1. Introduction
Marine litter is ubiquitous and presents a constant threat to oceans worldwide. It is a growing and complicated environmental issue as it comes from various sources and can move great distances from its source [1]. In this regard, marine litter is inherently a cross-border challenge [2]. Studies show that approximately 80% of marine litter originates from land-based sources, and when not handled properly, it runs into waterways and ends up at sea [3,4]. In addition, a portion of the litter is generated directly at sea, especially discarded fishing gear (e.g., ropes, buoys, and nets) and litter caused by marine activities [5,6,7]. Marine litter typically exists in all types of seas and coasts, where it accumulates on the bottom and water columns, and floats on the sea surface [8,9].
Marine litter, especially plastics decomposing slowly in the environment, poses dangers to marine species, ecosystems, and environments. For example, cigarette butts, the most frequent form of personal items found on beaches, liberate harmful compounds, including nicotine, tracmetals, and numerous other compounds, damaging the environment [10]. The ingestion of debris in 48 cetacean species and debris-induced mortality rates of 0–22% of stranded animals have been documented [11]. Arctic terns (Sterna paradisaea) tend to become entangled in fishing nets, mostly likely impeding hunting and foraging success, leading to starvation [12]. Corals and seagrass are covered with discarded fishing gear [13,14,15]. The associated microplastic pollution was further found to reduce the photosynthetic capabilities of specific coral species [16].
The presence of marine litter also creates significant economic costs affecting coastal and marine industries, such as plastic pollution affecting the visual and disturbing beach tourism [17,18], fishermen being faced with litter in places that are supposed to be fishing grounds [2], and the cost of cleaning seas and coasts [19,20]. As an illustration, removing plastic litter from Aldabra Atoll, a remote UNESCO World Heritage Site, in March 2019, cost approximately USD 4.68 million and required 18,000 person-hours of labor [20]. Annual crab loss due to lost traps in Washington waters of the Salish Sea was estimated at 178,874 male equivalents, translating to an economic loss of USD 744,296 or 4.5% of the value of harvest [21]. In addition, microplastics have been found in the human food chain, such as fish, shellfish, oysters, barnacles, and periwinkles, which are suspected to be related to human and ecosystem health [22,23,24]. A microplastic usually corresponds to the size range of 1–5 mm [25]. It was demonstrated that plastic particles are bioavailable for uptake into the human bloodstream, and the associated hazard needs to be studied in order to determine whether or not plastic exposure is a public health risk [26]. In addition, the interaction of heavy metals and microplastics in the aquatic environment can impact bioavailability, toxicity, and bioaccumulation potential in organisms [27].
A wide range of international, regional, and national measures have been developed to address marine litter [9]. These measures are multidimensional, such as behavior change [7,12,28], regulation and enforcement [29,30], the introduction of alternative materials [31], clean-up activities [32], and educational and outreach programs [29]. They are generally grouped into four types: prevention, mitigation, removal, and behavior changing [33]. The removal is recognized as one of the critical strategies to combat marine litter [34].
Fishing for Litter (hereafter, FFL) is one of the removal initiatives. It was initiated by KIMO in 2004, with an aim to reduce marine litter by engaging stakeholders, especially the fishing industry, in this initiative [35]. Illustratively, fishers collect plastic garbage, ghost gear, and others at sea during fishing activities and bring back the waste to ports. While FFL is not a new approach to eliminating marine litter around the world, there is a lack of studies on what features most in fishers’ decision-making regarding participation in this initiative.
Taiwan is surrounded by sea and inevitably faces marine litter problems, similar to other coastal states. To tackle such problems, concerned authorities have developed four major strategies—reduction at source, prevention and removal, research and investigation, and stakeholder collaboration and partnership. Specific actions corresponding to each strategy were further formulated [36]. FFL is one of the actions pertaining to the strategy of prevention and removal and has been actively promoted by concerned authorities. It was first initiated in 2017 and has been put into place till now. While an increasing trend was observed in the number of vessels participating in the program over the years, the percentage of vessels participating in the program was less than 50% in 2023 [37,38]. As the policy aims to engage a high number of vessels, it is critical to understand what motivates fishers to join or discourages them from joining the program. This paper aims to examine the motives and obstacles to fishers’ participation in the program and the relationship between motives/obstacles and fishers’ intention to participate.
The paper is organized as follows: It starts with an overview of the FFL program. A description of research methods and an analysis of results follows. Finally, management implications from the results are discussed and recommendations are proposed for mobilizing more fishers to join the FFL program as well as serving as a reference for other states who intend to initiate this program.
2. An Overview of the FFL Program
Fishing for Litter is one of the strategies developed by Taiwanese authority to address marine litter problems by removing debris at sea. It was first initiated by the Fisheries Administration in 2017. When a new cabinet, the Ocean Affairs Council (OAC), was established in April 2018, the FFL program was then transferred to the Ocean Conservation Administration (OCA), a subsidiary body of the OAC. The OCA has a mandate for the prevention, reduction, and control of marine pollution. While this program is voluntary by nature, with the increased funding allocated to local governments by the OCA to encourage fishers to join the program, the number of fishing vessels joining this initiative is increasing. In 2019, a total of 2594 vessels joined the program. The number of vessels increased to 5327 in 2022 and 5969 in 2023 [37]. This indicates that fishers (referring to owners or operators of fishing vessels who have decision-making power on joining the program or not) increasingly support this program, and a collaborative partnership, to some extent, has been established among the central government, local governments, and fishers.
The program is funded by the OCA. Each local government bordering the coast has financial support annually to implement the program. The funding is used for two major parts. One is the recruitment of fishers. The other is the implementation of the program when fishers are recruited. For the former, how to talk fishers into joining the program is very critical. In this regard, local fishers’ associations play a significant role in bridging fishers and local governments. The associations help find fishers to attend recruitment meetings held by the team who is tasked with the implementation of the program by local governments (Figure 1). For the latter, the expenditure includes the purchase of bags for fishers to carry on board; the rewards offered to fishers if they carry back garbage from the sea; the personnel cost; and the fees incurred from, including but not limited to, the setting and the maintenance of trash reception stations at fishing ports.
Fishers who participate in the program are given bags. The bags are used to store trash that is collected at sea during fishing operations. When fishers pull the net, they pick up the debris that was left in the net and put it in the bags. They also collect floating trash at sea (Figure 2). The trash is then brought back to ports, sorted, and weighted or counted for records (Figure 3). Fishers earn points based on the types of trash and the weight/number of each type of trash [37]. The trash items are diverse, including PET bottles, plastic bags, nylon ropes, derelict fishing gear, iron cans, aluminum cans, glass bottles, and general trash. Fishers receive rewards with the points they earn. The rewards are often daily-use items, including tissue paper, toilet paper, LED bulbs, engine oil, toothpaste, shampoo, clothes detergent, dish-washing liquid, and hand wash. Furthermore, the amount of trash brought back to ports per fishing trip is recorded, and the total amount of trash in one year is calculated. The ones having the top total amount of trash are further provided with gift coupons and publicly honored at the ceremony annually held by the OCA.
3. Research Methods
3.1. Interviews
A two-step approach was employed. First, in-depth personal interviews were conducted to obtain information regarding fishers’ understanding of the program, views toward the disposal ways of waste, and the possible factors that either promote or hinder fishers’ participation in the program. Interviewing stakeholders is an approach and procedure for gaining an understanding of a system by means of identifying the key actors and assessing their respective interests in the system [39]. The interviews were conducted in a conversational manner to maintain openness and flexibility, allowing interviewees to express their opinions freely [40].
The research used purposive sampling techniques to select interviewees based on the information provided by fishers’ associations. It should be noted that there are 38 local fishers’ associations. Four associations that were active in recruiting fishers to join the program were consulted first, and interviews were then conducted with fishers recommended by these associations. A total of 10 fishers participated in the interviews.
Each interview normally lasted about 1 h, and each interviewee was initially informed of the purpose of the program, what has been achieved to date regarding the program, and the possible reasons for fishers’ participation and non-participation in the program. The interviewees were then asked to identify what they think may have the potential to either promote or hinder fishers’ participation.
Frequently mentioned factors generally revolved around the reward, inconveniences of handling trash at ports as well as on board, other fishers’ participation, fishers’ position on the program, and views regarding the protection of the marine environment. For the second factor, it should be noted that fishers need to immediately handle catch when they return to ports. They often do not have spare time and energy to deal with trash, when they are physically exhausted after fishing operations. It is therefore important to devise an easy way for fishers to handle trash at ports without causing them conveniences or troubles. As for the last factor, it is interesting to note that there were divergent perspectives. Some stated that they cared about the health of the marine environment and were more likely to join the program so that their fishing-dependent livelihood could be sustained. However, others noted that littering occurs almost everywhere at sea and carrying back only a modest amount of garbage during fishing operations cannot change or improve the status quo. Given these two divergent viewpoints, an interesting dimension would be to examine whether fishers’ attitudes toward the marine environment could have a behavioral impact on their participation in the program.
Data from interviews were coded with thematic analysis [41]. Codes and major themes were developed to reflect the insights of interviewees, particularly regarding the factors that either promote or hinder fishers’ participation. Codes and major themes were updated throughout the data analysis process as codes were added, edited, or reorganized. The analysis of data from the interviews was conducted using NVivo 10 [42]. The analysis generated eight potential factors, which were, based on their attributes, classified into two categories (motive and constraint) and utilized in the second step. Each category contained four factors.
3.2. Questionnaire Survey
The second step applied a questionnaire survey. It consisted of five sections. The first section focused on fishers’ demographic information with five questions including gender, age, education, fishing methods, and years of working in the fishing sector. It should be noted that the answer options for the question of fishing methods were designed based on the fishing methods that are commonly used in Taiwan. Gillnetting, trawling, and longlining are the major fishing methods.
The second section examined fishers’ understanding of and participation in the FFL program as well as their intention to participate. It is worth noting that for the question of intention to participate, two answer options were specifically designed to allow for the binary logit regression, which will be detailed in the next subsection. This binary classification was used to divide the intention into ‘yes’ and ‘no’. The intention was viewed as an immediate antecedent of actual behavior; that is, an individual who expresses an intention to take action will be more likely to engage in the action than an individual who expresses no such intention. Previous studies have found a link between behavioral intention and behavior [43,44]. The third section deals with the factors that potentially encourage or hinder fishers’ participation in the program. It should be noted that these factors are the results of the thematic analysis of the first-step interviews. The questions related to the factors were grouped into two parts. One was the motive part and the other was the constraint part. Respondents were asked to indicate their agreement level for the factors on a five-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree) [45].
The draft of the questionnaire was amended and finalized considering the feedback from two staff members of fishers’ associations and a pilot sample of five fishers. They were chosen to check if the questionnaire was suitable in terms of the clarity of language, the flow of the questions, the length of the questionnaire, and the time taken to answer the questions. For obvious reasons, the survey was conducted in Taiwan, and some adjustments were made to cater to local circumstances. The final version of the questionnaire is provided in Appendix A. Cronbach’s alpha coefficient was used to analyze the reliability of items. The alpha values were 0.87 and 0.80 for the motive items and constraint items, respectively. This shows that the items have high reliability [46].
Fishers who own fishing vessels or are fishing operators and work in coastal or offshore fisheries were the target population. To select the samples, the survey used a two-tier sampling method procedure. First, 10 of 38 fishers’ associations were chosen. These associations are responsible for recruiting fishers to join the program and are located in the north, south, east, and west of Taiwan, as well as adjacent islets. Second, a convenience sampling procedure was employed to select samples from the 10 associations [47,48]. We aimed to select around 40 samples from each association. With the help of the associations, the researchers managed to conduct face-to-face surveys with fishers. A total of 412 valid samples were collected between March and August 2021. The sample size was calculated at a 95% confidence interval, with a 5% sample error utilizing Cochran’s formula [49]. The respondent sample, though not a random sample of the target population, comprised fishers affiliated with the major associations that are located in different areas of Taiwan.
3.3. Data Analysis of Surveys
Data analysis was performed with descriptive statistics and binary logit regression. The former involves summarizing large quantities of data using measures and presenting a list of measurements for each characteristic observed. Descriptive statistics consist of graphical and numerical techniques for summarizing data, i.e., reducing a large volume of data to simpler, more understandable terms. For example, the mean and standard deviation (SD) are used to show the measure of central tendency and dispersal or variability, respectively [50]. Descriptive statistics were employed to calculate the frequency, and the mean and SD of questions were used in order to understand respondents’ profile and their factor ratings.
The binary logistic regression involves examining the relationship between a dichotomous outcome and a set of covariables with a logit link function [51]. The regression has a categorical response outcome variable with a binary attribute, which is calculated through continuous or categorical explanatory variables [52]. It analyzes fishers’ behavioral intention by estimating which of the two categories he or she is likely to belong to given certain information, as indicated in the explanatory variables. This regression model has been employed in several empirical studies [53,54,55,56,57]. It was used in this research to investigate response variables (intention to participate, 0 = no, 1 = yes) as a function of motive and constraint predictor variables, which were measured using a five-point Likert scale.
All data analysis was conducted using the SPSS software (version 14.0), and a p value of 0.05 was used to determine significant differences in the association between variables.
4. Results
4.1. Sample Profile
The demographic profile of the respondents shows that the sample was dominantly (90%) male, 52% were aged between 51 and 60 years, and 32% were aged 61 years and above. Notably, 42% and 34% of the respondents had elementary and middle school educational levels, respectively, and 62% of the respondents have worked in the fishing sector for more than 20 years, whereas 31% have worked for 11–20 years. Overall, 35% of the respondents were engaged in gillnetting, 26% in trawling, and 20% in longlining. The results noted above are summarized in Table 1.
4.2. Fishers’ Understanding of and Participation in the FFL Program
As high as 87% of the respondents have heard of the program, indicating fishers have been widely informed of this program by their affiliated fishers’ associations. However, a lower percentage of 65% and 56% of the respondents reported that they knew this program well and had participated in the program, respectively. This shows that while fishers had heard of the program, they did not quite make sense of it. Furthermore, when it comes to taking action regarding cleaning out litter at sea during fishing operations, fishers may have concerns even though they may have heard of and know the program. For participation intention, 66% of the respondents expressed their intention to take part in the program. The results mentioned above are presented in Figure 4.
4.3. Mean Ratings of Factors
As previously noted, the factors that either promote or hinder fisher’s participation were classified into two groups: motive and constraint. Each item in the group was indexed, and its mean score and standard deviation are presented in Figure 5. For the motive items (M1–M4), ‘M2: fisher friends have participated in the program’ and ‘M1: providing rewards for fishers’ bringing trash back to ports’ were rated as the items with high agreement (4.15 and 4.12, respectively). This indicates that these two factors feature most in fishers’ decision-making on participating in the program. The items ‘M3: marine debris destroys marine environments’ and ‘M4: fishers should be obliged to protect marine environments’ came second, with mean scores of 3.78 and 3.64, respectively. A t-test showed that the mean scores were significantly greater than 3 but less than 4, indicating that fishers slightly agreed with these two items. In addition, the standard deviation of factor M3 was slightly larger, compared with other items. This suggests a greater extent of different views among fishers toward marine debris obstructing fishing operations.
For the constraint items, the two most agreed items that pose a barrier to fishers’ participation were ‘C2: handling trash at ports is troublesome when fishing vessels return to ports’ and ‘C1: collecting trash on board interferes with fishing operations’, with mean scores of 4.04 and 4.02, respectively. They were followed by ‘C3: fishing operations would be disrupted if the trash found is too big or heavy (e.g., a big chunk of entangled derelict fishing nets)’, with a mean score of 3.72. A t-test showed that the score was significantly greater than 3 but less than 4, indicating that fishers slightly agreed with this item. ‘C4: indifference to the debris littering at sea’ was found to have a mean score of 3.27. A t-test showed that the score was significantly equal to 3, indicating fishers keep a neutral view toward this factor.
4.4. Association Between Factors and Intention of Participation
Table 2 presents the empirical results from the regression to examine the association between factors and fishers’ intention to participate. With a Nagelkerge R-squared of 0.411 and a Chi-square of 158.421, the model was statistically significant and could effectively differentiate fishers with intention (yes) from those without intention (no). The binary model correctly classified 81.4% of the cases. Among group 1 cases (total = 272), 225 were correctly classified; among group 0 cases (total = 140), 112 were correctly classified.
As indicated by the coefficients of independent variables in Table 2, the positively significant factors (at 5% level) in determining fishers’ intention to participate were identified as ‘M1: providing rewards for fishers’ bring trash back to ports’, ‘M2: fisher friends have participated in the program’, and ‘M4: fishers should be obliged to protect marine environments’. This suggests that the higher agreement fishers have for these factors, the higher intention they have to act on removing marine debris during fishing operations. It is worth noting that M4 is particularly relevant to fishers’ view of the marine environment. On the other hand, the negatively significant factors were ‘C2: handling trash at ports is troublesome when fishing vessels return back to ports’ and ‘C3: fishing operations would be disrupted if the trash found is too big or heavy’. The negative coefficients indicate that the higher agreement fishers reveal for these factors, the lower intention they have to participate in the program.
5. Discussion and Policy Implications
This study identified four motive factors and four constraint factors that potentially either promote or hinder fishers’ participation in the program. Of these eight factors, most were agreed upon among the respondents, while neutral views were found for one factor. In addition, five factors significantly influenced the respondents’ intention to participate in the program. From a policy perspective, it would be interesting to infer potential management measures from the results.
5.1. Implications for Reducing Inconveniences Incurred from Handling Trash on Board and at Ports
As indicated in the agreement on the factors of C1, C2, and C3, fishers generally regard that collecting trash on board or handling trash at ports poses barriers to participation in the program. In addition, the binary logit regression analysis showed the C2 factor had a negatively significant correlation with the intention. This obviously suggests that what fishers are concerned about the most is the inconveniences or troubles incurred from handling trash. From a policy perspective, it is therefore critical to mitigate such concerns if the policy is intended to engage more fishers in the debris-removing campaign. For this, three practical ways were recommended. Firstly, it should be noted that fishers are busy either on board, during fishing operations, or at ports when fishing operations are over. Specifically, fishers are occupied with unloading and selling their catch and cleaning out the vessels, as well as fishing gear after an exhausting fishing trip. They almost have no spare time and energy to handle trash. It is therefore critical to ensure the availability of and easy access to reception facilities. This argument gained support from Ball [58], who stated that the inadequacy of port reception facilities is one of the main reasons for the illegal discharge of oil and garbage from ships.
Secondly, it is recommended that fishers’ associations regularly offer fishers an adequate number of bags or bins. Fishers would easily put the trash collected from the sea into bags or bins without bothering to handle it on board. Lastly, a communication channel is established for fishers to report the location of a giant chunk of trash if they find it hard to remove. The data of location show the ‘hot spots’ of trash at sea. Concerned authorities can then remove it by employing more effective ways (e.g., mobilizing divers to go to the hot spot sites) to cut and retrieve trash and bring it to ports.
5.2. Implication for Providing Rewards for Fishers’ Participation
It is not surprising to find that fishers regard rewards as a motive for removing trash at sea. Rewards act as an incentive to encourage people to act on a new policy, especially during the early stage of policy implementation [59]. However, it should be noted that if the perceived benefit of the reward is less than the additional inconveniences associated with removing trash at sea, then the reward would be meaningless. Currently, rewards are offered in the form of various kinds of daily-use commodities. While this approach has won support from fishers, it is hoped that fishers would eventually develop a long-term sustainable behavior (i.e., removing debris at sea during fishing operations) through the provision of rewards.
5.3. Implications for Sponsoring Environmental Outreach Programs
The agreed factors regarding fishers’ views toward the protection of marine environments (i.e., M3 and M4) revealed that fishers’ intention might be dictated by altruistic environmental goals, which provides supporting evidence for encouraging environmental education of fishers. Moreover, as the binary logit regression analysis showed, M4 had a significant correlation with the intention. This finding suggests a link between an individual’s attitude toward environmental protection and his or her actions regarding removing debris at sea. This link can be strengthened through education. Environmental education holds that behavior can be changed by making people more knowledgeable about the environment and its associated issues [60]. It is also argued that raising awareness and education are needed to reduce marine litter [61]. In this regard, concerned authorities are encouraged to sponsor educational and outreach programs for fishers with a focus on marine debris issues. In practical terms, the programs may be embodied in the meetings held by local governments or fishers’ associations to disseminate the program information and recruit fishers to join the program.
5.4. Implications for Distributing Information Regarding the Program and Fishers’ Participation
The results showed that while as high as 87% of respondents had heard of the program, only 65% understood it well. This indicates that fishers’ associations still have a long way to go in terms of having more fishers understand the programs. In addition, the M2 factor (other fishers have participated in the program) revealed high agreement and warrants further discussion. It is worth noting that the theory of planned behavior argues that subjective norm is a precursor to individual actions or intentions [62]. Subjective norm refers to the pressure an individual perceives from significant others or groups about whether they should engage in a specific behavior [62]. This implies that when people are uncertain about how to act on a particular behavior, they seek support from trusted cohorts [63]. For example, when it comes to purchasing green products, several studies have confirmed that subjective norm has a positive impact on consumers’ behavior or intention to purchase such products [64,65,66]. In this regard, it is therefore not surprising to find that fishers consider the participation of other fishers a strong motive. In other words, under a subjective norm setting, fishers will be more inclined to join the program if they know other fishers have already joined the program.
It is noted that fishers affiliated with the same fishers’ association are a closed group. Words spread easily in the group. It is therefore recommended that local fishers’ associations strengthen the bonds with their fisher members and provide them with information regarding the program.
6. Conclusions
This paper focuses on a removal approach to marine litter problems. It involves a specific stakeholder group of fishers to engage in a debris-removing activity during fishing operations. It illustrated several points of general significance regarding fishers’ participation in this debris-removing program (i.e., Fishing for Litter). First, factors that either promote or hinder fishers’ participation were identified and rated. Rewards, participation of other fishers, inconveniences or troubles incurred from handling trash were the primary factors involved in fishers’ decision-making regarding participation in the program. Second, fishers’ views toward marine environments also had a behavioral impact on their participation in the program. Thirdly, the proposed management measures could potentially engage more fishers in participating in the program, including reducing inconveniences incurred from handling trash on board as well at ports (e.g., ensuring availability of and easy access to reception facilities at ports and offering garbage bags or bins), providing rewards for fishers’ bringing trash back to ports, encouraging environmental education for fishers, and disseminating information regarding the program and fishers’ participation.
Despite the results with practical significance derived from this study, limitations do exist. Firstly, this study did not test the robustness of the prediction model using binary logit regression. Therefore, it would be a meaningful topic for future research to test the real explanatory power of the motive and constrain items by targeting more fishers. The results can serve as a benchmark to refine the model established herein. Secondly, this paper divided the intention into only two categories, which may not be able to depict fishers’ intention in a more precise way. A more refined model can be tested with more categories of intention. This can be done with discriminant analysis, which allows for more than two categories in the dependent variable. Thirdly, the FFL program is basically a reward-driven program. A direction for future research may focus on the factors influencing fishers’ voluntary participation, independent of the provision of rewards. The implications of this research would be meaningful in terms of building environmental stewardship among fishers. It is hoped that fishers will eventually make it a normal on-board practice to collect trash at sea during fishing operations and bring it back to ports.
Conceptualization, C.-L.C.; methodology, C.-L.C., X.-N.J. and T.-Y.W.; software, X.-N.J. and T.-Y.W.; validation, X.-N.J., T.-Y.W. and S.-W.H.; formal analysis, C.-L.C., X.-N.J. and T.-Y.W.; investigation, X.-N.J. and T.-Y.W.; resources, C.-L.C.; data curation, X.-N.J., T.-Y.W. and S.-W.H.; writing—original draft preparation, X.-N.J.; writing—review and editing, C.-L.C.; visualization, C.-L.C. and X.-N.J.; supervision, C.-L.C.; project administration, C.-L.C.; funding acquisition, C.-L.C. All authors have read and agreed to the published version of the manuscript.
The raw data supporting the conclusions of this article will be made available by the authors upon request.
The authors wish to express sincere thanks to staff members from fishers’ associations for providing valuable on-site information and assistance. Thanks are also due to the 10 anonymous interviewees and the 412 fishers involved in the research. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors, and any errors remain the sole responsibility of the authors.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. The recruitment meeting was held to introduce the FFL program and encourage fishers to join it (photo courtesy of the Environmental Protection Bureau of Tainan City Government).
Enlarge this image.Figure 2. A fisher collecting floating debris during the fishing operation (photo courtesy of the Environmental Protection Bureau of Tainan City Government).
Enlarge this image.Figure 3. Trash collected from the sea was weighed for records (photo courtesy of the Environmental Protection Bureau of Tainan City Government).
Enlarge this image.Figure 4. Fishers’ understanding of and participation in the FFL program (n = 412).
Enlarge this image.Figure 5. Mean ratings of the factors to the participation (n = 412). a Constraint factors, C1: collecting trash on board interferes with fishing operations; C2: handling trash at ports is troublesome when fishing vessels return to ports; C3: fishing operations would be disrupted if the trash found is too big or heavy (e.g., a big chunk of entangled derelict fishing nets); C4: indifference to the debris littering at sea; b Motive factors, M1: providing rewards for fishers bringing trash back to ports; M2: fisher friends have participated in the program; M3: marine debris destroys marine environments; M4: fishers should be obliged to protect marine environments; c from 1 (strongly disagree) to 5 (strongly agree).
Sample profile (n = 412).
| Items | Frequency | Percentage (%) | |
|---|---|---|---|
| Gender | Female | 42 | 10 |
| Male | 370 | 90 | |
| Age | 18–30 | 8 | 2 |
| 31–40 | 20 | 5 | |
| 41–50 | 38 | 9 | |
| 51–60 | 214 | 52 | |
| 61 and above | 132 | 32 | |
| Education | Elementary school | 173 | 42 |
| Middle school | 140 | 34 | |
| High school | 58 | 14 | |
| College and above | 41 | 10 | |
| Years of working in the fishing sector | Not greater than 10 years | 29 | 7 |
| 11–20 years | 128 | 31 | |
| More than 20 years | 255 | 62 | |
| Main fishing methods | Gillnetting | 145 | 35 |
| Trawling | 107 | 26 | |
| Longlining | 82 | 20 | |
| Others | 78 | 19 |
Results of the binary logit model.
| Factors | M1 | M2 | M3 | M4 | C1 | C2 | C3 | C4 |
|---|---|---|---|---|---|---|---|---|
| Coeff. | 0.332 * | 0.422 * | 0.062 | 0.318 * | 0.063 | −0.563 * | −0.414 * | 0.078 |
| Wald | 11.413 | 16.471 | 5.447 | 10.104 | 6.101 | 15.417 | 12.014 | 3.813 |
Notes: 1. n = 412, Chi-square = 158.421, p = 0.000, and Nagelkerge R-squared = 0.411. 2. The variables are defined as follows: intention = 1 if respondents have an intention to participate; 0 otherwise; M1–M4 and C1–C4 are motive items and constraint items, respectively (see
Appendix A
Questionnaire
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Background information
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Gender: male or female
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Age: 18–30, 31–40, 41–50, 51–60, 61 and above
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Education: elementary school, middle school, high school, college or above
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Years of working in the fishing sector: not greater than 10 years, 11–20 years, more than 20 years
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Fishing methods: gillnetting, trawling, longlining, others
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Understanding and participation of the FFL program
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Have you ever heard of the program? Yes or No
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Did you understand this program well? Yes or No
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Have you participated in the program? Yes or No
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Do you intend to participate in the program? Yes or No
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Factors to participation of the program (Five-point Likert scale)
M1: Providing rewards for fishers’ bringing trash back to ports.
M2: Other fishers have participated in the program.
M3: Marine debris destroys marine environments.
M4: Fishers should be obliged to protect marine environments.
C1: Collecting trash on board causes interference to fishing operations.
C2: Handling trash at ports is troublesome when fishing vessels return back to ports.
C3: Fishing operations would be disrupted if the trash found is too big or heavy (e.g., a big chunk of entangled derelict fishing nets).
C4: Indifference to the debris littering at the sea.
References
1. Werner, S.; Budziak, A.; Franeker Jv Galgani, F.; Hanke, G.; Maes, T.; Matiddi, M.; Nilsson, P.; Oosterbaan, L.; Priestland, E.; Thompson, R. et al. Harm Caused by Marine Litter; European Union: Luxembourg, 2016; 89p. Available online: https://data.europa.eu/doi/10.2788/690366 (accessed on 19 September 2024).
2. Ronchi, F.; Galgani, F.; Binda, F.; Mandić, M.; Peterlin, M.; Tutman, P.; Anastasopoulou, A.; Fortibuoni, T. Fishing for Litter in the Adriatic-Ionian macroregion (Mediterranean Sea): Strengths, weaknesses, opportunities and threats. Mar. Policy; 2019; 100, pp. 226-237. [DOI: https://dx.doi.org/10.1016/j.marpol.2018.11.041]
3. Jambeck, J.R.; Geyer, R.; Wilcox, C.; Siegler, T.R.; Perryman, M.; Andrady, A.; Narayan, R.; Law, K.L. Plastic waste inputs from land into the ocean. Science; 2015; 347, pp. 768-771. [DOI: https://dx.doi.org/10.1126/science.1260352]
4. Verlis, K.M.; Wilson, S.P. Paradise trashed: Sources and solutions to marine litter in a small island developing state. Waste Manag.; 2020; 103, pp. 128-136. [DOI: https://dx.doi.org/10.1016/j.wasman.2019.12.020] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31874416]
5. Vlachogianni, T.; Fortibuoni, T.; Ronchi, F.; Zeri, C.; Mazziotti, C.; Tutman, P.; Varezić, D.B.; Palatinus, A.; Trdan, Š.; Peterlin, M. et al. Marine litter on the beaches of the Adriatic and Ionian Seas: An assessment of their abundance, composition and sources. Mar. Poll. Bull.; 2018; 131, pp. 745-756. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2018.05.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29887002]
6. Chen, C.L.; Kuo, P.H.; Lee, T.C.; Liu, C.H. Snow lines on shorelines: Solving Styrofoam buoy marine debris from oyster culture in Taiwan. Ocean Coast. Manag.; 2018; 165, pp. 346-355. [DOI: https://dx.doi.org/10.1016/j.ocecoaman.2018.09.005]
7. Panwanitdumrong, K.; Chen, C.L. Investigating factors influencing tourists’ environmentally responsible behavior with extended theory of planned behavior for coastal tourism in Thailand. Mar. Poll. Bull.; 2021; 169, 112507. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2021.112507]
8. Basurko, O.C.; Gabiña, G.; Andrés, M.; Rubio, A.; Uriarte, A.; Krug, I. Fishing for floating marine litter in SE Bay of Biscay: Review and feasibility study. Mar. Policy; 2015; 61, pp. 103-112. [DOI: https://dx.doi.org/10.1016/j.marpol.2015.07.010]
9. UNEP. Marine Litter, An Analytical Overview; United Nations Environment Programme: Nairobi, Kenya, 2005; 47p.
10. Araújo, M.C.B.; Costa, M.F. A critical review of the issue of cigarette butt pollution in coastal environments. Environ. Res.; 2019; 172, pp. 137-149. [DOI: https://dx.doi.org/10.1016/j.envres.2019.02.005]
11. Baulch, S.; Perry, C. Evaluating the impacts of marine debris on cetaceans. Mar. Poll. Bull.; 2014; 80, pp. 210-221. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2013.12.050] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24525134]
12. Bergmann, M.; Lutz, B.; Tekman, M.B.; Gutow, L. Citizen scientists reveal: Marine litter pollutes Arctic beaches and affects wild life. Mar. Poll. Bull.; 2017; 125, pp. 535-540. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2017.09.055]
13. Abu-Hilal, A.; Al-Najjar, T. Marine litter in coral reef areas along the Jordan Gulf of Aqaba, Red Sea. J. Environ. Manag.; 2009; 90, pp. 1043-1049. [DOI: https://dx.doi.org/10.1016/j.jenvman.2008.03.014]
14. Santodomingo, N.; Perry, C.; Waheed, Z.; bin Syed Hussein, M.A.; Rosedy, A.; Johnson, K.G. Marine litter pollution on coral reefs of Darvel Bay (East Sabah, Malaysia). Mar. Poll. Bull.; 2021; 173, 112998. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2021.112998]
15. Ballesteros, L.V.; Matthews, J.L.; Hoeksema, B.W. Pollution and coral damage caused by derelict fishing gear on coral reefs around Koh Tao, Gulf of Thailand. Mar. Poll. Bull.; 2018; 135, pp. 1107-1116. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2018.08.033] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30301009]
16. Mendrik, F.M.; Henry, T.B.; Burdett, H.; Hackney, C.R.; Waller, C.; Parsons, D.R.; Hennige, S.J. Species-specific impact of microplastics on coral physiology. Environ. Pollut.; 2021; 269, 116238. [DOI: https://dx.doi.org/10.1016/j.envpol.2020.116238]
17. Brouwer, R.; Hadzhiyska, D.; Ioakeimidis, C.; Ouderdorp, H. The social costs of marine litter along European coasts. Ocean Coast. Manag.; 2017; 138, pp. 38-49. [DOI: https://dx.doi.org/10.1016/j.ocecoaman.2017.01.011]
18. Stafford, R.; Jones, P.J. Viewpoint–Ocean plastic pollution: A convenient but distracting truth?. Mar. Policy; 2019; 103, pp. 187-191. [DOI: https://dx.doi.org/10.1016/j.marpol.2019.02.003]
19. Andres, M.; Delpey, M.; Ruiz, I.; Declerck, A.; Sarrade, C.; Bergeron, P.; Basurko, O.C. Measuring and comparing solutions for floating marine litter removal: Lessons learned in the south-east coast of the Bay of Biscay from an economic perspective. Mar. Policy; 2021; 127, 104450. [DOI: https://dx.doi.org/10.1016/j.marpol.2021.104450]
20. Burt, A.J.; Raguain, J.; Sanchez, C.; Brice, J.; Fleischer-Dogley, F.; Goldberg, R.; Talma, S.; Syposz, M.; Mahony, J.; Letori, J. et al. The costs of removing the unsanctioned import of marine plastic litter to small island states. Sci. Rep.; 2020; 10, 14458. [DOI: https://dx.doi.org/10.1038/s41598-020-71444-6]
21. Antonelis, K.; Huppert, D.; Velasquez, D.; June, J. Dungeness crab mortality due to lost traps and a cost–benefit analysis of trap removal in Washington State waters of the Salish Sea. N. Am. J. Fish. Manag.; 2021; 31, pp. 880-893. [DOI: https://dx.doi.org/10.1080/02755947.2011.590113]
22. Barboza, L.G.A.; Vethaak, A.D.; Lavorante, B.R.; Lundebye, A.K.; Guilhermino, L. Marine microplastic debris: An emerging issue for food security, food safety and human health. Mar. Poll. Bull.; 2018; 133, pp. 336-348. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2018.05.047] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30041323]
23. Browne, M.A.; Niven, S.J.; Galloway, T.S.; Rowland, S.J.; Thompson, R.C. Microplastic moves pollutants and additives to worms, reducing functions linked to health and biodiversity. Curr. Biol.; 2013; 23, pp. 2388-2392. [DOI: https://dx.doi.org/10.1016/j.cub.2013.10.012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24309271]
24. Thushari, G.G.N.; Senevirathna, J.D.M.; Yakupitiyage, A.; Chavanich, S. Effects of microplastics on sessile invertebrates in the eastern coast of Thailand: An approach to coastal zone conservation. Mar. Poll. Bull.; 2017; 124, pp. 349-355. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2017.06.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28760587]
25. Galgani, F.; Hanke, G.; Werner, S.D.V.L.; De Vrees, L. Marine litter within the European marine strategy framework directive. ICES J. Mar. Sci.; 2013; 70, pp. 1055-1064. [DOI: https://dx.doi.org/10.1093/icesjms/fst122]
26. Leslie, H.A.; Van Velzen, M.J.; Brandsma, S.H.; Vethaak, A.D.; Garcia-Vallejo, J.J.; Lamoree, M.H. Discovery and quantification of plastic particle pollution in human blood. Environ. Int.; 2022; 163, 107199. [DOI: https://dx.doi.org/10.1016/j.envint.2022.107199]
27. Narwal, N.; Kakakhel, M.A.; Katyal, D.; Yadav, S.; Rose, P.K.; Rene, E.R.; Rakib, R.J.; Khoo, K.S.; Kataria, N. Interactions between microplastic and heavy metals in the aquatic environment: Implications for toxicity and mitigation strategies. Water Air Soil. Pollut.; 2024; 235, 567. [DOI: https://dx.doi.org/10.1007/s11270-024-07343-7]
28. Asmuni, S.; Hussin, N.B.; Khalili, J.M.; Zain, Z.M. Public participation and effectiveness of the no plastic bag day program in Malaysia. Procedia Soc. Behav. Sci.; 2015; 168, pp. 328-340. [DOI: https://dx.doi.org/10.1016/j.sbspro.2014.10.238]
29. Xanthos, D.; Walker, T.R. International policies to reduce plastic marine pollution from single-use plastics (plastic bags and microbeads): A review. Mar. Poll. Bull.; 2017; 118, pp. 17-26. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2017.02.048] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28238328]
30. Zhu, L.; Zhao, Y.C. A feasibility assessment of the application of the Polluter-Pays Principle to ship-source pollution in Hong Kong. Mar. Policy; 2015; 57, pp. 36-44. [DOI: https://dx.doi.org/10.1016/j.marpol.2015.03.010]
31. Lee, J.; Hong, S.; Jang, Y.C.; Lee, M.J.; Kang, D.; Shim, W.J. Finding solutions for the styrofoam buoy debris problem through participatory workshops. Mar. Policy; 2015; 51, pp. 182-189. [DOI: https://dx.doi.org/10.1016/j.marpol.2014.08.008]
32. Frantzi, S.; Brouwer, R.; Watkins, E.; van Beukering, P.; Cunha, M.C.; Dijkstra, H.; Duijndam, S.; Jaziri, H.; Okoli, I.C.; Pantzar, M. et al. Adoption and diffusion of marine litter clean-up technologies across European seas: Legal, institutional and financial drivers and barriers. Mar. Poll. Bull.; 2021; 170, 112611. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2021.112611] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34144394]
33. Chen, C.L. Regulation and management of marine litter. Marine Anthropogenic Litter; Springer: Cham, Switzerland, 2015; pp. 395-428. [DOI: https://dx.doi.org/10.1007/978-3-319-16510-3_15]
34. Willis, K.; Maureaud, C.; Wilcox, C.; Hardesty, B.D. How successful are waste abatement campaigns and government policies at reducing plastic waste into the marine environment?. Mar. Policy; 2018; 96, pp. 243-249. [DOI: https://dx.doi.org/10.1016/j.marpol.2017.11.037]
35. KIMO. Fishing for Litter. Available online: https://fishingforlitter.org/ (accessed on 19 September 2024).
36. Ministry of Environment Taiwan. Marine Debris Governance Action Plan; 2nd ed. Ministry of Environment, Executive Yuan: Taipei, Taiwan, 2019; 19p. (In Chinese)
37. OCA. Statistics on Marine Litter Removal in Taiwan. Available online: https://www.oca.gov.tw/ch/home.jsp?id=132&parentpath=0,4,127 (accessed on 19 September 2024). (In Chinese)
38. FA. Fisheries Statistical Yearbook 2023, Taiwan, Kinmen and Matsu Area. Available online: https://www.fa.gov.tw/view.php?theme=FS_AR&subtheme=&id=26 (accessed on 21 November 2024). (In Chinese)
39. Grimble, R.; Chan, M.-K. Stakeholder analysis for natural resource management in developing countries. Nat. Resour. Forum; 1995; 19, pp. 113-124. [DOI: https://dx.doi.org/10.1111/j.1477-8947.1995.tb00599.x]
40. Hennink, M.; Hutter, I.; Bailey, A. Qualitative Research Methods; Sage: London, UK, 2020; 376p.
41. Braun, V.; Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol.; 2006; 3, pp. 77-101. [DOI: https://dx.doi.org/10.1191/1478088706qp063oa]
42. Bazeley, P.; Jackson, K. Qualitative Data Analysis with NVivo; 2nd ed. Sage: London, UK, 2013; 307p.
43. Ajzen, I.; Driver, B.L. Application of the theory of planned behavior to leisure choice. J. Leis. Res.; 1992; 24, pp. 207-224. [DOI: https://dx.doi.org/10.1080/00222216.1992.11969889]
44. Kaiser, F.G.; Wölfing, S.; Fuhrer, U. Environmental attitude and ecological behaviour. J. Environ. Psychol.; 1999; 19, pp. 1-19. [DOI: https://dx.doi.org/10.1006/jevp.1998.0107]
45. Likert, R. A technique for the measurement of attitudes. Arch. Psychol.; 1932; 22, pp. 5-55.
46. Hajjar, S.T. Statistical analysis: Internal-consistency reliability and construct validity. Int. J. Quant. Qual. Res. Methods; 2018; 6, pp. 27-38.
47. Eze, U.C.; Ndubisi, N.O. Green buyer behavior: Evidence from Asia consumers. J. Asian Afr. Stud.; 2013; 48, pp. 413-426. [DOI: https://dx.doi.org/10.1177/0021909613493602]
48. Joshi, Y.; Rahman, Z. Consumers’ sustainable purchase behaviour: Modeling the impact of psychological factors. Ecol. Econ.; 2019; 159, pp. 235-243. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2019.01.025]
49. Bartlett, J.E.; Kotrlik, J.W.; Higgins, C.C. Organizational research: Determining appropriate sample size in survey research. Inf. Technol. Learn. Perform. J.; 2001; 19, pp. 43-50.
50. Burns, R.B. Introduction to Research Methods; 4th ed. Sage: London, UK, 2000; 613p.
51. Hosmer, D.W., Jr.; Lemeshow, S.; Sturdivant, R.X. Applied Logistic Regression; 3rd ed. John Wiley & Sons: Hoboken, NJ, USA, 2013; 528p.
52. Backhaus, K.; Erichson, B.; Plinke, W.; Weiber, R. Multivariate Analysemethoden; Springer: Berlin/Heidelberg, Germany, 2016; 647p. [DOI: https://dx.doi.org/10.1007/978-3-662-46076-4]
53. Klein, F.; Emberger-Klein, A.; Menrad, K.; Möhring, W.; Blesin, J.M. Influencing factors for the purchase intention of consumers choosing bioplastic products in Germany. Sustain. Prod. Consum.; 2019; 19, pp. 33-43. [DOI: https://dx.doi.org/10.1016/j.spc.2019.01.004]
54. Chen, C.L.; Jian, X.N.; Jiang, W.H. The Last but Not the Least Piece of Marine Debris Management: Decoding Factors in Consumers’ Intentions to Purchase Recycled Marine Debris Products. Sustainability; 2024; 16, 3869. [DOI: https://dx.doi.org/10.3390/su16093869]
55. Chen, C.L. Factors influencing participation of ‘top-down but voluntary’fishery management—Empirical evidence from Taiwan. Mar. Policy; 2010; 34, pp. 150-155. [DOI: https://dx.doi.org/10.1016/j.marpol.2009.05.005]
56. Halkos, G.; Matsiori, S. Environmental attitude, motivations and values for marine biodiversity protection. J. Behav. Exp. Econ.; 2017; 69, pp. 61-70. [DOI: https://dx.doi.org/10.1016/j.socec.2017.05.009]
57. Wan, L.L.; Ha, H.Y. Sustainable green product adoption test using logistic regression: Comparison of glass and electronic products. Sustainability; 2021; 13, 5084. [DOI: https://dx.doi.org/10.3390/su13095084]
58. Ball, I. Port waste reception facilities in UK ports Iwan Ball. Mar. Policy; 1999; 23, pp. 307-327. [DOI: https://dx.doi.org/10.1016/S0308-597X(98)00057-8]
59. Chen, C.L.; Liu, T.K. Fill the gap: Developing management strategies to control garbage pollution from fishing vessels. Mar. Policy; 2013; 40, pp. 34-40. [DOI: https://dx.doi.org/10.1016/j.marpol.2013.01.002]
60. Hungerford, H.R.; Volk, T.L. Changing learner behavior through environmental education. J. Environ. Educ.; 1990; 21, pp. 8-21. [DOI: https://dx.doi.org/10.1080/00958964.1990.10753743]
61. Storrier, K.L.; McGlashan, D.J. Development and management of a coastal litter campaign: The voluntary coastal partnership approach. Mar. Policy; 2006; 30, pp. 189-196. [DOI: https://dx.doi.org/10.1016/j.marpol.2005.01.002]
62. Ajzen, I. From intentions to actions: A theory of planned behavior. Action Control: From Cognition to Behavior; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11-39. [DOI: https://dx.doi.org/10.1007/978-3-642-69746-3_2]
63. Bratt, C. The impact of norms and assumed consequences on recycling behavior. Environ. Behav.; 1999; 31, pp. 630-656. [DOI: https://dx.doi.org/10.1177/00139169921972272]
64. Liobikienė, G.; Mandravickaitė, J.; Bernatonienė, J. Theory of planned behavior approach to understand the green purchasing behavior in the EU: A cross-cultural study. Ecol. Econ.; 2016; 125, pp. 38-46. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2016.02.008]
65. Singh, A.; Verma, P. Factors influencing Indian consumers’ actual buying behaviour towards organic food products. J. Clean. Prod.; 2017; 167, pp. 473-483. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.08.106]
66. Matharu, M.; Jain, R.; Kamboj, S. Understanding the impact of lifestyle on sustainable consumption behavior: A sharing economy perspective. Manag. Environ. Qual. Int. J.; 2020; 32, pp. 20-40. [DOI: https://dx.doi.org/10.1108/MEQ-02-2020-0036]
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; Xiang-Nong Jian 1
; Ting-Yu, Wang 1 ; Shi-Wei, Huang 2 1 Department of Hydraulic and Ocean Engineering, Institute of Ocean Technology and Marine Affairs, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan;
2 Department of Urban Design and Sustainable Development, Ming Chuan University, No. 5, De Ming Road, Taoyuan 333, Taiwan;