• 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
    Location of Jiangsu Province, China
    Fig. 1. Location of Jiangsu Province, China
    Added value of the three types of marine industries in Jiangsu Province
    Fig. 2. Added value of the three types of marine industries in Jiangsu Province
    Comparison of dynamic trends of the evolution of marine sustainable development based on the average correlation coefficient method and the weighting method
    Fig. 3. Comparison of dynamic trends of the evolution of marine sustainable development based on the average correlation coefficient method and the weighting method
    Comparison of dynamic trend of the evolution of marine sustainable development based on the grey relational model and the comprehensive index model
    Fig. 4. Comparison of dynamic trend of the evolution of marine sustainable development based on the grey relational model and the comprehensive index model
    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

    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

    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

    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

    RR2Adjusted R2Std. Error of the estimate
    0.951a0.9050.8940.0287146
    Table 5.

    Model overview output

    Driving factorsThe correlation coefficient of YDirect path coefficientIndirect path coefficient total
    X80.9510.9510
    Table 6.

    Decomposition of simple correlation coefficients

    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

    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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