Analysis of Fishery Policies in Xinjiang Using the Policy Modeling Consistency Index Model

Abstract

Objective: To reach the goal of basically realizing fishery modernization by 2035, a goal which aims to achieve sustainable growth, Xinjiang Uygur Autonomous Region (hereinafter referred to as Xinjiang), a representative region of inland fisheries, was selected as the research object. Method: Taking the Policy Modeling Consistency (PMC) index model as a theoretical basis and using the Weiciyun word-frequency statistics tool, ten existing fishery policies were analyzed using a combination of quantitative and qualitative methods. Result: In this study, we observed that (1) the existing fishery policies in Xinjiang have a certain degree of reasonableness; (2) area of research, implementation suggestions, policy evaluation, and policy disclosure provide an effective theoretical basis for the policy implementation process; and (3) poor performance in terms of policy timeliness, policy subject, policy nature, policy content, incentive mechanism, and constraints mechanism needs to be improved. Conclusion: Although the fishery policy shows its effectiveness, it still needs to be improved. Policy recommendations are as follows: (1) accelerate inter-regional coordination to ensure its long-term sustainable development; (2) deepen the scope of fishery policies and enhance the industry’s competitiveness; (3) promote the modernization of the fishery governance system; and (4) strengthen the education and publicity activities of fishery practitioners.

1. Introduction

Over the past decades, China has placed significant emphasis on the development of the fishing industry as a critical pillar for supporting agriculture, increasing partitioners’ incomes, and ensuring national food security. As indicated in the 14th Five-Year National Fishery Development Plan (2021), fishery policies have been continuously adjusted and refined to promote industry restructuring, advance fishery technology, enhance product quality, and safeguard the sustainable use of fishery resources [1]. Zheng (2024) has also revealed that with the improvement in living standards, the growing demand for high-quality and diverse food products has further raised expectations for fisheries’ development, highlighting the importance of evaluating current fishery policies [2].

Scholars have adopted various methods to evaluate the status of fishery policies and propose policy recommendations. In China, Gan (2023) employed the Policy Modeling Consistency (PMC) index model to empirically analyze the marine fisheries management policies of Zhejiang Province [3]. Recommendations included strengthening the role of key stakeholders, optimizing the scientific layout of policies, and transforming the development model of marine fisheries management. Similarly, Zheng et al. (2021) used word-frequency analysis, co-word analysis, and multi-dimensional scaling analysis to examine the development of China’s fishery financial policies since 1982 [4]. Their recommendations emphasized enhancing governmental backing for fishery financing, diversifying collateral options, developing innovative equity financing models for fishery businesses, and crafting scientifically designed mariculture insurance products. Additionally, Sun et al. (2023), through policy bibliometrics, social network analysis, and text content analysis, quantitatively assessed and optimized the policy pathways for Jiangsu’s green fishery development. They proposed constructing an intergovernmental collaborative governance system, scientifically employing diverse policy tools, and enhancing central policy responsiveness [5].

Internationally, Marchal et al. (2009) analyzed and compared the objectives, strategies, measures, and processes of fishery management systems in New Zealand and the European Union [6]. They proposed formulating centralized rules aligned with the social objectives of the Common Fisheries Policy and establishing value assessments based on scientific and economic principles. Mora et al. (2009) employed an expert research methodology to evaluate the effectiveness of the global fisheries management system [7]. They argued that, regardless of other fishery attributes, translating scientific advice into policy through a participatory and transparent process is essential for achieving sustainability in fisheries. Archibald (2021) categorized the effectiveness of implementing Canadian fishery policies into four stages—completed, ongoing, delayed, and suspended [8]. By comparing these categories with corresponding expected outcomes, they identified significant deficiencies in Canadian fisheries’ management, particularly in the implementation of legislation and policy tools, which require further improvement and refinement.

Focusing on the research related to the development of fisheries in Xinjiang Uygur Autonomous Region (hereinafter referred to as Xinjiang), several significant highlights emerge that warrant scholarly attention: Jing, Zhang and An (2015) applied Exploratory Spatial Data Analysis (ESDA) methods, along with indicators such as the standard deviation index (S) and coefficient of variation (V) [9]. They concluded that while the annual growth rate of per capita output value in Xinjiang’s agriculture, forestry, animal husbandry, and fishery sectors has been steadily increasing, the spatial distribution of these sectors remains imbalanced. Sun (2016) employed SWOT-AHP analysis to study fishery development in Changji Prefecture, a representative region for leisure fisheries, proposing a strength-based, opportunity-driven strategy to expand the source market and further develop leisure fisheries [10]. Liu (2018) introduced the concept of designing and developing a fishery resource data platform using open-source GIS technology to support the sustainable utilization of aquatic ecosystems and fishery resources in Xinjiang [11].

An analysis of existing fishery policies reveals that most academic studies primarily focus on the current state of fishery development and offer unilateral policy recommendations, such as technological upgrades or ecological conservation measures. However, comprehensive evaluations of policy implementation effectiveness using methods like the PMC index model are noticeably lacking. This gap limits the understanding of the actual challenges facing the fishery industry, making it difficult to establish a robust scientific basis for future policy development and implementation. Furthermore, the overall body of literature on fishery policy in Xinjiang is relatively small and lacks systematic and in-depth research.

This paper seeks to address these shortcomings by examining the current status and challenges of fishery policies in Xinjiang, assessing the effectiveness of existing policies, and providing theoretical insights and actionable recommendations for the sustainable development of the region’s fishery industry. The findings aim to offer valuable guidance for advancing the broader fishery development strategy.

2. Status of Fisheries Development in Xinjiang

Walker and Yang (1999) revealed that in Xinjiang, the fishery industry started relatively late, lagging in development and facing several challenges [12]. On the one hand, as shown in Figure 1, Xinjiang is in the center of Eurasia and on the northwestern border of China. Far from the sea, it is truly landlocked. Thus, geographic and climatic limitations have resulted in a scarcity of fishery resources, leading to a relatively small scale and low level of fishery production. On the other hand, Xinjiang’s fishery policy system is underdeveloped and insufficient to effectively support the sustainable growth and advancement of the industry. Despite these challenges, Li and Su (2008) concluded that Xinjiang’s unique highland cold-water resources present significant natural advantages for the development of specialty fisheries, offering considerable growth potential [13]. Therefore, this paper aims to analyze and explore the fishery policies in Xinjiang.

The bar chart in Figure 2 illustrates the annual changes in the total output value of fisheries in Xinjiang over the past six years, showing a trend of year-on-year increase from CNY 28.09 billion in 2018 to CNY 33.56 billion in 2023. Notably, the total output value peaked at CNY 35.95 billion in 2021 before experiencing a slight decline.

The line graph in Figure 2 depicts the changes in the fisheries total output value index (with the previous year set as 100) for the same period. The index declined steadily from 109.5 in 2018 to 90.8 in 2020, indicating a slowdown or even negative growth during this time. From 2020 onward, the index began to recover, reaching 100.7 in 2022, signaling a brief resurgence in output value. In 2023, the index increased to 108.8, suggesting a significant growth in output value and further recovery in the fishery industry.

Overall, the total output value of fisheries in Xinjiang exhibited an upward trend, with fluctuations, from 2018 to 2023. Following the impact of the COVID-19 pandemic, the volatility in the fishery industry’s output value increased, highlighting greater risks within the sector. This suggests that Xinjiang’s fishery industry is in a post-challenge recovery phase and requires effective policy guidance to support its stabilization and growth.

3. Methodology

To gain a deeper understanding of the current state of fishery policies in Xinjiang, as shown in Figure 3, this study adopts a combination of literature review, qualitative analysis, and quantitative analysis to comprehensively evaluate policy implementation. Data samples were collected by reviewing and organizing the specific content of fishery-related policies in Xinjiang in recent years. The analysis encompasses ten highly relevant and representative fishery policy documents issued in Xinjiang between 2019 and 2023.

3.1. Literature Review Method

Using keywords such as “Xinjiang fishery”, “Xinjiang aquaculture”, and “Xinjiang leisure fishery”, we searched databases, including the China National Knowledge Infrastructure (CNKI); the National Bureau of Statistics website; and the official websites of the autonomous region, prefectures, and counties in Xinjiang. This process allowed us to systematically collect and analyze relevant fishery policies in Xinjiang, providing a solid foundation for the research in this paper.

3.2. Qualitative Analysis Method

Weiciyun online tool was employed to perform word-frequency analysis on Xinjiang fishery policy texts. By ranking high-frequency words, key information was extracted. This qualitative analysis visually illustrated the overall framework and features of Xinjiang’s fishery policies.

3.3. Quantitative Analysis Method

To evaluate the impact and effectiveness of Xinjiang’s fishery policies, the PMC (Policy Modeling Consistency) index model was applied. Proposed by Estrada in 2010, the PMC model provides a comprehensive approach to policy text evaluation by considering the interconnectedness of variables and analyzing policy content from multiple perspectives [14]. The main steps include (i) aggregation of policy texts; (ii) classification of variables and parameters in a multi-input–output table; (iii) measuring of PMC index; and and (iv) drawing of PMC surface.

This analysis aimed to quantify the effectiveness of fishery policies in Xinjiang, presenting the results in charts to highlight their strengths and weaknesses. By doing so, the study provides a scientific basis for further improvements and adjustments to Xinjiang’s fishery policies.

Equation (1): Policy Modeling Consistency (PMC) index model

$$X\sim \mathrm{N}[0,1]$$

(1)

$$X=\{XR:[0\sim 1\left]\right\}$$

(2)

$$Xt=({\sum }_{j=1}^n{\displaystyle \frac{X_{tj}}{T\left(X_{tj}\right)}})\mathrm{t}=\mathrm{1,2},3\dots$$

(3)

$$\begin{array}{ll}\mathrm{P}\mathrm{M}\mathrm{C}=& [X_1\left({\sum }_{\mathrm{a}=1}^4{\displaystyle \frac{X_{1\mathrm{a}}}{4}}\right)+X_2\left({\sum }_{\mathrm{b}=1}^2{\displaystyle \frac{X_{1\mathrm{b}}}{2}}\right)+X_3\left({\sum }_{c=1}^4{\displaystyle \frac{X_{1\mathrm{c}}}{4}}\right)+\\ & X_4\left({\sum }_{d=1}^4{\displaystyle \frac{X_{1\mathrm{d}}}{4}}\right)+X_5\left({\sum }_{e=1}^4{\displaystyle \frac{X_{1\mathrm{e}}}{4}}\right)+X_6\left({\sum }_{f=1}^3{\displaystyle \frac{X_{1\mathrm{f}}}{3}}\right)+\\ & X_7\left({\sum }_{\mathrm{g}=1}^5{\displaystyle \frac{X_{1\mathrm{g}}}{5}}\right)+X_8\left({\sum }_{\mathrm{h}=1}^4{\displaystyle \frac{X_{1\mathrm{h}}}{4}}\right)+X_9\left({\sum }_{\mathrm{k}=1}^5{\displaystyle \frac{X_{1\mathrm{k}}}{5}}\right)+X_{10}]\end{array}$$

(4)

$$\mathrm{PMC} \mathrm{surface} \mathrm{diagram}=\left\{\begin{array}{ccc}{\mathrm{X}}_1& {\mathrm{X}}_2& {\mathrm{X}}_3\\ {\mathrm{X}}_4& {\mathrm{X}}_5& {\mathrm{X}}_6\\ {\mathrm{X}}_7& {\mathrm{X}}_8& {\mathrm{X}}_9\end{array}\right\}$$

(5)

In Equation (1), X represents a binary random variable, which can take the value of either 0 or 1. It indicates whether the checked object meets the relevant requirements: 1 if it does, and 0 if it does not. Therefore, the value set for X is [0~1], as shown in Equation (2). Equations (3) and (4) detail the specific calculation process of the PMC index. In Equation (3), t and j indicate Level 1 Variable and Level 2 Variable, correspondingly. T(X_tj) stands for the total number of the Level 2 Variables under a specific Level 1 Variable, t. T(X_tj) in Equation (4) is replaced by the number of Level 2 Variables in the Xinjiang fishery case. Equation (5) shows the distribution of the PMC index in space as a matrix.

4. Results

4.1. Qualitative Analysis Results

Based on the retrieval and analysis of ten Xinjiang fishery policy texts using the Weiciyun word frequency statistics tool, the results are presented in

Table 1. Statistics of high-frequency words.

Word	Frequency	Word	Frequency	Word	Frequency
aquaculture	615	national	77	key points	53
fishery	532	scope	76	scientific	53
development	270	products	75	production	53
aquatic products	254	should	75	fishery administration	52
water area	151	conduct	75	system	51
construction	146	reservoir	72	protected areas	50
implementation	145	cold water	70	China	50
management	145	fishing	67	enterprises	49
protection	137	operate	67	autonomous region	48
agriculture	128	relevant	67	environment	46
enhancement	126	fishery resources	65	tourism	46
units	122	development	62	law enforcement	46
water surface	115	model	62	supervision	44
ponds	113	region	61	improvement	43
Kashgar region	110	ecology	61	fish species	42
strengthen	104	river	59	seedlings	41
execute	101	project	59	breeding	39
planning	99	organization	58	health	38
rural areas	98	project implementation	58	breeding areas	38
facilities	92	resources	57	establishment	38
technology	91	safety	57	autonomous prefecture	38
promotion	89	aquatic	56	fishing vessels	37
renovation	86	regulations	55	standardization	37
industry	78	competent authorities	55	equipment	36
tidal flats	78	lake	55	recycled water	36

. High-frequency terms such as “aquatic products” (frequency 254), “facilities” (frequency 92), and “technology” (frequency 91) suggest that the fishery industry in Xinjiang primarily focuses on aquaculture, with certain emphasis on improving equipment and technology. Additionally, relatively high-frequency terms, for instance, “ponds” (frequency 113), “tridal flats” (frequency 78), and “reservoir” (frequency 72), reflect the comprehensive consideration of various types of water bodies in the policy framework, highlighting an emphasis on fostering coordinated development among these interconnected aquatic ecosystems to achieve collective progress. Moreover, the relatively high-frequency words “ecology” (frequency 61), “environment” (frequency 46), and “recycled water” (frequency 36) indicate that Xinjiang’s fishery industry is mainly developing aquaculture in synergy with sustainable resource management and environmental protection efforts.

4.2. Quantitative Analysis Results

4.2.1. Aggregation of Policy Texts

We named the ten comprehensive, highly relevant and representative [1] fishery-related policy texts as P1–P10, and we then summarized the policy name and the issued date as follows in

Table 2. Summary of the ten Xinjiang fishery policies.

No.	Policy Name	Issued Date
P1	2020 Action Plan for Fishery Poverty Alleviation and Aid to Xinjiang and Tibet	6 March 2020
P2	Trial Administrative Measures for Tourism Development and Aquaculture on River and Lake Water Surfaces in the 10th Division of Beitun City	22 August 2022
P3	Regulations on Promoting the Cold-Water Fish Industry in Bortala Mongol Autonomous Prefecture	28 July 2023
P4	Tidal Flat Aquaculture Planning for Kashgar Region	27 August 2021
P5	Regulations on the Protection of Fishery Resources in Ili Kazakh Autonomous Prefecture	18 March 2019
P6	2023 Implementation Plan for Fishery Development Subsidy Projects Issued in Advance by the Central Finance	9 January 2023
P7	2023 Implementation Plan for Fishery Development Subsidy Projects Under Central Refined Oil Price Adjustment	9 January 2023
P8	Measures for Implementing the Fisheries Law of the People’s Republic of China in Xinjiang Uygur Autonomous Region	9 May 2019
P9	14th Five-Year Plan for National Fishery Development	29 December 2021
P10	2020 Implementation Plan for the Five Major Actions for Green and Healthy Aquaculture in the Xinjiang Production and Construction Corps	7 May 2020

.

4.2.2. Classification of Variables and Parameters in Multi-Input–Output Table

Based on the theoretical foundation of the PMC index model developed by Estrada in 2011 and referencing the Level 1 Variables from the PMC index model as identified in the literature of five other scholars, including Gan (2023), Estrada, (2011), Liu (2022), Sun et al. (2023), Qin et al. (2024), and Gao and Shi (2024), word-frequency statistics were conducted [3,14,15,16,17,18,19].

The results are compiled in

Table 3. Frequency statistics of content for Level 1 Variables in the literature.

Term	Frequency
Policy nature	5
Policy timeliness	5
Policy content	5
Policy subjects	4
Policy evaluation	4
Incentive mechanism	4
Area of research	3
Implementation suggestions	3
Policy disclosure	3
Constraint mechanism	2

, which serves as the basis for setting the Level 1 Variables of the PMC index model for Xinjiang’s fishery policies. Additionally, by combining high-frequency terms from qualitative analysis, objective terms are selected based on the specific characteristics of each policy. This approach ensures that the Level 2 Variables in the PMC index model align closely with the current state of fishery policy in Xinjiang. Finally, the PMC index evaluation system of Xinjiang fishery policies is constructed: there are 10 Level 1 Variables and 35 Level 2 Variables. The Level 1 Variables are X1 policy nature, X2 policy timeliness, X3 policy subject, X4 area of research, X5 implementation suggestions, X6 policy content, X7 policy evaluation, X8 incentive mechanism, X9 constraint mechanism, and X10 policy disclosure. Except for X10 policy disclosure, which has no Level 2 Variables, the Level 1 Variables X1–X9 contain four, two, four, four, four, three, five, four, and five Level 2 Variables, respectively, which are represented by X11–X14, X21–X22, X31–X34, X41–X44, X51–X54, X61–X63, X71–X75, X81–X84, and X91–X95. The variables are summarized in

Table 4. Multi-input–output table.

Level 1 Variable	Level 2 Variable
X1	X11	X12	X13	X14
Policy nature	Predicting	Monitoring	Descriptive	Guidance
X2	X21	X22
Policy timeliness	Non-one-time	One-time
X3	X31	X32	X33	X34
Policy subjects	National level	Autonomous region level	Prefectures level	Counties level
X4	X41	X42	X43	X44
Area of research	Economics	Social	Technological	Environmental
X5	X51	X52	X53	X54
Implementation suggestions	Technological innovation	Standardize production	Ecological protection	Strengthen supervision
X6	X61	X62	X63
Policy content	Industry integration	Cultivate distinctiveness	Implement accountability
X7	X71	X72	X73	X74	X75
Policy evaluation	Clear objectives	Detailed planning	Scientific methods	Policy-based approach	Green and low-carbon
X8	X81	X82	X83	X84
Incentive mechanism	Financial subsidies	Technical support	Legal protection	Others
X9	X91	X92	X93	X94	X95
Constraint mechanism	Supervision	Institutionalization	Accountability	Penalize	Publicity
X10
Policy disclosure

in the form of a multi-input–output table. Mentioned by Liu in 2022, without limiting the number of Level 2 Variables and weighting situations, the table is used to make an evaluation of a single element’s measurements from multiple perspectives [16]. In the subsequent sections of this article, the abbreviated variables referenced in the text correspond to the variables delineated in Table 4. Specifically, Level 1 Variable X1 denotes “policy nature”, Level 2 Variable X11 signifies “predicting”, and so forth.

4.2.3. Measuring of PMC Index

PMC Model Calculation

Referring to the PMC index model Formulas (1) and (2), the rating scale outlined in

Table 5. PMC index model variables and scoring criteria.

No.	Level 1 Variable	No.	Level 2 Variable	Criteria
				Satisfy	Lack
X1	Policy nature	X11	Predicting	1	0
		X12	Monitoring	1	0
		X13	Descriptive	1	0
		X14	Guidance	1	0
X2	Policy timeliness	X21	Non-one-time	1	0
		X22	One-time	1	0
X3	Policy subjects	X31	National level	1	0
		X32	Autonomous region level	1	0
		X33	Prefectures level	1	0
		X34	Counties level	1	0
X4	Area of research	X41	Economics	1	0
		X42	Social	1	0
		X43	Technological	1	0
		X44	Environmental	1	0
X5	Implementation suggestions	X51	Technological innovation	1	0
		X52	Standardize production	1	0
		X53	Ecological protection	1	0
		X54	Strengthen supervision	1	0
X6	Policy content	X61	Industry integration	1	0
		X62	Cultivate distinctiveness	1	0
		X63	Implement accountability	1	0
X7	Policy evaluation	X71	Clear objectives	1	0
		X72	Detailed planning	1	0
		X73	Scientific methods	1	0
		X74	Policy-based approach	1	0
		X75	Green and low-carbon	1	0
X8	Incentive mechanism	X81	Financial subsidies	1	0
		X82	Technical support	1	0
		X83	Legal protection	1	0
		X84	Others	1	0
X9	Constraint mechanism	X91	Supervision	1	0
		X92	Institutionalization	1	0
		X93	Accountability	1	0
		X94	Penalize	1	0
		X95	Publicity	1	0
X10	Policy Disclosure	X10	Policy disclosure	1	0

is used to determine whether a policy includes the content of a given variable. If it does, the score is 1; if not, the score is 0. Notably, for Level 1 Variables X2 policy timeliness and X3 policy subjects, only one corresponding Level 2 Variable can be fulfilled. In contrast, other Level 1 Variables can be associated with multiple Level 2 Variables simultaneously.

Subsequently, using the PMC index model in Formula (3), the ten policies P1–P10 are input into the constructed multi-input–output table. The results are summarized in

Table 6. Multi-input–output tables for ten policies.

Level 1 Variable	Level 2 Variable	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	Score
X1	X11				1							1
	X12		1	1	1	1	1	1	1	1		8
	X13				1					1		2
	X14	1	1	1	1	1	1	1	1	1	1	10
		1	2	2	4	2	2	2	2	3	1	21
X2	X21		1	1	1	1			1	1		6
	X22	1					1	1			1	4
		1	1	1	1	1	1	1	1	1	1	10
X3	X31	1								1		2
	X32				1		1	1	1		1	5
	X33			1		1						2
	X34		1									1
		1	1	1	1	1	1	1	1	1	1	10
X4	X41	1	1	1	1				1	1	1	7
	X42		1	1	1	1		1	1	1	1	8
	X43	1	1	1	1		1	1		1	1	8
	X44	1	1	1	1	1	1	1	1	1	1	10
		3	4	4	4	2	2	3	3	4	4	33
X5	X51	1	1	1	1		1	1		1	1	8
	X52	1	1	1	1	1		1	1	1	1	9
	X53	1	1	1	1	1	1	1	1	1	1	10
	X54		1	1	1	1		1	1	1		7
		3	4	4	4	3	2	4	3	4	3	34
X6	X61	1	1	1	1	1				1		6
	X62	1	1	1	1	1		1	1	1	1	9
	X63						1	1			1	3
		2	2	2	2	2	1	2	1	2	2	18
X7	X71	1	1	1	1		1	1		1	1	8
	X72	1	1	1	1	1	1	1	1	1	1	10
	X73	1	1	1	1	1	1	1	1	1	1	10
	X74		1	1	1	1	1	1	1	1	1	9
	X75	1	1	1	1	1	1	1	1	1	1	10
		4	5	5	5	4	5	5	4	5	5	47
X8	X81	1	1	1	1		1	1	1	1	1	9
	X82	1	1	1	1	1	1	1	1	1	1	10
	X83			1		1				1		3
	X84				1	1			1			3
		2	2	3	3	3	2	2	3	3	2	25
X9	X91	1	1	1	1	1	1	1	1	1		9
	X92		1	1	1	1	1	1		1		7
	X93			1		1	1	1	1			5
	X94			1		1			1			3
	X95	1	1	1	1	1		1	1	1	1	9
		2	3	5	3	5	3	4	4	3	1	33
X10	X10	1	1	1	1	1	1	1	1	1	1	10
		1	1	1	1	1	1	1	1	1	1	10
Score		20	25	28	28	24	20	25	23	27	21	241

, where the scores for each Level 1 Variable, X1–X10, are calculated. The bolded numbers in Table 6 represent the overall scores of different policies (P1–P10) across various different Level 1 Variables. Also, the overall score for each Level 1 Variable is highlighted in bold.

Furthermore, based on the PMC index model in Formula (4), the PMC index scores for the ten policy texts, P1–P10, are computed. Policies are then categorized into corresponding levels according to their scores, as detailed in

Table 7. PMC index rating scale.

PMC Index	[10, 9]	(9, 7]	(7, 5]	(5, 0]
Grade	Perfect	Excellent	Good	Not acceptable

.

PMC scores in the range [10, 9] are rated as “perfect”; scores in the range (9, 7] are rated as “excellent”; scores in the range (7, 5] are rated as “good”; and scores in the range (5, 0] are rated as “not acceptable”. The results are summarized in

Table 8. Summary of PMC index calculations.

Rank	Mean		P1	P2	P3	P4	P5	P6	P7	P8	P9	P10
8	0.53	X1	0.25	0.5	0.5	1	0.5	0.5	0.5	0.5	0.75	0.25
9	0.5	X2	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5
10	0.25	X3	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.25	0.25
4	0.83	X4	0.75	1	1	1	0.5	0.5	0.75	0.75	1	1
3	0.85	X5	0.75	1	1	1	0.75	0.5	1	0.75	1	0.75
7	0.6	X6	0.67	0.67	0.67	0.67	0.67	0.33	0.67	0.33	0.67	0.67
2	0.94	X7	0.8	1	1	1	0.8	1	1	0.8	1	1
6	0.63	X8	0.5	0.5	0.75	0.75	0.75	0.5	0.5	0.75	0.75	0.5
5	0.66	X9	0.4	0.6	1	0.6	1	0.6	0.8	0.8	0.6	0.2
1	1	X10	1	1	1	1	1	1	1	1	1	1
	6.78	PMC	5.87	7.02	7.67	7.77	6.72	5.68	6.97	6.43	7.52	6.12
		Grade	Good	Excellent	Excellent	Excellent	Good	Good	Good	Good	Excellent	Good
		Rank	9	4	2	1	6	10	5	7	3	8

. Finally, the ten policies (P1–P10) are ranked in descending order based on their PMC scores. The mean values of the Level 1 Variables (X1–X10) are also calculated and ranked in descending order, which will serve as the basis for subsequent analysis steps.

Analysis of the Overall Policy Perspective

In terms of PMC index scores, the ranking from highest to lowest is as follows: P4, P3, P9, P2, P7, P5, P8, P10, P1, and P6. Among these, four policies, namely P4, P3, P9, and P2, are rated at the “excellent” level, while the remaining six policies are at the “good” level. Notably, there is no policy rated as “perfect” or “not acceptable”.

The average PMC index score for the ten policies is 6.775, placing them within the “good” evaluation level, indicating overall reasonableness. Based on the average values of Level 1 Variables (X1–X10), the mean scores of X10 policy disclosure, X7 policy evaluation, X5 implementation suggestions, and X4 area of research are higher, reflecting better performance. The mean scores of X9 constraint mechanism, X8 incentive mechanism, and X6 policy content are close to the average, indicating moderate performance. However, the mean scores of X1 policy nature, X2 policy timeliness, and X3 policy subject are lower, suggesting areas for improvement.

Level 1 Variable Analysis

The mean value of X1 policy nature is 0.53, indicating that Xinjiang’s fishery policies are relatively comprehensive. The policies perform well in X11 predicting and X13 descriptive but require improvement in X12 monitoring and X14 guidance. Notably, the score for X14 guidance is 10, showing that all ten policies play a guiding role.

The mean value of X2 policy timeliness is 0.50, reflecting a balanced distribution between X21 non-one-time and X22 one-time. However, the target timeframe of these policies is relatively simplistic.

The mean value of X3 policy subjects is 0.25. Among these, X32 autonomous region-level policies have the highest proportion, followed by X31 national-level and X33 prefectures-level policies, while X34 county-level policies have the lowest proportion.

The mean value of X4 area of research is 0.83, suggesting that Xinjiang’s fishery policies are highly comprehensive, addressing economic, social, technological, and environmental dimensions for balanced development. Particularly, X44 environmental scored 10, indicating that all ten policies emphasize the coordinated development of fishery farming and environmental protection.

The mean value of X5 implementation suggestions is 0.85, showing that Xinjiang’s fishery policies focus on a variety of dimensions. Notably, X53 ecological protection scored 10, meaning all policies strongly support the preservation of the ecological environment.

The mean value of X6 policy content is 0.60, highlighting a focus on key content development. X62 cultivate distinctiveness and X61 industry integration scored high, while X63 implement accountability scored low.

The mean value of X7 policy evaluation is 0.94, indicating that Xinjiang’s fishery policies are rigorous. Scores of 10 were achieved in X72 detailed planning, X73 scientific methods, and X75 green and low-carbon. However, improvements are needed in X71 clear objectives and X74 policy-based approach.

The mean value of X8 incentive mechanism is 0.63, showing that Xinjiang’s fishery policies provide effective incentives, with X82 technical support scoring 10, making it the primary incentive. However, X83 legal protection and X84 others scored lower, limiting the overall incentive effect.

The mean value of X9 constraint mechanism is 0.66, reflecting the broad and effective governance methods in Xinjiang’s fishery policies. Performance is strong in X91 supervision and X95 publicity, while X92–X94 institutionalization, accountability, and penalize require further improvement.

The mean value of X10 policy disclosure is 1.00, demonstrating full policy disclosure across all ten texts. This shows that Xinjiang’s fishery policies have successfully enhanced transparency, facilitating their implementation and supervision.

4.2.4. Drawing of PMC Surface

The purpose of constructing the PMC surface graph is to visualize the PMC scores of the ten policies (P1–P10), providing an intuitive representation of the strengths and weaknesses of each policy and enabling the proposal of targeted policy recommendations. Since the X10 policy disclosure variable does not have secondary variables and its score is fixed at 1, it is excluded to ensure the symmetry of the PMC matrix and the balance of the surface [14]. Using the remaining Level 1 Variables (X1–X9), the matrices for the ten policies are summarized in the Supplementary Materials document, based on the PMC index model in Formula (5). A three-dimensional graph was generated using Excel, thus creating Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13. PMC images can reflect the smoothness and degree of concavity of the policy. The overall defectiveness of the policy and the degree of defectiveness at each indicator level can be visualized by the difference between the surface and the perfect plane.

P1 scored 39 items, achieving a PMC score of 5.87, ranking ninth overall, and is in the “good” category. Variables X1 policy nature, X4 area of research, X5 implementation suggestions, X7 policy evaluation, X8 incentive mechanism, and X9 constraint mechanism all fall below the average.

P2 scored 49 items, with a PMC score of 7.02, placing fourth overall, and is in the “excellent” category. Variables X1 policy nature, X8 incentive mechanism, and X9 constraint mechanism are below average. In contrast, X4 area of research, X5 implementation suggestions, and X7 policy evaluation show strong performance, with all Level 2 Variables fully satisfied.

P3 scored 55 items, attaining a PMC score of 7.67, ranking second, and is in the “excellent” category. Except for X1 policy nature being below average, all other variables perform well. Particularly, X4 area of research, X5 implementation suggestion, X7 policy evaluation, and X9 constraint mechanism fully satisfy all Level 2 Variables.

P4 scored 55 items, with a PMC score of 7.77, securing first place overall, and is in the “excellent” category. Only X9 constraint mechanism is below average, while all other variables perform strongly. Notably, X1 policy nature is the only variable across all ten policies that fully satisfies all Level 2 Variables.

P5 scored 47 items, achieving a PMC score of 6.72, ranking sixth overall, and is in the “good” category. X1 policy nature, X4 area of research, X5 implementation suggestion, and X7 policy evaluation are below average, while X9 constraint mechanism performs well.

P6 scored 39 items, with a PMC score of 5.68, placing 10th overall, and is in the “good” category. X1 policy nature, X4 area of research, X5 implementation suggestions, X6 policy content, X8 incentive mechanism, and X9 constraint mechanism are below average, requiring significant improvement. However, X7 policy evaluation performs well, with all Level 2 Variables fully satisfied.

P7 scored 49 items, attaining a PMC score of 6.97, ranking fifth, and is in the “good” category. X1 policy nature, X4 area of research, and X8 incentive mechanism are below average, while X5 implementation suggestion and X7 policy evaluation perform well, fully satisfying all Level 2 Variables.

P8 scored 45 items, achieving a PMC score of 6.43, ranking seventh overall, and is in the “good” category. X1 policy nature, X4 area of research, X5 implementation suggestion, X6 policy content, and X7 policy evaluation all fall below average.

P9 scored 53 items, with a PMC score of 7.52, ranking third overall, and is in the “excellent” category. Only X9 constraint mechanism is below average, while other variables, such as X4 area of research, X5 implementation suggestion, and X7 policy evaluation, perform strongly, with all Level 2 Variables fully satisfied.

P10 scored 41 items, with a PMC score of 6.12, ranking eighth overall, and is in the “good” category. X1 policy nature, X5 implementation suggestion, X8 incentive mechanism, and X9 constraint mechanism are below average, while X4 area of research and X7 policy evaluation perform well, fully satisfying all Level 2 Variables.

5. Discussions

5.1. Current Policy Status

The current fishery policy in Xinjiang is excellent in X4 area of research, X5 implementation suggestions, X7 policy evaluation, and X10 policy disclosure, providing an effective theoretical basis for the implementation process of the policy. Excluding the inherent requirements of X2 policy timeliness, and X3 policy subject due to the fact that only one of the secondary variables can be fulfilled in the setup, the results show that the fishery policy in Xinjiang still needs to be upgraded and carried out in terms of the breadth and depth of the temporal and spatial dimensions. In addition, Xinjiang fishery policy performs poorly in terms of X1 policy nature, X6 policy content, X8 incentive mechanism, and X9 constraint mechanism needs to be improved and perfected to a greater extent.

5.2. Policy Suggestions

5.2.1. Accelerating Inter-Regional Coordination and Integration Within Xinjiang’s Fishery Industry to Ensure Its Long-Term Sustainable Development

To promote the long-term and sustainable development of the fishery industry, it is recommended to closely monitor industry trends and clearly distinguish between short-term and long-term goals. Greater attention should be paid to the long-term sustainability of policies, and the proportion of long-term (non-one-time) policies should be gradually increased to meet the evolving requirements of the global market and environmental changes. Gan’s (2023) research indicates that Zhejiang Province, a coastal region known for its significant fishing industry in China, also faces a lack of long-term policies [3]. This suggests that fishery policies across China tend to focus primarily on short-term goals, with insufficient emphasis on long-term planning. Strengthening long-term policies will be a key direction for future development. Additionally, national and regional support for policy formulation should be increased, helping autonomous prefectures and counties to more actively develop and implement fishery policies tailored to local conditions. Providing more policy guidance and resource support will encourage local governments to become more proactive. Furthermore, local governments should be encouraged to learn from successful domestic and international cases to promote synergistic development across Xinjiang’s fisheries sector. However, it is crucial to adopt region-specific policies rather than blindly following others. While Parker (2024) highlights the potential of Community-Based Fisheries Management (CBFM) as a sustainable management approach, he also underscores the importance of adapting it to the unique ecological, cultural, and socio-economic context of each community [20]. This finding is particularly relevant to Xinjiang, where the dominance of top-down governance and the region’s unique geographic and cultural characteristics necessitate a hybrid model that combines centralized guidance with localized adaptation.

5.2.2. Deepening the Scope of Fishery Policies and Enhancing the Industry’s Competitiveness

It is recommended that the policy be enhanced by incorporating elements related to market forecasting and situational analysis. This would assist both policymakers and practitioners in gaining a clearer understanding of market and environmental trends, enabling them to craft more effective and timely responses. Furthermore, the emphasis on specialty cultivation should be strengthened, particularly in cold-water fish farming, by continuously increasing investment in scientific research. This will improve the competitiveness of fishery products and foster the development of specialized fishery species. In terms of industrial integration, Chang (2020) indicated that under the “Internet+” era, the integration of the modern fishery industry with the Internet is an inevitable trend [21]. Promoting the synergy between fishery production and the e-commerce sector will help broaden product sales channels, extend the fishery product industry chain, and increase the added value of fishery products. Additionally, Meneghello and Mingotto (2016) suggested that there are grand opportunities offered by eco-tourism, including supplementary income for fishermen, economic gains for destinations from tourist spending, enhanced quality of life, and increased awareness of ecosystem fragility and sustainable development [22]. Hence, integrating fishery production with eco-tourism to create a “production + tourism” model will encourage the development of leisure fisheries. Moreover, in terms of policy implementation, a comprehensive inspection and verification system should be established to ensure that policies are carried out effectively at the local level. Singh et al. (2019) proposed a method combining cloud computing and social media analytics to monitor and evaluate the implementation of government policies effectively. Their study highlighted that integrating advanced technological tools can significantly enhance policy oversight and ensure accountability. Drawing from their approach, establishing a comprehensive monitoring mechanism for fishery policies can help ensure that each policy is not only well-designed but also effectively executed at the local level [23].

5.2.3. Promoting the Modernization of the Fishery Governance System

While maintaining fiscal and technical incentives, it is crucial to introduce stronger legal and creative guarantees for practitioners. Practitioners are the heart of the industry. Grafton et al. (2006) emphasize that fishery management must prioritize practitioners’ motivation with incentive-based strategies that clearly define community and individual harvest rights or territorial rights; incorporate ecosystem service valuation; and are supported by public research, monitoring, and effective oversight, as doing so is key to promoting sustainable fisheries [24]. In Xinjiang, special attention should be given to the unique challenges and needs of local fishery practitioners, ensuring they receive adequate support and recognition. Strengthening their involvement and addressing their concerns can address the needs of practitioners and increase their motivation, thereby enhancing overall productivity in the fishery industry. Secondly, accountability must be reinforced by implementing stricter penalties for legal violations. Establishing a robust mechanism to penalize misconduct will ensure the seriousness and authority of policy implementation. According to Rini Fathonah and Mashuril Anwar’s study from 2023, criminal penalties are a powerful deterrent to illicit fishing. This approach encourages adherence to legal fishing standards and stops offenders from profiting from illicit activities by enforcing severe financial penalties. Furthermore, the study highlights that a transparent legal system and larger fines for repeat offenders strengthen the deterrent effect, increasing the efficacy of policy enforcement [25]. Strengthening the supervision and management of fishery practitioners is essential to promptly identify and address violations, reducing the adverse impact of irregularities on the industry’s development.

5.2.4. Strengthening the Education and Publicity Activities of Fishery Practitioners

Zornu et al. (2023) states that “education, research, and biosecurity have global recognition as strong pillars of sustainable aquaculture development”. Enhancing employee education and training is especially crucial for frontier regions like Xinjiang, where the educational level is not that high [26]. Or otherwise, the application of science and technology being used during the development of the fishery industry might be limited by the practitioner’s competence. Improving fishermen’s skills through the establishment of fishery technology promotion stations and vocational training programs will help them master advanced techniques and management practices, boosting productivity and sustainability in the sector. Also, Laver et al. (2024) proposed that establishing localized fishery technology hubs and culturally relevant training programs can address specific regional needs while respecting local traditions. Integrating advanced aquaculture practices with indigenous knowledge systems and facilitating intergenerational knowledge transmission from knowledge holders to youth further provide valuable insights for the sustainable growth of Xinjiang’s fisheries industry [27]. Finally, fostering public awareness and participation in fishery governance is vital. Cooke et al. (2021) propose that a knowledge transfer and communication strategy should be developed to strengthen communication among users, including recreational fishers; and conservation, cultural, and social stakeholders, covering all relevant values [28]. Through various publicity and educational activities, the legal awareness of fishermen and related enterprises can be elevated. This, in turn, strengthens their sense of responsibility and social accountability. A collaborative governance model—led by the government and supported by all societal stakeholders—will ensure the effective implementation of policies and promote sustainable, inclusive growth in Xinjiang’s fisheries industry.

6. Conclusions, Research Limitations, and Expectations

6.1. Conclusions

Overall, the existing fishery policies in Xinjiang as a whole have a certain degree of rationality and scientificity, but in view of the fact that most of the ten policies are in the “good” grade, a small number of them are in “excellent” grade, and there is a lack of a “perfect” grade, the focus should be on upgrading the policies in the excellent grade, so that they can be optimized into the perfect policies; at the same time, the policies in the “good” grade should be upgraded comprehensively, so as to make them closer to the excellent and perfect grades.

6.2. Research Limitations and Expectations

This study has some limitations. First, without considering any other type of database, this study is based on online databases, such as the China National Knowledge Infrastructure (CNKI) and the website of the National Bureau of Statistics. In addition, the selection process for the ten policies for analysis inevitably involves a degree of subjectivity. This subjectivity may result in incomplete coverage of all relevant variables or scenarios, thereby limiting the generalizability of the findings to broader contexts.

More research subjects and a wider variety of datasets may be used in future studies. This expansion would enhance the reliability and applicability of the study’s results. Furthermore, this study evaluates the rationality and scientific basis of Xinjiang’s fishery policies but does not assess the effectiveness of policy implementation. Future research could focus on examining the extent to which these policies are executed and their actual outcomes. Such research would fill gaps in the field, providing scientific recommendations for the sustainable development of the fishery industry.