The research area encompasses the main channel of the Qiantang River between Duji Bridge and Fuxing Bridge (29˚45'-30˚13'N, 119˚39'-120˚11'E), a critical freshwater fishery zone under the jurisdiction of Hangzhou’s Xihu, Fuyang, and Tonglu districts. A total of nine representative monitoring sites have been identified, as shown in Figure 1 and Table 1 .

This area is an important component of the Qiantang River. It has important and special ecological functions in maintaining ecological balance and biodiversity, protecting rare species resources, conserving water sources, and storing floods and droughts . Prior to the 2019 fishing ban, anthropogenic activities such as water conservancy projects, sand mining, and excessive development had led to serious damage to the area, including continuous pollution of water quality, gradual depletion of water sources, significant ecological degradation, and fishery resource depletion . Therefore, in order to curb the continuous deterioration of water ecosystems, starting from 2014, ecological restoration projects, including water pollution control, stock enhancement, and seasonal fishing bans, have been successively conducted in the section.

Determining appropriate temporal scales for monitoring is critical to ensure the accurate assessment of aquatic ecosystem restoration. Restoration trajectories depend inherently on degradation severity, regional climatic conditions, and socioeconomic contexts, with recovery periods spanning 3 - 5 years for moderately impacted systems to decades (>20 - 50 years) for severely impaired ecosystems [8] . Nevertheless, interim evaluations remain essential for adaptive management, enabling corrective interventions during restoration rather than exclusively assessing endpoint outcomes. Consequently, this study employs a comparative temporal framework analyzing ecological conditions at two strategic intervals: baseline (2018; pre-ban implementation) and post-restoration (2023; 5 years post-ban), providing a critical mid-term assessment of the fishing ban’s efficacy in the Lower Qiantang River.

Theoretical frameworks suggest that monitoring and evaluation accuracy increase with indicator specificity [5] . Based on multi-year fishery resource survey data from the lower Qiantang River basin, we developed an initial evaluation index system and designed a questionnaire for expert consultation. Experts were recruited based on the following criteria: 1) professional engagement in water ecological restoration, 2) ≥5 years of field experience, 3) possession of a bachelor’s degree or higher with an associate professor-level (or equivalent) professional title, and 4) demonstrated commitment to completing iterative consultation rounds. Eight eligible experts participated. Indicator selection proceeded through iterative questionnaire rounds, retaining items only if the expert consensus met both of the following thresholds: a mean importance score >4.0 and a coefficient of variation <0.25.

After two rounds of expert inquiries, this study finally established a three-tiered monitoring and evaluation framework. This framework comprises eleven specific indicators derived from the ecological, economic, and social domains to quantify key elements within the Lower Qiantang River system. The complete indicator system and corresponding data acquisition methodologies are detailed in Table 2 . Within the ecological domain, Chlorophyll-a concentration serves as a fundamental proxy for algal biomass and a key water quality indicator in freshwater ecosystems [9] . However, discrete water quality monitoring points offer spatially and temporally limited snapshots. To comprehensively assess ecosystem health, integrating indicators reflecting structural changes in biological communities is essential. Economically, fishery output provides a direct measure of economic dynamics pre- and post-fishing ban implementation. Socially, public acceptance and policy compliance are critical determinants for the long-term efficacy of conservation measures, as enhanced societal awareness underpins ecosystem resilience and sustainable resource management.

“+” represents a positive indicator, “−” represents a negative indicator.

Water samples were collected quarterly at 9 monitoring sites in both 2018 and 2023, followed by spectrophotometric determination of chlorophyll-a content. Fish resource surveys were conducted during peak (May-November) and off-peak (December-April) operational seasons in 2018 and 2023, with annual sampling campaigns temporally aligned across years to control for phenological variation. Each 10-day campaign documented species composition, abundance, and key biological parameters (e.g., length-weight relationships, fecundity). Sampling spanned nine spatially distributed points with 1 km river segments upstream and downstream. We employed standardized gear at all sites: drift gillnets (50 - 100 m length × 1.5 - 2 m height; 2.0 - 12 cm mesh) and shrimp traps (4.5 - 8 m length; 0.8 - 2.0 cm mesh). To account for rare species, field data with socio-ecological validation—structured interviews with fishers and verification of commercial landings at riverside piers—were supplemented.

The evaluation of the fishing ban in the Lower Qiantang River comprised two principal stages: determination of indicator weights and comprehensive evaluation. First, a hierarchical multi-factor evaluation index system was established, structured across three tiers: Level 1 (A: Comprehensive Objective), Level 2 (B1-B3: Ecological, Economic, Social Dimensions), and Level 3 (C1-C11: Specific Indicators) as detailed in Table 2 . Subsequently, indicator weights were determined, a critical step as these weights directly influence multi-indicator comprehensive evaluation outcomes. To integrate expert judgment with objective monitoring data, this study employed the AHP. Expert opinions were systematically gathered using pairwise comparisons, with indicator importance quantified via a standardized 1 - 9 scale [22] . AHP calculations generated the final weights, and the consistency of expert judgments was rigorously validated using the Consistency Ratio (CR), with CR < 0.1 confirming acceptable consistency.

Expert judgment was elicited via structured scoring to assess the relative importance of each indicator within the hierarchy. Specifically, a pairwise comparison approach was employed: experts evaluated all combinations of criteria at each hierarchical level, determining their relative influence on the corresponding upper-level objective [23] . These qualitative judgments were quantitatively scaled using the established 1 - 9 scale method ( Table 3 ), forming the judgment matrices necessary for the AHP.

By using the defined 1 - 9 scale ( Table 3 ) above, the elements $a_1,a_2,\cdots ,a_n$ could be compared pairwise to obtain the judgment matrix A for subsequent AHP calculations [24] :

$\text{A}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]$

From this, the judgment matrix of each level indicator in the evaluation index system for the fishing ban’s efficacy in the Lower Qiantang River was derived as shown in Table 4 .

Let the complementary judgment matrix $A={\left(a_{ij}\right)}_{n\times n}$, making $r_i={\displaystyle {\sum }_{j=1}^n{a}_{ij}}$, $i,j=1,2,\cdots ,n$ and obtain the sets $\left\{r_i\right\}$ and $\left\{r_j\right\}$, $i,j=1,2,\cdots ,n$. Then, sum the matrix A by columns, calculate the geoindicator mean $\left\{{\bar{R}}_j\right\}$ of $\left\{r_j\right\}$ to perform a numerical transformation, making $R_i={\displaystyle {\sum }_{j=1}^n{\bar{R}}_{ij}}$, $i,j=1,2,\cdots ,n$, and obtain the sets $\left\{R_i\right\}$ and $\left\{R_j\right\}$, $i,j=1,2,\cdots ,n$, which means that the complementary judgment matrix $A={\left(a_{ij}\right)}_{n\times n}$ was transformed into a consistent matrix $R={\left(r_{ij}\right)}_{n\times n}$. Finally, calculate the weight vector $W={\left[w_1,w_2,\cdots ,w_n\right]}^{\text{T}}$ of the consistent matrix R, where $w_i=R_i/{\displaystyle {\sum }_{j=1}^n{R}_i}$ ( $i,j=1,2,\cdots ,n$).

From this, the weight sets of A, B (B1-B3), and C (C1-C11) were obtained, with W_A = [0.6232, 0.2395, 0.1373]^T, W_B1 = [0.1063, 0.1545, 0.2584, 0.0852, 0.3956]^T, W_B2 = [0.1638, 0.5390, 0.2973]^T, and W_B3 = [0.1374, 0.2389, 0.6237]^T, respectively, as shown in Table 5 . It could be observed that for the indicators at level 2, the ecological environment had the highest weight, followed by economic output and social consciousness. Among the three-level indicators, the Shannon-Wiener diversity index (C5), fish resource density (C3) (ecological indicators), and catch per unit of fishing effort (C7) (economic indicator) were identified as the top three contributors. Conversely, fishermen’s satisfaction (C9) and community satisfaction (C10) were ranked lower, reflecting their relatively diminished influence on the overall evaluation.

To validate matrix consistency, a two-step approach was employed. First, calculate CI of matrix $R={\left(r_{ij}\right)}_{n\times n}$, making $CI=\left({\lambda }_{\max }-n\right)/\left(n-1\right)$, where ${\lambda }_{\max }$ denotes the largest eigenvalue of matrix R, calculated as ${\lambda }_{\max }=\left({\displaystyle {\sum }_{i=1}^{n}A{W}_i/w_i}\right)/n$, and AW represents the matrix product of the original judgment matrix A and weight vector $W={\left[w_1,w_2,\cdots ,w_n\right]}^{\text{T}}$. Then, calculate CR of matrix R, making CR = CI/RI, where RI is the Random Index value from published standards for matrix order n [22] . A CR < 0.10 indicates satisfactory consistency, confirming logical coherence in expert judgments. As shown in Table 6 , all hierarchical comparison matrices demonstrated satisfactory consistency (CR < 0.10), validating their use in subsequent analyses.

In order to make the evaluation results more scientific and objective, this study set the indicator values as positive and negative. Positive indicators are directly proportional to the effect of the fishing ban, whereas negative indicators show an inverse relationship. To eliminate the influence of different data scales, a benchmark value was selected to normalize the raw data before calculating the comprehensive evaluation index. This transformation converts the data into standardized values without scale differences. Based on the attributes and characteristics of the indicators used in this study, the specific gravity method was chosen for data normalization as follows:

$I_{ijt}=\{\begin{array}{l}{C}_{ijt}/C_{ij0},\text{when}{C}_{ij}\text{is a positive indicator};\\ C_{ij0}/C_{ijt},\text{when}{C}_{ij}\text{is a negative indicator}\end{array}$

where I_ijt is the dimensionless rating coefficient of indicator C_ij in year t, C_ijt is the value of indicator C_ij in year t, C_ij0 is the reference benchmark value of indicator C_ij. In the absence of nationally standardized thresholds, C_ij0 was derived by using the dataset geometric mean (specifically: annual monitoring averages across 2018 and 2023 sampling sites). This approach minimizes skewness from extreme values common in ecological datasets.

Based on $\left\{I_{ijt}\right\}$ and the weight vector W, the comprehensive assessment index I was calculated by using the formula $I={\displaystyle {\sum }_{i=1}^n{I}_i\times W_i}$.

Comparative monitoring outcomes across equivalent seasonal periods reveal distinct pre- (2018) and post-implementation (2023) effects of fishing restrictions in the Lower Qiantang River, as quantified through the eleven ecological-economic-social indicators detailed in Table 7 .

Standardization of indicator values was achieved using the specific gravity method, eliminating scale differences ( Table 8 ). Subsequent application of our integrated assessment framework revealed significant improvement in the Lower Qiantang River’s ecological and socioeconomic conditions following fishing bans. The comprehensive assessment index increased from 0.88 (2018 pre-ban baseline) to 1.16 (2023 implementation phase), representing a 31.67% enhancement. This quantifiable progression demonstrates successful policy implementation.

The Qiantang River fishing ban has demonstrated significant efficacy in augmenting fishers’ revenues while advancing ecological resource sustainability [25] . For example, Zhang et al. utilized hydroacoustic surveys to demonstrate that the 2019 four-month ban significantly enhanced core fish resource indicators—including density, biomass, and size structure—while shifting pelagic species distributions towards greater ecological equilibrium. Ge integrated field sampling with questionnaire data to document increased post-ban catches, juvenile fish abundance, and fish community complexity. Here, monitoring and survey data indicate significant improvements across nine key ecological, economic, and social indicators. For instance, Chlorophyll-a content decreased by 31.72% (from 3.24 ± 0.42 μg∙L⁻¹ to 2.21±0.33 μg∙L⁻¹), fish species richness increased by 41.18% (from 51 ± 2.00 to 72 ± 3.00 species), and fish stock density rose by 50.00% (from 0.008 ± 0.002 ind.·m⁻³ to 0.012 ± 0.003 ind.·m⁻³). These findings align strongly with previous research.

However, in contrast to other indicators, the Pielou evenness index exhibited a declining trend. This divergence is likely attributable to two factors: 1) heightened heterogeneity in species abundance driven by colonization pressures from invasive and rare species, and 2) selective fishing practices following the ban, which may have intensified interspecific resource competition. Fisher satisfaction represents another negatively impacted indicator, showing a small but notable decline of 4.62%. The four-month annual seasonal fishing ban directly disrupts livelihoods, as fishers previously generated income year-round but now face complete income loss during the closure period. Compounding this, older fishers with limited formal education constitute a vulnerable demographic—their diminished capacity to secure alternative short-term employment often eliminates household income streams during the ban. This socioeconomic vulnerability manifests as significantly reduced quality of life, resulting in diminished fisher satisfaction. Implementing the fishing quota system and establishing subsidies for fisherman re-training as well as ecological compensation funds might be important measures to improve the values of these two indicators.

By using the method of system analysis, a comprehensive assessment of the fishing ban’s efficacy for the Lower Qiantang River based on multiple indicators was also conducted. The comparison of the comprehensive evaluation index is shown in Table 8 , showing that over the five-year ban (2018-2023), the index increased from 0.88 to 1.16 (+31.67%). This improvement reflects continuously improved water quality, a certain degree of restoration of the aquatic ecosystem, and the continuous enhancement of the public’s awareness of ecological protection, aligning with the broader goal of sustainable fisheries development and providing a robust theoretical foundation for future policy iterations.

Due to the implementation of the ongoing annual fishing ban (March 1-June 30), this evaluation was mainly a phased evaluation, and further strengthening is needed for continuous tracking, monitoring, and evaluation in the future.

The structural-functional indicator method offers a comprehensive selection of indicators, incorporating ecological, economic, and social factors. This approach enhances the evaluation’s accuracy and intuitiveness by reflecting the actual impacts of fishing bans more precisely. Currently, limited research has been conducted on applying this method to assess fishing ban effectiveness in natural waters. The indicator system proposed in this study integrates indicators from previous studies [6] [25] while incorporating freshwater ecosystem characteristics and long-term data from our research group. Ultimately, 11 representative indicators were selected through comprehensive analysis, categorized into ecological, economic, and social benefits.

Notably, this system diverges from Lu’s [25] Qiantang River fishing ban evaluation framework. Here, Catch per Unit Effort serves as an economic indicator, chlorophyll a represents ecological health, and novel indicators such as the Pielou evenness index, input-output ratio, and ecological protection awareness were introduced. Conversely, the indicator number of dominant fish species was removed due to its limited comparability across studies.

This article is an attempt to study the fishing ban’s efficacy in freshwater areas based on the Analytic Hierarchy Process, and the result may be confounded by covarying restoration measures (e.g., artificial stock enhancement, water quality remediation) and remain vulnerable to climate-driven hydrological alterations. The indicator system adopted in this article still needs further research and improvement.