set of explanatory variables.

In the first hurdle, the latent variable underlying the fisher's decision to sell to *fish mothers* is represented as a binary process:

$$y\_{1i}^\* = \gamma\_1' \mathbf{X}\_{1i} + \pi\_i \qquad \pi\_i \sim N(0, 1) \tag{1}$$

where *X*1*i* is a vector of explanatory variables, and *πi* is the error term. The observed decision of the fisher is then modeled as:

$$y\_{1i} = \begin{cases} \quad 1 \text{ if } y\_{1i}^\* > 0\\ \quad 0 \text{ otherwise} \end{cases} \tag{2}$$

In the second hurdle, the latent variable describing the percentage of fish catch sold to *fish mothers* is represented as:

$$y\_{2i}^{\*} = \gamma\_2^{\prime} \mathbf{X}\_{2i} + \vartheta\_i \qquad \vartheta\_i \sim N\left(0, \sigma^2\right) \tag{3}$$

where *ϑi* is the error term, and the observed percentages is represented as:

$$y\_i = \begin{cases} \gamma\_2' \mathbf{X}\_{2i} + \theta\_i & \text{if } y\_{1i}^\* > 0 \text{ and } y\_{2i}^\* > 0\\ & 0 & \text{otherwise} \end{cases} \tag{4}$$

and the Υ*s* are coefficients to be estimated.

Given the cross-sectional nature of the data, the DH model is estimated using maximum likelihood estimation procedures and tested for potential presence of heteroscedasticity using a chi-squared test. If there is heteroscedasticity, the model estimates will be inconsistent. Consequently, the variance of the error term in Equation (4), *σ*2 is allowed to vary across observations. The standard deviation *σi* is specified as:

$$
\sigma\_{\bar{l}} = \sigma \ . \ \exp \left( \delta^{\prime} \mathbf{h}\_{2\bar{l}} \right) \tag{5}
$$

where **h2***i* is a vector of select continuous variables included in *X*2*i*, and *δ* is a vector of coefficients [22]. If the model is homoscedastic, *δ* = 0. The specification in Equation (5) ensures that the standard deviation is strictly positive [24,25].

The DH models outlined above with and without specification adjustments for heteroscedasticity were estimated for fishers' decisions to sell fish catch through *fish mothers* using the LIMDEP software version 10.0.

The data used in the study was collected in the summer of 2013 from artisanal fishers at major coastal fishing communities in Ghana, i.e., Chorkor, Jamestown, and Tema in the Greater Accra region, and Elmina, Cape Coast, and Moree in the Central region. The semi-structured questionnaire solicited information on the major activities and services of fishers, costs and revenues associated with their activities, fish landed, prices, business transaction methods, marketing channels, fishing gear used, and demographic factors. The subjects were selected randomly and surveyed at each fishing community.

#### **3. Data**

Data was collected from 202 artisanal fishers but only 96 responses were used in this analysis because they corresponded to the full responses provided for the relevant variables needed for the analysis. Information on the method of transaction was also collected, and whether they self-financed fishing operations. Information on the percentage of fish sold through *fish mothers* or other marketing outlets was also collected. Other outlets consist of mainly spouse and relatives, and direct to consumers. *Fish mothers* generally operate as wholesalers [10]. Other information includes the size of fishing boat.

The variables used in the estimation process are reported in Table 1. The proportion of artisanal fishers selling to *fish mothers* is about 77%, and on average, about 73% of their fish catch was sold through *fish mothers*. Fish prices generally varied with season and fish species. There are two major fishing seasons in Ghana: the major upwelling or bumper season, which is usually from the end of June or early July through late September or early October, and the minor upwelling season, which occurs from late December or early January through February [26]. Respondents reported the composition of fish landed in the past three fishing seasons. They also reported the prices received for the various species in both the major and minor seasons. The price of the fish landed depended on demand and supply, costs, and grade. A weighted average price was therefore calculated for the analysis based on the season and value of fish. Table 1 shows that prices on average ranging from \$1.50/kg to \$22.5/kg.

**Table 1.** Description and Summary Statistics of Variables.

Based on multiple responses, 30.6% of fishers reported landing *Sardinella* spp., 16.1% tuna, 15.4% mackerels, 13.5% burrito, 12.3% Atlantic bumper, 5.6% red fish, 2.7% anchovies, 2.5% *Soleidae* spp., and 1.2% white grouper. The FAO [2] also reported that sardines and mackerel are among the commonly landed fish species in Ghana, and constitute the most commercially important coastal fisheries. The fishers reported low landing of shrimps (mainly *Parapenaeopsis atlantica* and *Penaeus kerathurusthat*), Atlantic bigeye (*Priacanthus arenathus*), European barracuda (*Sphyraena sphyraena*), Trigger fish (*Balistes punctatus*/*capriscus*), and Bigeye Scad (*Selar crumenophthalmus*). White grouper, tuna, and red fish are considered high value fish.

Most of the fishers have at least a primary education (87%), and the average fishing experience was 19 years. Over half (55%) of the fishers were self-financing their fishing operations. Fishers used a range of canoe (boat) sizes varying from 8 m to 27 m, with an average of 16.4 m. The average number of trips per year ranged from 12 to as many as 317.

#### **4. Results and Discussion**

A chi-square test statistically rejected the homoscedastic model in favor of the heteroscedastic model with an alternative variance specification (Table 2). Tables 3 and 4 present the maximum likelihood estimates for the homoscedastic and heteroscedastic models respectively. To assess the total impact of the explanatory variables on fishers' decisions, the Average Partial Effects (APEs) from the heteroscedastic model are discussed (Table 5). The APEs are evaluated at the means of each variable. They are decomposed into three components, i.e., the effect on the probability of selling fish catch to *fish mothers* (Prob [*y* > 0]), the effect on the unconditional expected value of percentage fish sold to the *fish mothers* (E[*y*]), and the effect on expected value of percentage fish sold to the *fish mothers* conditional on positive expected value (E[*y*|*y* > 0]).

**Table 3.** Estimated parameters from homoscedastic double-hurdle model a.

a *p*-values are given in parentheses; \*\*\* and \*\* represent significance at α = 0.01 and α = 0.05 respectively.

**Table 4.** Estimated parameters from heteroscedastic double-hurdle model a.

a *p*-values are given in parentheses; \*\*\*, \*\*, and \* represent significance at α = 0.01, α = 0.05, and α = 0.10 respectively.

**Table 5.** Estimated average partial effects (APE) from heteroscedastic double-hurdle model.

\*\*\*, \*\*, and \* represent significance at α = 0.01, α = 0.05, and α = 0.10 respectively; b parameter × 103.

The choice of explanatory variables included in the two hurdles is based on some economic theory and reasoning. Fishers are considered as rational businessmen and to take decisions to maximize their economic returns. In the literature, the first hurdle, which is based on the discrete choice preference theory, is often specified as a function of non-economic factors because the theory assumes that sample selection is determined by non-economic factors such as demographic characteristics [22,23]. In Table 4, price, percentage of high value fish landed, and some characteristics such as boat size, number of fishing trips per year as well as experience (measured as number of years of fishing) have a positive and significant effect. The APEs reported in Table 5 are positive and significant as well, which indicates the positive responsiveness of fish sold to *fish mothers* to changes in these factors. Self-financing of fishing trips is found to negatively impact fish sold to *fish mothers*.

Self-financing fishing trips relative to other forms of financing fishing activities negatively impacts the decision to sell to *fish mothers* (Table 4). The magnitude of the estimated APEs (Table 5) suggests that it is important and that fishers are 19.0% less likely to sell to *fish mothers* if they self-finance their fishing operations. Also, self-financing will result in a 10.2% downward change on the unconditional percentage of fish sold to the *fish mothers*. As indicated earlier, some *fish mothers* pre-finance fishing trips of fishers to secure access to their catch. Consequently, self-financed fishers are not obligated to sell their fish through the *fish mothers*. Such fishers may be mindful of their economic portfolio and the flow back of resources to the household. They could probably sell through their spouses or relatives [10,27]. However, conditional on a positive decision to sell fish to the *fish mothers*, self-financing will result in just a 1.2% upward change in the percentage of fish catch sold.

From Tables 4 and 5, the positive effect of price, though relatively small is perhaps due to the ability of fishers to negotiate prices with the *fish mothers*. The change in price on both conditional and unconditional sale of fish catch to *fish mothers* is about 0.1% *ceteris paribus*, i.e., all else unchanged (Table 5). Traditionally, fish landed are priced by the *Fish Queen*, the Head of *fish mothers* in consultation with the Chief fisher [7,10,27]. However, some fishing communities have invested in improved landing facilities, and charge landing fees, which then allows the fishers to negotiate prices on individual basis with the *fish mothers* [7]. In addition, *fish mothers* buying fish to sell in markets with better prices may offer fishers more competitive prices. A similar interpretation could apply to the positive impact of high value fishes. Low value fish such as *Sardinellas* are often in commercial abundance, which impacts its value. To prevent waste, *Sardinellas* tend to be preserved through smoking or drying. However, the percentage catches of high value fishes such as red fish (*Dentex* spp.) are relatively low (25% in Table 1) and they are sold fresh. *Fish mothers* would likely offer higher prices due to the high demand and high profit.

The size of boat is found to be a significant variable and positively impact the probability of selling to *fish mothers* and the percentage of fish sold to them (Table 4). The magnitude of the estimated coefficient (0.32 in Hurdle 1 and 1.21 in Hurdle 2—Table 4) is the strongest compared to the other variables. The change in boat size on both conditional and unconditional sale of fish catch to *fish mothers* is about 146% and 91%, respectively (Table 5) suggesting a very strong effect. Larger boats operate in a wider area, from near landing sites to relatively more open waters, and as such exploit more catches compared to smaller boats [28]. Larger boats can also handle and store fish in good condition on board as they are motorized, and most are equipped with hydraulic hauling machinery, fish-finding electronics, ice, and storage facilities, which improves the efficiency of fishing and handling of fish [29]. Perhaps, the strong impact of having larger boats on fish sold through *fish mothers* results from higher fish catches and requires a higher purchasing power to absorb all the fish caught.

Regarding fishers' experience, a one-year change in fishing experience on both conditional and unconditional probability of sale of fish catch to *fish mothers* is about 0.8% and 0.5%, respectively (Table 5). Similar probabilities are found for the number of fishing trips taken in a year (Table 5). Though the experience variable has a relatively small effect, it is likely that experienced fishers have better understanding of fish movements, and consequently better fish catch. Also, experienced fishers may have a better knowledge of the fish markets and social connections that could make them to sell

to *fish mothers*. Knowledge gained from fishing experience is reported to influence relocation of fishers along the west coast of Africa [30] and loan repayments by fishers [11]. Regarding the number of trips, the probabilities imply that the higher the number of trips, the higher the effect on fish sold to the *fish mothers* as a more consistent relationship was set up.

#### **5. Conclusions**

This study assessed the determinants of marketing decisions of artisanal fishers regarding the fish catch sold through *fish mothers*. The empirical model was a double-hurdle that addressed the zero outcomes arising from the fishers' deliberate decisions not to sell through *fish mothers*.

Self-financing of fishing trips was found to negatively impact fish sold to *fish mothers*. It is the only variable that has a negative impact. The magnitude of the estimated coefficients suggests that self-financing has a strong effect, i.e., fishers were 19% less likely to sell to *fish mothers* if they self-finance their fishing activities as they will receive higher prices without the binding agreements with the *fish mothers*, and that self-financing will result in a 10% downward change on the unconditional percentage fish sold to the *fish mothers*. Such fishers would probably sell through their spouses or family relatives.

The impact of price, percentage of high value fish landed, boat size, number of fishing trips per year as well as fishing experience were found to have a positive and significant effect on the percentage of fish sold through *fish mothers*. The estimated average partial effects suggest that boat size had the strongest effect on fish sold through the *fish mothers*, with about 146% and 91% change, respectively on conditional and unconditional sale of fish catch, which is likely caused by higher catches. But price, percentage of high value fish landed, the number of fishing trips per year and fishing experience had relatively smaller effects.

Overall, the study shows that long-term consistent economic and investment considerations such as investing in larger boats are important drivers for fishers' choice of selling catches through *fish mothers*. Larger boats operate in wider and deep waters and have higher hauling capacity and as such exploit more catches. The main implication of the results is that fishers need some economic leverage such as access to formal capital and financial resources to incentivize them to exercise control over their marketing activities so that they can receive a higher profit from their fishing operations. This is important for the sustainability of coastal fisheries communities and the sector as a whole. Artisanal fishers need resources such as low interest loans and market information systems that will enable them to negotiate prices for their fish catch with *fish mothers*.

**Author Contributions:** Formal analysis, K.K.Q.; Writing—review & editing, J.C.

**Funding:** The research was funded by the World Bank Group, grant number 7832381.

**Acknowledgments:** The research was funded by the World Bank Group.

**Conflicts of Interest:** The authors have no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Estimating the Public's Preferences for Sustainable Aquaculture: A Country Comparison**

#### **Suzanne van Osch 1,\*, Stephen Hynes 1 , Shirra Freeman 2 and Tim O'Higgins 3**

Received: 11 December 2018; Accepted: 18 January 2019; Published: 22 January 2019

**Abstract:** Integrated Multi-Trophic Aquaculture (IMTA) is an alternative to the monoculture of fin fish species, in which several species are combined in the production process. This can have environmental advantages such as a lower environmental impact through nutrient cycling and natural filters; and can have economic advantages consisting of increased efficiency, product diversification and potential price premiums. In this paper, a choice experiment (CE) was conducted through an online survey in Ireland, the UK, Italy, Israel and Norway, to assess how the public makes decisions on what type of salmon or sea bream to buy based on the attributes of the product. Analysis assessed the Willingness-to-Pay (WTP) for more sustainable produced seafood using a Latent Class multinomial logit modelling approach. In the experiment, an ecolabel was used to distinguish between regularly produced (monoculture) products and sustainably produced (IMTA) products. The general public in each country showed a positive attitude towards the development of such an ecolabel and towards the payment of a price premium for the more sustainably produced salmon or sea bream.

**Keywords:** aquaculture; IMTA; ecolabel; choice experiments; latent class; WTP

#### **1. Introduction**

Seafood production is increasingly challenged in the context of human population growth, increasing global per capita seafood demand and diminishing wild fish stocks [1–3]. Aquaculture is viewed by many as an alternative to wild fisheries and in recent years the aquaculture industry has shown considerable growth. However, in 2014 the annual European aquaculture industry increased by 0.5 percent against a growth of 7 percent globally [4]. Due to the international character of the seafood market, the EU is currently looking into the marketing of the seafood industry at the transnational level through educating the public and stimulating producers to shift towards sustainable production methods. One of the proposed strategies is the use of an ecolabel for seafood products that indicates the degree of sustainability of the production of the seafood product [5].

Integrated Multi-Trophic Aquaculture (IMTA) has also been proposed by academics and policy and industry actors to reach the dual aim of economic growth and environmental sustainability [6–8]. Multiple experiments have been conducted with IMTA; in the Far East, aquatic species have traditionally been co-cultured for centuries and in recent years IMTA has been implemented experimentally in modern industrial forms in Canada [9], Israel [10] and the Netherlands [11]. However, despite this call for IMTA integration in the European industry, IMTA has not been adopted on an industrial scale. In an IMTA system, multiple species from different trophic levels are combined in the production process. The species are selected based on their function in the ecosystem and economic value. Combining them has the advantage of allowing for nutrient cycling in the production process. On the one hand, this allows for environmental benefits, as the added species extract organic and inorganic wastes that would otherwise be discarded from the fed finfish cages [12–15]. Additionally, bivalves consume sea lice copepods [16], the main parasitic infection contributed to intensive salmon farming [17]. The inclusion of bivalves in an aquaculture production system could contribute to the mitigation of such parasites thereby limiting the environmental impact of the farm. On the other hand, IMTA has potential economic benefits, as nutrient cycling may reduce feed costs per unit biomass. Some research results suggest that IMTA systems may have higher growth rates for the lower trophic species [15].

In addition, products from an IMTA system can produce higher profits than monoculture products as the public may have willingness-to-pay (WTP) for products from an IMTA farm as opposed to a monoculture farm. Research has indicated that the public indeed values an IMTA approach to fish farming [18–21]. A small-scale study in New York concluded that IMTA produce outperformed monoculture salmon on the main seafood purchase determinants of product quality, freshness and taste [18]. Regarding the public's preferences for IMTA produce, several studies identified a positive WTP for salmon produced in an environmentally friendly manner in Scotland [21], the US Pacific Northwest [19] and Canada [20]. Higher marginal WTP should translate to additional revenue, which could function to cover potentially additional costs to IMTA production practices, particularly in the short run, as opposed to monoculture production [12,15,22].

However, an essential element in the valuation of IMTA products is the public's ability to distinguish between monoculture and IMTA products [18,23]. Both industry and non-industry actors have proposed eco-labelling to create bottom up pressure to reform production systems to prevent the over-exploitation of natural stocks [24]. Eco-labelling is an increasingly used tool that can change purchasing behaviour by educating the public on the environmental impact of purchasing decisions. Eco-labelled seafood has been found to be preferred over unlabelled seafood in several studies [24–31]. Despite aquaculture products being perceived distinctly different from wild-caught products [28], according to our knowledge, research on preferences specifically for aquaculture ecolabels is limited to Roheim [28] and Yip et al. [19]. Individuals prefer wild products over farmed products, even when these farmed products are certified [28]. Yet within the aquaculture market, products produced in CC (Closed Containment) and IMTA systems are preferred over monoculture production, with strongest preferences expressed for IMTA [19]. Further research on public preferences for aquaculture labelling and production methods is essential if environmental labelling is to be used as a tool to internalize any external costs from the aquaculture industry.

In this paper, we explore the market potential of IMTA across five countries, Ireland, UK, Norway, Italy and Israel, by estimating the public's preferences for the products of sustainable aquaculture. Specifically, we answer the question: is the public willing to pay a price premium for fish cultured sustainably in European and Israeli farms? If so, then part of the European marketing strategy [5] should include the promotion of the sustainability and production location attributes. In this study, preferences are elicited using a choice experiment (CE) that asked respondents from the five countries to choose from among fish with different levels of sustainability, different locations of production and different prices. The WTP for the sustainability and location attributes was estimated based on the choices. The CE approach was taken due to its capacity to capture diversity among the countries and to enable a cross-country comparison. This adds to the current dearth of multinational research on preferences for IMTA.

Current aquaculture growth is taking place in a context of rising awareness and public concern over food production, specifically on issues such as food safety, food quality, health impacts, environmental sustainability and animal welfare [32]. Against this background, several seafood consumer awareness ecolabels have been developed such as the Earth Island Institute's Dolphin Safe label and Marine Stewardship Council (MSC) labels. Ecolabels consist of a physical label on a product, indicating that it fulfils the criteria defined for sustainable production, thus communicating the environmental effect of production of the good [3]. This differentiates eco-labelled seafood products

from unlabelled products, with the aim of stimulating environmentally friendly purchasing behaviour in order to increase demand for certified products, hence decreasing the environmental impact of the fast-growing industry [33].

The economic impact of eco-labelling is influenced by several factors. First, it depends on the degree of one's altruism [34]. Individuals weigh their utility for attributes related to self-interest, such as health, to their utility for attributes outside of their individual interest, such as sustainability [29]. Second, WTP is influenced by an individuals' income. As individuals with a high income have a lower marginal utility for income [35], their price sensitivity will be low and their WTP will be higher [29]. This implies that in the creation of the model and interpretation of model results, the income level of respondents must be taken into consideration to account for the difference in price sensitivity. Third, information plays a role in the value individuals attribute to a sustainability ecolabel. Individuals are more likely to use eco-labelled products when they have a higher degree of environmental awareness, are concerned about the environment and feel a responsibility towards contributing to its maintenance [36]. Lastly, one's WTP for a seafood product with a sustainability ecolabel is influenced by the perception of the product. Individuals will weight a product with their preferred level of sustainability against other products and attributes such as quality and value for money [37]. This has been accounted for in the experiment design deployed in this paper by including an opt-out option in every choice card.

#### **2. Materials and Methods**

#### *2.1. Sample, Questionnaire and Data*

Data was retrieved by ICM Research, an independent survey firm in the UK, who distributed the survey online among a population of randomly selected contracted clients. After selection, this sample was stratified according to the proportions of age, sex and region in each country, to ensure a representative sample. A sample of 2520 surveys was collected from five countries; Ireland (*n* = 500), Israel (*n* = 500), Italy (*n* = 508), Norway (*n* = 501) and the United Kingdom (*n* = 511). The countries were selected based on their geographical location and the characteristics of their aquaculture industries, aiming to include countries with both Atlantic and Mediterranean production sites. In the Atlantic area, the countries selected were Ireland, the UK and Norway. Ireland and the UK were selected as aquaculture production countries, with Ireland being characterized by its focus on the niche market of organic salmon aquaculture. Norway is included as European but non-EU member state that is characterized by its intensive large-scale salmon production. Outside of the Atlantic area, Italy and Israel were chosen as key sea bream farming countries operating in the Mediterranean area.

Respondents to the survey were all in the market as potential fish consumers. The choice experiment was part of a broader survey. Respondents were first told what the survey was about and given background information on what IMTA was and how it operated. It is recognized that providing respondents information about IMTA before the choice experiment can alter the expressed preferences. However, considering the lack of familiarity of respondents with IMTA, the CE could not be conducted without a basic understanding of the production method to allow a well-informed decision for every choice card presented. The survey consisted of four parts. The first part asked respondents about their perception of the aquaculture industry and marine environmental problems, followed by a section on seafood consuming behavior, focusing on respondents' use of ecolabels to make informed purchasing decisions. In the third part, respondents were presented with the choice experiment. In the final part respondents were asked about their socio-demographic indicators.

#### *2.2. The Choice Experiment Methodology*

The choice experiment (CE) method is a stated preference approach widely used to estimate the public's preferences and willingness to pay (WTP) for changes in environmental quality or new products. Choice experiments consist of a set of choice cards, each containing a set of alternatives from

which respondents select their most preferred alternative. Each alternative consists of a combination of attributes that vary on attribute levels. In making their choices, respondents have to weigh the utility they derive from the different combination of attribute levels presented in each alternative. The assumption underlying this method is that respondents make fully rational decisions and therefore maximise their utility in every choice [38].

Choice cards often include two elements that assist the modelling process. A baseline alternative that reflects the status quo is often included in the choice cards. The inclusion of this alternative is necessary to mimic actual purchasing decisions when a new product enters the market; a consumer can choose to opt-out of a purchase altogether. This allows the statistical models to generate more welfare-consistent estimates [39]. The choice alternatives also include a monetary attribute. This allows the elicitation of an implicit price for each parameter, which reflects the respondents' WTP for a relative change in the attribute, given the changes in the other attributes [40]. The status quo alternative is always associated with a zero prize as no product is purchased in that decision. The aim of the choice experiment is to derive marginal values for attribute levels from the respondents' choices.

The choice experiment in this paper was designed to assess the public's marginal WTP for the attributes associated with IMTA products. In the choice experiment, respondents were presented with eight choice cards each, with each choice card consisting of three alternatives; two purchasing options and one opt-out option (no purchase). An example of a choice card for Ireland is given in Appendix A. The purchasing options were presented as a fillet of salmon for the Northern Atlantic countries and Sea Bream for the Mediterranean countries, as those are the main farmed species in each region. Respondents were first asked to "imagine that you walk into a supermarket and fresh (unfrozen) salmon [sea bream] is on your shopping list. The supermarket has several types of salmon [sea bream]. The packs are identical in size and quality, but some salmon [sea bream] is produced locally, some is produced abroad, and some is produced in a way that is better for the environment. They also vary in price. You are asked to indicate which of the salmon [sea bream] presented you would buy".

Respondents were then told what attributes they would have information on in the choice situation and to consider what they could afford to pay given their groceries budget. The attributes and attribute levels are described in Table 1. A pilot study conducted to determine the main attributes to present for seafood products found that a production location indicator was important in several sample countries. Literature suggests that consumers prefer locally produced food over imported produce [41,42] and that consumers' WTP for sustainability is partially influenced by attitudes towards other labels present in the market [43]. The experiment therefore includes production location as an attribute.

The sustainability attribute reflects the change in environmental pressure due to a change to an IMTA production system. Research has indicated that consumers have a WTP for sustainability in seafood, suggesting that production method should be included in the CE design [28]. Previous studies estimating WTP for specific production methods included an ecolabel reflecting the production system [19]. However, we decided against this approach for two reasons. First, the change in environmental pressure due to a shift to an IMTA production system can vary greatly, depending on species selection and the amount of the extractive species added to the production process, but also on environmental conditions, such as strength and direction of water currents [20,22]. A singular ecolabel would not confer such variations to individuals and therefore not inform individuals on environmental effects. Second, a common critique towards ecolabels is their dichotomous nature, which does not contribute to informing the public on the environmental impact of the product, but rather is limited on a defined set of indicators. Neither does it stimulate producers to continue to develop innovations to decrease environmental pressure once the ecolabel has been obtained. Indeed, by providing consumers with imperfect information, ecolabels can stimulate greenwashing [29]. Therefore, the inclusion of an IMTA label was not considered a fitting method to confer information to the public. Rather, a rating ecolabel was used.

#### **Table 1.** Overview of CE attributes and attribute levels.

A rated ecolabel approach was also used by Martinez-Espiñeira et al. [23] who estimated WTP for sustainability in food production processes and suggested by Aarset et al. [32] as a more viable approach to create bottom-up market incentives to shift to more sustainable production methods. In line with Martinez-Espiñeira et al. [23], the label design rates the environmental impact of the more sustainable production systems relative to conventional monoculture farms. The label design was based on the EU energy rating label [44]. This label is common on the European market for electronic appliances to indicate the amount of energy necessary to run an appliance in comparison to the product group. Several EU countries are using such environmental rating labels to inform consumer choices. The label used in the CE presents four levels of sustainability, ranging from A–D. The D label reflects the status-quo, or the environmental pressure resulting from monoculture production techniques. Respondents were informed that "integrated aquaculture attempts to mimic the natural ecosystem and produces less pollution. Depending on how the farms are set up, the amount of pollution will be different. The sustainability labels show you how good or bad the farming method is for the environment. The labels range from A–D, with A being best and D being worst for the environment. The labels show how much the environmental pressure of producing the salmon in the package has decreased from what we currently consider normal aquaculture (monoculture). Every level has a step of a 10% improvement in environmental sustainability." The labels A therefore being equivalent to a 30% decrease in environmental pressure. This information was shown on the labels to the respondents prior to the choice experiment.

Lastly, a monetary attribute was included in the CE to reflect a market situation and to enable the statistical derivation of the public's WTP for the other attribute levels. The CE price levels are based on the price range found in a survey of supermarket prices in each country. Based on these price ranges, a low, medium and high price were selected for each sample country. Within the model, the medium price level was taken as a base case. After the selection of the attributes and attribute levels, sixteen profiles were blocked into 4 versions of eight choice cards. Each choice card contained two alternatives and one opt-out option. The number of choice cards per respondent was limited to eight to avoid respondent fatigue. Variation in the attribute levels was achieved by using an efficient Bayesian experimental design based on the minimisation of the Db error criterion [45]. D-efficiency is a common approach for measuring experimental design efficiency [46]. A pilot study (*n* = 201) indicated

that the attribute levels were appropriate as respondents selected the full price range when taking the choice experiment and indicated that they found the price range realistic when interviewed.

#### *2.3. Modelling Approach*

Discrete choice modelling is rooted in the concept of utility maximization. Lancaster [47] states that individuals derive utility, not from the consumption of a good, but from the set of attributes embodied by the good. The value of a good is therefore comprised of the bundle of attributes that the good holds. In a choice experiment, the value of specific attributes is elicited through statistical analysis. The Random Utility Model (RUM), developed by Mc Fadden [48] forms the basis for the statistical derivation of respondents' utility from the choice experiment data. The RUM states that the utility an individual derives from the consumption of a good consists of an observable and unobservable component. This can be expressed as

$$\mathbf{U}\_{\rm in} = \mathbf{U}(Z\_{\rm in}\mathbf{S}\_{\rm n}) + \varepsilon (Z\_{\rm in}\mathbf{S}\_{\rm n}),\tag{1}$$

where Uin reflects the utility of alternative i as perceived by individual n in choice set C. Alternative i will be chosen over alternative j if Ui > Uj. This utility is comprised of two parts. First, the observable component (U) consist of the CE attributes (Zin) and socioeconomic characteristics of the respondents (Sn), which can be captured by the model. Second, the unobservable component (εn) reflects unmeasured variations that fall outside the influence of the experiment. Utility cannot be estimated precisely due to the existence of the latent unobservable component, but utility measures can be maximised by minimising the error terms. The design of the CE aims to capture as much of the public's utility derived from the product as possible in the CE attributes, thus minimising the unobservable component. In the modelling process, the error term can be minimized by including information about the respondent and choice context indicators in the model, thereby including the influence of these terms on the choices made into the model.

Multiple econometric techniques to elicit utility from CE data have been developed. The basic discrete choice model is the conditional logit (CL). However, the CL is limited in that it carries the independence from irrelevant alternatives (IIA) assumption and it does not control for unobserved preference heterogeneity, i.e., variations of preferences across the sample. Therefore, other models have been developed, of which the most prominent ones are the random parameter logit (RPL) and the latent class (LC) model. Both models account for preference heterogeneity, albeit in different ways and relax the IIA assumption. The RPL generalizes the CL by allowing the coefficients of observed variables to vary randomly over people rather than being fixed [49]. In contrast, the LC model assesses the source of preference heterogeneity by identifying underlying latent groups from the data set. Both the RPL and LC models have been created for this study, but this paper presents the results of the LC analysis, as this model gave a better fit with regards to statistical information criteria, as will be elaborated on in the next section.

The LC model specifies that the mixing of preference intensities takes place over a finite group of c preference classes [40]. Members of each class have similar preferences that are not directly observable from the CE data, i.e., they are latent. The parameters of *u*, expressed as β, can be estimated by the LC model while assessing preference heterogeneity sources across classes of respondents. The LC model assumes that preferences vary across the classes based on a non-parametric distribution. Following Greene & Henscher [50], suppose β takes C possible values labelled β1, ... ; βc with probability probc reflecting the LC model estimates. The choice probability can be expressed as:

$$\text{Prob}\_{\text{ri}} = \sum\_{\text{c}=1}^{\text{C}} \text{prob}\_{\text{c}} \frac{\exp\left(\beta\_{\text{C}}^{\prime} \mathbf{x}\_{\text{ri}}\right)}{\sum\_{\text{j}} \exp\left(\beta\_{\text{C}}^{\prime} \mathbf{x}\_{\text{nj}}\right)},\tag{2}$$

where the expected probability of alternative *i* being chosen by any respondent is dependent upon the expected value of the class probability. Class probability, expressed as probc is estimated in each LC model, along with the parameters of *u* (expressed as β).

The number of classes in the model is assessed using information criteria (IC) statistics for each of the models. There is no common accepted IC to determine the correct number of classes [51] so several IC are used in combination. In this case we examined the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Hannan Quinn Information Criterion (HQ) statistics. These IC penalise for additional parameters to be included to determine the classes. The LC model has been used repeatedly in environmental economics [52,53] and, more specifically, for consumer preferences in seafood [54,55] and sustainability of seafood production [19,29].

The LC model was run for the pooled data set, containing the CE data of all the countries, and for each individual country. The model output is reported for the pooled and individual country level models. The model was additionally run on a subset of the CE data, excluding instances where sea bream was the product specified (Italy and Israel), as one could argue that the consumers' perceptions of production and preferences can vary across seafood species. As the results were similar in trends, coefficient strengths and marginal WTP estimates for CE attributes, the results of the full pooled data set including all country data are reported in this paper. (Results of the model, excluding the seabream CE data, are available from the authors upon request).

It should also be noted that there exists a high degree of heterogeneity in international responses towards seafood ecolabels. Several studies reviewed by Brécard et al. [29] point out numerous factors that determine the public's WTP. A number of these factors are the influence of culture, socioeconomic status, social norms, customs, political and moral values, institutions, political interests and political awareness [56–58]. Country specific dummies have been included in the model specification of the pooled data set to account for the effect of variations of these elements across the states. The comparison of several countries through a CE allows the researcher to assess if sustainability in the aquaculture production process is more valued in certain countries than others.

#### **3. Results**

Summary statistics for the entire sample of respondents (2520) are shown in Table 2. Overall, the sample was comparable to the national proportions in terms of gender, marital status, age and income. Even though 14% of the sample indicated they had heard of IMTA previously, a follow-up question asking for a description of IMTA resulted in no adequate description of IMTA being provided, suggesting that the true proportion of consumers familiar with IMTA is lower. This shows that consumers are not familiar with IMTA, regardless of whether or not IMTA experiments are taking place in the sample country. The majority of respondents indicated use of ecolabels either sometimes, most of the time, or always (1.86 average out of Likert 1–5).

**Table 2.** Summary statistics of choice experiment respondents.

1 When you are buying seafood, do you look at ecolabels to decide which product you want to buy? Likert score (1–4): 1. Always, 2. Most of the time, 3. Sometimes, 4. Never.

The choice experiment data was analysed using NLOGIT6 statistical software. The search for the most appropriate specification (in terms of number of classes in model) consisted of modelling data assuming different numbers of latent classes for the individual country data sets and the pooled data set. The aim of this search was to develop a model that minimises the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Hannan Quinn Information Criterion (HQ) statistics. These statistics are measures of how well the model expresses variation in observed data. In the interpretation of the model results, differences in preferences as expressed by differences in the attribute level parameter across the classes. Statistics of model performance are listed in Table 3. The pooled model and the models for Ireland and Israel failed to converge when specified with 4 classes. Interestingly, all statistics indicated the same class selection for each country, leading to the selection of a latent class (LC) model with three classes for the pooled data, three classes for Ireland and Israel and four classes for Italy, Norway and the UK. It has been shown that the different criteria can indicate different optimum class numbers and the researcher needs to then make a judgement call based on other factors such as variable significance across classes [40].

Notes: Numbers in bold are maximised criterion for model selection.

#### *3.1. Cross Country Models*

Tables 4–8 present the individual country latent class models while the pooled cross country model is presented in Table 9. A set of consumer profiles based on the results of the latent class models are also presented in Table 10. Weighted marginal WTP estimates for the CE model attributes of production location and sustainability levels are presented separately in Table 11 and as a product containing a set of attributes in Table 12. Finally, an overview of the marginal WTP estimates per class in each of the country specific models and the combined pooled model is given in Appendix B.

**Table 4.** Model Ireland.

The latent class model output for the Irish data contained three latent classes, where the highest probability assigned was for class 3 (48%), followed by class 1 (39%) and class 2 (12%). The model showed significant positive consumer preference for seafood produced in Irish waters for the first (1.6, *P* < 0.01) and third (1.4, *P* < 0.01) classes. The second class showed a negative preference for the Irish produced attribute, but this coefficient was insignificant. With regard to sustainability in seafood production, Irish respondents showed a positive preference towards products with sustainability label C in class 1 (0.7, *P* < 0.01) and class 2 (0.9, *P* < 0.01), towards label B in class 1 (0.9, *P* < 0.01) and class 2 (0.9, *P* < 0.01) and towards label A in class 1 (1.9, *P* < 0.01), class 2 (1.2, *P* < 0.01) and class 3 (1.8, *P* < 0.01).

Overall, all latent classes were characterised by a positive preference for more sustainably produced seafood, although this preference was more clearly defined in class 1. Irish respondents had a significant negative preference in all classes for the price attribute (class 1: −0.05, *P* < 0.01; class 2: −0.4, *P* < 0.01; class 3: −0.1, *P* < 0.01) and the opt-out option (class 1: −11.9, *P* < 0.01; class 2: −3.9, *P* < 0.01; class 3: −2.5, *P* < 0.01). The models also include socio-demographic interaction terms where being male, married, having third-level education and income were interacted with the opt-out alternative specific constant (ASC). The interaction terms yielded insignificant parameters in the majority of classes in the Irish model. In class 2, demographic groups that opted out significantly more were male (1.5, *P* < 0.01) and highly educated (0.8, *P* < 0.01) respondents. In class 3 the elderly opted out significantly more than the other groups (0.04, *P* < 0.01).

**Table 5.** Model Israel.

The preferred model for the Israel data contained three classes, where the highest-class probability was for class 2 (53%) and smaller probabilities for class 1 (32%) and class 3 (15%). Respondents from Israel showed a positive preference for nationally produced aquaculture produce in all classes (class 1: 1.89 *P* < 0.01; class 2: 1.1, *P* < 0.01; class 3: 0.6, *P* < 0.01). Across all classes, positive preferences for more sustainable aquaculture production were expressed that increased as sustainability levels increased, from label C (class 1: 0.9, *P* < 0.01; class 2: 0.5, *P* < 0.01; class 3: 1, *P* < 0.01) to B (class 1: 1.7, *P* < 0.01; class 2: 1.3, *P* < 0.01; class 3: 1.3, *P* < 0.01) to A (class 1: 2.8, *P* < 0.01; class 2: 2.1, *P* < 0.01; class 3: 2.8, *P* < 0.01). Israeli respondents expressed negative preferences for the price attribute (class 2: −0.04, *P* < 0.01; class 3: −0.04, *P* < 0.01), although this was insignificant for class 1 (−5.9, *P* > 0.1). With regard to the opt-out option, respondents showed a negative preference towards the opt-out option in class 2 (−5.6, *P* < 0.01) and class 3 (−1.6, *P* < 0.05), but this remained insignificant in class 1 (−5.9, *P* < 0.1). The interaction terms were insignificant in class 1. In class 2, the elderly opted out more (0.02, *P* < 0.5) and in class 3, respondents that opted out were more likely to be female (−0.7, *P* < 0.05) and unmarried (−0.7, *P* < 0.05).

**Table 6.** Model Italy.

The Italian sample was divided into four latent classes, with the probability of class 3 being the largest (45%), followed by class 2 (22%), class 1 (18%) and class 4 (14%). Overall, preferences for the attributes followed similar patterns, although differences exist between the classes in terms of attribute coefficient significance. Italian respondents have a positive preference for aquaculture produced in national waters across all classes (class 1: 2.6, *P* < 0.01; class 2: 3.8, *P* < 0.01; class 3: 0.8, *P* < 0.01; class 4: 1.1, *P* < 0.01). Respondents predominantly show a preference for aquaculture products with higher sustainability labels, although some differences exist. In class 1 and 3, preference is significant and positively related to sustainability across label C (class 1: 0.7, *P* < 0.05; class 3: 1.2, *P* < 0.01), label B (class 1: 1.5, *P* < 0.01; class 3: 2.5, *P* < 0.01) and label A (class 1: 2.4, *P* < 0.01; class 3: 3.8, *P* < 0.01). Although the model produced statistically insignificant results on label C (class 4) and label B (class 2), respondents expressed a positive and significant preference for aquaculture products with the highest sustainability label (A) across all classes. Respondents had a negative preference for products with a higher price across all classes. Preferences were negative for the opt-out option in the choice cards, although these were insignificant in class 1 (−3, *P* > 0.1) and class 2 (−0.1, *P* > 0.1). The interaction terms between demographic groups and the opt-out option were insignificant in most cases. None of the interaction terms were significant in class 1, while in other classes respondents were more likely to opt out when they were male (class 2: −0.7, *P* < 0.01; class 3: 0.4, *P* < 0.05; class 4: 0.6, *P* < 0.01), educated below third-level education (class 2: −1.3, *P* < 0.01), married (class 2: 0.5, *P* < 0.05; class 4:

0.4, *P* < 0.05), over 45 years of age (class 3: 0.06, *P* < 0.01; class 4: 0.03, *P* < 0.01) or had an income below the national average (class 3: −1.4, *P* < 0.01; class: 4 0.5, *P* < 0.01).

**Table 7.** Model Norway.

\*: *P* < 0.1; \*\*: *P* < 0.05; \*\*\*: *P* < 0.01.

The preferred model for the Norwegian data divided the respondents up into four latent classes, where class membership was the highest for class 1 (48%), followed by class 4 (27%), class 3 (17%) and class 2 (9%). Considerable preference differences were modelled across the Norwegian classes. National production location was preferred by class 1 (1.2, *P* < 0.01), class 3 (1.2, *P* < 0.01) and class 4 (3.5, *P* < 0.01), but remained insignificant for class 2. Sustainable aquaculture produce was preferred across all sustainability labels in class 1 (label C: 0.4, *P* < 0.01; label B: 1.2, *P* < 0.01; label A: 2, *P* < 0.01) and class 3 (label C: 1.1, *P* < 0.05; label B: 3, *P* < 0.01; label A: 4.4, *P* < 0.01). Preferences for the sustainability of aquaculture production were however insignificant for class 2 and class 4. All latent classes in the Norwegian data had a statistically significant preference for products with lower prices and a negative preference for the opt-out option, with the smallest class (2) being statistically insignificant for the alternative specific constant. With regard to the interactions between demographic variables and the alternative specific constant, Norwegian respondents were more likely to opt-out when male in class 1 (1.3, *P* < 0.05), when not having completed third-level education in class 2 (−2.5, *P* < 0.01), when under the age of 45 in class 2 (−0.1, *P* < 0.01) and over 45 in class 3 (0.2, *P* < 0.01), when married in class 2 (1.5, *P* < 0.01) and unmarried in class 3 (−2.2, *P* < 0.01) and when the respondent's income is below the national average in class 2 (−1.4, *P* < 0.05).

**Table 8.** Model United Kingdom.

Data from the British respondents were modelled assuming four classes, consisting of two classes with a class probability of 39% (class 1) and 34% (class 4) and two classes with lower class probabilities of 17% and 10%. British respondents showed a positive preference for aquaculture products produced in national waters (class 1: 1.4, *P* < 0.01; class 2: 1.5, *P* < 0.01; class 4: 0.6, *P* < 0.01), although this preference was statistically insignificant for class 3 (0.02, *P* > 0.1). Respondents also expressed a positive preference for products from more sustainable aquaculture production processes, across all labels; although label C was insignificant in class 2 (*P* > 0.5) and class 4 (*P* >.01). This suggests that these classes prefer only larger increases in sustainability. All classes showed a statistically significant negative preference for the price attribute (*P* < 0.01) and all but class 3 (*P* > 0.1) had a negative preference for selecting the opt-out option in the choice cards (*P* < 0.01). In terms of interaction terms, in no demographic group opted out significantly more on average in class 1. In class 2 however, respondents selected the "I would not purchase either" option significantly more when they were male (1.1, *P* < 0.05), when their highest completed education level was below third level (−1.2, *P* < 0.01) or if they were married (2.7, *P* < 0.01). In class 3, the opt-out was selected more by respondents that were male (0.6, *P* < 0.05), under 45 years of age (−0.1, *P* < 0.01) or unmarried (−2.2, *P* < 0.01). In class 4, respondents opted out more when male (0.9, *P* < 0.05), completed third level education or higher (0.7, *P* < 0.05), are over 45 years of age (0.1, *P* < 0.01) and have an income higher than the national average (1.1, *P* < 0.01).

**Table 9.** Model pooled data.

In the 3-class model for the pooled data set, 54% (*n* = 1,161) of the respondents we assigned to the first class, 28% (*n* = 705) to the second class and 18% (*n* = 454) to the third class. The coefficients of all parameters are significant on the 1% level, with the exception of the third class, where the lower sustainability level (c) was insignificant. The interaction terms with the opt-out ASC varied strongly between the classes.

#### *3.2. Consumer Profiling across Classes*

In Table 10, consumer profiles based on the latent class parameters are described. Here it should be noted that the classes are not mutually exclusive but latent, meaning that respondents are assigned a probability of membership in each case. Respondents are thus not assigned 100% to any single class. Consumer profiling is based on the coefficient values in each class. A total of five consumer profiles were created. The profiles identified in the latent classes are (1) the "Green Buyer", (2) the "Local Buyer", (3) the "Determined Buyer", (4) the "Flexible Buyer" and (5) the "Economic Buyer".

**Table 10.** Classification of preferred models of pooled and country data.

#### 3.2.1. The Green Buyer

The first two profiles relate to the main CE attributes; production location and sustainability level. Respondents in the "Green Buyer" class are characterised by a strong preference for sustainable seafood production. The class represents a positive coefficient for the sustainability attributes, which translates into a WTP for the sustainability attributes that is significantly higher than in the other classes and compared to other attributes.

The second class from the pooled data model is labelled as the green buyer due to its positive preference for the sustainability attributes C, B and A (attribute coefficients (and standard error) of 0.48 (0.05), 2.02 (0.13) and 3.21 (0.14) respectively) and the comparatively low preference for production location. Elder people were more likely to select the opt–out (0.03 (0.004)), while highly educated respondents (−0.23 (0.09)), low income respondents (−0.24 (0.09)) and married respondents (−0.16 (0.09)) were less likely to select the opt-out. With regard to the country interactions, respondents from this class are more likely to select the opt-out when they are Italian (0.71 (0.16)), Norwegian (3.71 (0.23)) or British (0.76 (0.17)), compared to the base case.

Latent classes characterised by their high utility associated with sustainability are modelled for every country and represented a large proportion of the population of Ireland (class 3, probability 48%) and Italy (class 3, probability 45%) and smaller proportions for Norway (class 3, probability 17%), the UK (class 3, probability 17%) and Israel (class 3, probability 15%). The profile of the Green Consumer is represented by the largest proportion of respondents (class 2, probability 28%) and the only latent class to be modelled in every data set. The allocation towards green or local buyer profiles is based on the comparative coefficients of the sustainability attribute and on the height of the WTP for the individual attributes in the specific class. Although characterised by their preference for sustainability, the Irish, Israeli and Norwegian respondents in this profile also have a positive utility for national production location paired with their preference for higher sustainability.

#### 3.2.2. The Local Buyer

The "Local Buyer" has a strong positive preference for seafood that is produced in national water, as opposed to imported seafood. This preference is elicited from a positive coefficient for the location attribute and a WTP for the location attribute that is high, both in comparison to the other attributes as well as for the location attribute WTP in other classes.

Members of the third class from the pooled model had strong positive preferences for fish produced in national waters (3.14 (0.16)). The sustainability attribute coefficients did not show a clear preference pattern, although the highest sustainability attribute is significant (0.5 (0.13)). With regard to representativeness of the sample, male respondents in this class were more likely to select one of the purchasing decisions than women (0.5 (0.13)). Compared to the base case, male respondents from Ireland (−0.5, (0.13)), Italy (−3.31 (0.25)) and Norway (−3.33 (0.25)) are less likely to select the opt-out, while respondents from the UK select the opt-out significantly more (0.49 (0.24)). However, the preference for the opt-out is still negative for all countries, i.e., respondents from all countries prefer to select a purchasing option over the "I would not buy any of these products, even if this product was on my shopping list" option.

In the single country analysis, the profile of the local buyer can be attributed to 27% of the respondents, spread over Israel (class 2, probability 53%), the United Kingdom (class 4, probability 34%), Norway (class 4, probability 27%) and Italy (class 2, probability 22%). Although Irish respondents have among the highest WTP for nationally produced seafood in the sample, no latent class was allocated to the local buyer profile in the Irish model.

#### 3.2.3. The Determined Buyer

The determined and flexible buyer profiles relate to the shopping habits of respondents and their resoluteness in making purchasing decisions. Respondents in the "Determined Buyer" profile are characterised by their determination to buy what is on their shopping list, regardless of the attributes of the product. This is reflected by a negative coefficient for the alternative specific constant in the model.

The first class from the pooled data model is labelled as 'determined buyer' due to its strong negative preference for the opt-out option (−10.57 (0.51)). The class is further characterised by positive coefficients for the national production location (1.13 (0.05)) and the sustainability attributes C, B and A (0.48 (0.05), 1.04 (0.07) and 1.73 (0.07) respectively). The country interactions terms in the pooled model indicate that Irish respondents have a stronger tendency to select one of the purchasing options (−6.05 (0.51)) compared to the base case of Israelis, whereas Norwegian and the British respondents selected the opt-out option more (in 2 out of the 3 classes in the Norwegian case and for all classes in the UK case) in comparison to the base case (1.94 (0.29) and 2.64 (0.29), respectively). This means that the results of the model cannot be generalised over the Norwegian and British public as much as over the other sample countries, as they have selected the opt-out more. However, considering the strong negative coefficient for the constant, Norwegian and British respondents have a negative preference for the opt-out; i.e., they prefer to select a purchasing option over the opt-out option, but this preference is significantly weaker than in the other sample countries. This tendency is consistent for these countries across the different classes.

Across the individual country models, the profile of the determined buyer was allocated to latent classes in Ireland (class 1, probability 39%), Norway (class 1, probability 48%) and the United Kingdom (class 1, probability 39%), together accounting for 25% of the sample population. The modelled classes produced significant coefficients for all attributes, but were identified as determined buyer classes due to the strength of the negative coefficient for the opt-out ASC; it was over 6 times the nearest coefficient in the class in Ireland, over 5 times in the Norwegian class and 4.5 in the UK class. The modelling of these classes is based on the purchasing habits rather than preference for one of the attributes, but the respondents represented by these classes did display significant preferences for the CE attributes.

#### 3.2.4. The Flexible Buyer

In contrast to the determined buyer, the "Flexible Buyer" shifts easily from one product to the other when the product attributes do not fit the respondents' preferences. This is expressed by the absence of a significant negative coefficient for the alternative specific constant. The determined and flexible buyers can however still have strong preferences for the CE attributes, which should be recognised in the interpretation of the latent class models.

In the individual country models, a latent class flexible buyer are assumed for Israel (class 1, probability 32%) and Italy (class 1, probability 18%), together representing 10% of the total sample population. Classes were allocated to the flexible buyer profile based on their respondents' tendency to switch to other (substitute) products and their lack of a preference for lower prices. They were

allocated to the flexible profile due to insignificant coefficients for the constant, which reflects a lack of a detectable preference for buying the type of product the respondent intended to buy when entering the shop. In the Israel class, no significant preference for lower priced products was visible. As the classification of these latent classes is based on shopping habits, it should be recognised that the classes in Israel and Italy both have significant positive coefficients for the CE attributes production location and sustainability, thus contributing to the WTP of those attributes.

#### 3.2.5. The Economic Buyer

The "Economic Buyer" profile captures respondents' price sensitivity. Respondents in this class have a preference for products with lower prices. Although not observed in the pooled data set, the economic buyer profile is allocated to classes in Italy (class 4, probability 14%) and Norway (class 2, probability 9%), together representing 5% of the total sample population. The economic buyer profile was allocated according to the negative preference for the price variable. The Italian class holds positive preferences for the sustainability and location attributes, but these preferences are only significant for the higher sustainability attribute levels. This suggests that these individuals pay extra only for larger increases in sustainability. In the Norwegian class 2, price was the only variable that was significant (at the 5% level). In terms of representativeness of the latent classes for the sample population, this profile is comparatively weakly represented.

**Table 11.** Weighted marginal WTP for CE attributes across countries by the preferred models.

\*: *P* < 0.1; \*\*: *P* < 0.05; \*\*\*: *P* < 0.01.

**Table 12.** Price premium for seafood with varying attributes of pooled data.

Table 11 lists the marginal willingness-to-pay for the change in attribute levels. Estimates reported are weighted for the latent class membership probabilities. The public's marginal WTP for seafood produced in national waters varies from €3.41 (in Israel) to €11.33 (in Italy) and is estimated at €6.02 for the pooled data set, ceteris paribus. The marginal WTP for the sustainability attributes vary from €1.40 (Norway) to €4.28 (Italy) for sustainability label C, from €2.88 (Norway) to €9.44 (UK) for sustainability label B and from €4.88 (Norway) to €15.18 (UK) for sustainability label A. The pooled data analysis produced estimates of €2.45, €4.16 and €6.89 per kilo of unfrozen product that has sustainability label C, B and A, respectively, ceteris paribus. The pattern in WTP estimate is comparable between countries. Marginal WTP for both sustainability and national production location is positive and increases as sustainability increases. This pattern holds over all countries. However, difference does exist in the

magnitude of marginal WTP estimates. The average marginal WTP for CE attributes is the highest in the UK, followed by Italy, Ireland, Israel and lastly Norway.

#### **4. Discussion**

This paper presented the results of a latent class analysis of CE data with the aim of assessing the market potential of sustainable seafood production techniques, such as IMTA. Latent classes were modelled for data sampled in Ireland, Israel, Italy, Norway and the United Kingdom both separately and for the pooled data from all countries. Classes were allocated to consumer profiles according to the estimated attribute preference parameters signs and significance, leading to respondents in each class being labelled as green, local, determined, flexible, or economic buyers. More specifically, latent classes in the pooled data model were identified as green (28%), local (18%) and determined (54%) buyers. While these profiles remain dominant in the country data analysis, a number of smaller profiles were detected that remained unidentified under the three class model on the pooled data set.

The profiles of the green and local consumer reflect respondents' preferences for the CE attributes sustainability and production location, whereas the determined and flexible profiles reflect how easily an individual shifts to an alternative product based on the product attributes. Interestingly, the proportion of the public assigned to the profile of green consumer (28%) is comparable to the proportion of consumers labelled green consumers by the OECD [59] (27%). As the determined buyer class covers a relatively large proportion of the respondents in Ireland (39%), Norway (48%) and the UK (39%) the general public of the sampled countries seems to be characterised as determined buyers rather than as a flexible buyer.

The public's WTP is positive for all CE attributes and across the sample countries and the pooled data, indicating that the public is willing to pay a price premium for products produced in a more sustainable method such as IMTA. Marginal WTP for the locally produced attribute is estimated in the pooled data at €6.02, ceteris paribus, indicating a willingness to pay a price premium for seafood products that are produced in national waters. The results also indicate that the public is willing to pay a price premium for products that are produced more sustainably. This WTP increases as the degree of sustainability increases by decreasing the environmental impact by 10% (€2.45—sustainability label C), 20% (€4.16—sustainability label B) or 30% (€6.89—sustainability label A) per kilo of product.

Price premiums are mentioned as a method of stimulating seafood producers to shift to more sustainable production techniques such as IMTA [60]. However, this is conditional upon the additional profits going to producers, rather than to retailers. Considering the international character of the seafood market and the power of retailers in the market, some form of governance may be necessary if bottom-up pressure by the public is to steer the industry to become more sustainable. The EU may well be the best suited institution to govern seafood market steering policies in Europe, as it holds some transnational power and can create a standardised instrument that transcends national market boundaries and works at the European level [61] in contrast with the fragmented approach of national governments.

Given the strong competition in the globalised seafood market, the European aquaculture industry can match the global industry only when it creates a valuable market position for itself. For example, the Irish salmon industry recovered from a decline in production after a combination of disease and negative market conditions by carving out a high-value niche market for organic salmon [62]. The European aquaculture industry could come closer to matching global growth rates by creating a similar niche for its products. Production methods such as IMTA could expedite the development of such a sustainable niche market.

The significant WTP for more sustainable forms of aquaculture does not in itself guarantee higher profits for producers that switch to such production methods. This will also depend on the marginal costs of providing higher sustainable levels in production. Given the potential synergies across the products produced in an IMTA system with possible reduction in costs associated with feed loss and waste processing (plus increasing demand for sustainable produced food), the marginal cost could actually decrease in the long run but this remains an area for future research. Further research is also needed in terms of the pooled model specification. The model presented controls for non-observed heterogeneity in mean preferences. In this type of cross-country study however, consumers may interpret and process choice tasks and situations differently [63]. Control for this scale heterogeneity could improve the fit of the pooled model.

The creation of an ecolabel as demonstrated in this survey could simultaneously fulfil multiple functions; for the public, they provide previously hidden information on the environmental impact of a product, allowing them to maximize their utility; for a producer, they provide the opportunity to differentiate their product and increase their market value; while governments use ecolabels as a policy instrument to stimulate environmentally friendlier production to reach policy goals. An increasing proportion of the European public is aware of the environmental impact of seafood production and is willing to pay a price premium for more sustainable products. Public awareness and willingness-to-pay analysis can therefore be considered a tool to stimulate the European aquaculture industry while facilitating blue growth and reaching the goal of 'Good Environmental Status'. Simultaneously, an EU ecolabel guarantees the public that the product observes EU production standards, which, as Magennis et al. [64] points out could reassure those individuals reluctant to purchase seafood that is imported from countries where regulations are perceived as insufficient.

Furthermore, an ecolabel as proposed in this paper provides information to the public on the degree of environmental impact. A common criticism towards ecolabels is that they do not provide information on the degree of environmental improvement, and green grading schemes have been proposed by scholars [32]. A sustainability grading label provides this information to the public and continually stimulates producers to decrease the environmental impact of their production process, so as to reach a higher ecolabel tier. Lastly, a lack of knowledge and trust in regulatory organisations, paired with distrust towards the food industry, and ignorance and scepticism about the independence of certifiers, has been identified as elements that are decreasing the ecolabel impact [32]. A grading label as proposed has the advantage of consumer familiarity and trust, as this is a widely used and accepted label across EU countries.

Seafood preferences for sustainability and production location can be generalized over countries in terms of preference patterns, but WTP estimates were heterogeneous in degree. Differences among countries can have implications for the success of eco-labelling programs. Differences in socioeconomic status, knowledge on environmental issues, price sensitivity or the perceived importance of other seafood attributes that the ecolabel competes with may all impact on the success of a new ecolabel [24]. These differences highlight the need for flexible implementation of cross border eco-labelling programs, as well as the need for education of the European public on environmental issues and their role as responsible consumers. Finally, a key aspect of investment in IMTA will be the extent to which consumers are willing to pay higher prices for fish and shellfish which are produced using this technique. The results presented here highlight the fact that consumers are willing to pay a price premium for the additional sustainability IMTA systems could provide, although the location of such systems will also remain an important consideration in any purchasing decision.

**Author Contributions:** Conceptualization, S.v.O., S.H. and T.O.; Data curation, S.v.O.; Formal analysis, S.v.O. and S.H.; Investigation, S.F.; Methodology, S.v.O. and S.H.; Resources, S.H.; Software, S.v.O. and S.H.; Validation, S.v.O., S.H. and S.F.; Visualization, T.O.; Writing—original draft, S.v.O.; Writing—review & editing, S.v.O., S.H., T.O. and S.F.

**Funding:** The authors wish to acknowledge the funding received from the Marine Institute, under the Marine Research Programme 2014–2020 by the Irish Government (Grant-Aid Agreement No. PBA/SE/16/01). The data was collected for the Increasing Industrial Resource Efficiency in European Mariculture (IDREEM) project of the EU 7th Framework Programme under grant agreement number 308571.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

#### **Appendix B**

**Table A1.** *Cont.*

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Sustainability Descriptive Labels on Farmed Salmon: Do Young Educated Consumers Like It More?**

#### **Djin Gie Liem 1,\* ID , Giovanni M. Turchini 2, Uracha Wanich 1 and Russell Keast 1 ID**

Received: 13 May 2018; Accepted: 7 July 2018; Published: 10 July 2018

**Abstract:** Despite the efforts to make fish sustainable, it is largely unknown if young educated consumers' taste of fish and their willingness to pay more for fish is influenced by positively framed messages regarding sustainable farming practices. This research investigated if a positively framed description of sustainable farming opposed to positively framed descriptions of flavour, health benefits, or socially responsible farming, influences young consumers' liking, and willingness to pay for farmed salmon. Young consumers of fish (*n* = 119) randomly tasted Fresh and hot Smoked salmon and rated their liking and willingness to pay more on structured line scales. The salmon were labelled with either a description of sustainable farming practices, flavour benefits, nutrition/health benefits, socially responsible farming practices, or no descriptions. Descriptive labelling about Sustainability (*p* = 0.04), Flavour (*p* = 0.01), and Health/nutrition (*p* = 0.01) significantly increased consumers' liking of Fresh salmon compared to Fresh salmon without labelling. No such a difference was found between the social responsibility label and the sample without labels (*p* = 0.2). Participants were willing to pay more for 250 g of Fresh and Smoked Salmon with descriptive labels (Fresh: \$9.3 ± \$0.003; Smoked: \$10.1 ± \$0.003), than for the same Salmon without such labels (Fresh: \$9.0 ± \$0.06; Smoked: \$9.8 ± \$0.08) (*p* < 0.001). The sustainability descriptive label had no added benefit above other descriptive labels. The liking and buying intent were, for all labels and fish types, strongly correlated (*r* = 0.80, *p* < 0.001). In conclusion, sustainability labelling is promising, but does not differentiate from other positively framed messages.

**Keywords:** sustainability; sensory; seafood; consumers

#### **1. Introduction**

Global fish consumption has rapidly increased in the past decades. The health benefits associated with fish consumption are well recognized and health organisations across the world recommend that consumers eat around 2 to 3 servings of fish per week [1,2]. Especial young consumers consume suboptimal levels of fish [3,4]. Large quantities of fish consumption, although beneficial to health, are thought to put an unsustainable strain on the current fish supply [5]. The FAO has estimated that about 31.4% of the commercialised fish stocks are overfished and biologically unsustainable. The focus on sustainable practices, including farming fish in a sustainable manner [6,7], is therefore important from an industry as well as a consumer point of view.

There is an increased focus on sustainable seafood practices, which is reflected by an increase in sustainability certifications shown on the front of the pack, which aims to inform consumers about the positive sustainability or eco-friendly profile of the product. It is estimated that there are close to 50 different labels worldwide related to sustainability and eco-friendliness of seafood, some of those are supported by official certifications as assessed by independent organisations (see Reference [8] for review). Although some studies have suggested that information about sustainability has a positive influence on product choice [9,10] and willingness to pay [11,12], other studies failed to see such an effect [13]. More importantly, to our knowledge, none of the studies which investigated the impact of sustainability information on seafood perception actually included consumers tasting the product and assessed their liking of the taste of seafood with sustainability information.

Considering that food liking is one of the most important determinants of food choice, including fish [14,15], the importance to positively influence consumers' food liking and buying intentions of sustainable fish is instrumental to increase the economic viability of the aquaculture sector. Taste is determined by the food, but also by information, which is provided to consumers at the point of purchase and at the point of consumption. Previous research with a variety of commodities suggests that the liking of products can be influenced by descriptive labelling [16]. Descriptive labelling provides consumers with additional information about the product (e.g., country of origin, production methods, ingredients, health and taste benefits) and is mostly placed on the front of the food package (also referred to as "front of the pack"), to attract consumers' attention. Descriptive labelling in this context can either be a single word or phrase (e.g., made in Australia, made by high-pressure technology), or a more elaborated marketing description such as "Belgium black forest double chocolate cake [17]". A variety of studies have suggested that descriptive labels influence consumers' taste perception and buying intention. For example, descriptive labelling on wine bottles focused on the region of origin (e.g., Dakota vs. California) can increase consumers' liking of wine [18]. Descriptions about the production process of milk (e.g., long vs. short shelf life milk) can alter consumers' liking of milk, depending on consumers' beliefs about the different milk production processes [19]. Ingredient information associated with health and/or taste benefits placed on the front of packages, such as "contains soy" [20], "reduced in salt" [21,22], "low fat" [23], and labelling emphasising the exclusivity of a product (e.g., "Belgium black forest double chocolate cake" [17] or "X-ray vision carrots" [24] can alter consumers' liking of unhealthy, as well as healthy, products. In addition, logos and messages related to social responsibility, commonly seen on food products such as coffee, chocolate [25,26], rice [27], as well as seafood, seem to increase consumers' willingness to buy the product [28].

Along the same line, labelling focused on sustainability can influence consumers' liking of food as shown with various commodities such as chocolate, coffee, and lamb (see Reference [16] for review). However, consumers need to understand the sustainability label [29] and feel a high involvement with sustainability [30]. For some consumers, sustainable practices concerning fish are of high importance and relevance [10,15,16]. However, not all consumers place a high importance on sustainability. For them, labelling focused on sustainability is less likely to influence food liking, partly because they do not see an immediate personal benefit of sustainable foods [31]. Egocentric consumers who do not feel highly connected with "sustainability" might view products labelled as "sustainable" as not benefitting them in the near future. Whereas for those who are less egocentric and those who feel a strong involvement with the welfare of others and the greater good, descriptions focused on sustainability are more likely to influence their liking of foods. It is therefore likely that descriptive labelling concerning sustainability is specifically powerful for consumers who are highly concerned and involved in sustainability [10].

Research in Western countries such as the UK, USA, and Spain, suggest that young highly educated adults show a positive attitude towards sustainability [32] and are concerned about sustainability [33,34]. Research specifically focused on fish with a sustainable label suggests that those who buy fish with a sustainability label are mainly either young singles or families with older aged children living at home [35]. Several studies suggest that the sustainability of fish is valued by consumers as evidenced by the increased preference for seafood products with a sustainability label [9], but the influence of sustainability-related information on young educated consumers' liking of the taste of seafood is underexplored

The present study investigated if a positively framed description of sustainable farming as opposed to positively framed descriptions of flavour, health benefits, or socially responsible farming, influences young highly educated consumers' liking and willingness to pay for farmed salmon. Specifically, two differently prepared Atlantic salmon (*Salmo salar*) products, fresh raw salmon and hot smoked salmon, were tested, and the messages/labels tested in this study were focused on the production methods of the feed (aquafeed) used in salmon farms. The focus on aquafeed, rather than on other production variables, was selected to represent one of the highest recurrent costs of aquaculture practices and has attracted some criticisms with respect to its sustainability. For example, at the moment, the amount of fish from the ocean needed to feed farmed salmon can be seen as not sustainable. However, aquafeed has the capability of directly affecting all the parameters studied (including nutrition/health and flavour). In addition, feed companies are typically the largest players in the aquaculture production chain, and accordingly have a greater capability of intervention, R&D investment, and marketing. A positive consumer perception of aquafeed will help the industry to further develop and sell sustainable fish products.

#### **2. Materials and Methods**

Regular consumers of salmon tasted and rated their liking and willingness to pay for two different types of salmon products; fresh raw salmon and hot smoked salmon. The four randomly assigned descriptive marketing type labels were focused either on sustainability, flavour, nutrition/health, or social responsibility (Table 1). Each combination of the type of salmon and labelling was presented once to each consumer. In addition, they tasted the two types of salmon without descriptive labelling. All participants tasted the fresh raw, as well as smoked, salmon in all label combinations, which resulted in 10 samples for each participant. After tasting all samples, participants rated the perceived importance of sustainability, flavour, nutrition/health, or social responsibility on food in general. In addition, they were asked how much they considered sustainability, flavour, nutrition/health and social responsibility when actually buying food in general or fish specifically.

**Table 1.** The four marketing descriptive labels used for the consumer taste test.

The testing was conducted in computerised, partitioned sensory booths in the Centre for Advanced Sensory Science at Deakin University, and data was collected using Compusense Cloud as part of the Compusense Academic Consortium (Compusense Inc., Ontario, Canada. The study design and protocol was approved by the Deakin University Human Ethics Committee (HREC 2012-162); all consumers provided informed, written consent prior to study commencement. The consumers were asked to refrain from eating, drinking (except room temperature water), brushing teeth or chewing gum for one hour prior to testing.

*Stimuli:* The fresh Atlantic salmon was collected from a wholesaler the morning prior to the sensory tests (Clamms Seafood, Yarraville, Victoria, Australia). All fresh salmon originated from the same batch, same farm, and was harvested and processed the evening before. The hot smoked salmon was sourced by the same wholesaler and came from the same farm as the fresh salmon. The smoked salmon originating all from the same batch and were collected frozen on the morning prior to the sensory tests. Salmon products were offered to the consumers to taste in portions of 5 g at room temperature in clear medicine cups. In total, the participants tasted five fresh and five smoked pieces of salmon.

*Participants*: All consumers were students enrolled in an undergraduate degree at Deakin University and consumed fish at least once every month. Eight participants were excluded because they did not want to taste the salmon. A total of 119 participants (108 female, 11 male, 23 ± 3.9 yrs) were included in the final analyses of the study.

*Procedure*: At the start of the test, consumers were told that they were going to taste sashimi (fresh raw salmon, hereafter referred to as Fresh) and hot smoked salmon (hereafter referred to as Smoked). They were also told the following: "Just to inform you, we have given you some more information about the fish you will be tasting". Consumers tasted four samples of Fresh and Smoked salmon to which a descriptive label was attached and a sample without labelling in a randomised order (see Table 1). In order to make sure the participants read the description without making them aware of the real purpose of the study, they were told: "in the interest of time could you please read the description while you are tasting, this will make the tasting and rating a bit quicker". After the five salmon samples were tasted, consumers waited one minute before another control and four samples with randomly assigned descriptive labels were presented. The design was balanced and randomised in such a way that all participants tasted all combinations of fish type and descriptive labels. The offering of Fresh and Smoked salmon was alternated. Consumers rinsed their mouth with water after tasting each piece of salmon.

*Measurement of liking and willingness to pay*: While tasting each sample consumers were asked to score on a 10 cm structured line scale how much they liked the salmon (hereafter referred to as "liking"). The lines were anchored with "not liked at all" (−5 cm) on the left side to "like very much" on the right side (5 cm), and neutral in the middle (0 cm). The line was separated by three evenly spread markers. After consumers rated liking they were asked how much they wanted to pay for the salmon they just tasted by showing the following text on screen: "regular salmon sells for about \$10 per 250 g. How much would you be willing to pay for 250 g of the salmon you just tasted?" Underneath this question, they were presented with a 10 cm structured line scale with the following evenly spread markers \$8, \$9, \$10, \$11, \$12. Participants could mark the line anywhere between \$8 and \$12. The reference price was deliberately provided to make sure that all participants had the same reference price point in mind when answering the question.

*Measurement attitudes and behaviour*: After all fish samples were tasted, participants were asked the following questions. "You are about to buy fish.  Participants were presented with 4 statements (e.g., the fish is tasty; the way the fish is farmed is good for the environment; the way the fish is farmed is good for the community and farms; Consuming the fish will be good for your health) and were asked to rank these statements from most to least decisive. In addition, they were asked the following questions: "How important is it to you that the fish you buy (a) gives fair wages to farmers? (b) is produced with sustainable methods? (c) is tasty? (d) is good for your health? Participants were asked to score these statements on a 5 points scale (e.g., 1 = not important at all; 2 = not important; 3 = between not important and important; 4 = important; and 5 = very important. This score is referred to as the "importancy score"). Next, they were asked to rate how often they considered Sustainability, Flavour, Health/nutrition, or Social responsibility at the point of purchase when buying fish (1 = never, 2 = sometimes, 3 = often, 4 = always). Next, consumers were asked how often (i.e., less than once a month, 1–3 times per month, once per week, twice per week, 3–4 times per week, 5–6 times per week, every day) they consumed raw, steamed/grilled/baked, fried and/or canned fish. Basic demographic data (i.e., age, gender) were collected at the end of the questionnaire.

Two separate linear univariate models were used to investigate the role of the salmon type and label type on (1) liking and (2) willingness to pay. More specifically, a general linear univariate model with "descriptive labelling" and "Salmon type" as fixed factors and "liking" as the dependent variable was applied to investigate the influence of "descriptive labelling" and "salmon type" on "liking". A second linear model with "descriptive labelling" and "Salmon type" as fixed factors and the "price willing to pay for salmon" as the dependent variable was applied to investigate the influence of "descriptive labelling" and "salmon type" on "willingness to pay". Post-hoc analyses with Bonferroni correction were carried out to identify if the "sustainability label" influenced liking and willingness to pay in a different way as the remaining labels did. In order to take into account that the data was not independent (e.g., all participants rated all samples on liking and willingness to pay), participants' ID was included as a random factor but not as variable. *p*-values < 0.05 were considered statistically significant.

To investigate the importance of sustainability, flavour, health/nutrition, and social responsibility, the importancy score of sustainability was compared against the importancy score of flavour, health/nutrition, and social responsibility by using a Wilcoxon non-parametric test for two related samples. In which, not important at all = 1, not important = 2, between not important and important = 3, important = 4, very important = 5). A similar analysis was conducted to investigate if sustainability was more often considered when buying fish than flavour, health/nutrition, or social responsibility.

Sustainability-focused consumers were identified as those who reported often or always to consider "sustainability" when buying food. Taste focused consumers were identified as those who reported always to consider "taste" when buying food. Health-focused consumers were identified as those who reported always to consider "health" when buying food. When consumers reported to always consider taste and health when buying food, they were counted as both taste focused and health-focused consumers. It is important to point out that taste and health focus was used as a way to look at the sample set, rather than a comparison of taste versus health-focused consumers. All statistical procedures were performed with IBM SPSS statistics Version 22, 64-bit edition.

#### **3. Results**

#### *3.1. Subject Characteristics*

The majority of consumers were female (91%) and had at least completed year 12 of secondary school (Table 2). As shown in Table 3, fish was most frequently consumed (more than once a month) as steamed/grilled/ baked and/or canned fish. Raw and fried were the least popular preparation methods. Virtually all consumers reported that flavour and/or health were important to very important when it came to food choices. Sustainability and social responsibility were the least important aspects of food (*p* < 0.0001). This is also a reflection of how often participants considered sustainability when buying fish. That is, 4.2% of participants "always" considered sustainability when buying fish. This was significantly lower than the frequency at which they considered flavour, health/nutrition, or social responsibility when buying fish (*p* < 0.0001) (see Tables 3–5).

**Table 2.** The demographic characteristics (mean ± sem) of participants in the consumer taste test.

1 Taste focused consumers were identified as those who reported to always consider "taste" when buying food. Health-focused consumers were identified as those who reported to always consider "health" when buying food. When consumers reported to always consider taste and health when buying food, they were counted as both taste focused and health-focused consumers. Taste and health focus was used as a way to look at the sample set, rather than a comparison of taste versus health-focused consumers.

**Table 3.** The participants' frequency of fish and fish product consumption.

**Table 4.** The percentage of participants who judged sustainability, flavour, health/nutrition, or social responsibility as not important at all to very important.

1 Sustainability was significantly less important than flavour and health/nutrition *p* < 0.0001.

**Table 5.** The percentage of participants who considered sustainability, flavour, health/nutrition, or social responsibility when buying fish (point of purchase).

1 Sustainability was significantly less considered when buying fish than flavour, health/nutrition, and social responsibility *p* < 0.0001.

#### *3.2. Influence of Descriptive Labelling on Liking and Willingness to Pay*

The general linear univariate model showed a main effect of type of salmon offered on liking (*p* < 0.05) and a borderline significant main effect of descriptive labels on liking (*p* = 0.07). Fresh salmon was liked less than Smoked salmon (Fresh: −1.7 cm ± 0.13 cm vs. Smoked: 1.4 cm ± 0.14 cm) (*p* < 0.001). When investigating the influence of labels on each salmon type individually, it was found that descriptive labelling about Sustainability (−1.1 cm ± 0.37 cm, *p* = 0.04), Flavour (−0.95 cm ± 0.37 cm, *p* = 0.01), and Health/nutrition (−1.3 cm ± 0.37 cm, *p* = 0.01) significantly increased consumers' liking for Fresh salmon compared to the same fish without labels (−1.8 cm ± 0.37 mm). No such difference was found between the Social responsibility label (−1.1 cm ± 0.35 cm) and the fish without labels (*p* = 0.2). Post hoc analyses revealed no significant difference between the Sustainability descriptive label and the remaining descriptive labels (Figure 1). Descriptive labelling did not influence consumers' liking for Smoked salmon (no labels: 1.3 cm ± 0.36 cm vs. Smoked labelled: 1.6 cm ± 0.07 mm) (*p* = 0.60) (Figure 1).

**Figure 1.** The relative difference (cm) (mean ± sem) in liking compared to fish without labels (control), shown for different descriptive labels (i.e., health, taste, social responsibility and sustainability). Positive (maximum = +10 or negative difference (maximum = −10) indicates that fish with a descriptive label is more or less liked respectively, than the control fish. \* indicates a significant difference (*p* < 0.05) compared to the same salmon without a descriptive label.

The general linear univariate model showed a main effect of the type of salmon offered (*p* < 0.001) and a main effect of the descriptive labels (*p* < 0.001) on the willingness to pay. Participants were willing to pay more for Fresh and Smoked Salmon with descriptive labels (Fresh: \$9.3 ± \$0.003; Smoked: \$10.1 ± \$0.003), than for the same Salmons without such labels (Fresh: \$9.0 ± \$0.06; Smoked: \$9.8 ± \$0.08) (*p* < 0.001). No significant differences were observed between the different descriptive labels (Figure 2). Liking and buying intent were, for all labels and fish types, strongly correlated *r* = 0.80, *p* < 0.001.

**Figure 2.** The difference in \$ amount (mean ± sem) that participants are willing to pay more for 250 g of salmon (e.g., fresh, smoked) with different labels (i.e., health, taste, social responsibility and sustainability) compared to control fish without labels (cm). \* indicates a significant difference (*p* < 0.05) compared to the same salmon without a descriptive label.

#### *3.3. Sustainability, Health, and Taste Focused Consumers*

Seventy-one consumers (60%) were classified as sustainability-focused consumers. The influence of sustainability labelling on fish liking and buying intentions of sustainably focused consumers was not different from the influence of these labels on none-sustainable focused consumers.

When only selecting consumers who highly focused on taste, it was found that the flavour descriptive label increased liking of fresh salmon (control: −1.8 ± 0.4 vs. flavour label: −1.0 ± 0.4) (*p* < 0.05). The remaining descriptive labels did not influence liking. The health-focused consumers

were, however, more willing to pay more for any of the fresh salmon with descriptive labels, than for the same salmon without such labels. Although health-focused consumers' liking of the hot smoked salmon was not influenced by any of the descriptive labels, they were willing to pay more for smoked salmon which carried either the sustainability or the social responsibility descriptive label compared to the same salmon without such labels (*p* < 0.05).

Descriptive labels did not influence taste focused consumers' liking of fresh nor smoked salmon. However, taste focused consumers were willing to pay more for the fresh salmon with a flavour description, than for the same fresh salmon without a descriptive label. Such difference was not found for the nutrition/health, social responsibility or sustainability label.

#### **4. Discussion**