MCDM-Based Ranking and Prioritization of Fisheries’ Risks: A Case Study of Sindh, Pakistan

Abstract

The fisheries sector in developing countries, including Pakistan, faces various risks that have not been comprehensively studied and addressed through policy measures. This study aims to analyze fisheries’ risks in Pakistan by following a risk management process and using statistical analysis. The data was collected through structured questionnaire surveys, and subsequently, fuzzy analytic hierarchy process (fuzzy AHP) and importance performance analysis (IPA) were utilized to analyze the data. The study ranked the top five risks in order of importance as management, technical, economic, environmental, and occupational risks. The study also identified high-importance, low-performance sub-factors, including inadequate legislative implementation, overexploitation, and infrastructure shortages. It was found that there is low risk perception and inadequate management regulations in the sector. The findings suggest that risk management strategies, such as risk avoidance and risk transfer, can be used to mitigate fisheries’ risks. The study highlights the need for policy measures to revitalize the fisheries sector in Pakistan and provides recommendations for further research.

1. Introduction

In a risk analysis, several risks with similar grading or severity might be detected. Identifying which risks to respond to and where to allocate scarce resources becomes more difficult when a lot of risks are grouped together. Therefore, it is important to align a management method with management needs in order to maximize resource use [1]. In order to formulate a risk management program that is successful, it is vital to develop a prioritization model that is logical [2]. It is common for risks to be prioritized by their severity when all risks are equal. A risk response priority is determined based on both the likelihood of the risk occurring and its impact. It is imperative to analyze the sensitivity of each risk so that we can determine which risks will likely have the most impact on the sector. In this regard, it is usually necessary to scrutinize each risk’s uncertainty level, and compare it with the uncertainty level of other risks in order to determine whether it is comparable or not. Management should not be prioritized according to the resources available in general [3,4]. The reason for this is that assessing and/or controlling involves team members whose availability is limited. There is a direct correlation between a sector’s risk appetite, risk tolerance, and risk thresholds and its risk attitude. When a sector has a low level of tolerance for risk, it tends to prioritize its risk response according to the level of expected impact of a risk event. In order to determine how much and what type of risk can be managed, it is imperative that we know when the risk might arise and what steps can be taken to minimize its impact in order to determine the amount and type of risk that needs to be managed [3].

Risk analysis has become a crucial tool for managing the complex and dynamic risks facing the fisheries sector. In the past, fisheries management relied on anecdotal evidence and limited scientific knowledge, leading to overexploitation and declines in fish stock [5,6]. However, a risk-based approach to fisheries management has emerged, involving identifying and assessing risks, developing and implementing mitigation strategies, and communicating with stakeholders. With new tools and techniques, such as bioeconomic modeling, social network analysis, and ecosystem-based management, risk analysis has become more sophisticated and evidence-based. These developments have led to more sustainable and profitable fisheries [2,3,7]. It is important to realize that risk management can make or break the success or failure of a sector, depending on the effectiveness with which they manage risk [8]. Effective risk management relies on information, continuous improvement, and the application of tools. It is essential to devote sufficient time to risk management. Ranking and prioritizing risks is a critical part of the process to ensure that they are addressed before they become major problems, and the consequences cannot be avoided [9]. Sectors can be at risk for a variety of reasons [3]. Risk management contributes to the sector’s success by leveraging its potential [10]. Therefore, for successful management, ranking and prioritizing risks is very important. The ranking and prioritization of risks can only be accomplished with a complete picture of existing risks.

It is necessary to adopt a variety of strategies to solve fisheries management issues. A famous tool for managing fisheries’ risks is known as decision analysis or MCDM [3,8]. It is possible to resolve conflicts of interest by combining qualitative and quantitative data and selecting an appropriate course of action with decision analysis tools [11]. Along with their multi-use properties, they also consider a wide variety of stakeholders throughout the entire decision-making process and ensure transparency throughout the entire process. This makes them particularly well suited for fisheries applications. When taken into account with stakeholder preferences, decision analysis can qualify as risk management, since it addresses exposure to risk as well as minimizing its effects. This process involves the following steps: (i) identify the issue and goals of stakeholders; (ii) develop action plans and their corresponding evaluation criteria; (iii) identify options and assess their effectiveness; (iv) select the performance analysis method; (v) consider stakeholder opinion; and (vi) recommend alternate options. Typically, decision analysis includes the concept of assessing risk, which does not have a clear definition [3,11].

In many applications of decision analysis to fisheries management, different and competing objectives are involved, for example, harvest, stock size, impacts on habitat, and employment [12]. Programming with dynamic variables can be used for tasks with single objectives, such as increasing a stock’s current value [3]. The decision to allocate resources will ultimately have to be made based on trade-offs among the interests of the stakeholders, even if the goal is to increase the present value. It is generally not possible to achieve each component objective concurrently in multi-goal scenarios because the goals are linked. The achievement of one goal is accompanied by a decrease in another, resulting in compromises. By integrating stakeholder needs into objective functions, MCDM tools facilitate the navigation of trade-offs [3,13].

Decision aids are developed techniques that focus on stakeholders’ preferences. This allows for identifying choices among discrete alternative options and incorporating them quantitatively. A decision aid fosters a full understanding of management objectives by extracting stakeholder preferences, thereby preventing failure of management. As part of MCDM, there are several types of algorithms, for example, AHP, Importance Performance Analysis (IPA), Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), Importance Performance Analysis (IPA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), aggregation operators, Simple Additive Weighting (SAW), and Analytic Network Process (ANP) [14]. ANP can take into account both inner and outer dependencies when making decisions [15]. The fuzzy TOPSIS algorithm can be used to evaluate the management of risks [16]. Managers, however, who need to solve real-life problems quickly may not be able to use this approach, since it is computationally complex [17]. It is very important to mention that the statistical techniques employed in this research, namely fuzzy AHP and IPA, were selected based on their availability, suitability, and relevance to this study. These routines have a number of advantages over other models of decision making, such as they are attribute-based approaches that prevent bias in judgment.

The fisheries sector is an important contributor to the economy of Pakistan, providing employment opportunities to over 3 million people and generating significant foreign exchange earnings. The sector is diverse and includes marine, coastal, and inland fisheries. Pakistan’s coastline is about 1050 km long and is home to a variety of fish species, making it an important fishing ground for both artisanal and commercial fishermen [7]. The inland fisheries sector, meanwhile, includes rivers, canals, reservoirs, and ponds, and is a major source of fish for domestic consumption. However, despite its economic importance, the fisheries sector in Pakistan faces a number of challenges. The fisheries sector in Pakistan faces various risks, such as overfishing, illegal fishing, and unsustainable practices, leading to the depletion of fish stocks [18,19]. Inadequate legislative implementation, poor management practices, and weak enforcement of regulations are also major challenges [7]. Other risks include infrastructure shortages, such as inadequate cold storage facilities and poor transportation networks, which contribute to post-harvest losses and reduced profitability [6]. Climate change and environmental degradation, such as pollution and habitat destruction, also pose significant risks to the fisheries sector in Pakistan [7,20]. These risks can have serious consequences for the livelihoods of the millions of people who depend on the fisheries sector and the economy of the country as a whole.

Pakistan’s fisheries sector faces a lot of challenges, especially when it comes to technological advancements and performance efficiency, both of which are lacking. There are also a number of major shortcomings in marketing practices. These include the absence of branded products, inadequate packaging, inadequate product labels, and the lack of a planned promotional campaign. These are just some of the main deficiencies [21]. Moreover, the production process has significant flaws that need to be addressed [22]. In short, Pakistan’s fisheries sector is confronted with a number of risks [7]. To manage these risks, management measures have been implemented [23]. However, risk ranking-based management is not adopted in Pakistan. As a result, the desired management goals have not yet been achieved [7]. In order to better manage fisheries, risks must be ranked and prioritized. Therefore, the objective of this research is to systematically access and prioritize risks confronted by the fisheries sector in Pakistan. It will assist management in identifying risk ranking hierarchies. This will assist in identifying risks that require more management measures to improve the sector. Thus, this will also contribute to efficient resource utilization and effective fisheries management.

2. Types of Risks

2.1. Economic Risk

Pakistan can benefit from fishery exports to boost export-led growth; however, many trade problems are hindering the contribution of this sector to the national economy at present [21]. Aquatic product trade has a variety of risks involving the price of fishery products, as these products are susceptible to price fluctuations owing to their sensitivity to price changes [24]. Because of the large variation in their annual incomes and the fluctuating prices of fishery products, fishermen are at considerable economic risk [25]. Information asymmetry, variations in costs, and shifts in user rights all influence the supply of fishery products, making economic risks more dynamic [3,26]. Certain studies have suggested that fishery risk can be categorized as technical, natural, economic, and managerial risks. Information asymmetry, the chaotic rivalry within fisheries, as well as shifting market decisions combine to create economic risk for fishery producers [5].

The escalating operational costs have impeded the advancement of Pakistan’s fisheries sector in the past few years. There are a variety of reasons for this, such as high equipment costs, expensive materials, a lack of qualified workers, inadequate research, and stagnant technology [27]. In the current economic environment, increasing the costs of fishing equipment and equipment means a constant increase in risk for fishermen’s operation [28]. Fisheries are also hampered by the lack of funding that is available as well as a difficult economic situation that restricts the flow of capital. Generally, farmers are not inclined to take risks. Providing loan support is difficult, with no credit guarantee. There is not much financing available for potentially risky businesses. Despite this risky sector’s importance, generally the government does not have a strategy to promote it [29]. Unfortunately, the processing and packaging technology in Pakistan is very old. Therefore, several bans have been implemented on the export of fishery products from Pakistan in the past. Through technological advancements and meeting international standards, it would be possible to increase fish trade. The technology for processing and packaging in Pakistan is very old. Henceforth, numerous bans have been instituted on the shipment of seafood items from Pakistan in past instances. The fish trade could be increased through technological advancements and meeting international standards [21].

2.2. Environmental Risk

Excessive fishing, contamination, and loss of habitat pose significant threats to some of the most important industrial fisheries in Pakistan [18]. Coasts have seen increased pollution due to industry expansion and growth. In the Karachi Port area, the marine environment is affected by heavy metal pollution, which has been exacerbated by commercialization, harming the ecosystem [18,30]. Among the various ecological effects associated with fisheries, the destruction of habitats, fishing gear loss or disposal, environmental degradation, and pollution of the oceans are some of the most prevalent impacts. These factors may lead to the unsustainable development of a tuna fishery in Pakistan. There have been many instances over the years where hurricanes, earthquakes, storms, and other forms of environmental catastrophes have severely affected the fishery and caused substantial devastations [31]. Commercial catches of fish are dropping due to water pollution, excessive fishing, loss of natural habitats, and the introduction of hard-to-kill exotic species [20]. It is now common practice to set targets for sustainable fisheries. Protecting biodiversity, environmental degradation, invasive species, and exploitation are just a few of the possible objectives. Moreover, fish growth is highly dependent on temperature. Natural and artificial factors both threaten the diversity of fishery species along Pakistan’s coastline. Among the natural factors that affect fisheries are temperature fluctuations, water movement, hypoxic water diffusion, and salinity variations [6]. According to studies, a rising sea surface temperature poses a serious risk to fisheries [32].

Fishermen face a combination of environmental and social risks that can affect their businesses, including typhoons, storm surges, and red tides [5]. The introduction of exotic species can also cause environmental disruption, such as sea lice infestation in a salmon farm mentioned by Huang and Perrings [33]. Newman et al. [34] proposed a plan to avoid the risks associated with multi-species fisheries resources, including threats posed by exotic species introductions, genetic changes in fish populations, and the interactions between species and ecosystem changes. According to Grafton and Little [35], one possible strategy for mitigating the impact of habitat degradation on the fishery is to implement environmental offsets. Additionally, an artificial disaster, like a radioactive leak, can cause a decline in the quality of fishery products, as radionuclides are released into the sea and absorbed by marine organisms and sediments [36]. Troubled waters also pose a risk to aquaculture, according to Holley et al. [37].

Concerns about the environmental risks associated with farm-based fisheries have been raised in recent years. Enhanced fisheries resources can lead to a loss of species diversity in natural populations [38]. Coastal restoration programs have taken over fishing grounds traditionally used by fishermen, and increasing industrial development has led to the occupation of large areas of the sea by telecom companies and port facilities. The utilization of island resources has additionally exerted stress on coastal and offshore reserves, resulting in ecological limits being exceeded [24]. CO₂ emissions and other greenhouse gases contribute to global warming, which affects marine life, further destabilizing and compromising the safety of marine fisheries. A number of environmental catastrophes, such as eutrophication, toxic air pollution, radioactive waste disposal in water, petroleum exploration and spills, and poor waste handling, have significantly threatened the sustainability and security of oceanic stocks. The topic of mercury exposure in aquatic products and its health consequences has garnered substantial attention in Chinese research [39,40].

Coastal countries that rely on fishing are greatly impacted by the deteriorating ocean climate, which is difficult to forecast. For instance, Chile’s Jack mackerel fishery was threatened by El Niño, a weather phenomenon that causes warm water currents in the Pacific Ocean, according to studies by Martinet et al. [41] and McKelvey and Golubtsov [42]. The rise in sea level is a critical aspect of the changing climate, leading to the loss of mangroves, which provide wood, food, and fuel for people and serve as breeding grounds for shrimp populations. Hence, mangrove forests are indispensable for preserving fishery resources. In addition, man-made pressures on beaches, such as Indus River water discharge and coastal sand depletion, increase turbidity and accelerate erosion. Moreover, the presence of vessels in fishing areas can also create disturbances that hinder fishing activities, according to Astles et al. [43]. Furthermore, ocean acidification severely affects marine organisms, which ultimately impacts local commercial harvests. In their study, Mathis et al. [44] utilized risk assessment techniques to examine the impact of climate change on marine chemistry and found a distressing increase in the risk of ocean acidification. Additionally, ecological adversities, such as tremors and bores, can cause significant damage to infrastructure, and Mabon and Kawabe [45] studied the effects of these catastrophes on infrastructure and discussed measures for managing risk after disasters.

2.3. Technical Risk

Fisheries face a range of risks, including technical and environmental risks. Technical risks can arise due to issues such as invasive species or problems with breeding techniques. In Pakistan, a lack of coordination between agencies has seriously affected the fishing sector. On the environmental side, the use of unreasonable fishing gear can negatively affect marine habitats, while the presence of demersal trawlers can endanger many marine organisms [7]. Equipment failures can decrease the profitability of fishing vessels, while fuel- and oil-related tasks can result in accidental spills or leaks into the sea or air. These factors can impact fisheries production and sustainability [32,43].

As the fishing industry continues to grow, there is a concern about the impact on marine resources due to the lack of expertise and equipment in some fishing communities [46]. The use of smaller mesh-sized fishing nets and illegal killing practices may lead to the discarding and harm of juvenile fish populations in creeks [7]. In China, aquaculture in deep water cages has made progress, but due to the low levels of mechanization, automation, and industrialization, the industry is limited in its ability to transform and upgrade itself [47]. Encircling nets and estuarine set bag nets, commonly referred to as Katra and Bullo, respectively, are among the gear types that can pose a threat to juvenile fish populations [7]. By raising these concerns, the importance of sustainable fishing practices and regulatory frameworks to preserve marine resources is emphasized.

2.4. Management Risk

The management of fisheries faces many challenges and risks that may impact their ability to sustainably manage fish populations. One of the biggest problems is the lack of proper plans for harvesting and operational errors made by the management authority [48]. This is often compounded by limited resources, which may make it difficult for fishery management agencies to solve problems due to competing stakeholder demands [49]. Uncertainty associated with stock assessments and the difficulty of conducting ecological risk assessments in areas with limited data further add to the complexity of fisheries management [43]. In addition to these risks, Sethi [3] highlights management risks associated with inadequate social welfare objectives and insufficient ecological protection.

The management of fisheries is a complex issue that affects both management authorities and fishing communities [50]. One of the major risks associated with fisheries is overfishing, which can lead to the collapse of fisheries due to a lack of understanding of fishermen’s behavior [51]. Fishing is also considered a hazardous occupation with high mortality rates [52]. When fish are excessively harvested, it can exhaust the fish population and cause a shift in the fishery from being prosperous to unprofitable due to diminishing resources. This impact has been widely studied, with research demonstrating the extensive effects of overfishing [53]. Fishing may bring about indirect risks that are connected to the ecological relationship between the captured species and their habitats. Direct contact without capture can damage habitats and indirectly influence the captured species [54].

The decline of high sea fisheries resources is a growing concern, as the stocks continue to deplete, and the fishery may not have enough capacity to sustain itself in the long run. To prevent the collapse of fish stocks, several countries have established quotas that can be transferred. However, this may also put the fishery at risk [55,56]. In addition, fishing gear loss can jeopardize fishing activities, and fish farm escapes can result in competition with wild fish, hybridization between them, and the spread of pathogens [43]. The argument states that fishing mortality is a significant risk factor connected with fishing activities and that the only method to prevent irreversible ecological and socio-economic consequences is to decrease fishing mortality at the current level [56]. The inability of single-species management to sustain fish stocks raises questions about marine ecosystem safety and sustainability [57].

Changes in the international fishery order during the 1990s resulted in overfishing and unreasonable aquaculture production plans, leading to a loss of fishery production due to poor management practices or errors. The lack of fishery insurance is a significant problem, making it necessary to have a fishery disaster insurance policy that complies with local laws to provide support to fishermen in case of disasters or accidents. Fishermen’s operations are vulnerable to external factors, such as waves and wind, and they face risks, such as fishing accidents and mishandling of tools, highlighting the need for effective management practices. However, the shortage of the national security system has resulted in fishermen in some parts of China being encouraged to transfer jobs, which ignores the need for proper job transfers and puts them at higher risk. This policy also increases the likelihood of illegal fishing, as some fishermen are willing to take risks. Therefore, proper management practices during this transformation are crucial to avoiding negative consequences [39,40,58].

2.5. Occupational Risk

A common risk in fisheries is the transmission of disease [59]. A stressed working environment and a strenuous work routine often result in health problems for fishermen. Moreover, it is common for fishermen to sustain injuries due to trips and falls while working [60]. Manual handling of catches, as well as intense labor, causes extreme stress on fishermen’s muscles and joints. It is not uncommon for injuries to occur on vessels because the decks are always in motion with a lot of people around them. Fishing safety risks can also be exacerbated by long working hours, sleep deprivation in remote areas, and risky conditions in the water [61]. The high demands of the job heavily affect the health of fishermen. Long sea voyages by boat place fishermen in an uncomfortable working environment due to the open air they work in [62]. Since fishermen spend a lot of time away from home, they are often deprived of sleep, physically spent, and required to work long hours [63].

Fishermen are exposed to various occupational hazards, including increased risks of skin irritation, physical injuries, and infections. Frequent contact with water can increase the risk of skin irritation, physical injuries, and infection being introduced into the body [64]. Although minor injuries such as cuts are common, they can cause infections when bacteria enter the body through the wounds. Fungal infections are more common among fishermen due to sweating, which creates a conducive environment for fungal growth [65]. Three types of infections are common among fishermen: desquamation, hair follicle infection, and dermatitis. Parasitic infections can also occur due to constant moist conditions and excessive perspiration associated with high temperatures and elevated levels of humidity [64]. Fishermen are also at risk of viral infections, such as herpes simplex and skin lesions, while parasitic diseases, such as scabies, are rare [65].

3. Materials and Methods

3.1. Risk Classification

A reliable and trustworthy fisheries’ risk classification was proposed by Tingley et al. [66] for “The European Commission”. This classification was used in this study. Additionally, it should be pointed out that this study did not encompass all the fisheries’ risks put forth by Tingley et al. [66]. First, a list of risks and their corresponding sub-risks was prepared. Second, the list was discussed with various stakeholders, including fishery experts, to determine whether specific risks existed in Pakistan. Furthermore, it is crucial to note that all stakeholders who took part in this survey were impacted by these risks, and their opinions were formed based on their experiences and expertise. As a result, the survey respondents represent those stakeholders who have been affected by these risks. Third, the published literature was reviewed. Fourth, following this step-by-step investigation of Pakistani fisheries’ risks, some risks that were not significant were deleted. As a result, this study used fisheries’ risks that were evident in Pakistan and were appropriately categorized. Figure 1 presents the risk classification.

3.2. Data Collection

To obtain data, a structured questionnaire survey was designed and implemented. Survey respondents were selected by using the snowball technique. This technique requires the recruitment of one subject, which refers to multiple data sources. These sources of data refer to another set of multiple sources of data later on. This process continues until sufficient data are collected [66,67]. Data collection was done from three main districts of Sind, viz., Karachi (193; 73.8% response rate), Thatta (72; 61.2% response rate), and Sujawal (81; 68.7% response rate) between 2 February 2021 and 16 July 2021. In Balochistan, data were collected from Lasbela (212; 81.3% response rate) and Gwadar (105; 63.3% response rate) between 16 February 2021 and 28 July 2021. Particulars of the survey respondents are given in

Table 1. Demographic features of survey respondents.

Features		Frequency	Percent
Relationship status	Unmarried	79	22.9
	Married	267	77.1
Gender	Male	301	86.9
	Female	45	13.1
Age	25~34 years	91	26.3
	35~54 years	215	62.1
	55~65 years	40	11.6
Professional Experience	5–9 years	56	16.2
	10–14 years	215	62.1
	15 years or more	75	21.7
Qualification	Primary school	44	12.7
	Secondary school to Masters	265	76.6
	Ph.D.	37	10.7
Area	Karachi	193	55.8
	Thatta	72	20.8
	Sujawal	81	23.4
Stakeholder group	Fishermen	91	26.3
	Fishing companies	74	21.4
	Public or private organizations	85	24.6
	Researchers	47	13.6
	Consumers (well-aware)	49	14.1
	Total	346	100.0

.

3.3. Data Analysis

The response rates for the survey were computed. To arrive at the response rates, the completed surveys were divided by the total number of surveys sent, and the result was multiplied by 100. For statistical analysis, the Expert Choice 2000 computer package was employed using fuzzy AHP programming. The selection of IPA and AHP as analysis tools in this study was based on their appropriateness for tackling the research problem at hand. IPA was chosen for its effectiveness in evaluating performance and pinpointing areas for improvement, while AHP was selected for its ability to tackle complex decision-making problems. Both techniques have been widely employed in various fields, including marketing and management, and they have been validated by prior research. In terms of relative importance, IPA and fuzzy AHP were given equal weight. However, IPA was utilized to assess performance attributes, whereas AHP was applied to supplement the IPA’s findings and provide a more robust analysis [64,68].

3.3.1. Fuzzy AHP

AHP is recognized as one of the most prevalent decision aid methods [17,68]. When decision makers use AHP, they focus on judgments based on comparative worth rather than logical presumptions [11]. An AHP approach divides a decision issue into components arranged in a hierarchy. A variety of AHP algorithms are utilized to generate rankings of action choices based on preferences for problem sub-part results. For instance, option A has three times as much preference as option B. Fisheries management can benefit from AHP, since it emphasizes trade-offs among different parties in situations where it might be more important to achieve a satisfying outcome than maximizing efficiency [69].

It was Saaty who developed the AHP method in 1977 [17]. Several revisions of the method have been made and employed in various sectors since then. For example, Teniwut et al. [70] determined ocean-wide distributions of seaweed using AHP and geographic analysis. An assessment tool employing AHP for green construction was designed by Vyas et al. [71]. Using AHP, Giamalaki and Tsoutsos investigated the installation of photovoltaic systems [72]. The AHP analysis was applied to the shipping industry in a study conducted by Havle et al. [73]. The fuzzy AHP method was employed to evaluate water quality. It was also proposed by Dursun and Karsak to apply fuzzy theory to solve complicated issues [74].

A fuzzy set can simplify the process of assessing human reasoning when vague information is available in unclear situations. The use of items that are linguistically related, thus serving as judgment models, works well for the MCDA approach. With the goal of providing higher-quality outcomes, fuzzy logic supports AHP as a key component of the decision-making process. Stakeholders’ priority ranking of risks in AHP leads to rational assumptions. The problem is decomposed into tiers of components in a hierarchical manner. AHP techniques quantify options against different action choices in the case of stakeholder preference for one option in comparison to another. In cases where optimization is the goal, AHP and IPA offer a flawless reconciliation of stakeholders and goals [3,75].

Expert opinions are employed in AHP to determine the priority and weight of pairs of comparisons [76]. AHP scales are based on human evaluations. Therefore, they are based on an assessment of what is important for a user in relation to the value that they place on other factors. Information that is ambiguous or uncertain is not possible to incorporate with this method in ambiguous settings. In order to evaluate measures that are not precise, accurate, and arbitrary, fuzzy AHP theory can be applied. It allows the user to make explicit judgments based on the uncertainty and ambiguity of all the expressions that are presented to him under uncertain circumstances, so he can make explicit judgments based on all the expressions [77]. There are times when subjectivity in fuzzy theory can provide a solution to these problems. Past studies have employed fuzzy AHP and IPA to assess the performance of the risk management process in several ways [78]. The following is a detailed description of the fuzzy AHP process of data analysis.

Matrix of Fuzzy Relations

A matrix of fuzzy relations between criteria with the properties of positivity and reciprocity was taken into account. The matrix expression is outlined below: $\tilde{A}={\left[{\tilde{a}}_{ij}\right]}_{n\times n}$. In the above mathematical expression, a_ij represents a special number, i.e., fuzzy triangular number. This number is given as follows: ${\tilde{a}}_{ij}=\left[l_{ij},m_{ij},u_{ij}\right]$. Moreover, various parameters were described in the following way: $\left[l_{ij},m_{ij},u_{ij}\right]=\left\{\begin{array}{c}\left[1,1,1,\right],ifi=j;\\ \left[\frac{1}{u_{ij}},\frac{1}{m_{ij}},\frac{1}{l_{ij}}\right],ifi\ne j.\end{array}\right.$. We constructed comparison pairs, shown as A^(k) by relating each risk factor to each survey respondent (kth). In this expression, the value of k varies from 1 to 23. These comparison pairs were combined with fuzzy sets of inverse functions. Furthermore, a fuzzy number (triangular) was estimated using the following formula: ${\tilde{a}}_{ij}=\left[\underset{1\ll k\ll 30}{\min }\left\{a_{ij}^{\left(k\right)}\right\},{\left(\prod _{k=1}^{30}{a}_{ij}^{\left(k\right)}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$30$}\right.},\underset{1\ll k\ll 30}{\max }\left\{a_{ij}^{\left(k\right)}\right\}\right]$. In the above equation, the values of i and j range from 1 to n. Fuzzy digits were estimated as shown here [79]: ${\tilde{a}}_{ij}=\left\{\begin{array}{c}\left[1,1,1,\right],ifi=j;\\ {{\tilde{a}}_{ij}}^{-1},ifi\ne j.\end{array}\right.$.

Estimation of Weights and Fuzzy Number (Triangular)

In this study, the local weights of variables ($\tilde{A}$) associated with risk were determined by implementing a technique proposed by Saaty [17] involving geometric means. To determine the local weights, it was necessary to apply a certain layout to the data. A mathematical calculation for fuzzy numbers (triangular) was used to compute geometric averaging for variables associated with risk. Here, the values of the items (ith) ranged from 1 to n. This can be expressed mathematically as follows: ${\tilde{w}}_i={\left(\prod _{j=1}^n{\tilde{a}}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}=\left[{\left(\prod _{j=1}^n{l}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.},{\left(\prod _{j=1}^n{m}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.},{\left(\prod _{j=1}^n{u}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}\right],i=1,2,\dots ,n$. Following that, the estimates of ${\tilde{W}}_i$ were summed up. This summation is expressed below: ${\sum }_{i=1}^n{\tilde{w}}_i=\left[{\sum }_{i=1}^n{\left(\prod _{j=1}^n{l}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.},{\sum }_{i=1}^n{\left(\prod _{j=1}^n{m}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.},{\sum }_{i=1}^n{\left(\prod _{j=1}^n{u}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}\right]$. This can be used to write fuzzy weights for variables associated with risk, as shown below:

$${\tilde{W}}_i=\frac{{\tilde{w}}_i}{{\sum }_{i=1}^n{\tilde{w}}_i}=\left[\frac{{\left(\prod _{j=1}^n{l}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{{\sum }_{i=1}^n{\left(\prod _{j=1}^n{l}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}},\frac{{\left(\prod _{j=1}^n{m}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{{\sum }_{i=1}^n{\left(\prod _{j=1}^n{m}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}},\frac{{\left(\prod _{j=1}^n{u}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{{\sum }_{i=1}^n{\left(\prod _{j=1}^n{u}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}\right],i=1,2,\dots ,n.$$

Approximation of Crisp Number

A defuzzification process (${\tilde{W}}_i$) was used in order to obtain crisp numbers. Here, the values of items i ranged from 1 to n. The following is a mathematical expression of the defuzzification process: $W_i=\left(l_i^W+2m_i^W+u_i^W\right)/4,i=1,2,\dots ,n$. In order to obtain the corresponding local weights for all the variables associated with risk, the ${\tilde{W}}_i$ values for each variable were standardized. The standardization process is expressed as follows: $W_i=W_i/{\sum }_{i=1}^n{W}_i,i=1,2,\dots ,n$ [79].

3.3.2. IPA

A variety of research fields have used the IPA framework to examine the relationships between factor performance and its importance [64,80]. There are four areas in which importance and performance ratings appear: concentrate here, low priority, potential overkill, and keep up the good work (Figure 2).

The concept of assessing importance in relation to the performance of any sector was proposed by Martilla and James [81]. The technique has been found to be an extremely useful and easy-to-use tool that has been widely used by researchers and managers across a wide range of industries. It facilitates the interpretation of data and makes it easier for strategic decisions to be made, resulting in the development of effective management programs [82]. Each risk element studied is represented by a pair of coordinate axes measuring its performance (x-axis) and importance (y-axis). With regard to the four quadrants, it is essential to remember that each quadrant reflects a combination of the significance and performance attributes attributed by survey respondents to a specific risk element, resulting in a different management value that reflects this entirely different opinion. Basically, the aim of the matrix is to determine the mean value of the importance and performance data in order to compare the two. The following is a description of the four IPA quadrant areas:

• Low performance, high importance (Concentrate here): represents weaknesses that must be addressed immediately and need immediate improvement;
• High performance, high importance (Keep up with the good work): highlights major strengths that can be used to achieve or maintain competitive advantage;
• Low performance, low importance (Low priority): indicates neither requires additional effort nor is it a major weakness;
• High performance, low importance (Possible overkill): resources should be diverted elsewhere instead of committing them to these attributes [82].

4. Results

4.1. Demographic Features of Survey Respondents

A reliable result was obtained by obtaining sufficient data for statistical analysis. Overall, data were collected from 346 survey respondents who completed the questionnaires in all respects and were suitable for analysis. Table 1 highlights the salient features of survey respondents in detail. With respect to relationship status, 79 respondents (22.9%) were unmarried, whereas 267 respondents (77.1%) were married. Male respondents 301 (86.9%) dominated the research sample compared to female respondents 45 (13.1%). In terms of age, 91 respondents (26.3%) belonged to the 25~36 years age group, 215 respondents (62.1%) belonged to the 35~54 years age group, and 40 respondents (11.6%) belonged to the 55~65 years age group. It was also noted that 44 respondents (12.7%) had primary school, 265 respondents (76.6%) had secondary school to master’s, and 37 respondents (10.7%) had Ph.D. education. In terms of the area, 193 respondents (55.8%) were from Karachi, 72 respondents (20.8%) were from Thatta, and 81 respondents (23.4%) were from Sujawal. As far as professional experience is concerned, 56 respondents (16.2%) had 5–9 years of working experience, 215 respondents (62.1%) had 10~14 years of experience, and 75 respondents (21.7%) had 15 or more years of experience. Regarding stakeholder group, 91 (26.3%) were fishermen, 74 respondents (21.4%) were fishing companies, 85 respondents (24.6%) belonged to public or private organizations, 47 respondents (13.6%) were researchers and 49 respondents (14.1%) were consumers (well-aware).

4.2. Fuzzy AHP Ranking of Main Risk Factors

Computed fuzzy AHP rankings of the main risk factors based on their local weights are portrayed in

Table 2. Fuzzy AHP ranking of main risk factors using estimated local weights.

Risk Category	Importance	Rank
Management	0.353	1
Technical	0.264	2
Economic	0.213	3
Environmental	0.097	4
Occupational	0.073	5

. ‘Management risk’ (0.353) was ranked first, followed by ‘technical risk’ (0.264). It is also noteworthy that ‘economic risk’ (0.213) was ranked third, ‘environmental risk’ (0.097) was ranked fourth and ‘occupational risk’ (0.073) was ranked fifth.

4.3. Ranking of Risk Sub-Factors

Computed fuzzy AHP rankings of risk sub-factors for Sindh based on their local weights are graphically presented in Figure 3. Economic risk sub-factors were ranked from most to least important as increasing the costs of fishing (0.361), problems related to trade (0.299), price volatility (0.187), and technological advancements (0.153), respectively. Moreover, management risk sub-factors were ranked from most to least important as inadequate legislative implementation (0.321), intense fishing (0.301), operational issues (0.213), excessive discard ratio (0.096), and scientific knowledge about fisheries (0.069), in that order.

4.4. IPA Ranking of Main Risk Factors

The estimated IPA rankings of the main risk factors for Sindh based on their local weights are depicted in

Table 3. IPA ranking of main risk factors using estimated local weights.

Risk Category	Performance	Rank
Technical risk	4.518	1
Management risk	4.014	2
Occupational risk	3.864	3
Economic risk	3.229	4
Environmental risk	3.198	5

. ‘Technical risk’ ranked first with a score of 4.518, while ‘management risk’ was a close second with a score of 4.014. Moreover, ‘occupational risk’ (3.864) was ranked third, ‘economic risk’ (3.229) was ranked fourth, and ‘environmental risk’ (3.198) was ranked fifth.

4.5. IPA Ranking of Risk Sub-Factors

The estimated IPA rankings of risk sub-factors for Sindh based on their local weights are represented visually in Figure 4. Economic risk sub-factors were ranked from most to least important as ‘problems related to trade’ (4.778), ‘technical advancements’ (4.123), ‘increasing costs of fishing’ (3.846), and ‘price volatility’ (3.112), in that order. Furthermore, ‘management risk’ sub-factors were ranked from most to least important as ‘intense fishing’ (4.731), ‘operational issues’ (3.984), ‘excessive discard ratio’ (3.546), ‘scientific knowledge about fisheries’ (3.443), and ‘inadequate legislative implementation’ (2.891), correspondingly. Besides, ‘environmental risk’ sub-factors were ranked from most to least important as ‘damage to habitats’ (4.653), ‘pollution’ (4.478), ‘erratic temperature’ (3.789), and ‘environmental disruption’ (3.325), accordingly. Furthermore, ‘technical risk’ sub-factors were ranked from most to least important as ‘destructive techniques for fishing’ (4.951), ‘lack of coordination between agencies’ (4.165), ‘infrastructure shortage’ (3.845), ‘lack of skilled and educated workers’ (3.457), and ‘equipment failure’ (3.326), in that order. In addition, ‘occupational risk’ sub-factors were ranked from most to least important as ‘contagious disease’ (4.242), ‘high job demands’ (3.554), ‘issues related to personal safety’ (3.426), ‘job insecurity’ (3.251), and ‘work-life imbalance’ (2.654), respectively.

4.6. IPA Quadrant Analysis

The IPA distribution of risk sub-factors based on their performance evaluation is presented in Figure 5. Sub-factors with high importance but low performance were placed in Area 1. Thus, these factors call for improvement and included ‘problems related to trade’, ‘inadequate legislative implementation’, ‘excessive discard ratio’, ‘damage to habitats’, ‘pollution’, ‘destructive techniques for fishing’, ‘contagious disease’, ‘issues related to personal safety’, and ‘intense fishing’. Sub-factors with both high importance and performance were placed in Area 2. Thus, these factors continued to be managed. These factors include ‘scientific knowledge about fisheries’, ‘technological advancements’, ‘lack of coordination between agencies’, ‘job insecurity’, ‘lack of skilled and educated manpower’, and ‘equipment failure’. Sub-factors having both low importance as well as performance were placed in Area 3, indicating low priority. These factors include ‘environmental disruption’, ‘operational issues’, ‘high job demands’, ‘increasing costs of fishing’, and ‘erratic temperature’. Sub-factors with low importance but high performance were placed in Area 4. The resources spent on these sub-factors should be better allocated to manage sub-factors placed in Area 1. These factors include ‘price volatility’, ‘infrastructure shortage’, and ‘work-life imbalance’.

Table 4. IPA distribution of risk sub-factors by their corresponding areas.

Area 1	Area 2
Increasing costs of fishing	Scientific knowledge about fisheries
Problems related to trade	Lack of coordination between agencies
Operational issues	Lack of skilled and educated manpower
Inadequate legislative implementation	Job insecurity
Environmental risk	Contagious disease
Intense fishing
Excessive discard ratio
Pollution
Damage to habitats
Infrastructure shortage
Destructive techniques of fishing
Issues related to personal safety
Area 3	Area 4
Price volatility	Technological advancements
Environmental disruption	Erratic temperature
Equipment failure	Work-life imbalance
High job demands

presents the IPA distribution of risk sub-factors by their corresponding areas. Codes have been assigned to every risk sub-factor. The risk sub-factors are highlighted with their corresponding codes in

Table 5. IPA quadrant analysis output.

Code	Category	Importance	Performance
1	Problems related to trade	4.381	3.801
2	Inadequate legislative implementation	4.518	3.473
3	Scientific knowledge about fisheries	4.711	3.884
4	Excessive discard ratio	4.991	3.549
5	Technological advancements	4.801	3.786
6	Damage to habitats	4.651	3.553
7	Pollution	4.118	3.213
8	Destructive techniques of fishing	4.139	3.914
9	Price volatility	4.932	3.887
10	Environmental disruption	4.857	4.429
11	Lack of coordination between agencies	4.621	4.901
12	Job insecurity	4.591	4.197
13	Contagious disease	4.018	4.332
14	Lack of skilled and educated manpower	4.431	4.121
15	Issues related to personal safety	4.453	4.599
16	Operational issues	3.564	3.909
17	High job demands	3.781	3.619
18	Equipment failure	3.458	3.551
19	Infrastructure shortage	3.759	3.454
20	Increasing costs of fishing	3.539	3.111
21	Intense fishing	3.469	4.217
22	Work-life imbalance	3.687	4.549
23	Erratic temperature	3.543	4.827

.

5. Discussion

In this section, we provide a comprehensive discussion of the significant risk types that Sindh fisheries face. Specifically, we focus on management risk and technical risk, as these are the major risk types that have been identified. To provide a well-rounded understanding of these risks, we will also discuss the results of our study in conjunction with those found in the existing literature. Finally, we conclude this section by addressing the limitations of our study, discussing the implications of our findings, and suggesting future research directions. By examining these risk types in detail, we hope to provide valuable insights for the management and sustainability of Sindh fisheries, while also identifying areas where further research is needed.

5.1. Management Risk

According to the findings of a recent study, the sustainability of Sindh fisheries is under threat due to a number of significant risks. The study identified intense fishing and inadequate legislative implementation as the most pressing management risks faced by the region’s fisheries, a result that is supported by previous research [19,21]. Overexploitation of fisheries is a global issue, as it occurs when the amount of fish caught exceeds the maximum amount that can be sustainably harvested [83]. The problem is compounded by advances in fishing technology, which allow even small boats to use highly effective fishing equipment that prevents fish from reproducing or escaping [84]. As a result, approximately 75% of commercial fisheries have been harvested beyond sustainable yields, causing collapses or severe impacts on ecosystems [7]. To address this issue, the FAO’s Code of Conduct for Responsible Fisheries requires states to take all necessary steps to ensure appropriate fishing operation documentation and data collection for stock assessments (article 8.4.3). The FAO report UTF/PAK/108/PAK uses modern and reliable methods to gather and evaluate fisheries data, which can be used by managers and policymakers to assess fisheries, collect statistics, and better understand ecosystems [5,83].

Researchers using Marine Fisheries Department statistics indicate that Pakistan’s commercial fisheries are heavily overfished, with overexploitation identified as the cause. Fisheries such as molluscan, Portunus spp., and Acanthopagrus berda have been reported to be overexploited [18,19,21]. The decline in fisheries capture production in Sindh in recent decades can be attributed to the overexploitation risk. To address this issue, Section 2A.2 of the National Fisheries Policy (NFP) prioritizes sustainable fishing and overfishing control [7]. Regulations such as ‘The Sindh Fisheries Ordinance of 1980’ have also been implemented in the past to curb overexploitation. However, there is a low level of risk perception among Pakistanis, with 85% of survey respondents indicating this belief. Moreover, 67% of respondents felt that existing management practices did not adequately address risks, and 59% felt that there was poor implementation of current management policies. A majority of participants (71%) were unaware of how to improve existing management practices. This reflects the prevailing situation in Pakistan regarding risk management and human behavior. People tend to ignore common risks when they perceive them to be routine [85], which might be the case with overexploitation, as it has been a consistent risk for several decades.

In conclusion, Sindh fisheries face significant management risks that threaten their sustainability. Intense fishing and inadequate legislative implementation are the major risks, with overexploitation identified as the cause. It is imperative that measures are taken to ensure that marine aquatic resources are managed sustainably and that overfishing is curbed. Effective management practices that address risks and are properly implemented are essential to achieving this goal. Education and awareness-raising campaigns can also help improve risk perception among the population, as well as their willingness to take action to mitigate risks. By taking these steps, we can help ensure that Sindh fisheries remain sustainable for future generations.

5.2. Technical Risk

One way to mitigate technical risks in Sindh fisheries is by investing in administrative practices that are more advanced. For example, remote sensing is a technology that can provide precise data about the abundance of wild fish populations, keep a check on fishing practices, and provide customers with information on the source and condition of fish products [86]. Furthermore, genetic modification and biotechnology can help eliminate infectious diseases and enhance marine fisheries [87]. The reduction of technical risks in fisheries management, ecosystems, and ecosystem-based management principles can be achieved through the provision of training programs and support [88]. In the fisheries industry, a significant portion of the difficulties encountered can be attributed to inadequately trained personnel. Consequently, training plays a critical role in enhancing the capabilities of the workforce and minimizing associated risks. Poorly managed fisheries in Pakistan have led to severe damage to the marine environment, with destructive fishing practices being a major cause. This has been primarily attributed to the lack of coordination between different departments involved in the management of fisheries [7]. Hence, it is essential to have efficient government involvement and alliances among different departments to tackle this problem.

The survey findings revealed that the majority of respondents in Sindh reported low levels of knowledge about performance management. In Sindh, 81% of respondents reported low levels of knowledge. Moreover, a significant proportion of respondents in this province expressed dissatisfaction with their current performance management systems, with 57% of respondents in Sindh reporting dissatisfaction. To improve performance, respondents in this province recommended various measures, with training being the most commonly recommended solution. 49% of respondents recommended training, followed by performance evaluation systems (31%) and reward systems (12%). Pakistan’s fisheries sector has seen a growing trend in employee training in recent years, particularly in Sindh, where training was conducted on shrimp farming and cage culture [7,21]. In 2012, training of fishermen in Sindh was conducted to improve their knowledge of aquaculture. The Trade Related Technical Assistance Program II also conducted training in 2010 throughout the employee chain associated with the fisheries sector on operational procedures, good hygienic and manufacturing practices, and quality control. The World Wildlife Fund funded a project in 2020 that trained 280 fishermen on safe fishing, post-harvest loss control, and data collection. Additionally, the Sindh government launched a special program to train fishermen and develop their skills, knowledge, and capacity building from 2021 to 2026 [7]. The Food and Agriculture Organization has also arranged different training programs to develop fishermen’s skills. However, according to the survey participants, training effectiveness needs improvement. Survey respondents reported a lack of communication on training details, inter-departmental training, and low interaction between trainers and participants. Furthermore, there is no developed system for evaluating training sessions or obtaining feedback from attendees. Participants also reported that some training was conducted without the necessary equipment. To increase training effectiveness in Pakistan’s fisheries sector, there is a need to implement training evaluation and feedback systems and provide the necessary equipment during training sessions.

To address the most significant risks faced by the Pakistani fisheries sector, it is essential to allocate resources in a strategic and effective manner. First, there needs to be a comprehensive legislative framework that incorporates the latest scientific findings and best practices to regulate the industry. This will require the involvement of multiple stakeholders, including government officials, fisheries management experts, and representatives from the fishing industry, to collaborate and develop regulations that are enforceable, transparent, and adaptable. Second, the overexploitation of fisheries resources can be addressed through effective fisheries management practices, such as stock assessments, monitoring, and enforcement of catch limits. This will require investment in both human and technological resources to build capacity for fisheries management, such as training programs, hiring additional staff, and acquiring new technologies. In addition, there needs to be a focus on promoting sustainable fishing practices, such as the use of selective fishing gear, and establishing marine protected areas to allow fish stocks to recover. Lastly, infrastructure shortages can be addressed through targeted investments in port facilities, cold storage, and transportation infrastructure to improve the handling and transportation of fish products. This will require collaboration between the government, private sector, and international organizations to identify areas of need, develop cost-effective solutions, and ensure the sustainable and equitable distribution of benefits.

5.3. Limitations

This study has certain limitations, including the snowball technique used to collect data, which can introduce sampling bias and margin of error due to the referral process being based on known individuals with similar characteristics. These limitations may restrict the number of participants studied, resulting in less compelling results than if a larger group were studied. Additionally, even after referrals are made, participants may refuse to participate in the study. The use of fuzzy AHP as an analysis tool also has limitations, such as the high weighting of variables and the possibility of mathematically illogical outcomes. Furthermore, factors such as threats, ambiguities, and the costs and benefits of decisions may be overlooked. However, fuzzy AHP is still widely used globally for strategic resource allocation under various risks. Other methods, such as VIKOR and TOPSIS, can also be used for data analysis, but each has its own advantages and disadvantages.

In addition, this research is limited to a single sector and country, and caution should be exercised when generalizing the findings to other contexts due to the dynamic nature of the risks studied. Further in-depth research using more extensive surveys and other statistical routines is needed to verify the findings. Future research should also focus on investigating each risk sub-type individually and proposing management strategies based on the specific type of risk sub-factor. Finally, each risk sub-type should be investigated according to each stakeholder type.

5.4. Implications

This study offers valuable insights into fisheries management, highlighting the need for a comprehensive risk management approach that prioritizes ongoing risks and identifies strategies for reducing risk. The findings suggest that the fisheries in Sindh are particularly vulnerable to management and technical risks, with various main categories and subcategories of risks identified. Effective management efforts should focus on identifying and addressing these risks while also considering the potential of different methods and techniques for improving practices. Policy proposals, such as the NFP, have the potential to play a crucial role in revitalizing the fisheries sector, and there have been efforts at the provincial level to enhance and modernize the sector, protect resources, and promote sustainability. Future policy reforms should aim to strengthen infrastructure, improve knowledge and safety, and maintain a competitive and viable fishery firm while ensuring legislative coherence and efficient resource use. A rational management system and a sustainable growth framework are key to achieving these goals, and strategic management decisions are essential for promoting sustainable economic and ecological fishing practices. By integrating provincial and national development policies, it is possible to support the growth and sustainability of the fisheries sector while improving the livelihoods of fishermen and protecting valuable resources.

5.5. Future Research Directions

This study used an ample amount of data to obtain reliable results through statistical analysis. However, future research can enhance the findings of this study by using larger data sets. Furthermore, to gain a more comprehensive understanding of the identified risks, future studies should focus on a single risk sub-type. Lastly, data can also be analyzed and compared using other suitable analysis methods, which can provide further insights into the identified risks and their management strategies.

6. Conclusions

Managing fisheries’ risks is a challenging task that requires a systematic approach to identifying and managing risks in increasingly complex environments. The study findings revealed that the Pakistani fisheries sector faces several risk factors, including economic, environmental, technical, management, and occupational risks. Despite the presence of fisheries regulations, these risks persist, which raises questions about the effectiveness of fisheries management in the country. To better manage these risks, prioritizing, and ranking them are crucial. The study employed fuzzy AHP and IPA methods to rank and prioritize risks. The results showed that management and technical risks were the top two risks in Sindh, while technical, management, and occupational risks were the top three risks in the overall ranking, based on their importance. However, the study found that there were differences between the importance and performance of these risks, indicating the need for more attention and resources to improve the performance of some risks. Risk perception was identified as the biggest challenge to effective risk management in Pakistan. Thus, raising awareness about fisheries’ risks can help bridge the gap between risk management and risk performance. The study suggested several measures to improve fisheries management performance, such as delivering training, implementing performance evaluation systems, and rewarding employees. Overall, the study provides valuable insights into the state of fisheries’ risk management in Pakistan. The results can guide policymakers and stakeholders in prioritizing and allocating resources to better manage fisheries’ risks and improve the performance of fisheries management in the country.