Abstract
Accurately grasping the comprehensive cost-benefit and influencing factors of various marine development projects is of great significance to the promotion of high-quality marine development. This work screened and constructed the performance audit evaluation index system of comprehensive cost-benefit of marine development projects from the four aspects including society, economy, resources, and environment. The entropy weight set pair analysis (SPA) was used to audit and evaluate the comprehensive cost-benefit of 15 marine development projects during the construction and operation period. The back propagation (BP) neural network model was used to test the results. The main influencing factors were analyzed by the grey relational analysis (GRA) model. It was found that the comprehensive cost-benefit performance audit evaluation index of the six projects of marine protected area and offshore wind power were higher than the other nine projects of mariculture, sewage dumping, and port industry. The main influencing factors were economic net income, loss of wetland ecological service value, environmental pollution prevention and control cost, new addition employment rate, etc. The countermeasures suggestions were put forward.
1 Introduction
The coastal region is one of the regions with the fastest economic development speed, the largest economic aggregate scale, and the highest development vitality and development potential in the world. In addition, it is also an area with high development and utilization intensity and concentrated pollutant discharge, which leads to a series of problems and risks, such as low efficiency of marine development and utilization, tense relationship between human and land, increasing marine pollution, extensive mode of marine economic growth, and so on. Unreasonable development of marine resources will eventually restrict the high-quality development of coastal areas. The rational selection of marine development and utilization types according to local conditions is an important assurance to promote the sustainable development of the ocean. Therefore, it is very critical to audit and evaluate the comprehensive cost-benefit of various marine development activities. Current research works on the development, utilization, and management of marine resources mainly focus on the supporting role of marine development activities in economic development. However, the social cost-benefit caused by improper marine development and utilization are not fully evaluated. The marine development engineering technology and integrated management attract too much attention, which led to improper neglection of the resource and environmental problems caused by marine development activities. The relevant research involves a variety of types of marine development activities, the construction of comprehensive cost-benefit index system, and evaluation models.
In terms of evaluation objects, Robertson et al. [1] made an overall assessment of marine energy resources from two aspects: offshore wind energy and wave energy. Ward et al. [2] investigated the value of lobster fishery in Taishan National Park marine reserve. Kuleli [3] assessed the contribution of coastal resources to the sustainable development of Turkey’s national economy. Fang et al. [4] summarized the development and utilization of marine fishery resources in African coastal states. Satumanatpan et al. [5] assessed the governance of marine and coastal resources of the small island states in the western Gulf of Thailand. Bao et al. [6] used the principle of risk factor decomposition method and analytic hierarchy process to identify and evaluate the risk factors in the implementation of beach development projects. Wang et al. [7] comprehensively evaluated the marine resource utilization efficiency of China’s coastal provinces and cities, and revealed the regional differences in the convergence of marine resource intensity based on the resource intensity and economic growth convergence theory. Tian et al. [8] calculated the coastal development index through ecological environment indicators and socio-economic indicators, and assessed the sustainability of coastal development.
In terms of index system screening and evaluation model construction, Wang et al. [9] screened and constructed the comprehensive benefit evaluation index system of coastal beach development and utilization from four aspects, including economic growth, social development, resource utilization, and ecological environment. Jones et al. [10] evaluated an ecological restoration project to reduce coastal erosion in southern Texas from the aspects of engineering technology, ecological environment, and social economy. Liu et al. [11] established an environmental impact assessment system for coastal reclamation projects. Huang et al. [12] constructed the Xiamen bay ecosystem service evaluation index system. The comprehensive benefit and internal composition of marine development types are evaluated by Gao et al. [13] based on the calculation of net economic benefit, loss value of ecosystem service function value, and loss value of fishery resources and ecological cost-benefit value. Du et al. [14] analyzed and evaluated the current situation of space development and utilization in Laizhou Bay from three aspects: structure, intensity, and coordination. Song et al. [15] established a comprehensive evaluation system for the utilization of coastal marine resources in China from the perspectives of science, technology, environment, and public management. The comprehensive benefits of marine resources utilization in China’s coastal provinces and cities are evaluated by using the methods of system dynamics and mean square error weight. Gao et al. [16] established an evaluation index system for marine resources development from the four aspects of economy, society, resources, and environment. The comprehensive indexes of five types of marine resources development are evaluated by using the linear weighted sum model. Liu et al. [17] established an evaluation index system containing economic benefits, social benefits, resource depletion and environmental costs. The comprehensive benefit model was used to evaluate the comprehensive benefits of seven typical types of marine resources development in China. Du and Gao [18] developed the MRES index system based on the PSR model, and used the TOPSIS method of AHP entropy to evaluate the ecological security of marine pasture. Tang and He [19] established an accounting index system for comprehensive benefits of marine resources utilization, and calculated the comprehensive benefits of major marine resources utilization types in Liaoning Province by using the comprehensive benefit model. Avelino et al. [20] used DEA to measure the sustainability assessment index of marine protected areas. Zhang and Chu [21] established an evaluation model for the comprehensive benefits of reclamation from the sea. Zhang and Zhou [22] established the impact assessment model of project cost-benefit, and assessed the cost-benefit of each project in the development of marine resources in the EPC mode. Liu and Xu [23] used ratio analysis method, market value method, achievement reference method, and other methods to evaluate the comprehensive benefits of reclamation. Geng et al. [24] used the system dynamics model to predict the change trend of tail gas emissions and economic benefits of Qingdao port under different scenarios from 2015 to 2025.
In order to serve the growing demand for land and energy, all coastal countries are accelerating marine development. Accurate audit evaluation of the comprehensive cost-benefit of various types of marine development activities can provide a scientific basis for the formulation of marine development strategies and the planning of marine functional areas. It helps to improve the rationality of marine development and utilization, promote high-quality marine development, and accelerate the construction of a marine power [25]. It is of great significance to promote the coordination and stability of marine economy, resources, and environment. This study used the entropy weight SPA method to calculate the comprehensive cost-benefit performance audit evaluation index of 15 marine development projects. BP neural network model was used to test the results. The main influencing factors of marine development projects were identified and analyzed by the GRA model, and the countermeasures and suggestions were put forward. The research results can provide a scientific basis for each sea area to choose the marine development type suitable for priority development.
2 Materials and methods
2.1 Materials
Combined with the current situation of China’s marine development and utilization, this study selected five types of marine development activities, including marine protected areas, mariculture, offshore wind power, sewage dumping, and port industry. Fifteen typical marine development projects were taken as cases for comprehensive cost-benefit audit evaluation (Table 1). The case selection is representative, and it is convenient to compare the cost-benefit of marine exploitation activities in different sea-use types and industry categories. The data mainly come from the social and economic statistical data, the monitoring data, and the pollution declaration data of the environmental protection department during the construction and operation of the marine development projects, the field visit survey data, etc.
Five types of marine resources development and representative project cases
| Five types of marine resources development | Representative projects of marine development | Number |
|---|---|---|
| Marine protected areas | A national marine park in Lianyungang | A1 |
| A national marine park in Haimen | A2 | |
| A national marine park in Yangkou | A3 | |
| Mariculture | A mariculture project in Ganyu | B4 |
| A mariculture project in Dongtai | B5 | |
| A mariculture project in Rudong | B6 | |
| Offshore wind power | A coastal offshore wind power project | C7 |
| The first phase of an intertidal wind farm in Rudong | C8 | |
| The first and second phases of a wind farm in Qidong | C9 | |
| Sewage dumping | A sewage discharge project in Nantong | D10 |
| A tailwater discharge project in Binhai | D11 | |
| 1–4 units project of a nuclear power plant | D12 | |
| Port industry | A coastal power plant project | E13 |
| A heavy equipment manufacturing project | E14 | |
| A petrochemical storage project in Rudong | E15 |
2.2 Construction of comprehensive cost-benefit index system
The performance audit evaluation index system of comprehensive cost-benefit established in this study consists of three layers. The target layer is the comprehensive cost-benefit performance audit evaluation index of marine development projects. The standard layer includes the social cost-benefit, economic cost-benefit, resource cost-benefit, and environmental cost-benefit of various marine development projects. The indicator layer contains 32 specific indicators (Table 2).
Performance audit evaluation index system of comprehensive cost-benefit of marine development projects
| Criteria layer | Indicator layer | Type | Sort | Entropy weight |
|---|---|---|---|---|
| Social cost-benefit | Mass satisfaction (%) | Benefit | F1 | 0.0059 |
| New addition employment rate (%) | Benefit | F2 | 0.0572 | |
| Improvement rate of residents’ living standards (%) | Benefit | F3 | 0.0164 | |
| Completion degree of residents’ interest coordination (%) | Benefit | F4 | 0.0073 | |
| Residents’ lives are adversely affected | Cost | F5 | 0.0208 | |
| Contradiction between management and development | Cost | F6 | 0.0189 | |
| Population density per unit area | Cost | F7 | 0.0146 | |
| Change rate of tourist number (%) | Benefit | F8 | 0.02 | |
| Economic cost-benefit | Economic net income per unit area (10,000 yuan/hm2) | Benefit | F9 | 0.0903 |
| Economic net income per unit shoreline (10,000 yuan/km) | Benefit | F10 | 0.0851 | |
| Geometric average annual growth rate of per capita income (10,000 yuan/annum) | Benefit | F11 | 0.0154 | |
| Annual average tax payment (10,000 yuan/hm2) | Benefit | F12 | 0.0493 | |
| Investment and development cost per unit area (10,000 yuan/hm2) | Cost | F13 | 0.0379 | |
| Economic losses of marine disasters per unit coastline (10,000 yuan/km) | Cost | F14 | 0.0304 | |
| Charge for sea area utilization (10,000 yuan/hm2) | Cost | F15 | 0.0409 | |
| Environmental pollution prevention and control cost (10,000 yuan/hm2) | Cost | F16 | 0.052 | |
| Resource cost-benefit | Change rate of total output of aquatic products (%) | Benefit | F17 | 0.0101 |
| Marine space development rate (%) | Benefit | F18 | 0.0293 | |
| Change rate of coastal tourist attractions (%) | Benefit | F19 | 0.0196 | |
| Biodiversity index | Benefit | F20 | 0.014 | |
| Loss of marine tourism resources value | Cost | F21 | 0.0208 | |
| Shoreline loss rate (%) | Cost | F22 | 0.0307 | |
| Dynamic loss degree of tidal flat wetland (%) | Cost | F23 | 0.033 | |
| Loss of marine living resources per unit area (10,000 yuan/hm2) | Cost | F24 | 0.019 | |
| Environmental cost-benefit | Environmental purification capacity | Benefit | F25 | 0.0139 |
| Ecological environment carrying capacity | Benefit | F26 | 0.0143 | |
| Ecological environment sensitivity | Cost | F27 | 0.0183 | |
| The water quality compliance rate (%) | Benefit | F28 | 0.0174 | |
| Loss of wetland ecological service value (10,000 yuan/hm2 annum) | Cost | F29 | 0.0824 | |
| Ecological compensation cost (10,000 yuan/hm2 annum) | Cost | F30 | 0.0404 | |
| Dynamic sediment environmental change rate (%) | Cost | F31 | 0.0414 | |
| Scouring and silting coefficient (%) | Cost | F32 | 0.033 |
The calculation process of some important indicators in Table 2 is as follows. Improvement rate of residents’ living standards (F3) is expressed by the ratio of Engel’s coefficient before and after the project operation.
where E P refers to the improvement rate of residents’ living standards. E o and E n , respectively, represent Engel’s coefficient before and after project operation.
The economic net income per unit area (F9) within the period is expressed by deducting relevant expenditure costs from the total income of the project.
where B k is the net economic income per unit sea area, unit: 10,000 yuan/hm2. P is the predicted total project income within the project period, unit: 10,000 yuan. C P is the construction cost of the project. C s is the operating cost of the project. C u is the resource occupancy cost. C r refers to the demolition compensation paid, unit: 10,000 yuan. S is the marine area, unit: hm2.
Geometric average annual growth rate of per capita income (F11) is expressed by the geometric mean of per capita income before and after the project operation.
where I k is the geometric average annual growth rate of per capita income. I n is the per capita income in the nth year of project operation. I n−1 is the per capita income in the n − 1 year of project operation.
The calculation formula of shoreline loss rate (F22) is as follows:
where L is the shoreline loss rate. L 0 is the length of shoreline before project development. L n is the length of the shoreline after the development of the project.
The calculation formula of dynamic loss degree of tidal flat wetland (F23) is as follows:
where B is the dynamic loss degree of tidal flat wetland within a certain time range in the study area. W a refers to the area of tidal flat wetland at the initial stage of the marine development project. W b refers to the area of tidal flat wetland at the end of the marine development project. T is the study period.
The calculation formula of loss of wetland ecological service value (F29) is as follows:
where E is the loss value of service function value of wetland ecosystem of marine development project. E i is the functional value of ecosystem service per unit area of marine development project, unit: 10,000 yuan/hm2 annum. S j is the area of the jth marine development type, unit: hm2. d j is the damage coefficient of the jth type of marine development to wetland ecological service function.
The calculation formula of dynamic sediment environmental change rate (F31) is as follows:
where D v is the dynamic sediment environmental change rate. V a is the flow velocity of the characteristic point after the project construction. V b is the flow velocity of the characteristic point before the project construction.
The calculation formula of scouring and silting coefficient (F32) is as follows:
where G k is the impact of engineering development on the scour and silt environment. F i is the maximum scouring and silting strength caused by engineering construction, unit: cm/annum. F s is the variation range of scour and siltation caused by engineering construction, unit: km2.
2.3 Entropy weight method
The entropy method determines the index weight according to the amount of information transmitted by each indicator to the decision maker [26]. In this study, the range standard method was used to eliminate the influence of different unit data dimensions, and to calculate the normalized value and entropy weight.
The benefit indicator:
The cost indicator:
where x ij is the index value. y ij is the standard value. x min is the minimum value of the indicator. x max is the maximum value of the indicator. i = 1, 2,…n, j = 1, 2,…m. h j is the information entropy of index j. y ij is the normalized value.
Based on the information entropy of the obtained evaluation indicators, the weight value of each evaluation index is further determined. The weight W j is calculated as follows:
where e ij is the proportion of the standard value of the jth index standard value y ij in the total value of the ith sample year. h j is the information entropy value of the jth index. w j is the weight of the jth index, and 0 ≤ w j ≤ 1, ∑w j = 1. i = 1, 2,… n, j = 1, 2,… m. n is the number of samples, and m is the number of evaluation indicators. The weight calculation results are shown in Table 2.
2.4 SPA
SPA is a system analysis method that uses connection degree to deal with uncertain problems [27]. In this theory, the relationship between certainty and uncertainty among research objects is analyzed and processed as an uncertainty system. Certainty includes identity and opposition, while uncertainty refers to difference. SPA analyzes things and their systems through identity, difference, and opposition. Take two associated sets X and Y as a set pair B, and the correlation formula is as follows:
where μ(X,Y) is the connection degree of set pair. S is the number of elements with the same attribute in two sets. P is the number of elements with opposite characteristics in two sets. F is the number of elements in the difference state in the two sets, that is, the number of characteristics in the set that are neither one nor opposite. N is the total number of elements in the set, N = S + P + F. i is the difference coefficient, which is taken in the range of [−1,1]. j is the opposite identification coefficient, often taken as −1. a, b, and c represent the degree of identity, difference, and opposition of the two sets, a + b + c = 1.
This study constructs set pair B{X,Y}, where set X is the performance audit evaluation index system of marine development projects, and set Y is the index evaluation standard. Transform the performance audit evaluation of various marine development projects into a comparative analysis of set X and set Y. Record the multi-attribute evaluation question as Q = [F,Z,W,D]. Set F = {f 1,f 2,…,f m }(m = 15) is the evaluation scheme. Z = {z 1,z 2,…,z n }(n = 32) is the evaluation index. W = {w 1,w 2,…,w n } is the index entropy weight. D is a matrix composed of specific values of evaluation indicators. If the indicator value is recorded as d kp (k = 1,2,…,m; p = 1,2,…,n), then the matrix D is given as follows:
Select the best and worst value of the index from the index system to form the best scheme set U = (u 1,u 2,…,u n ), and the worst scheme set V = (v 1,v 2,…,v n ). The connection degree of set pair B{X k ,Y} in [V,U] interval is given as follows:
where a k is the identity degree, b k is the difference degree, c k is the opposition degree, μ(X k ,Y) is the connection degree of set pairs, a kp and c kp are the degree of identity and opposition of evaluation index d kp and set, respectively, [V p ,U p ].
When the evaluation matrix d kp has a positive effect on the evaluation results,
When the evaluation matrix d kp has a negative effect on the evaluation results,
The formula of the closeness degree r k between the scheme F k and the optimal scheme is
where r k is the proximity degree, which reflects the closeness between the evaluated scheme F k and the optimal scheme set. The larger the r k , the closer the evaluation object is to the optimal evaluation standard. The smaller the r k , the lower the performance of the marine development project. The larger the r k , the higher the performance of the marine development project.
2.5 Grey relational analysis (GRA) model
GRA measures the relational degree of factors according to the similarity or difference of the trends of changes between the factors [28]. The grey relational degree was calculated based on the entropy weights, which can directly reveal the main factors affecting the performance of marine development projects. The formula is as follows:
where R i is the grey relational degree; w j is the entropy weight of the jth evaluation indicator; ξ i (k) is the correlation coefficient of the kth element between the comparison sequence x i (k) and the reference sequence x 0 (k),0＜ξ i (k) ≤ 1.
2.6 BP neural network model
BP neural network is a multilayer feedforward network with strong adaptability and relatively accurate simulation and prediction function in dealing with complex nonlinear relations [26,27]. The neurons in the network correspond to each index in the performance audit evaluation index system of marine development projects. In this study, 32 indicator values were used as input nodes and put the Proximity degree as the output node. According to the operation method of the network topology, the number of nodes in the inputnum, hiddennum, and outputnum was determined to be 32-8-1 (Figure 1). The BP neural network was trained with Matlab2020a software. The calculated input and output data were used as the basic database for BP neural network training and testing.
BP neural network topology.
3 Results
3.1 Comprehensive cost-benefit performance audit evaluation index
The entropy weight SPA method was used to calculate the proximity degree of the comprehensive cost-benefit of 15 marine development projects as the performance audit evaluation index (Table 3). Among the five types of marine development, A2 and A1 of marine protected areas had the highest comprehensive cost-benefit performance audit evaluation index, 0.6292 and 0.6253, respectively. In addition, A3 was 0.6163, ranking fourth and relatively high. The comprehensive cost-benefit performance audit evaluation index of offshore wind power also ranks high, with C9 being 0.6218, ranking third. C7 and C8 were 0.6025 and 0.5894, respectively, ranking fifth and sixth. The comprehensive cost-benefit performance audit evaluation index of the nine offshore development projects of the other three types of offshore wind power, offshore dumping, and port industry was less than 0.5. The smallest items were E13, B5, and D10, which were 0.2728, 0.2844 and 0.2861, respectively.
Comprehensive cost-benefit performance audit evaluation index of marine development projects
| Projects | Identity degree ( a k ) | Opposition degree ( c k ) | Difference degree ( b k ) | Proximity degree ( r k ) | Sort |
|---|---|---|---|---|---|
| A1 | 0.3994 | 0.2393 | 0.3613 | 0.6253 | 2 |
| A2 | 0.3910 | 0.2304 | 0.3786 | 0.6292 | 1 |
| A3 | 0.3891 | 0.2422 | 0.3687 | 0.6163 | 4 |
| B4 | 0.1366 | 0.2788 | 0.5846 | 0.3289 | 12 |
| B5 | 0.1408 | 0.3543 | 0.5050 | 0.2844 | 14 |
| B6 | 0.1412 | 0.2677 | 0.5911 | 0.3453 | 10 |
| C7 | 0.1920 | 0.1267 | 0.6813 | 0.6025 | 5 |
| C8 | 0.1938 | 0.1350 | 0.6712 | 0.5894 | 6 |
| C9 | 0.1965 | 0.1195 | 0.6840 | 0.6218 | 3 |
| D10 | 0.1633 | 0.4075 | 0.4292 | 0.2861 | 13 |
| D11 | 0.1927 | 0.3426 | 0.4647 | 0.3600 | 9 |
| D12 | 0.3772 | 0.4842 | 0.1386 | 0.4379 | 8 |
| E13 | 0.1657 | 0.4418 | 0.3924 | 0.2728 | 15 |
| E14 | 0.2897 | 0.3355 | 0.3748 | 0.4634 | 7 |
| E15 | 0.1779 | 0.3395 | 0.4826 | 0.3438 | 11 |
3.2 Analysis of influencing factors
The main influencing factors of marine development projects were identified and analyzed by the GRA model’s grey relational degree based on entropy weight. According to the grey relational degree of specific indicators in each marine development project (Table 4)，it was found that the main influencing factors of marine protected areas were F9, F10, F29, F8, F2, and F19. The main influencing factors of mariculture were F14, F31, F9, F10, F29, F2, and F16. The main influencing factors of offshore wind power were F9, F10, F29, F2, F16, and F12. The main influencing factors of sewage dumping were F9, F10, F29, F2, F13, F16, F12, and F5. The main influencing factors of the port industry were F16, F12, F9, F30, F15, F29, F32, F10, and F23.
Main influencing factors of comprehensive cost-benefit of marine development projects
| Sort | A1 | A2 | A3 | B4 | B5 | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | F9 | 0.0301 | F9 | 0.0301 | F9 | 0.0301 | F14 | 0.0304 | F31 | 0.0414 |
| 2 | F10 | 0.0284 | F10 | 0.0284 | F10 | 0.0284 | F9 | 0.0301 | F9 | 0.0301 |
| 3 | F29 | 0.0275 | F29 | 0.0275 | F29 | 0.0275 | F10 | 0.0284 | F14 | 0.0289 |
| 4 | F2 | 0.0192 | F19 | 0.0196 | F8 | 0.0200 | F29 | 0.0276 | F10 | 0.0284 |
| 5 | F8 | 0.0188 | F2 | 0.0192 | F2 | 0.0192 | F2 | 0.0197 | F29 | 0.0275 |
| 6 | F19 | 0.0188 | F8 | 0.0178 | F19 | 0.0182 | F16 | 0.0176 | F2 | 0.0208 |
| Sort | B6 | C7 | C8 | C9 | D10 | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | F9 | 0.0301 | F9 | 0.0301 | F9 | 0.0301 | F9 | 0.0301 | F9 | 0.0301 |
| 2 | F10 | 0.0284 | F10 | 0.0284 | F10 | 0.0284 | F10 | 0.0284 | F10 | 0.0284 |
| 3 | F29 | 0.0276 | F29 | 0.0277 | F29 | 0.0277 | F29 | 0.0278 | F29 | 0.0275 |
| 4 | F14 | 0.0276 | F2 | 0.0193 | F2 | 0.0192 | F2 | 0.0195 | F2 | 0.0191 |
| 5 | F2 | 0.0202 | F16 | 0.0174 | F16 | 0.0174 | F16 | 0.0174 | F16 | 0.0185 |
| 6 | F31 | 0.0190 | F12 | 0.0167 | F12 | 0.0166 | F12 | 0.0167 | F12 | 0.0164 |
| Sort | D11 | D12 | E13 | E14 | E15 | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | F9 | 0.0301 | F9 | 0.0903 | F16 | 0.0458 | F12 | 0.0493 | F16 | 0.0520 |
| 2 | F10 | 0.0284 | F10 | 0.0851 | F30 | 0.0404 | F9 | 0.0432 | F15 | 0.0390 |
| 3 | F29 | 0.0275 | F29 | 0.0824 | F15 | 0.0384 | F16 | 0.0432 | F9 | 0.0309 |
| 4 | F5 | 0.0199 | F2 | 0.0572 | F29 | 0.0383 | F2 | 0.0429 | F10 | 0.0307 |
| 5 | F2 | 0.0191 | F13 | 0.0379 | F32 | 0.0330 | F15 | 0.0409 | F23 | 0.0293 |
| 6 | F16 | 0.0185 | F16 | 0.0346 | F10 | 0.0318 | F23 | 0.0330 | F29 | 0.0290 |
According to the summary of grey relational degree of each index, it was found that among the 32 evaluation indicators, the seven indicators with the highest aggregate grey relational degree were F9, F10, F29, F16, F2, F15, and F12 (Figure 2). That is, the main influencing factors of comprehensive cost-benefit performance audit evaluation index of marine development projects were economic net income per unit area, economic net income per unit shoreline, loss of wetland economic service value, environmental pollution prevention and control cost, new addition employment rate, charge for sea area utilization, and annual average tax payment.
Seven indicators with grey correlation degree greater than 0.3 after summary.
4 Discussion
Marine development activities involve many elements such as society, economy, resources, and environment. In this study, social cost-benefit index and ecological cost-benefit index had been added to the comprehensive cost-benefit audit evaluation index system of marine development activities. However, the index system, safety threshold, evaluation model, and evaluation criteria need to be further improved [29]. It is necessary to further summarize the connotation, theory and practical experience of sustainable utilization of marine resources, and pay attention to the sustainability of coastal zone development. In the element layer of environmental cost, the loss of wetland ecosystem service function value can be calculated according to the coastal section, location, type, area, and ecological damage coefficient [30]. This study is performed directly based on the average ecological service value of Jiangsu tidal flats of 40,000 yuan/(hm2 annum), and the ecological service value of each bank section obtained by Gao et al. [31]. The dynamic sediment environmental change rate is to select the characteristic points in the area where the flow velocity changes most significantly. Through the numerical simulation of tidal current, the maximum velocity change rate is selected from the velocity change rates of several characteristic points, so as to reflect the maximum impact of offshore engineering construction on the hydrological dynamic environment. The change in hydrological and sediment dynamic field caused by marine development projects will be measured according to the scouring and silting coefficient. The maximum scouring and silting intensity and scope shall be comprehensively considered and the dimensional influence shall be eliminated [32].
In this study, BP neural network model was used to predict and test audit evaluation results. The linear interpolation method was used to increase the training data, and 200 equidistant interpolation samples were generated within the distribution range of each index and randomly arranged as input parameters. SPA was used to calculate the corresponding relative pasting progress as the output parameter. 150 samples were randomly selected as training samples. The number of training times was set to 1,000, the learning rate was set to 0.01, and the minimum error of training target was set to 0.0001. After 1,000 times of training on the network by matlab2020a software, the training sample error of comprehensive cost-benefit of marine development activities reaches the target value. The relevant data of 15 samples were imported to the trained network model. Finally, the comprehensive cost-benefit performance audit evaluation index of 15 marine development projects were obtained, and the overall trend chart was drawn (Figure 3). According to the comparison between the predicted value of BP neural network model and the actual value of SPA, it was found that the trend of comprehensive cost-benefit performance audit evaluation index of 15 marine development projects was basically the same. It shows that the combination of entropy weight SPA method and BP neural network model can better achieve complementary advantages and improve accuracy.
Comprehensive cost-benefit performance audit evaluation index of 15 marine development projects based on BP neural network model.
Marine development is an effective way to alleviate the dual pressure of population growth and land use reduction in China. However, improper development has also led to social and economic costs, in addition with resource and environmental costs, which slows down economic and social development. Nowadays, the theoretical research and framework system of comprehensive cost-benefit audit evaluation on marine development activities are not unified, and the research methods are not fully optimized. It mainly reflected in the uncertainty of evaluation factors, evaluation systems, evaluation models, evaluation methods, evaluation standards, and the simplification of evaluation types. There is less in-depth discussion on the relationship between various types of marine development activities and marine economy, resources, and environment. It seldom involves the function, structure, interactive stress relationship and spatial-temporal evolution characteristics of the marine resources, environment, and economy complex system. The article still has deficiencies, which need further study. First, due to the difficulty in obtaining statistical data and information, this study is not comprehensive and perfect in the construction of the performance audit evaluation index system of comprehensive cost-benefit of marine development projects. It needs further improvement and optimization. Second, in the follow-up study, it is suggested to use a combination of multiple methods for evaluation. Then, the BP neural network is used for training prediction, and the prediction results of various evaluation methods are further compared and analyzed. We need to expand the index system, combine a variety of evaluation methods, build complex models, and enhance the accuracy of empirical results. It is necessary to deeply explore the relationship among social and economic benefits and resource and environmental costs of various marine development activities, so as to provide a scientific basis for the selection and management of types of marine development activities. Third, there are huge differences in the geographical location, natural environment, and social and economic development of marine resources development projects. The performance audit evaluation results also show different localization characteristics and multi-factor interaction. How to accurately identify the core influencing factors and seek methods and strategies to improve the comprehensive benefits are also the key points that need to be broken through in the current and future research. In short, in future research, we need to combine the dynamic evolution perspective with the visual perspective. Dynamically analyze the temporal and spatial distribution characteristics, dynamic evolution mechanism, coupling and coordination relationship, influencing factors, driving mechanism, and formation path of uncoordinated state of comprehensive cost-benefit of marine development activities. We need to focus on the sustainable use of marine resources, high-quality development level of marine economy, and carrying capacity of marine ecological environment. We need to deeply explore the impact of marine economic growth on the loss of marine resources and marine ecological environment. It is necessary to reveal the dynamic balance and coordinated development relationship among marine resources, environment, and economy.
5 Conclusion
First, based on the calculation results of the entropy weight SPA method and BP neural network, it was found that the comprehensive cost-benefit performance audit evaluation index of the six projects of marine protected area and offshore wind power was high. The comprehensive cost-benefit performance audit evaluation index of nine marine development projects in mariculture, sewage dumping, and port industry was low. The predicted value of BP neural network model was basically consistent with the actual value of SPA.
Second, according to the grey relational degree of specific indicators in each marine development project, it was found that the three most important influencing factors of mariculture, sewage dumping, and port industry were economic net income per unit area, economic net income per unit shoreline, and loss of land ecological service value. The three main influencing factors of offshore wind power were economic losses of marine disasters per unit coastline, dynamic sedimentation environmental change rate, and economic net income per unit area. The three main influencing factors of port industry were environmental pollution prevention and control cost, annual average tax payment, and economic net income per unit area.
Third, according to the summary grey relational degree of each index, it was found that the main influencing factors of comprehensive cost-benefit performance audit evaluation index of marine development projects were economic net income per unit area, economic net income per unit shoreline, loss of wetland economic service value, environmental pollution prevention and control cost, new addition employment rate, charge for sea area utilization, and annual average tax payment.
Fourth, it was suggested to focus on the types of marine development activities that will generate higher economic net income, effectively increase employment rate, and increase the amount of tax payment. It was suggested to focus on reducing the loss of wetland ecological service value and the cost of environmental pollution prevention. At the same time, the rate of marine space development, the rate of change of coastal tourist attractions, and the rate of water quality are important decision-making reference index. Accordingly, the types of marine development activities suitable for preferential development are selected to optimize the development of marine resources and the distribution of marine industries.
Acknowledgements
The authors are very grateful to the anonymous reviewers for reading our manuscript and providing suggestions.
-
Funding information: This work was supported by the Jiangsu University Philosophy and Social Science research project (2022SJYB0342); Jiangsu university student innovation and entrepreneurship training program (202211287021Z & 202211287086Y); Teaching and Research Project of Nanjing Audit University (2022JG024); Jiangsu Science and Technology Think Tank Youth Talent Plan (JSKJZK2023044).
-
Author contributions: Sheng Gao proposed the research framework and wrote the article. Huihui Sun and Shutong Ge was in charge of translation worked and analyzed the data. Yu Hui conducted SPA. Xiaoyan Huang established the BP neural network model. All authors have read and approved the final manuscript.
-
Conflict of interest: The authors declare no competing interest.
-
Ethics approval and consent to participate: This study involves the macrodata of human economy and society. All the data are from the official statistical yearbook. The data collection process is in line with the ethical and moral standards. The research method of this study is BP neural network model, and there is no need for ethical approval and animal experiment content. The authors guarantee that the process, content, and conclusion of this study do not violate the theory and moral principles.
-
Consent for publication: Not applicable.
-
Institutional review board statement: Not applicable.
-
Informed consent statement: Not applicable.
-
Data availability statement: Relevant data can be provided according to reasonable demand.
References
[1] Robertson B, Dunkle G, Gadasi J, Garcia-Medina G, Yang Z. Holistic marine energy resource assessments: A wave and offshore wind perspective of metocean conditions. Renew Energy. 2021;170:286–301.10.1016/j.renene.2021.01.136Search in Google Scholar
[2] Ward M, Possingham H, Rhodes JR, Mumby P. Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals. Ecosyst Serv. 2018;29:56–69.10.1016/j.ecoser.2017.10.023Search in Google Scholar
[3] Kuleli T. The socio–economic significance of the Turkish coastal environment for sustainable development. Environ Monit Assess. 2015;5:231.10.1007/s10661-015-4477-7Search in Google Scholar PubMed
[4] Fang JH, Ren H, Luo Y, Zhang ZK. Evaluation utilization state of marine fishery resources of Africa. Trop Geogr. 2019;39(2):288–97 (in Chinese).Search in Google Scholar
[5] Satumanatpan S, Moore P, Lentisco A, Kirkman H. An assessment of governance of marine and coastal resources on Koh Tao, Thailand. Ocean Coast Manag. 2017;148:143–57.10.1016/j.ocecoaman.2017.08.001Search in Google Scholar
[6] Bao HJ, Shi JL, Yuan DH, Hu XT. The Risk Identification and its countermeasures of tidal flat resources development project based on AHP. Ecol Econ. 2014;30(05):93–7 (in Chinese).Search in Google Scholar
[7] Wang ZY, Yuan F, Han ZL. Convergence and management policy of marine resource utilization efficiency in coastal regions of China. Ocean & Coast Manag. 2019;178:104854.10.1016/j.ocecoaman.2019.104854Search in Google Scholar
[8] Tian HY, Lindenmayer DB, Wong GT, Mao Z, Huang Y, Xue XZ. A methodological framework for coastal development assessment: A case study of Fujian Province, China. Sci Total Environ. 2018;615:572–80.10.1016/j.scitotenv.2017.09.309Search in Google Scholar PubMed
[9] Wang XZ, Hao YN, Wang P, Yu YH, Liu YN. Comprehensive benefit of tidal flat development and utilization. Ocean Dev Manag. 2019;36(6):21–3 (in Chinese).Search in Google Scholar
[10] Jones K, Pan X, Garza A, Lloyd-Reilley J. Multi-level assessment of ecological coastal restoration in South Texas. Ecol Eng. 2010;36(4):435–40.10.1016/j.ecoleng.2009.11.010Search in Google Scholar
[11] Liu DH, Yu Y, Li XX, Li YP. Progress on the environmental impact assessment of coastal reclamation in China. Coast Eng. 2016;03:74–82 (in Chinese).Search in Google Scholar
[12] Huang H, Chen B, Ma ZY, Liu ZH, Zheng SL. Assessing the ecological security of the estuary in view of the ecological services-A case study of the Xiamen estuary. Ocean Coast Manag. 2017;13(1):12–23.10.1016/j.ocecoaman.2016.12.003Search in Google Scholar
[13] Gao S, Sun H, Ma M, Lu Y, Cao G, Zhao L, et al. Comprehensive benefit evaluation of several typical types of marine development: A case study of Jiangsu province, China. J Coast Res. 2020;104(sp1):512–9.10.2112/JCR-SI104-090.1Search in Google Scholar
[14] Du BB, Wu XQ, Du XY, Liu BJ, Yu L. Situation evaluation on the marine spatial development and utilization in the Laizhou Bay. Mar Sci Bull. 2017;36(01):19–26 (in Chinese).Search in Google Scholar
[15] Song ML, Pan XF, Pan XY. Comprehensive benefit evaluation of marine resource utilization in China. Sustainable Marine Resource Utilization in China–A Comprehensive Evaluation. Elsevier; 2020. p. 145–72.10.1016/B978-0-12-819911-4.00005-9Search in Google Scholar
[16] Zhuang Y, Zhang L, Gao X, Shae ZY, Tsai J, Li P, et al. Comprehensive audit evaluation and driving force analysis of various types of marine resources development. Environ Dev Sustainability. 2022;24:4371–86.10.1007/s10668-021-01619-2Search in Google Scholar
[17] Liu BQ, Wang J, Xu M, Wang ZF, Zhao L. Evaluation of the comprehensive benefit of various marine exploitation activities in China. Mar Policy. 2020;116:103924.10.1016/j.marpol.2020.103924Search in Google Scholar
[18] Du YW, Gao K. Ecological security evaluation of marine ranching with AHP-entropy-based TOPSIS: A case study of Yantai, China. Mar Policy. 2020;122:104223.10.1016/j.marpol.2020.104223Search in Google Scholar
[19] Tang Y, He YX. Research on comprehensive benefit accounting of utilization of marine resources. J Dongbei Univ Financ Econ. 2019;6:32–8 (in Chinese).Search in Google Scholar
[20] Avelino JE, Sasaki J, Esteban M, Salah P, Jamero ML, Valenzuela VP. Sustainability evaluation of marine protected areas index (SEMPAI): A multi-criteria decision-making method to determine the effectiveness of the El Nido-Taytay managed resource protected area. Ocean Coast Manag. 2019;181:104891.10.1016/j.ocecoaman.2019.104891Search in Google Scholar
[21] Zhang JX, Chu C. Comprehensive benefits evaluation model of land reclamation from sea and its application. J Eng Manag. 2011;25(5):526–9 (in Chinese).Search in Google Scholar
[22] Zhang S, Zhou H. Evaluation of the influence of various factors on project performance under the EPC general contracting mode in the development of marine resources. J Coast Res. 2019;93(sp1):762–7.10.2112/SI93-106.1Search in Google Scholar
[23] Liu Q, Xu M. Comprehensive benefits evaluation for sea reclamation projects in Jiangsu province. J Nanjing Norm Univ (Nat Sci Ed). 2013;3:125–30 (in Chinese).Search in Google Scholar
[24] Geng X, Wen Y, Zhou C, Xiao C. Establishment of the sustainable ecosystem for the regional shipping industry based on system dynamics. Sustainability. 2017;9(5):742.10.3390/su9050742Search in Google Scholar
[25] Zhou T, Chen J, Wang T, Yan H, Xu Y, Li Y, et al. Research on comprehensive benefits and reasonable selection of marine resources development types. Open Geosci. 2022;14(1):141–50.10.1515/geo-2022-0341Search in Google Scholar
[26] Gao S, Zhao L, Sun HH, Liu W. Comprehensive evaluation and main influencing factors of sustainable development of marine economy based on GRA and LWCI models. J Coast Res. 2020;104(sp1):566–74.10.2112/JCR-SI104-097.1Search in Google Scholar
[27] Wang Y, Xie Y, Qi L, He Y, Bo H. Synergies evaluation and influencing factors analysis of the water-energy-food nexus from symbiosis perspective: A case study in the Beijing-Tianjin-Hebei region. Sci Total Environ. 2021;818:151731.10.1016/j.scitotenv.2021.151731Search in Google Scholar PubMed
[28] Gao S, Zhao L, Sun HH, Cao GX, Liu W. Evaluation and driving force analysis of marine sustainable development based on the grey relational model and path analysis. J Resour Ecol. 2020;11(6):570–9.10.5814/j.issn.1674-764x.2020.06.004Search in Google Scholar
[29] Wei C, Guo Z, Wu J, Ye S. Constructing an assessment indices system to analyze integrated regional carrying capacity in the coastal zones–A case in Nantong. Ocean & Coast Manag. 2014;93:51–9.10.1016/j.ocecoaman.2014.02.009Search in Google Scholar
[30] Gao S, Sun H, Zhao L, Cao G, Liu W. Research on the comprehensive benefits and driving forces of various typical types of marine development in Jiangsu Province. IOP Conf Ser Earth Environ Sci. 2020;569(1):1–8.10.1088/1755-1315/569/1/012034Search in Google Scholar
[31] Gao S, Xu M, Liu BQ, Cao GX, Zhao L. Comprehensive benefits evaluation of various marine development types. Mar Sci Bull. 2018;37(1):17–24 (in Chinese).Search in Google Scholar
[32] Min X, Li PY, Lu PD. Research on the suitable enclosure scale of silted tidal flats: A case study of Jiangsu Province. Beijing: Science Press; 2012. p. 66–92 (in Chinese).Search in Google Scholar
© 2023 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
