• Journal of Resources and Ecology
  • Vol. 11, Issue 6, 570 (2020)
Sheng GAO1、2, Lin ZHAO2, Huihui SUN1, Guangxi CAO3, and Wei LIU1、*
Author Affiliations
  • 1Institute of Natural Resources and Environmental Audits, Nanjing Audit University, Nanjing 211815, China
  • 2College of Ocean Science and Engineering, Nanjing Normal University, Nanjing 210023, China
  • 3School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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    DOI: 10.5814/j.issn.1674-764x.2020.06.004 Cite this Article
    Sheng GAO, Lin ZHAO, Huihui SUN, Guangxi CAO, Wei LIU. Evaluation and Driving Force Analysis of Marine Sustainable Development based on the Grey Relational Model and Path Analysis[J]. Journal of Resources and Ecology, 2020, 11(6): 570 Copy Citation Text show less

    Abstract

    With the rapid development of the marine economy, the demand for marine resources development and the pressure on marine environmental protection are gradually increasing. It is critical to evaluate and analyze the driving forces of marine sustainable development in order to promote the coordinated development of the marine economy, resources and environment. Taking Jiangsu Province of China as an example, this paper constructs an evaluation index system for marine sustainable development from the three aspects of marine economy, resources and environment, and calculates the weight of the variation coefficient for each indicator. Based on the grey relational model, the average value of the relational degree, calculated by the average value method of correlation coefficients and the weighting method, is then used to evaluate the status of marine sustainable development in this province. The comprehensive index model is used to analyze the dynamic trend of the evolution of marine sustainable development. The driving forces of marine sustainable development are analyzed by the path analysis method combined with the average values of the grey relational degree for each indicator. This analysis found that the marine sustainable development in 2016 and 2012 was good, the situation in 2007 was bad, and the remaining years were intermediate. Compared with the previous years, the optimal conditions of 2008 and 2012 were obvious. The main driving factors of marine sustainable development are cargo throughput of coastal ports, economic losses caused by storm surges in coastal areas, the area of marine nature reserves in coastal areas, coastal wind power generation capacity, and marine biodiversity.

    1 Introduction

    With the increasing demand for resources for consumption, coastal countries all over the world consider marine sustainable development as an important development strategy. Marine sustainable development is reflected by several factors, such as the total amount of marine economy that has increased to a certain stage, marine industrial structure is optimized, marine resource development is efficient and reasonable, the marine ecological environment is harmonious, and marine comprehensive strength is improved, which together realize the dynamic balance of the “marine economy-resources-environment” system (Eikeset et al., 2018). Marine resources and the environment are the basis and guarantee for the development of the marine economy, but with increasing marine development, the pressures on marine resources and the environment are increasing day by day (Selina, 2018). The contradictions between marine resources, environment and economy are becoming increasingly prominent. As a result, the system is faced with a series of problems and risks, such as low utilization efficiency of marine resources, decline of biodiversity, frequent occurrence of marine disasters, tension between man and land, aggravation of environmental pollution, degradation of the ecological environment, an extensive mode of economic growth, and increasing transformation pressures (Zhang et al., 2019a). Therefore, with the rapid development of the marine economy, marine sustainable development has gradually become the focus of attention and the key for enhancing the comprehensive national strength of coastal countries.

    At present, the research on marine sustainable development at home and abroad pays more attention to measuring the development of the marine economy, resources and environment, but ignores the coupling of coordination analysis and driving force analysis (Laura, 2018). Researchers in this field pay more attention to the role of marine resource development in promoting economic development, while ignoring the negative impacts of resource waste and environmental problems on marine development (Wang and Zhang, 2019; Cao et al., 2020). They pay more attention to the impact of marine economic development on the marine environment, but ignore the analysis of the interactions between the marine economy and environment (Zhu et al., 2019); and more attention to the spatial distribution of the marine economy, resources and environment, but ignore the analysis of the spatiotemporal dynamic characteristics of the evolution of coordinated development (Li et al., 2018). Marine sustainable development involves many subsystems, such as economy, resources, and environment. The construction of the index system, selection of the evaluation indicators, standardization of the processing method, calculation of the index weights, determination of the safety threshold and selection of the evaluation models all need to be further discussed (Liang and Li, 2020). In short, the current approach mainly suffers from two limitations. On the one hand, there is a lack of comprehensive analysis of the composite system of the marine economy, resources and environment, and a unified evaluation index system has not yet been formed (Ntona and Morgera, 2018). On the other hand, although the evaluation model method is diversified, it is still not perfect, and the evaluation standard has not been unified (Wang et al., 2019).

    In the context of maritime power, the assessment of marine sustainable development is helpful to understand the characteristics of the dynamic evolution of development, and to formulate and adjust effective marine development policies in time. There is a great need to promote the coordinated development of the marine economy, resources and environment (Zhang et al., 2019b). Based on the related research results on the evaluation of marine sustainable development at home and abroad, the marine economy, resources and environment are included in the evaluation index system of marine sustainable development (Liu et al., 2018). In this study, the dynamic trend of change of marine sustainable development from 2006 to 2016 was analyzed by using the grey relational model and the comprehensive index model. The path analysis method is used to analyze the main driving factors of marine sustainable development, combined with the average values of the relational degrees of each evaluation indicator. The corresponding countermeasures and suggestions are put forward to provide the basis for the high-quality development of the marine economy, the rational development and effective utilization of marine resources, and the protection and optimization of the marine environment. The results provide a reference for future marine sustainable development research.

    2 Materials and methods

    2.1 Research area status and data sources

    Jiangsu Province is located on the eastern coast of the Chinese mainland, 30°45'N-35°20'N, 116°18'E-121°57'E, and is bordered to the north by Shandong, to the east by the Yellow Sea, to the southeast by Zhejiang and Shanghai, and to the west by Anhui (Fig. 1). The total area is 10.72×104 km2, accounting for 1.06% of China’s total land area, and the coastal zone area is 3.5×104 km2. The total length of coastline is 888.9 km. The total coastline of the island is 84.74 km, with a total area of 59.15 km2.

    Location of Jiangsu Province, China

    Figure 1.Location of Jiangsu Province, China

    Jiangsu Province is located in the Yangtze River economic belt. The Yangtze River Delta urban agglomeration, which is composed of Jiangsu, Shanghai, Zhejiang and Anhui, is one of the six world-class urban agglomerations in the world. Jiangsu Province’s per capita GDP, comprehensive economic competitiveness, regional development and people’s livelihood index (DLI) are among the highest for China’s provinces. By the end of 2019, the permanent population was 80.7 million, making it the largest province with the largest population density in China. The GDP was 9963.152 billion yuan and the per capita GDP was 123607 yuan. Among the three types of added value within the marine industry, the marine tertiary industry has developed rapidly, from 151.755 billion yuan in 2010 to 352.28 billion yuan in 2017. The added value of marine secondary industry increased from 197.022 billion yuan in 2010 to 340.24 billion yuan in 2017. The primary industry rose slowly, from 17.698 billion yuan in 2010 to 30.185.18 billion yuan in 2016, but dropped to 29.18 billion yuan in 2017, indicating that Jiangsu’s marine GDP is gradually accelerating the development of the secondary and tertiary industries (Fig. 2).

    Added value of the three types of marine industries in Jiangsu Province

    Figure 2.Added value of the three types of marine industries in Jiangsu Province

    The data in this article mainly come from the China Marine Statistics Yearbook, China Fishery Statistics Yearbook, China Port Yearbook, Jiangsu Yearbook, Jiangsu Marine Economic Development Report, Marine Industry Development Report, environmental monitoring data, and other official sources. According to the framework of China’s environmental economic accounting system and the framework of China’s resource and environmental economic accounting system, the evaluation index system of marine sustainable development in this study is selected and constructed from the three aspects of the marine economy, resources and environment.

    2.2 Coefficient of variation method

    In the evaluation indicator system, a larger numerical difference indicates greater difficulty in achieving the goal, but this system can better reflect the gaps between the evaluated objects. Due to the different dimensions of each indicator in the evaluation indicator system, direct difference comparisons are not possible. Therefore, the coefficient of variation of each indicator is calculated to measure the degree of difference for each indicator, thereby eliminating the influence of different evaluation indicator units. The coefficient of variation method is an objective weighting method, which directly uses the information contained in each indicator to calculate the index weight (Qian and Jiang, 2019).

    The evaluation indicator source data for marine sustainable development is constructed into a matrix [X11×21]. In order to make the evaluation indicators of different unit values comparable, the norm method is used to normalize the index data by converting it into standardized values in the interval of 0-1 to form a standardized decision matrix [Z°11×21], using the following formula:

    ${{{Z}'}_{ij}}=\frac{{{f}_{ij}}}{\sqrt{\sum\limits_{i=1}^{n}{f_{ij}^{2}}}}\ (i=1,2,\cdots ,n;j=1,2,\cdots ,m)$

    In formula (1), fij is the original data in the decision matrix, n is the number of evaluation objects, m is the number of indicators, and Z°ij is the normalized value.

    In order to eliminate the influence of different dimensions for each evaluation indicator, it is necessary to measure the difference of each indicator value through the coefficient of variation, and calculate the weights of coefficient of variation method (Zhu and Zhang, 2019). The calculation formula for the coefficient of variation and weight of each indicator is as follows:

    ${{w}_{i}}=\frac{{{V}_{i}}}{\sum\limits_{i=1}^{n}{{{V}_{i}}}}\text{ }\!\!~\!\!\text{ }(i=1,2,\cdots ,n)$

    In formula (2), Vi is the coefficient of variation of index i, also known as the coefficient of standard deviation, σi is the standard deviation of index i, \[{{\bar{x}}_{i}}\] is the average of index i, and wi is the weight of the coefficient of variation method.

    2.3 Grey relation analysis model

    The grey relational method measures the relational degree of factors according to the similarity or difference of the trends of changes between the factors. If the synchronous degrees of change of two factors are consistent, it indicates that the relational degree is higher; otherwise, it is lower. A greater relational degree indicates a closer relationship between the comparison sequence and the reference sequence (Liu et al., 2019). The correlation coefficient ξ(Xi) of the comparison sequence and the reference sequence at point k is calculated as follows:

    $\begin{align} & {{\xi }_{i}}\left( k \right)= \\ & \frac{\underset{i}{\mathop{\text{min}}}\,\underset{k}{\mathop{\text{min}}}\,\left| {{x}_{0}}\left( k \right)-{{x}_{i}}\left( k \right) \right|+\rho \times \underset{i}{\mathop{\text{max}}}\,\underset{k}{\mathop{\text{max}}}\,\left| {{x}_{0}}\left( k \right)-{{x}_{i}}\left( k \right) \right|}{\left| {{x}_{0}}\left( k \right)-{{x}_{i}}\left( k \right) \right|+\rho \times \underset{i}{\mathop{\text{max}}}\,\underset{k}{\mathop{\text{max}}}\,\left| {{x}_{0}}\left( k \right)-{{x}_{i}}\left( k \right) \right|} \\ \end{align}$

    In formula (3), ξi(k) is the correlation coefficient of the k-th element between the comparison sequence xi (k) and the reference sequence x0 (k), 0i(k)1, and ρ is the resolution coefficient. A smaller ρ indicates a larger resolution. The value range is generally 0<ρ<1. When ρ≤0.5463, the resolution is the best, and it is generally 0.5.

    Using the average value method of correlation coefficients, the correlation coefficient of each point in the curve is concentrated into one value. The calculated average value is taken as the relational degree between the comparison sequence and the reference sequence, by the following formula:

    ${{R}_{i}}=\frac{1}{n}\sum\limits_{k=1}^{n}{{{\xi }_{i}}\left( k \right)}\ $

    In formula (4), Ri is the relational degree, which reflects the relative importance of each indicator. ξi(k) is the correlation coefficient between the comparison sequence and the reference sequence of the k-th index.

    The grey relational degree is calculated by the weighting method based on the index weights, which can directly reflect the contribution of each evaluation indicator to marine sustainable development. The formula is as follows:

    ${{R}_{i}}=\sum\limits_{j=1}^{n}{{{w}_{j}}{{\xi }_{i}}\left( k \right)}$

    In formula (5), wj is the weight of the j-th evaluation indicator and Ri is the relational degree.

    According to the research results of the evaluation standard and grade division, this paper divides the evaluation standard of marine sustainable development from [0, 1] into 5 levels (Table 1). Among them, the interval [0, 0.2) belongs to the fifth grade of “very bad”. The interval [0.2, 0.4) belongs to the fourth grade of “bad”. The interval [0.4, 0.6) belongs to the third level of “neutral”. The interval [0.6, 0.8) belongs to the second grade of “good”. The interval [0.8, 1.0] belongs to the first level of “very good”.

    Grade
    Evaluation indicator value[0, 0.2)[0.2, 0.4)[0.4, 0.6)[0.6, 0.8)[0.8, 1.0]
    StateVery badBadNeutralGoodVery good

    Table 1.

    Evaluation standard of marine sustainable development

    In this paper, the average value method of correlation coefficients and the weighting method are used to calculate the grey relational degree. The average value of the relational degree of the two methods is used as the main basis for the evaluation of marine sustainable development.

    2.4 Composite index model

    The comprehensive index model is used to compare and verify the dynamic trend of the evolution of marine sustainable development as calculated by the grey relational model. The comprehensive index is the cumulative sum of the standardized values of each indicator multiplied by the weight of the coefficient of variation. The formula is as follows:

    ${{S}_{i}}=\sum\limits_{j=1}^{m}{{{y}_{ij}}{{w}_{j}}}$

    In formula (6), Si is a comprehensive index, yij is the standard value of the j-th index in the i-th sample (i=1, 2, ..., n, j=1, 2, ..., m), wj is the weight of the j-th evaluation indicator, n is the number of samples, and m is the number of evaluation indicators.

    2.5 Main driving force analysis

    2.5.1 Path analysis

    On the basis of multiple regression, path analysis decomposes the interactions (correlation coefficient) of dependent and independent variables into direct influence (direct path coefficient) and indirect influence (indirect path coefficient). These indicate either the direct effect of an independent variable on the dependent variable, or the indirect effect of the other variables on the dependent variable (Hang et al., 2019). For the general multiple linear regression analysis, the independent variables are X1, X2, …, Xn and the dependent variable is Y. Therefore, the multiple linear regression equations based on standardization are as follows:

    $\left\{ \begin{align} & {{r}_{1Y}}={{b}_{1}}+{{r}_{12}}{{b}_{2}}+\cdots +{{r}_{1n}}{{b}_{n}} \\ & {{r}_{2Y}}={{r}_{21}}{{b}_{1}}+{{b}_{2}}+\cdots +{{r}_{2n}}{{b}_{n}} \\ & \vdots \\ & {{r}_{nY}}={{r}_{n1}}{{b}_{1}}+{{r}_{n2}}{{b}_{2}}+\cdots +{{b}_{n}} \\ \end{align} \right.$

    In formula (7), bi is the partial regression coefficient of Xi as the direct path coefficient of Xi to Y, rij is the correlation coefficient between Xi and Xj, rijbj is the indirect path coefficient of Xi to Y through Xj, and riY is the correlation coefficient between Xi and Y, for i=1, 2, … n and j=1, 2, … m.

    Path analysis can be used to test whether X1, X2, …, Xn has a significant effect on Y. The most important aspect is to analyze how Xi affects Y directly and indirectly through other independent variables.

    ${{R}_{1}}=\sum\limits_{i=1}^{n}{{{b}_{i}}},\begin{matrix} {} & {{R}_{2}}= \\ \end{matrix}\sum\limits_{i\ne j}^{n}{{{b}_{i}}{{r}_{ij}}}$

    $R=\sum\limits_{i=1}^{n}{\left( {{b}_{i}}+\sum\limits_{j\ne i}^{n}{{{b}_{i}}{{r}_{ij}}} \right)}$

    In formulas (8-9), R1 is the direct determination coefficient of Xi to Y, and R2 is the indirect determination coefficient of Xi to Y through Xj.

    Because of the complex relationships among the marine economy resources and environmental indicators, it is impossible to account for all the factors driving the dependent variable Y when setting the model. Therefore, we should further calculate the path effect coefficient (bYe) of the missing variable and error term on the dependent variable Y. This residual effect is calculated as follows:

    ${{b}_{Ye}}=\sqrt{1-{{R}^{2}}}$

    In formula (10), bYe is the path effect coefficient. If bYe < 0.05, this indicates that the path analysis has found the main driving factors, otherwise it indicates that some driving factors may be missing. Some driving factors outside of the model should be considered in the analysis.

    2.5.2 Grey relational degree of each indicator

    The grey relational model is used to calculate the grey relational weight of each evaluation indicator, reflecting the importance of that index in the whole index matrix. The formula ${{Q}_{i}}={{{w}_{i}}}/{\sum\limits_{i=1}^{n}{{{w}_{i}}}}\;$ is used to normalize the relational

    degrees of the average method and the weighting method for each evaluation indicator, respectively. Taking the aver-age value as the driving force, the main driving factors of marine sustainable development are then analyzed.

    3 Results

    3.1 Evaluation indicators and weights of marine sustainable development

    The evaluation indicator system of marine sustainable development is divided into three layers. The target layer is the status of marine sustainable development. The system layer includes the marine economy, marine resources and marine environment. The indicator layer includes 21 evaluation indicators (Table 2).

    System layerIndicator layerUnitCoefficient of variation weight
    Marine economyAdded value of marine industry (X1)×108 yuan0.0460
    Gross marine product of coastal areas (X2)×108 yuan0.0488
    The proportion of marine GDP to coastal GDP (X3)%0.0120
    Proportion of marine secondary industry in marine GDP in coastal areas (X4)%0.0074
    Proportion of marine tertiary industry in marine GDP in coastal areas (X5)%0.0083
    Number of employed personnel involved in the sea (X6)×104 person0.0138
    Passenger traffic volume in coastal areas (X7)×104 person0.0770
    Marine resourcesCargo throughput of coastal ports (X8)×104 t0.0419
    Per capita water resources in coastal areas (X9)m3 person-10.0273
    Mariculture area in coastal area (X10)×104 ha0.0093
    Coastal wind power generation capacity (X11)×104 kW0.0758
    Coastal wetland area (X12)×104 ha0.0288
    Area of marine nature reserves in coastal areas (X13)×104 ha0.1950
    Marine biodiversity (X14)0.0548
    Marine environmentEconomic losses caused by storm surges in coastal areas (X15)×108 yuan0.2351
    Industrial wastewater discharge in coastal areas (X16)×104 t0.0110
    Standard rate of industrial wastewater discharge in coastal areas (X17)%0.0011
    Industrial waste gas emissions in coastal areas (X18)×108 m30.0242
    Industrial smoke (dust) emission in coastal areas (X19)×108 m30.0073
    Disposal capacity of industrial solid waste in coastal areas (X20)×104 t0.0583
    Comprehensive utilization of industrial solid waste in coastal areas (X21)×104 t0.0165

    Table 2.

    Evaluation indicator system of marine sustainable development

    3.2 Evaluation and analysis of marine sustainable development

    The average value method of correlation coefficients and the weighting method are used to calculate the relational degree. According to the standard of marine sustainable development (Table 3), the status of marine sustainable development in each year is analyzed. The average value method of correlation coefficients indicates that in 2016, 2014, 2015 and 2013, the values were 0.7146, 0.6345, 0.6273 and 0.6097 respectively, all of which are in the range of 0.6-0.8, so the marine sustainable development status was at a good level. The remaining years were between 0.4 and 0.6, which belong to the neutral level. The coefficient of variation weighting method shows that the value in 2012 was 0.6246, indicating the marine sustainable development was at a good level. In 2009, 2006 and 2007, the values were 0.3834, 0.3634 and 0.3632, which were all between 0.2 and 0.4, indicating the bad level. The remaining years had values between 0.4 and 0.6, belonging to the neutral level.

    The average value of grey relational degree is sorted according to the size, which is the basis of the marine sustainable development evaluation. In 2016 and 2012, the values were 0.6513 and 0.6014 respectively, which are in the range of 0.6-0.8, indicating that the marine sustainable development status belongs to a good level. In 2007, it was 0.3850, in the range of 0.2-0.4, belonging to the bad level. Values in the remaining years were in the range of 0.4-0.6, belonging to the neutral level. The dynamic changes of marine sustainable development based on average correlation coefficient method and the weighting method are slightly different, but the overall trends are basically the same (Table 3, Fig. 3).

    YearGrey relational degree of correlation coefficient average methodGrey relational degree of weighting methodAverage value of grey relational degreeRank
    20160.71460.58810.65131
    20120.57820.62460.60142
    20140.63450.52590.58023
    20150.62730.5210.57414
    20130.60970.53820.57395
    20110.53830.44550.49196
    20100.54870.43230.49057
    20080.45900.49410.47668
    20090.45710.38340.42039
    20060.44740.36340.405410
    20070.40690.36320.385011

    Table 3.

    Marine sustainable development based on the grey relational model

    Comparison of dynamic trends of the evolution of marine sustainable development based on the average correlation coefficient method and the weighting method

    Figure 3.Comparison of dynamic trends of the evolution of marine sustainable development based on the average correlation coefficient method and the weighting method

    3.3 Comparative analysis of dynamic trends of marine sustainable development

    The dynamic trends of the evolution of marine sustainable development calculated by the comprehensive index model and the grey relational model are basically consistent (Fig. 4), and the growth fluctuations of 2008 and 2012 are obvious compared with those of the previous year.

    Comparison of dynamic trend of the evolution of marine sustainable development based on the grey relational model and the comprehensive index model

    Figure 4.Comparison of dynamic trend of the evolution of marine sustainable development based on the grey relational model and the comprehensive index model

    3.4 The main driving forces of marine sustainable development

    3.4.1 Results of path analysis

    According to the principle of path analysis, the average value of the grey relational degree of marine sustainable development from 2006 to 2016 is taken as the dependent variable (Y). The standard values of X1-X21 were used as independent variables to test the normality of the dependent variable Y, and the output results are shown in Table 4.

    Dependent variable (Y)Kolmogorov-Smirnov(a)Shapiro-Wilk
    StatisticdfSig.StatisticdfSig.
    Average value of grey relational degree of marine sustainable development0.19030.99830.905

    Table 4.

    Output results of normality test

    The Kolmogorov-Smirnov and Shapiro-Wilk tests were used to test the normality. The former is more suitable for large samples, while the latter is more suitable for small samples. Therefore, after the normality test of dependent variable Y, the output of Shapiro-Wilk test was used. The Shapiro-Wilk statistic was 0.905, Sig. = 0.905>0.05, indicating that the dependent variable Y obeyed a normal distribution, so since Y is a normal variable and can be used for regression analysis. Through stepwise regression analysis, the linear regression equation was established to obtain the path coefficient (Table 5).

    RR2Adjusted R2Std. Error of the estimate
    0.951a0.9050.8940.0287146

    Table 5.

    Model overview output

    The coefficient of determination (R2=0.905) and the residual factor (${{b}_{Ye}}=\sqrt{1-{{R}^{2}}}$=0.3257>0.05) indicated that in addition to the above indicators, there are other factors affecting marine sustainable development. Because there are still some main driving factors not taken into account, the comprehensive analysis of the driving factors needs to be further refined. Based on the results of path analysis (Table 6), cargo throughput of coastal ports (X8) is found to be the main driving factor, and its direct path coefficient is 0.951, which indicates that it has a great direct impact on marine sustainable development.

    Driving factorsThe correlation coefficient of YDirect path coefficientIndirect path coefficient total
    X80.9510.9510

    Table 6.

    Decomposition of simple correlation coefficients

    3.4.2 Results of the grey relational analysis of each indicator

    Based on the grey relational coefficient table of the marine sustainable development index system, the grey relational weight of each indicator is calculated (Table 7). According to the order of the weights, the main driving factors of marine sustainable development are as follows: Economic losses caused by storm surges in coastal areas (0.1163), Area of marine nature reserves in coastal areas (0.1012), Coastal wind power generation capacity (0.0639), and Marine biodiversity (0.060).

    IndicatorGrey relational degree of correlation coefficient average methodGrey relational degree of weighting methodAverage value of grey relational degreeDriving force ranking
    X150.03510.19750.11631
    X130.03570.16660.10122
    X110.04540.08240.06393
    X140.05190.06810.06004
    X70.04050.07460.05755
    X20.04980.05830.05406
    X10.04750.05240.05007
    X200.04100.05720.04918
    X80.04730.04740.04749
    X60.06590.02180.043810
    X30.06730.01930.043311
    X120.05010.03460.042312
    X210.05250.02070.036613
    X90.04260.02790.035314
    X100.05640.01260.034515
    X40.05360.00950.031616
    X170.05830.00160.029917
    X160.04640.01230.029318
    X180.03520.02050.027919
    X50.04150.00830.024920
    X190.03600.00630.021221

    Table 7.

    Main driving factors of marine sustainable development

    4 Discussion

    The evaluation index system for marine sustainable development is a complex system, which is influenced and restricted by many factors. The main driving factors in different coastal areas are also different (Gu and Li, 2018). The selection of indicators is the basis of marine sustainable development evaluation, which directly affects the evaluation results. Therefore, indicator selection must conform to the principles of science, integrity, hierarchy, dynamics and stability, operability and practicability (Ren et al., 2018). At present, the difficulties of marine sustainable development evaluation are mainly reflected in the uncertainties of the evaluation model method, evaluation system framework, evaluation index, evaluation standard and simplification of the evaluation type (Zhang et al., 2019b).

    This paper presents a systematic study on the core issues and main links, such as index system construction, marine sustainable development evaluation and driving force analysis. By revealing the characteristics of dynamic changes and the main driving factors of marine sustainable development, this paper provides an important basis for the formulation and adjustment of marine sustainable development policies. Firstly, referring to the results of sustainable development index system research at home and abroad, the evaluation index system of marine sustainable development is constructed from the three aspects of the marine economy, resources and environment. The index data from 2006 to 2016 are constructed into a sample judgment matrix [X11×21], which represents 11 evaluation schemes, and each scheme has 21 evaluation indicators. A normalized matrix [Y11×21] is obtained by dimensionless treatment of the judgment matrix by the norm method. The norm method is used to standardize the index data, which will not increase the subjective information, but it also eliminates the adverse impact of extreme values on the evaluation, and improves the accuracy of the weights. Then, the coefficient of variation method was used to calculate the weight of the variation coefficient for each evaluation index. The average value method of correlation coefficients and the weighting method are used to calculate the degree of correlation of marine sustainable development. According to the mean value of the grey correlation degree, the sustainable development of the ocean is analyzed. The comprehensive index model is used to compare and analyze the characteristics of the dynamic trends of marine sustainable development of the grey relational model. Finally, the grey relational degree of each evaluation index in the evaluation index system is calculated, and the driving forces of marine sustainable development are analyzed combined with path analysis. Finally, this paper puts forward the corresponding countermeasures and suggestions to provide a scientific basis for the formulation of marine development strategies, provide decision support for the rational development and utilization of marine resources and their scientific management, and promote the coordination and sustainable development of the marine economy, resources and environment.

    At present, the sustainable development index system mainly includes the sustainable development index system of Commission on Sustainable Development (CSD), sustainable development index system of Organization for Economic Co-operation and Development (OECD), sustainable development index system of World Bank, sustainable development strategy research report index system of china, related indicators of global sustainability assessment system (Phondani et al., 2016), and the ecological sustainable development index system of urban coastal zone (Georgia et al., 2013). This paper constructs an index system of marine sustainable development, carries out monitoring evaluation and driving force analysis, provides methods for accurately measuring the status of marine sustainable development in coastal areas, provides a reference for regional development quality assessment objectives, and provides a basis for the timely adjustment of marine development planning and management countermeasures (Hull, 2020). It is of vital importance to reduce the contradictions between marine economic development and the marine resources and environment, and to promote the coordination and sustainable development of marine systems.

    In future research, more attention should be paid to the objective selection and simplification of the evaluation indicators of marine sustainable development, so as to reduce the subjectivity in the selection of evaluation indicators. It is necessary to continue to supplement the theoretical research framework of the marine economy, resources and environment, and to sort out the evaluation factors, evaluation systems, evaluation models, evaluation methods, evaluation standards and evaluation types (Di et al., 2018). It is also necessary to explore the interactions among the marine economy, resources and environment and the mechanisms of coupling and coordination. It is necessary to explore the mechanisms of disharmony among the marine economy, resources and environment, and the principle of coordinated governance (Sherman et al., 2019). It is necessary to explore the basic problems of the marine economy-resources-environment composite system, such as the functions, structures, interactive stress relationships and temporal and spatial evolution regulation.

    5 Conclusions

    In this paper, an evaluation index system of marine sustainable development is constructed, and the average value of the relational degree, as calculated by the average correlation coefficient method and the weighting method, is used as the final basis for evaluating the status of marine sustainable development. The comprehensive index is calculated and compared with the average value of grey relational degree, and the chart is drawn to verify and analyze the evolution of dynamic trends of marine sustainable development. Path analysis is combined with the grey relational degree of each indicator to analyze the driving forces. These analyses led to four main conclusions.

    Firstly, the evaluation index system of marine sustainable development in 2006-2016 is developed from the three aspects of the marine economy, resources and environment. The average value method of correlation coefficients revealed that the marine sustainable development in 2016, 2014, 2015 and 2013 was good, and in the remaining years it was neutral. The coefficient of variation weighting method showed that the marine sustainable development in 2012 was good; while in 2009, 2006 and 2007 it was bad and in the remaining years it was neutral. The dynamic changes of marine sustainable development calculated by the average correlation coefficient method and the weighting method are slightly different, but the overall trends are basically the same.

    Secondly, according to the average value of the correlation degree, as calculated by the average value method of correlation coefficients and the weighting method, the sustainable development of the marine industry in 2016 and 2012 was good, the situation in 2007 was bad, and in the remaining years it was neutral. By comparing and analyzing the dynamic trends in the evolution of marine sustainable development between the comprehensive index model and the grey correlation model, the overall trends of the dynamic changes obtained by the two models are basically the same. Compared with the previous year, the growth in 2008 and 2012 fluctuated significantly.

    Thirdly, the direct path coefficient of X8 is 0.951. Through path analysis, cargo throughput of coastal ports is found to be the main driving factor of marine sustainable development. The residual factor ${{b}_{Ye}}=\sqrt{1-{{R}^{2}}}$=0.3257> 0.05 indicates that there are other main driving factors that need further analysis. Therefore, combined with the grey relational degree of each indicator, we found that the economic losses caused by storm surges in coastal areas, area of marine nature reserves in coastal areas, coastal wind power generation capacity, and marine biodiversity are the main driving factors of marine sustainable development.

    Fourth, there are close interactions between the marine economy, resources and environment. Therefore, the blind pursuit of high economic growth will aggravate the depletion of resources and environmental degradation, and will ultimately be detrimental to further marine development. China's marine development stage is gradually changing from high growth to high quality. At present, China is in the critical period of changing the development mode and the driving force behind the growth. Thus, it is urgent to seek the coordination of the marine economy, resources and environment and to promote the marine sustainable development. On the one hand, we need to pay more attention to the characteristics and adjustment of economic structure, the development and utilization of resources, and environmental protection and optimization in the process of marine development. On the other hand, we need to deal with the coordination and developmental relationships among economy, resources and environment, emphasize the growth of the marine economy, reduce the consumption of resources and protect the environment.

    References

    [1] CaoQ, SunC, ZhaoL et al. Marine resource congestion in china: identifying, measuring, and assessing its impact on sustainable development of the marine economy. Plos One, 15, e0227211(2020). https://www.ncbi.nlm.nih.gov/pubmed/31978061

    [2] Di QB, Zheng JH, YuZ. Measuring chinese marine environmental efficiency: A spatiotemporal pattern analysis. Chinese Geographical Science, 28, 823-835(2018).

    [3] Eikeset AM, Mazzarella AB, DavíesdóttirB et al. What is blue growth? The semantics of “sustainable development” of marine environments. Marine Policy, 87, 177-179(2018).

    [4] HangX, Li XY, Xie XP et al. The quantitative meteorological evaluation model of cyanobacterial bloom in Lake Taihu based on path analysis. Journal of Lake Sciences, 31, 43-52(2019).

    [5] HullS. DOI, 183-194(2020).

    [6] GeorgiaM, KostasB, PanayiotisP. Operationalizing sustainability in urban coastal systems: A system dynamics analysis. Water Research, 47, 7235-7250(2013).

    [7] Gu YQ, LiP. Sustainable development of marine tourism economy in Zhoushan Archipelago new district. Ecological Economy, 14, 249-256(2018).

    [8] Laura RV. A preliminary assessment of the indicators for sustainable development goal (SDG) 14 “conserve and sustainably use the oceans, seas and marine resources for sustainable development”. Marine Policy, 98, 47-57(2018).

    [9] LiB, Shi ZY, TianC. Spatio-temporal difference and influencing factors of environmental adaptability measurement of human-sea economic system in Liaoning coastal area. Chinese Geographical Science, 28, 313-324(2018).

    [10] Liang JJ, Li YF. Resilience and sustainable development goals based social-ecological indicators and assessment of coastal urban areas —A case study of Dapeng New District, Shenzhen, China. Watershed Ecology and the Environment, 2, 6-15(2020).

    [11] LiuX, LiuH, ChenJ et al. Evaluating the sustainability of marine industrial parks based on the DPSIR framework. Journal of Cleaner Production, 188, 158-170(2018).

    [12] LiuZ, Dang YG, Xie YM. Space vector-based multivariate grey incidence model and its application. Statistics & Decision, 35, 13-17(2019).

    [13] NtonaM, MorgeraE. Connecting SDG 14 with the other sustainable development goals through marine spatial planning. Marine Policy, 93, 214-222(2018).

    [14] Phondani PC, BhattA, ElsarragE et al. Criteria and indicator approach of global sustainability assessment system for sustainable landscaping using native plants in Qatar. Ecological Indicators, 69, 381-389(2016).

    [15] Qian XH, Jiang QB. Application of variation coefficient method based on combination weights in quality evaluation. Journal of Nanjing Normal University (Natural Science Edition), 42, 44-48(2019).

    [16] RenW, JiJ, ChenL et al. Evaluation of China’s marine economic efficiency under environmental constraints—An empirical analysis of China’s eleven coastal regions. Journal of Cleaner Production, 184, 806-814(2018).

    [17] Selina MS. Rethinking marine resource governance for the united nations sustainable development goals. Current Opinion in Environmental Sustainability, 34, 54-61(2018).

    [18] ShermanK, PetersonB, DamarA et al. Towards sustainable development of Asian large marine ecosystems. Deep Sea Research Part II: Topical Studies in Oceanography, 163, 1-5(2019).

    [19] Wang LL, Zhang HQ. The impact of marine tourism resources development on sustainable development of marine economy. Journal of Coastal Research, 94, 589(2019). https://bioone.org/journals/journal-of-coastal-research/volume-94/issue-sp1/SI94-117.1/The-Impact-of-Marine-Tourism-Resources-Development-on-Sustainable-Development/10.2112/SI94-117.1.full

    [20] WangL, Liu BQ, XuM et al. Study of the sustainable development of green marine economy of Jiangsu Province based on energy analysis. Marine Science, 43, 46-54(2019).

    [21] ZhangC, Quan YB, Zhong HY. The building of marine ecological civilization and sustainable development: Conference report. Marine Policy, 110, e103627(2019).

    [22] Zhang ZH, Qu FY, Wang SQ. Sustainable development of the Yellow sea large marine ecosystem. Deep Sea Research Part II: Topical Studies in Oceanography, 163, 102-107(2019).

    [23] ZhuX, ZhaoZ, YanR. Coupling coordinated development of population, marine economy, and environment system: A case in Hainan Province, China. Journal of Coastal Research, 98, 18-21(2019).

    [24] Zhu ZC, ZhangZ. Modified G2 weighting method and demonstration based on coefficient of variation. Statistics & Decision, 35, 72-76(2019).

    Sheng GAO, Lin ZHAO, Huihui SUN, Guangxi CAO, Wei LIU. Evaluation and Driving Force Analysis of Marine Sustainable Development based on the Grey Relational Model and Path Analysis[J]. Journal of Resources and Ecology, 2020, 11(6): 570
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