Volume 2 Number 1 ISSN 2516-158X Marine Marine Economics ...

Marine Economics and Management

Volume 2 Number 1ISSN 2516-158X

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1 Research on optimization of index system design and its inspection method: indicator design and expert assessment quality inspectionYin Kedong, Shiwei Zhou and Tongtong Xu

29 Evaluation of the marine economic development quality in Qingdao based on entropy and grey relational analysisPeide Liu, Xiaoxiao Liu and Hongyu Yang

39 Analysis of China’s coastal zone management reform based on land-sea integrationDahai Liu and Wenxiu Xing

50 Inter-basin water transfer supply chain coordination with the fairness concern under capacity constraint and random precipitationZhisong Chen and Huimin Wang



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EDITOR-IN-CHIEFYin KedongOcean University of China, China Email [email protected]

HONORARY EDITOR-IN-CHIEF(S)Keith W. HipelUniversity of Waterloo, CanadaLi JingwenBeijing University of Technology, China

ASSOCIATE EDITORSFang LipingRyerson University, CanadaKevin LiUniversity of Windsor, CanadaLiu PeideShandong University of Finance and Economics, ChinaGan jianpingHong Kong University of Science and Technology, Hong Kong, ChinaWu KejianOcean University of China, ChinaLi XuemeiOcean University of China, China

ISSN 2516-158X © Ocean University of China

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Marine Economics and Management Indexed and abstracted by:British Library

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Marine Economics and Management (MAEM) is an international, transdisciplinary journal focusing on marine economics and management research and practice. Through published articles, the journal aims to help societies become more sustainable and harmonious.MAEM covers research in integrated marine systems, marine modeling and prediction, marine planning and marine management, presenting theoretical insights and developments, as well as real-world case studies. Major articles are contributed by specialists in marine affairs, including marine economists and marine resource managers, and marine scientists. Articles from all relevant disciplines are invited, but all contributions must make clear the link between fundamental concepts and the central improvement of marine economics and management.

Research on optimizationof index system design and

its inspection methodIndicator design and expert assessment

quality inspectionYin Kedong, Shiwei Zhou and Tongtong Xu

Ocean University of China, Qingdao, China

AbstractPurpose – To construct a scientific and reasonable indicator system, it is necessary to design a set ofstandardized indicator primary selection and optimization inspection process. The purpose of this paper is toprovide theoretical guidance and reference standards for the indicator system design process, laying a solidfoundation for the application of the indicator system, by systematically exploring the expert evaluationmethod to optimize the index system to enhance its credibility and reliability, to improve its resolution andaccuracy and reduce its objectivity and randomness.Design/methodology/approach – The paper is based on system theory and statistics, and it designs themain line of “relevant theoretical analysis – identification of indicators – expert assignment and qualityinspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis,relevant factor analysis and physical process description are used to clarify the comprehensive evaluationproblem and the correlation mechanism. Second, the system structure analysis, hierarchical decompositionand indicator set identification are used to complete the initial establishment of the indicator system. Third,based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliabilityand validity test is used to perform single-index assignment correction and consistency test is used forKENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.Findings – Compared with the traditional index system construction method, the optimization process usedin the study standardizes the process of index establishment, reduces subjectivity and randomness, andenhances objectivity and scientificity.Originality/value – The innovation point and value of the paper are embodied in three aspects. First, thesystem design process of the combined indicator system, the multi-dimensional index screening and systemoptimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second,the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment areanalyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment,conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance ofmultiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.Keywords Expert assessment quality inspectionPaper type Research paper

1. IntroductionThe indicator system is a set of organic wholes with relevant, scientific, dynamic andpurpose characteristics. It consists of a series of interrelated, complementary, clear andwell-structured statistical indicators. The index system mainly includes the theoretical

Marine Economics andManagement

Vol. 2 No. 1, 2019pp. 1-28

Emerald Publishing Limited2516-158X

DOI 10.1108/MAEM-10-2019-0010

Received 16 October 2019Revised 19 October 2019

Accepted 19 October 2019

The current issue and full text archive of this journal is available on Emerald Insight at:www.emeraldinsight.com/2516-158X.htm

© Yin Kedong, Shiwei Zhou and Tongtong Xu. Published in Marine Economics and Management.Published by Emerald Publishing Limited. This article is published under the Creative CommonsAttribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivativeworks of this article ( for both commercial & non-commercial purposes), subject to full attribution tothe original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

This research is supported by the National Social Science Fund Major Projects (14ZDB151), Ministryof Education Philosophy and Social Sciences Development Report Cultivation Project (13JBGP005).

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system design

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index system and the evaluation index system. The theoretical index system is a set ofcomplete indicators; it analyzes and evaluates the research objects from the macro levelsystematically and comprehensively according to the relevant theoretical system. Theevaluation index system is based on the actual phenomenon of the researched problem.It analyzes the theoretical index system or related literature; the design is according to theavailability of the indicator data, the accuracy of the definition of the index and thefeasibility of the operation of the index. Fully consideration of design principles (Table I),using a series of consistency, validity, credibility test methods, set up a set of simple,practical and scientific indicators to optimize through multi-layer screening.

Saisana and Tarantola introduced polymerization systems, multiple linear regressionmodels, principal component analysis and factor analysis (Saisana and Tarantola). Muriaset al. (2008) used data enveloped analysis method to construct index aggregation andweighting in the index system, which could determine the weights of some indexesinternally. Shiau and Liu (2013) used fuzzy cognitive map and analytic hierarchy process(AHP) to construct the causal relationship between key indicators and evaluate sustainabletransportation strategies (Shiau and Liu, 2013). Zheng (2017) proposed a new method usingfuzzy theory and established an evaluation index system based on the development modelof water BOT (Zheng, 2017). Domestic scholars have also made great achievements.Bao Yidan proposed an improved AHP, combined with the orthogonal design ideas, tofurther improve the objectivity and impartiality of the evaluation process (Yidan et al., 2005).Yu Shunkun proposed a multi-index system for evaluating the performance of powersupply enterprises and applied the neural network and entropy method to the correlationanalysis of the index system. The results showed that the model and method had highaccuracy (Lisha et al., 2011). Liu Weijun combined the coefficient of variation method andGIS spatial analysis to establish a floor water inrush risk assessment model (Weitao et al.,2016). Xi Peiyu et al. established an evaluation index system for power purchase schemes,which combined AHP, coefficient of variation method and gray triangle whitening weightfunction to comprehensively evaluate different power purchase schemes (Peiyu et al.).

In summary, domestic and foreign scholars have studied a lot of methods for indexsystem construction. In general, they have used the multi-statistical analysis methods suchas DEA method, principal component analysis and AHP to construct the index system andconduct comprehensive evaluation. However, at present, there is no scholar system topropose the optimization and test of the index system design, which is the core of indexsystem design and construction; among them, the expert assignment quality test is one ofthe most important links of the index system optimization and testing. In the optimizationand testing of the indicator system, if there is no systematic expert assessment qualityinspection method, no matter how accurate and objective the indicator data are, howinnovative and scientific the index screening method is and how forward-looking andcomprehensive the index system test is, the final indicator system application will inevitablybe worse. In view of this, based on the analysis of the index system construction and testing

Logical-related design principles Scientific and holistic principlesPrinciple of compatibility and comprehensivenessComprehensive and balanced principlePotential and forward-looking principles

Data-related design principles Comparability principlePrinciple of operabilityEvaluation principle

Trend-related design principles Trend reflection principleTrue objective principle

Table I.Design principles ofevaluation indexsystem

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methods, this paper systematically studies and proposes the optimization and testingprocess of the index system design based on the expert evaluation quality test, in order toprovide a comprehensive evaluation index system for each field.

2. Relevant theoretical analysisTheoretical analysis is an important reference system of index system design. It is mainlyaimed at revealing the prior theoretical features of the development and change process ofthe studied object, analyzing relevant influencing factors and describing the internalstructure of the studied object and the basic principles and physical processes followed bythe dynamic evolution process.

The role of theoretical analysis is mainly reflected in the following aspects (Haibo, 2004):

(1) Analyzing the statistical results according to the theory. The statistical analysis ofthe survey data needs to be further explained through theoretical analysis in orderto further reveal the essential phenomena of the problem.

(2) Verifying and demonstrating the basic assumptions. When the statistical analysisresults are inconsistent with the research hypothesis, it needs to be explained bytheoretical analysis. Even if the statistical analysis results are consistent with theresearch hypothesis, it is also necessary to combine the specific theoretical analysisto demonstrate the correctness of the hypothesis from different angles.

(3) Theoretical abstraction and sublimation of practical experience. Some typical caseanalysis data are limited, and only by means of relevant theoretical analysis,problems can be found from specific phenomena, and the essential characteristics ofthe research problems can be grasped through representation.

2.1 Theoretical basis analysisTheoretical basis analysis is a scientific analysis method put forward by Li QingZhen in1999. Compared with the empirical analysis method, it is a method to explore and study thenature of problems and their development rules by using rational thinking on the basis ofperceptual knowledge (Qingzhen, 1999). In 2011, HeYun further defined theoretical analysisas a series of systematic analysis on the basic principles, related concepts, connotationextension, attribute characteristics, classification characteristics, basic theories andrepresentation of practical problems involved in the research object (Yun, 2011).

Through theoretical analysis, the research object can be further explored in a systematic,scientific, logical, relevant, general and frontier way, and the process, rules and mechanismof the occurrence, development and evolution of the research object can be distinguishedand analyzed, and its future development trend can be explored and predicted. However,due to the timeliness, particularity, limitation and complexity of practical conditions, thereare still some differences between relevant theoretical analysis and research objects,objective facts and constraints. Therefore, theoretical analysis must be closely related toreality, must deeply analyze the attributes of the research object, must master sufficientdata, must ensure the objectivity, accuracy and scientificity of the analysis conclusion andmust be able to pass the test of relevant theory and practice.

Theoretical analysis is a priori or empirical indirect research method, which is the basisof analysis of many practical problems. After failing to fully know, understand and graspthe internal mechanism and the essential characteristics of the actual research object, thetheoretical analysis provides researchers with an empirical research train of thought,reveals the common property of this kind of research object and also provides researcherswith a relatively efficient analysis method, on the basis of in-depth and creative research.When the theoretical analysis system becomes more and more abundant and perfect,

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researchers can make full use of the previous research literature and existing materials toget relevant research conclusions quickly through theoretical analysis, which is moreefficient than empirical analysis.

2.2 Correlation factor analysisFactor analysis is a multivariate analysis method first proposed by C. Spearman in 1904 inthe field of psychology. In addition to being influenced by potential factors, a certainresearch object is more influenced by external factors. If there is no external conditionconstraint, the research can only be conducted according to the observed data (Spearman,1923). Spearman’s two-factor theory of intelligence holds that intelligence is a combinationof general factors and special factors. Later, Thurston et al. proposed “thurston’s intelligencegroup factor theory” in 1938 (Xiting et al., 2003). In 1961, Vernon divided intelligence intodifferent levels of factor branches through factor analysis and established the factorhierarchy structure (Mingyuan, 1998). Carroll (1993) proposed a three-level model ofcognitive ability by integrating some viewpoints of two-factor theory, multi-factor theoryand information processing theory. In 2012, Yao Ligen et al. defined factor analysis methodas a qualitative analysis method, which mainly analyzed various factors affecting theresearch object based on the empirical knowledge of researchers, and then carried out theanalysis (Ligen and Xuewen, 2012).

Factor analysis is an empirical analysis method. In fact, it analyzes the correlation factors ofthe research objects and judges the relationship among various factors. The correlation betweenvariables is mathematically described by the correlation coefficient. The correlation coefficient istaken as [−1, 1], −1 is the complete negative correlation between the two variables and +1 is thecomplete positive correlation between the two variables. The larger the absolute value of thecorrelation coefficient, the stronger is the correlation.

The factor analysis method can carry out the internal and external features of theresearch object, such as roughing and refining, de-authentication, and thus processing fromthe table and the inside, and then excavating the inherent essential relationship of objectivethings. However, the factor analysis method can simplify the research problem, ensure acertain amount of information and greatly improve the efficiency of research.

2.3 Description of physical processPhysical description was proposed by Meigang Zhang and others in 2008, which is mainly asystematic analysis and description of the state of things, including the attributes,characteristics and corresponding terminology of things (Meigang et al., 2008). It mainlyincludes general description, description of object and substance and description of concept.It mainly explains and describes the meaning of relevant terms and concepts in detail. It canbe compared and described with other research objects, and at the same time, keywords canbe appropriately used to simplify the description process or brief explanation can be madethrough cases.

Process expression was put forward by Lan Wang and others in 2004. It means toexpress the theoretical knowledge and research method of the studied problem into asolution process. A series of in-depth studies can be carried out smoothly only when theanalysis process of the problem is fully mastered (Wei and Yujun, 2004). Process expressionincludes some objective laws, development changes, correlation relations and researchprogress of the research object. All research information is included in the processexpression. Process presentation clearly represents the problem background, theoreticalknowledge, solution method, etc., of the research object as a series of research processes,which is a process and focuses on the description of dynamic process. However, some otherdescription methods mainly focus on the static description of the research object.

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Description of relationship mechanism (analysis of mechanism) is based on the analysisof the system’s internal reasons (mechanism). It is a scientific research method that studiesthe intrinsic working mode of each element in a certain system structure and the operatingrules and principles of the interconnection and interaction of various elements under certainenvironmental conditions, so as to find out the law of its development and change.

3. Identification of complete set of indicators3.1 Analysis of system structureThe concept of system exists in all things in human society and nature, and system thoughtruns through the whole practice process of human society. A system is an organic wholecomposed of two or more organic elements with a certain structure and specific functions.System structure analysis (Yun, 2011) is mainly used to identify the components of the systemelements of the research object and the relationship between the elements and the system, soas to provide analysis basis for revealing the hierarchical structure among the elements, thehierarchical division of the elements and the causal relationship among the elements.

First, system method starts from wholeness; it comprehensively inspects the relationshipbetween system and elements, elements and elements, system and environment as well asits rule of movement, so as to achieve the purpose of optimizing the overall function of thesystem. It was first proposed by Bertalanffy in 1937.

The main principles of the general system theory analysis are as follows: integrity,connectedness, order, structure, hierarchy, dynamic, environmental adaptability,optimization and so on. Integrity is the first basic principle, which requires that theunderstanding of the system must start from the integrity, thus achieving the integrity ofthe understanding; connectedness emphasizes the organic connection between each elementand the system, and emphasizes the structural and integral integrity of the system.The structure of the system is hierarchical, and the hierarchy of the structure shows thehigh order of the system. Any objective system is an open and evolving one. In order todevelop from low order to high order, the system in dynamic change must adapt to theenvironment and constantly optimize its structure and function.

The general system theory analysis method must not only raise the problemcomprehensively and systematically, but it must also accurately explain the problem andestablish the system goal. At the same time, it must formulate the plan and establish andverify the relevant mathematical model, evaluate and select the optimal solution to solve theproblem. Making decisions and implementing plans require constant review, feedback andrevision from practice.

Second, method of cybernetics is a scientific method for applying control theory toresearch, identify and solve system control problems. It mainly analyzes the interactionbetween system and environment, elements and elements, and elements and systems(positive and negative feedback relationship), and it reveals and describes the behavioralcharacteristics of the system. It was first proposed by Norbert Wiener in 1948. At present, amethod group including a feedback method, a control method, a function simulationmethod, a black box method, a white box method, a gray box method and a systemidentification method has been formed.

Third, the method of information is a modern scientific research method that abstractsthe motion change process of the object system into the information transformation processand then recognizes the law of the system motion according to the informationcharacteristics. It was first proposed by Shannon in 1948.

Fourth, methods of synergetics are scientific methods for studying how systemsspontaneously produce ordered structures and study the common laws of how elements(or subsystems) in various types of systems can produce overall effects through synergy.It was founded by the famous German theoretical physicist H. Haken in the 1960s.

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Haken discovered that the system evolved from disorder to order. Non-equilibrium phasetransitions and equilibrium phase transitions have their commonalities. They are thecoordinated results of each element (or subsystem) through nonlinear interaction andcoherence effects, which can be processed by the same mathematical model.

Fifth, the method of catastrophe theory is a scientific method for studying the non-continuous qualitative transformation process and its laws by using mathematical toolssuch as topology, singularity theory and structural stability. It was first proposed by theFrench Mathematician R. Thom in 1972. Qualitative change is a common phenomenon in theobjective world. The combination of catastrophe theory and synergistic system methods isconducive to the formation, structure and development of the system of in-depth study andreveals the system’s transformation mechanism from disorder to order.

3.2 Hierarchical decomposition of the systemThe social and economic complex system generally has the characteristics of deep structure,higher system order, strong element correlation and more object conflicts. “Decomposition‒coordination” method is an effective approach to the study of complex large system.“Decomposition” is to decompose a complex system into simple subsystems (or elements)and solve them separately. Due to the complex association between the elements, thesolution of the subsystem is not a system solution and even has a conflict. “Coordination”coordinates the consistency and compatibility of subsystem solutions, and it is achievedthrough the overall goals of the system and associated constraints. The “decomposition‒coordination” of complex systems can make the system present a hierarchical multi-levelstructure, which is a hierarchical structure. The results of systematic hierarchicaldecomposition provide an important reference for hierarchical structure and classification ofindex system.

3.2.1 System hierarchical structure. Messervik proposed three types of hierarchicalstructures (Yonghua, 1997):

(1) Multiple hierarchical structure, mainly focusing on multi-level structure division offunctions of natural attributes of complex systems. The function of the system needsto start from the specific principle corresponding to various hierarchies and multipledescriptions of the corresponding features of the system, so as to fully analyze anddescribe the overall function of the system. The multiple hierarchies of systemfunctions have their own different rules and principles, inputs and outputs, andfinally the multiple hierarchies are coupled.

(2) Multi-level hierarchical structure, mainly focusing on the hierarchical division of thecontrol, management, decision-making and other objectives of complex systems.The stratification of goals such as control, management and decision-makingfocuses on the stratification of the concept of “what to do” rather than thestratification of “task details” and “how to.” The higher the target level, the greater isthe strategic significance and the more complex is the decision; the lower the level,the more frequent is the decision.

(3) Multi-level hierarchical structure, mainly focusing on the hierarchical decompositionof the organizational composition of complex systems, including horizontaldecomposition and vertical decomposition. The multi-level hierarchical structure is apyramid-shaped mesh structure or a tree-shaped mesh structure. The lower levelstructural units are coordinated by local controllers, and the higher level structuralunits are coordinated by associations and constraints to achieve global optimization.The descriptions and problems of higher level structural units are less structural,have greater uncertainty and are generally more complex than lower level units.

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3.2.2 Decomposition and coordination of hierarchical structure. Decomposition ofhierarchical structure: in general, the total objective function of complex systemoptimization is the objective function that can be decomposed into multiple subsystems,and the subsystem is the local optimization problem when the coordination parameters arerelatively fixed. It is necessary to determine the inter-relationship between subsystems andtheir local objective function and constraints.

Coordination of hierarchical structure: the optimal result of the subsystem is closelyrelated to its coordination parameters. Therefore, it is necessary to verify or improvethe coordination parameters according to the iterative coordination rules to make theglobal optimization.

3.2.3 Conceptual model design. The conceptual model is a description of real-worldproblems or things (Environmental Engineering Evaluation Center of the Ministry ofEnvironmental Protection, 2016), which is the first layer of abstraction from the realworld to the information world. The conceptual model must have four characteristics:first, rich in semantic expression, able to express various needs of users, fully reflect thereal world, including the connection between things and the user’s processingrequirements for data; second, easy to communicate and understand, expressednaturally, intuitive and easy to understand, in order to exchange ideas with users who arenot familiar with the computer; third, easy to modify and expand, can be flexibly changedto reflect changes in user needs and real-world environment; fourth, easy to convert tovarious data models and can be easily converted to various data models such as relationalmodels and hierarchical models.

3.3 Identification of the complete set of indicatorsAn indicator is a concept that describes the quantity or quality characteristics of thepopulation. It is the unit or method of measuring the target. It represents the expected index,specification, target or standard, and it is generally expressed by data (quantitative data andqualitative data). The index is generally composed of two parts: the name of the indicatorand the value of the indicator (data). It embodies the characteristics of the prescribing natureof the substance and the prescriptiveness of the quantity.

Indicators and signs are different and cannot be mixed. First, the indicators indicate theoverall characteristics, reflecting the overall quantity or quality characteristics, and mustuse the quality or quantity data (quantitative, qualitative, etc., quantitative, qualitativedescription) to answer questions, cannot answer questions with questions, the indicator datais after certain The summary obtained, a complete indicator should have the time, place,scope, data and other conditions. Second, the logo indicates the characteristics of the overallunit, which reflects both the characteristics of the overall unit quantity and the qualitycharacteristics of the overall unit. Only the quantity mark uses the number to answer thequestion, and the quality mark uses the word to answer the question. The quantity mark inthe mark may not be aggregated or obtained directly. The logo generally does not haveconditions such as time and place.

The identification of the complete set of indicators is mainly for the multi-dimensionalanalysis and analysis of factors in the process of “theoretical analysis, factor analysis,process analysis, structural analysis, hierarchical decomposition” and so on, to clarify themeaning of indicators, the role of indicators, the attributes of indicators, indicator data,calculation methods, measurement units, as well as indicator type, indicator relationshipand other characteristics, build indicator collection warehouse or index set dictionary andprovide a basis for screening, testing and optimization of indicator system.

Indicator attributes include subjectivity and objectivity indicators, randomness,certainty indicators, discreteness, continuity indicators, quantitative and qualitative

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indicators, and positive, negative and neutral indicators. Indicator types include thefollowing: semantic indicators, statistical indicators, calculation indicators, comprehensiveindicators, end indicators, time point indicators, period indicators, physical indicators,value indicators, descriptive indicators, evaluation indicators, early warning indicators,technical indicators, quantitative indicators, qualitative indicators.

Quantitative indicators are mainly a quantitative result, statistical data, such as totalindicators, relative indicators, average indicators, incremental indicators and growthindicators. Qualitative indicators, usually unstructured, empirical, revealing and difficult tocategorize, are generally subjective, descriptive and semantic:

(1) Total indicator: it reflects the size, quantity and total difference of a research object,which is the basic statistical indicator. The size of this indicator is directly affectedby the overall size, reflecting the problem with a certain one-sidedness.

(2) Relative indicators: also known as “relative numbers,” the ratio of two relatedindicator values reflects the degree of development, structure, intensity, prevalence orproportionality of the research object. Common relative indicators are structuralrelative number (specific gravity), proportional relative number, comparative relativenumber, intensity relative number, dynamic relative number and elastic coefficient.

(3) Average indicators: analytical indicators reflecting the general level of the researchobject, divided into static average and dynamic average. The static average is thegeneral horizontal state at a certain time, and the dynamic average is the horizontalstate at different points in a period of time.

(4) Variability indicators: indicators that reflect the extent of the overall difference in thestudy subjects. Common ones are extreme difference, mean difference, standarddeviation, coefficient of variation, etc.

3.4 Indicator system primary selectionThe construction of the indicator system is generally divided into three steps: initialconstruction of indicator system, screening of indicator system and optimization of indicatorsystem structure. The initial construction methods of the indicator system includequalitative methods and quantitative methods. At present, most of the practical applicationsuse qualitative methods to select indicators.

For the initial construction of the indicator system, first, it is necessary to clarify theevaluation object and the purpose of the evaluation. The evaluation object and the evaluationpurpose directly determine the selection of the indicator system and the evaluation method.Second, it is necessary to determine the primary selection method of the indicator system(such as the system analysis method, Delphi method), determine the “top-down” or“bottom-up” index system construction sequence, obtain the evaluation index set, determinethe mutual index and structural relationships, such as using the target hierarchy to evaluatethe level and using factor decomposition structure (DuPont indicator system) to analyze thefactors. Third, it is necessary to determine the connotation of indicators, the role of indicators,data sources, calculation methods and measurement units.

The initial construction methods of the index system in the existing literature are shownin Table II. The traditional methods have their own characteristics and different emphasis.They are subjective and random, and lack the basis of the normative method. It is difficult toadapt to the research requirements of the rapidly developing indicator system.

The primary selection method of the index system in this paper follows a series ofstandardization and scientific processes from “the theoretical basis analysis, relevant factoranalysis, process mechanism description” to “system structure analysis, system hierarchical

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decomposition, indicator complete set identification.” On the basis of comprehensiveanalysis and reference to traditional methods, the system engineering and hierarchicalstructure decomposition theory are used to optimize the standardization process andanalysis method of the primary selection of index system, which reduces subjectivityand arbitrariness, enhances objectivity and scientificity, and agrees with the normative andtransparent nature of the primary selection method.

4. Indicator expert assignmentIn the design of the indicator system, there are three tasks that require consulting expertsor an expert seminar to confirm the relevant content. First, through the primaries, theindicators are fully concentrated, the reliability and feasibility, importance and necessityof each indicator, validity and rationality, particularity and forward-lookingcharacteristics require expert evaluation scores in relevant fields to classify eachindicator. Second, there are no quantitative indicators and qualitative indicators ofindicator attribute values. Experts in related fields need to be assigned scoring. Third, theweighting of each indicator in the indicator system requires expert assignment scoring inthe relevant field.

Method Principle Application field Advantages Disadvantages

Thecomprehensivemethod

Refer to the existingindicators,comprehensivelyanalyze andsummarize to formnew indicators, andclassify the moremature socioeconomicevaluation indexsystem.

More maturesocioeconomicevaluation indexsystem

Drawing on researchexperience andresults, avoidingsubjectiverandomness andintegrating multipleperspectives

Indicators are oldor even misleadingand difficult toreflect new changes

Analysis Subdivide researchobjects andmeasurement targets,partition subsystemsand their functionalmodules

Sustainabledevelopmentevaluation indexsystem, economicbenefit evaluationindex system

Systematic analysisof different featureattributes of researchobjects

Influenced byknowledgestructure, researchlevel, familiarity,etc., it has certainsubjectivity

Target level According to theresearch objectives,build the target layerand the criteria layerto form an indicatorsystem.

Comprehensiveevaluation system forplanning scheme

Easy to understand,simple and practical,the target structurecan reduce thecrossover ofindicators

Subjective andsubjectivecategorization

Cross method Through multi-dimensional cross-analysis, a series ofindicators are derivedto form an indicatorsystem

Economic benefitevaluation, evaluationindex system ofcoordinateddevelopment of socialeconomy and science

Can reflect thecontrast orcoordination betweenmultiple elements

Cross-analysis isdifficult to controland has a limitedrange ofapplications

Index attributegrouping

Determine theindicator compositionbased on the indicatorproperties (dynamic,static, absolute,relative)

Unemployment statusevaluation indexsystem, etc.

The indicatorattributes are clearand considered morecomprehensive

Indicators are easyto repeat, singleindicator type Table II.

Initial constructionmethod of the

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4.1 Expert background analysisExpert assignment or scoring is an important task in the process of index design. Whether itis the attribute value score of qualitative indicators or the importance and rationality ofindicators, the determination of index weights, etc., it is necessary to consult experts inrelevant fields and pass expert seminars. In other forms, the relevant experts of theorganization assign or score the different attribute requirements of the indicator. However,due to the different academic background, influence and judgment basis of the experts, it isnecessary to uniformly measure the representativeness and authority of the expertassignment or scoring value. Therefore, it is necessary to fully understand the academicbackground and research fields of relevant experts.

The representativeness or authority of expert opinions is generally determined by threemain factors: first, the educational background, age and professional title of the experts;second, the academic level, influence and credibility of the experts; third, the practicalexperience and problems of the experts, and the level of familiarity and the basis for judging.The degree of representation or authority of expert opinions is mainly self-evaluation.

The educational background, age and title of the experts themselves: the educationalbackground of the experts themselves mainly includes the basic situation of domestic andforeign educational experience, age, administrative duties, professional technical titles andacademic tutor level.

The academic level, influence and credibility of the experts themselves: the academiclevel of the experts themselves mainly include the number of academic articles, the numberand level of monographs and their citations, the number and level of relevant scientificresearch topics, the number and level of awards for major scientific research achievements,the number of academicians and other talents in related fields, the level of academic grouporganization in related fields, recognition and popularity of peer experts.

The expert’s practical experience and familiarity with the problem mainly include thetime of research in the professional field, the nature and level of the work unit of the expert,the number of patents applied in related fields, the number of scientific research projects, thesocial and economic benefits, and review expert level, etc.

The attribute value range or scoring interval of the three main factors of the expert is[0,100] points. The attribute assignment or scoring of each major factor is formed bythe project organizer based on the relevant information of each expert and soliciting theopinions of other relevant experts.

The relative importance of the three main factors of the degree of authority ofexpert opinions can be determined by a pairwise comparison method. Among the threemain factors, “the academic level and influence of the experts themselves and thecredibility” are the most important and can be assigned 100 points. Then, “the expert’spractical experience and familiarity with the problem” can be assigned 85 points.The importance of background, age and title for the experts themselves can be assigned70 points. According to a large number of practical comparisons, consultations andempirical judgments, the weight coefficients of the three factors are 0.40–0.45, 0.30–0.35and 0.25–0.30.

The degree of authority of i expert opinion is expressed by Ei, which can be calculated byweighted average. The larger the weighted average value, the higher is the authority of theexpert opinion (Table III).

4.2 Indicator attribute value domainThe indicator attribute range is the value range of the expert’s evaluation of the importance,rationality and forward-looking attributes of the indicator. It is often determined accordingto the meaning and role of the attribute of the indicator, and it is also an important referencefor the expert assignment score. Indicators have different attribute types, such as

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quantifiable indicators, non-quantitative indicators, positive indicators, negative indicators,neutral indicators, etc. The attribute values or data types of its indicators can beappropriately adjusted and transformed accordingly.

Non-quantitative indicator attribute value ranges can take many forms, and otherindicators that lack data can determine the value range based on their indicator attributes:

(1) Quantity formula: 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 0 or 10, 9, 8, 7, 6, 5, 4, 3, 2, 1.

(2) Interval: 90–100, 80–90, 70–80, 60–70, 50–60, 40–50, 30–40, 20–30, 10–20, 0–10.

(3) Quantifiers: excellent, good, medium, qualified, poor, very poor or good, good,average, poor, very poor.

(4) Level: special, first, second, third, fourth, and fifth (level scale can also berepresented by different numbers).

(5) Definition formula: A, B, C, D, E.

4.3 Expert assignment of indexesThe expert assignment or score of the index is mainly based on the importance of the index,the attribute value of the qualitative index and the weight of the index. In essence, it is aform of consultation, correspondence, discussion, research, questionnaire, discussion, etc., toorganize experts and scholars in related fields, respectively, to conduct diagnosticevaluation and score on relevant indicators, mainly including direct evaluation, pairwisecomparison, fuzzy evaluation and interval evaluation.

The assignment score of experts can take the independent assignment score of eachindex in the same level index under a certain kind of research (evaluation) object, and it canalso take the pairwise comparison assignment score of the importance of the same levelindex under a certain kind of research (evaluation) object. Common expert assignmentscoring methods are detailed next.

4.3.1 Delphi assignment method. Delphi assignment method is essentially a feedbackanonymous consultation method, which is a collective anonymous thought exchangeprocess in the form of correspondence consultation. It was first pioneered by Olaf Helmerand Norman Dalkey in the 1940s. In 1946, RAND Corporation first used this method toconduct qualitative rating and prediction in order to avoid the defects of subjection toauthority or blind obedience to the majority in group discussion. Later, this method wasquickly and widely adopted, and enjoyed high credibility in evaluation, decision-makingand planning (Environmental Engineering Evaluation Center of the Ministry ofEnvironmental Protection, 2016). By improving Delphi’s process, Vakil et al. (2006)adopted the method of repeated iterative voting to reach consensus and improve thetransparency and reliability of decision-making (Yuxiang and Donghua, 1990). NavdeepKaur et al. proposed a multi-round progressive improvement of Delphi method to enhancethe universality of analysis (Vakil et al., 2006).

Factor

Expert’s owneducationalbackground, age,title (X1)

The academic level andinfluence of the expertsthemselves and theircredibility (X2)

Expert experienceand familiarity withthe problem (X3)

The degree ofauthority of expertopinions (Ei)

Weight(value range) 0.25–0.30 (0–100) 0.40–0.45 (0–100) 0.30–0.35 (0–100) 1.00 (0–100)

Table III.Authoritative opinion

degree and itsfactor weight

distribution table

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Wang Chengbin (1991) used Bayesian estimation to improve the Delphi method to enhancethe prediction accuracy (Kaur and Pluye, 2019). Lian Jinyi (1992) quantified expert opinionsto improve Delphi method (Chengbin, 1991). Cai Hui et al. (1995), based on the credibility ofexpert opinions and the overall evaluation of experts, improved the coordination degree ofexpert evaluation ( Jinyi, 1992). Zong Jinfeng et al. introduced the idea of set value statisticsto convert the evaluation results that are difficult or impossible to be quantitative intointerval valuation, thus improving the Delphi method (Hui et al., 1995). Daowu et al. (2004)adjusted the improved decision-making procedure of the Delphi method based on theevolutionary game, so as to reach a consensus group game example ( Jinfeng and Shuqin,1997). Guan Chun et al. modified the Delphi method based on the weighted averagecalculation model of BP neural network, so as to reduce the influence of subjective factors onweight distribution (Daowu et al., 2004). Yuan Jixue et al. improved Delphi method based onconsistency analysis and listed the personnel composition and working procedure diagramof group decision-making (Chun and Jun, 2006). Zhang Qun et al. (2010) introduced Vagueset and its similarity measurement formula and established the Delphi method processbased on Vague set theory, so as to make more use of expert opinions ( Jixue and Weijin,2010). Yuan Qinjian et al. used Citespace to analyze the development and application ofDelphi method in China and found that “index system” and “comprehensive evaluation”were the first application fields (Qun et al., 2010).

Delphi assignment method generally requires 8–20 experts and has obvious decision-making advantages:

(1) The authority of experts: it makes full use of the experience, background andexpertise of famous experts.

(2) Anonymity of experts: experts are anonymous and back-to-back, and each expert isfree to make his or her own judgment.

(3) Convergence of assignments: the opinions of experts are summarized and fed back,and the opinions of experts are adjusted and modified.

(4) Statistical quantification: one median and two quartiles of all expert assignments arecounted, 50 percent of which are in the two quartiles and 50 percent are outside thetwo quartiles.

4.3.2 Likert score. In 1932, Likert scale was proposed on the basis of the improvement of theoriginal total addition scale. Likert scale is composed of a set of problem statements andtheir expert ratings, indicating the strength or state of the expert’s attitude (Qinwei et al.,2011). Schuman and Presser (1981) used words such as “uncertain” or “unclear” to set up noresponse options and improved Likert scale to make the separation test of information moreaccurate(Chonghua and Wenfu, 2013). Bai Kai (2011) added “no response” option in Likertscale to improve the integrity of data information (Schuman and Presser, 1981). HanGuanghua et al. studied the influence of different semantic expressions on scientificmeasurement in Likert scale and used numerical analysis and mathematical reasoningmethods to determine the range of semantic differences with reliability (Kai, 2011).

Common Likert score include four-stage grading method, five-stage gradingmethod, six-stage grading method, seven-stage grading method and sometimesten-stage grading method. The determination of grade is often more arbitrary, and therecognition of the advantages and disadvantages of various grades is based more onperceptual experience (Table IV ).

4.3.3 Fuzzy semantic scale scoring method. Fuzzy semantic scale score was producedafter American mathematician L.A. Zadeh proposed “fuzzy mathematics” in 1965. In reallife, in answer to a question or judgment, there are often “is also the essential” in the middle

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of the state, people use natural language description of “very clear, clear, not too clear, notclear, very not clear” (very good, good, relatively good, average, poor, very poor), as well aslikert rating, etc., with strong fuzziness, expert assignment or score results are often mustrely on subjective judgment in fuzzy several options to determine an option.

It is more objective to assign fuzzy language with fuzzy semantic scale (Guanghua andBo, 2017). The fuzzy semantic scale not only considers the general fuzziness of language, butalso synthesizes the subjective difference of expert cognition, so the score is more accurate.

It is assumed that there are k states for the answer or evaluation of a certain question X,and the corresponding score of each state is X1, X2,…, Xk. According to his/her ownpsychological feelings or cognitive level, expert i needs to fill in his/her perception level foreach state j, which is expressed as percentage Aij. The score quantification value of expert’sfuzzy semantic scale is expressed as follows:

Fi ¼Xkj¼1

kþ1�jð Þ � Aij ¼Xkj¼1

Xj � Aij;

where the corresponding score of state j is Xj¼ k + 1−j.For example, there are five states, “very satisfied, somewhat satisfied, satisfied,

dissatisfied, very dissatisfied” on a scale of 0–5 (or 0–10), concerning the question “are yousatisfied with your ability to do your job?”. According to the degree of perception,respondents (or experts) filled in the corresponding degree of perception (percentage) foreach of the five states. The fuzzy semantic scale of this respondent is shown in Table V.

The score quantification value is F¼ 5×5% + 4×80% + 3×5% + 2×10%¼ 3.62.This score indicates that the expert’s true perception is between “satisfied” and “slightly

satisfied,” rather than “satisfied” with the traditional assignment rating options. Therefore,the fuzzy semantic scale scoring method is more realistic and accurate.

4.3.4 Pairwise comparison assignment. Based on the analysis of the backgroundinformation of the problem, each expert analyzed and sorted the target indicators,gave 100 points for the best indicator and 0 points for the worst indicator, and thencompared other indicators with these two indicators and gave corresponding assignmentor score.

Grade Content of grading options

Four-stage grading method 1 – not important; 2 – generally important; 3 – important; 4 – very importantFive-stage grading method 1 – very unimportant; 2 – not important; 3 – generally important; 4 – relatively

important; 5 – very importantSix-stage grading method 1 – very unimportant; 2 – not important; 3 – not too important; 4 – important;

5 – relatively important; 6 – very importantSeven-stage grading method 1 – very unimportant; 2 – not important; 3 – not too important; 4 – generally

important; 5 – important; 6 – relatively important; 7 – very importantTen-stage grading method Use any number from 1 to 10 to indicate importance. A score of 10 out of 10

indicates very importantTable IV.

Likert score method

Fuzzy semantic scale score Very satisfied Satisfied Slightly satisfied Dissatisfied Very dissatisfied

State score 5 4 3 2 1Percentile of perception (%) 5 80 5 10 0

Table V.Expert fuzzy

semantic scale

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It is also possible to assign values according to the pairwise comparison method of1–9 scales in the AHP method. The scale values of pairwise comparison and quantificationare shown in Table VI.

5. Expert assignment quality inspection of individual indicatorsThrough expert assignment scoring, each indicator has an expert assignment set, and allsets can form an expert assignment matrix. The quality evaluation of the expert evaluationof a single indicator is mainly for the validity of the evaluation of the expert, the consistencyof the assignment of multiple experts, etc., and the rationality of the design of the indicatorcan also be tested.

The quality of assignment refers to the degree to which the consistency, correctness andintegrity of the expert assignment are satisfied in the information system. The quality testof assignment is mainly to test the reliability of the expert assignment and to check theabnormal points in the expert assignment in order to exclude or correct the errorassignment. At the same time, it is also possible to judge whether there is a difference in theunderstanding of the indicator by the quality test result of the expert assignment and to testthe rationality of the indicator design.

Suppose n represents the number of experts, k for the number of indicators, and xij for thevalue of the ith expert assignment to the jth indicator. xj(max) represents the maximumvalue in the jth indicator value(data) range.

5.1 Statistical analysis of expert assignments with single indicatorsThe statistical analysis of expert assignment of individual indicators mainly includes threeaspects: expert participation, concentration of expert opinions and coordination of expertopinions. Among them, the expert participation degree is measured by the positivecoefficient of the expert assignment; the expert opinion concentration degree is measured bythe average number, the perfect rate and the dispersion degree of the expert assignment;and the coordination degree of the expert opinion is measured by the coefficient of variationand the coordination coefficient.

5.1.1 Expert participation analysis. The enthusiasm or participation of experts isreflected by the positive coefficient Pj. Pj is used to reflect the level of concern, participation,and cooperation of experts on research issues, which can be measured by the ratio of thenumber of experts participating in the evaluation to the total number of experts generally.This ratio can be measured by the recovery rate and efficiency of the questionnaire.In general, Pj ⩾ 50% is the lowest ratio that can be used for analysis, and Pj⩾80% indicatesthat the expert’s participation is relatively high.

5.1.2 Analysis of the concentration of expert opinions. The importance of the indicatorcan be expressed by the average value xj and the full rate Kj of the jth indicator by the mjthexperts. The degree of concentration of expert opinions indicates the degree of consistencyof experts on the assignment of indicators, which can be expressed by the dispersion of theassignment of the j indicator by mj experts.

Scale 1 3 5 7 9 2, 4, 6, 8

Index i relativeto index j

Equallyimportant

Slightlyimportant

Obviouslyimportant

Strongimportant

Extremeimportant

Somewhere in the middle ofthe previous number

Table VI.Pairwise comparisonof 1–9 quantitativescales of importanceof indicators

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The average of expert assignment is given as follows:

xj ¼1mj

Xmj

i¼1

xij; (1)

wheremj represents the total number of experts assigned to the jth indicator. The closer theratio of xj=xj maxð Þ is to 1, the more important the experts think the jth indicator is.

The full rate of expert assignment is given as follows:

Kj ¼~mj

mj; (2)

where mj represents the total number of experts assigned to the jth indicator and ~mj

represents the number of experts who have a full score for the jth indicator. The value of Kjranges from 0 to 1, and the full rate Kj can be used as a supplementary indicator of averagexj. The larger the Kjd, the larger is the proportion of experts who give the full value to theindicator and the more important is the indicator.

The dispersion of expert assignment is given as follows:

d2j ¼1mj

Xmj

i¼1

xij�xj� �2

: (3)

The dispersion d2j of the expert assignment is the variance of the expert assignment,indicating the degree of dispersion of the expert assignment to the jth indicator and theconcentration of the expert assignment from another aspect. The greater the dispersion d2j ,more inconsistent is the expert’s awareness of the importance of the jth indicator.The smaller the dispersion d2j , more consistent is the experts’ cognition of the importanceof the jth indicator and higher is the concentration of expert opinions.

5.1.3 Analysis of coordination of expert opinions. The degree of coordination of expertopinions is based on the coefficient of variation (Vj). The coefficient of variation describesthe extent to which the mjth experts coordinate the assignment of the jth indicator.By calculating the coefficient of variation, it can be judged whether there is a bigdisagreement between the experts on the assignment (score) of a single indicator. Thespecific calculation method is as follows:

Vj ¼djxj; (4)

where Vj represents the coefficient of variation of the mjth experts assigned to the jthindicator; δj represents the expert assignment’s standard deviation of the jth indicator; andxj represents the average assignment of the expert to the jth indicator. The coefficient ofvariation indicates the degree of fluctuation of the expert assignment to the jth indicator.The smaller the Vj, the higher is the consistency of the expert assignment to the jth indicatorand the higher is the degree of coordination of the expert assignment.

5.2 Single-index expert assignment t-testIn general, there are two types of ideas for quality inspection methods. One is the overallimpact analysis. The basic idea is to delete one or more assignment points in theassignment set of an indicator and then examine the impact on the statistical inference

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after deleting the assignment point. The other type is local impact analysis. The basic ideais to make a small perturbation of some assignment points in the assignment set. Then, theinfluence of the assignment point perturbation on statistical inference is examined.

The quality test of the assignment of a single indicator expert is based on the overallimpact analysis, testing the consistency, validity and reliability of the assignment ofmultiple experts, judging whether the experts have a large disagreement on the score ofeach indicator and checking and analyzing experts with abnormal assignments. When theexpert assignment distribution obeys the normal distribution, the commonly used statisticsare used to test.

5.2.1 Consistency test of expert assignment. Whether all expert assignments areconsistent can be tested with t-statistic, that is t-test. The inspection steps are as follows:

(1) Randomly dividing all expert assignments of an indicator into two groups (ordividing into two groups according to the size of the assignment).

(2) Establishing assumptions:

• The null hypothesis H0. All expert assignments are consistent.

• Alternative hypothesis H1. All expert assignments are not consistent.

(3) Constructing t-statistic:

t ¼ x1j�x2jffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffin1�1ð ÞS2

1 þ n2�1ð ÞS22

n1 þn2�21n1þ 1

n2

� �r � t n�2ð Þ; (5)

where x1j is the average of all expert assignments of the first group of the jth indicator,x2j is the average of all expert assignments of the second group of the jth indicator,S21 is the variance of all experts assigned to the first group of the jth indicator, S

22 is the

variance of all experts assigned to the second group of the jth indicator, n1 isthe number of the first grouping experts and n2 is the number of the second groupof experts.

(4) Checking the t distribution table to obtain the critical value t(α/2)(n−2), given thesignificance level α.

(5) Comparing and judging:

• If |t| ⩽ t(α/2)(n−2), then H0 is accepted and H1 is rejected, thus considering theexpert assignment to be consistent.

• If |t|W t(α/2)(n−2), then H0 is rejected and H1 is accepted, thus considering theexpert assignment to be not consistent.

5.2.2 Validity test of expert assignment. The validity and reliability of an expert assignmentcan also be tested using the t-statistic, that is verifying that the expert assignment isconsistent with the mean of all expert assignments for that indicator. The t-test stepsare as follows:

(1) Arbitrarily selecting an expert assignment xij to establish the following hypotheses:

• The null hypothesis H0. The expert assignment has consistency with the mean,and the assignment is valid.

• Alternative hypothesis H1. The expert assignment has no consistency with themean, and the assignment is invalid.

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(2) Constructing t-statistic:

t ¼ xij�xjdj

� t n�2ð Þ; (6)

where xij represents the assignment of the jth indicator by the ith expert, xj is theaverage value of all the experts of the jth indicator and δj is the standard deviation ofthe assignment of all the experts of the jth indicator.

(3) Checking the t distribution table to obtain the critical value t(α/2)(n−2), given thesignificance level α.


• If |t|⩽t(α/2)(n−2), then H0 is accepted and H1 is rejected; thus, the expertassignment is consistent with the mean, and the assignment is valid.

• If |t|⩽t(α/2)(n−2), then H0 is rejected and H1 is accepted; thus, the expertassignment and the mean have no consistency, and the assignment is invalid.

5.2.3 Outlier test of expert assignment. The t-test is only to test the consistency and validityof the expert assignment of a single indicator, but it is impossible to test whether an expert’sabnormal assignment has a significant impact on the overall evaluation of the indicator.Therefore, on the basis of the t-test, it is also necessary to perform an F-test on a singleindicator. The F-test steps are as follows:

(1) Sorting the expert assignments of individual indicators from small to large. Theassignment sequence of the originalN experts setting the jth indicator is representedas xj¼ {x1j, x2j,…, xnj}, j¼ 1, 2,…,m, and the sequence after sorting isx0j ¼ fx01j; x02j; . . .; x0njg; j ¼ 1; 2; . . .; m.


• The null hypothesis H0. There is no significant difference between the expertassignment after removing the maximum and minimum values and all expertassignments.

• Alternative hypothesis H1. There is a significant difference between the expertassignment after removing the maximum and minimum values and allexpert assignments.

(3) Constructing F-statistic:

F ¼ SS1�SS2ð Þ=2SS1= m�3ð Þ � Fa 2;m�3ð Þ; (7)

where SS1 ¼Pn

i¼1 xij�xj� �2 represents the sum of squared deviations of all expert

assignments and SS2 ¼Pn�1

i¼2 ðx0ij�x0jÞ2 represents the sum of squared deviationsafter removing the maximum and minimum values of expert assignments.

(4) Checking the F distribution table to obtain the critical value Fα(2, m−3), given thesignificance level α.


• If F⩽Fα(2, m−3), then H0 is accepted and H1 is rejected, thus considering thatthere is no significant difference between the expert assignment after removingthe maximum and minimum values and all expert assignments.

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• If FWFα(2, m−3), then H0 is rejected and H1 is accepted, thus considering thatthe expert assignment after removing the maximum and minimum values andthe total expert assignment is significantly different.

5.3 Experts assignment normal distribution transformationIf the expert assignment does not obey the normal distribution, a normal distributiontransformation should be considered. Common transformation methods include logarithmictransformation, square root transformation, reciprocal transformation, square root inversesine transformation, etc., and the appropriate transformation method should be selectedaccording to the nature of expert assignment data (Wei, 2012).

5.3.1 Data normal distribution transformation method

(1) Calculating the distribution of the data and two parameters: skewness and kurtosis.

(2) Determining whether transformation is needed according to the distribution shapeand parameters of the data (Wei, 2012):

• Symmetric judgment: the value of skewness should be observed. If the skewness is0, it is completely symmetrical (but rare); if the skewness is positive, thedistribution of the data is positively skewed; and if the skewness is negative, it isnegatively skewed. However, the skewness value cannot fully judge whether thedistribution of the skewness is significantly different from the normal distribution,and it is also necessary to make a significance test. If the test results aresignificant, the transformation can be used to achieve or approach symmetry.

• Kurtosis test: the kurtosis is an indicator that judges the curve to be steep andgentle. If the kurtosis is 3, the shape of the distribution peak is the same as thenormal distribution; if the kurtosis is greater than 3, the distribution is steep. Onthe contrary, it shows that the distribution of data is gentle. The kurtosis alsoneeds to be judged by significance tests to see if there is a significant differencefrom the normal distribution. If the test results are significant, the conversion canbe used to achieve or approximate a normal distribution.

(3) Determining the corresponding transformation formula according to the distributionshape of the data, if a normal transformation is required. There are three commonnormal transformation methods:

• Moderately skewed: if the skewness is two to three times its standard error, theroot value for transformation should be considered.

• Highly skewed: if the skewness is more than three times its standard error, it canbe transformed in logarithmic form.

• Bimodal or multimodal data: the Blom function is used to calculate the normalscore, and then the data are transformed into a normal distribution.

(4) Retesting the distribution after data conversion. If the problem is not solved or evenworsened, it is necessary to restart from (2) or (3) and then carry out relevant testsuntil a satisfactory result is achieved.

5.3.2 Several problems of normal distribution transformation

(1) The normal transformation method of data is not universal. It is necessary to select asuitable or improved transformation method according to the data distribution.After the transformation, the transformation effect must be verified, and finally thetransformation purpose is achieved.

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(2) Not all non-normally distributed data can be normally distributed. Non-normallydistributed data can also be analyzed using non-parametric methods.

(3) The standard error of skewness and kurtosis is directly related to the sample size;generally, the larger the sample size, the smaller is the standard deviation ofskewness and kurtosis.

(4) When the expert assignment set does not obey the normal distribution, theconsistency and validity of the experts assignment can use the Mann‒WhitneyU-test and the Wilcoxon Signed rank test.

5.4 Non-parametric test of experts assignmentWhen the expert assignment data do not satisfy the normal distribution and cannot benormally transformed, the consistency of the assignment data cannot be used for parameterverification. The following non-parametric test methods can be used: Mann–Whitney U-testand Wilcoxon Signed rank test.

5.4.1 Mann–Whitney U-test. The Mann‒Whitney U-test, also known as the “Mann‒Whitney rank sum test,” was proposed by H.B. Mann and D.R. Whitney in 1947. This test ismainly to test whether two independent samples are from the population of the same mean,that is to test the assignment scores of different experts. The specific steps (Guoxiang, 2014)are as follows:

(1) Randomly searching for two groups of experts to assign indicators or randomlyassigning expert values to two groups of samples A and B. The expert assignmentsof the two groups are graded in ascending order according to the numerical value.The minimum data level is 1, the second smallest data level is 2, and so on. If there isa case where the assignments are equal in the sorted data, the rank values of thesame data should be the same and take the average of the unranked ranks. If thedata are sorted {3, 5, 5, 9}, then their rating value should be {1, 2.5, 2.5, 4}.

(2) Obtaining, respectively, the rank and sum RA, RB of the two sets of assignmentsamples, according to the rank value of (1).

(3) Establishing hypothesis:

• H0. There is no significant difference between the mean values of the two groups.

• H1. There is a significant difference between the mean values of the two groups.

(4) Mann‒Whitney U-test. Calculating U: its sum is always equal to nAnB, that is UA +UB¼ nAnB. If nA ⩽ 20; nB ⩽ 20, its test statistic is given as follows:

UA ¼ nAnBþnA nAþ1ð Þ

2�RA; (8)

UB ¼ nAnBþnB nBþ1ð Þ

2�RB; (9)

where nA and nB represent the sample sizes of samples A and B, respectively.The threshold table for the Mann‒Whitney U-test only gives a small threshold,

so the smaller U value in UA, UB, b is used as the test statistic.

(5) Choosing the smaller of these values to compare with the threshold, given the levelof significance α:

• If U is less than Uα, H0 is accepted and H1 is rejected.

• if U is greater than Uα, H0 is rejected.

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The U-test are also small and large samples for inspection. In small samples, the criticalvalue of U is shown in the table. In large samples, the distribution of U approaches a normaldistribution, so normal approximation can be used.

5.4.2 Wilcoxon Signed rank test. Wilcoxon Signed rank test, also known as Wilcoxon’ssign rank test, was proposed by F. Wilcoxon in 1945. This method is developed on the basisof the symbolic test of paired observation data and is suitable for testing the data of two setsof associated samples. The specific steps are as follows (Xuemin, 2010):

(1) Randomly finding a group of experts to assign two independent scores to the sameindex and getting two groups of assigned samples.


• H0. There is no significant difference in the assignment results between thetwo groups.

• H1. There are significant differences in the assignment results between thetwo groups.

(3) Calculating the difference di of expert pair assignment and arranging the rank Ri ofabsolute value of di in order from large to small. If di is equal, the rank of the same datais the same and the average of the unranked array is taken. If di takes {9, 5, 5, 3}, thenits grade value should be {1, 2.5, 2.5, 4}.

(4) Restoring the positive and negative signs and calculating the sum of positive gradesT + and negative grades T−, respectively, after the completion of grade numbering.The smaller of T+ and T− was selected as the Wilkerson test statistic:

T ¼ min T þ ;T�� : (10)

Thereinto, the sum of positive grades T þ ¼Pdi 40Ri and negative grades T� ¼Pdi o 0Ri:

(5) Making a judgment: the critical value table should be checked according to thesignificance level α and the critical value Tα should be obtained; if ToTα, we willreject null hypothesis H0.

6. Reliability test and validity test6.1 Reliability test of expert assignmentReliability refers to the consistency and reliability of the evaluation index, and the consistencyof the results obtained when the same method is used for repeated evaluation of the sameindex, that is the degree to reflect the actual situation. The higher the reliability coefficient, themore consistent and reliable are the evaluation results. The most commonly used reliabilitymeasurement method is Cronbach’s α reliability, which was put forward by Americaneducator Lee Cronbach in 1951. The calculation formula of α reliability coefficient is based onthe early internal consistency calculation formula developed by G. Frederick kuder ( Johnsonet al., 2014) and M.W. Richardson in 1937. Its calculation formula is as follows:

a ¼ mm�1

1�Pm

j¼1 s2Yj

s2X

!; (11)

where m represents the number of indicators, s2Yjrepresents the variance of the expert

assignment of a single indicator and s2X represents the variance of the sum of the expertassignments of all indicators.

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Cronbach’s α coefficient is usually between 0 and 1. If the coefficient α does not exceed 0.6, itis generally considered that the internal reliability is insufficient. When it reaches 0.7~0.8, thescale has a good reliability, and when it reaches 0.8~0.9, the scale has a very good reliability.

Steps of reliability analysis test are given as follows:

(1) According to the designed index system, the reliability of the index should beassessed by experts twice and the evaluation results should be counted.

(2) Cronbach’s α coefficient should be calculated.

(3) Indicators should be selected by reliability.

6.2 Validity test of expert assignmentValidity is a complex concept that continues to evolve. In 1952, the committee on testingstandards of the American psychological association published preliminary technicalrecommendations on psychological testing and diagnostic techniques, which indicated for thefirst time that the types of validity included predictive validity, simultaneity validity, contentvalidity and consistency validity (Committee on Test Standards of American PsychologicalAssociation, 1952). In 1974, the standards of education and psychological testing definedvalidity clearly for the first time, believing that validity is the appropriateness of speculatingon test scores and other evaluation results (American Psychological Association, AmericanEducational Research Association, National Council on Measurement in Education, 1974).American Educational Research Association, American Psychological Association, NationalCouncil on Measurement in Education (1985) supplemented and improved the definition ofvalidity, believing that validity refers to the appropriateness, richness and usefulness ofspecific predictions made by indicators on test scores (American Educational ResearchAssociation, American Psychological Association, National Council on Measurement inEducation, 1985). American Educational Research Association, American PsychologicalAssociation, National Council on Measurement in Education (2014) pointed out that thevalidity is degree of evidence and theory support for the interpretation of test scores, andincludes content based on the test of evidence, evidence based on the reaction process, basedon the internal structure of the evidence, evidence based on the relationship with othervariables and based on the test results of five kinds of validity of the evidence sources ofevidence (American Educational Research Association, American Psychological Association,National Council on Measurement in Education, 2014).

Reliability is the premise of validity, which means the degree to which the indexscreening results are consistent with the target index system. It cannot be said that it isabsolutely “invalid” or “effective.” The value of validity ranges from −1 to 1, and the largerthe value, the higher is the validity of the index.

The validity test of screening indicators usually adopts “content validity ratio,” whoseformula is given as follows:

CVR ¼PN

i¼1 eij�10� N=210� N=2

; (12)

where eij is the score of necessity and importance of indicator j given by expert i and N is thenumber of experts.

It is generally believed that when CVR is greater than 0.8, content validity is better, andthis index is adopted, otherwise it is removed or modified.

The steps of validity analysis and test are as follows:

(1) According to the designed index system, the necessity (importance) of the indexshould be evaluated by experts and the evaluation results should be counted.

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(2) Content validity ratio, CVR, should be calculated.

(3) Indicators should be filtered through CVR.

6.3 Single-index expert assignment correctionWhen the validity of the expert assignment with a single indicator cannot pass the t-test, if thefailure rate is greater than 20 percent, the theoretical indicator system is unreasonablyconstructed, and the indicators and classification stratification need to be re-screened; if thefailure rate is less than or equal to 20 percent, it means that there is a large deviation inthe assignment of a few experts, and it is necessary to re-evaluate the assignment.

When the expert assignment of a single indicator cannot pass the F-test or the reliabilityand validity test, in order not to lose the expert assignment, the expert assignment needs tobe corrected. The correction methods are as follows:

(1) The average value of the maximum value, the minimum value and the adjacentvalue of the single indicator expert assignment should be taken as the new valueinstead of the original assignment.

(2) After the introduction of the new value, the test t, F-test and the reliability and validitytest should be re-expanded. If the new t and F tests are still not passed, it means that theexpert’s cognition of the indicator is controversial and needs to be re-assigned.

(3) If the second expert assignment is still controversial, it means that the design of theindicator is wrong, and the indicator needs to be redefined.

7. Multi-index expert assignment quality testThe multi-indicator expert assignment quality test is mainly based on the validity of theexpert assignment of multiple indicators and the consistency of multiple expert assignments.

n Represents the number of experts, the number of the representative index k, xijrepresents the ith expert scores assigned to the jth index. xj(max) represents the maximumvalue in the jth indicator attribute value range (data).

7.1 Multi-index expert assignment consistency testBecause of the correlation between multiple attributes between indicators, it is often difficultfor an expert to accurately judge the complex relationship between multiple indicators. Forexample, it has been judged that C1‒C2 is important, and C2‒C3 is important. Naturally, C1‒C3is more important, but if the expert considers C3‒C1 important or an equally importantconclusion, a logical error will occur. Therefore, it is necessary to judge the reliability of theexpert assignment or the accuracy of the judgment by judging the consistency of the matrix.

The consistency test (Shufeng, 2006) is mainly to test whether each expert assignment ofmultiple indicators has a logical consistency test (or a consistency test of multiple experts’judgment results) to avoid the contradiction between the two evaluation results. Consistencyindicators and random consistency ratios are used in the AHP for consistency testing.

7.1.1 Consistency index. For the evaluation of multiple indicators, one is to test whethereach expert’s evaluation scores are consistent, and the other is to test whether all expertsjudge the consistency of these indicators. For the m indicators that need to be assignedscores, based on the quality test of the individual indicator experts and the expert evaluationscores of the two pairs, the judgment matrix is constructed, and the maximum eigenvalueλmax of the judgment matrix is calculated. If the number is m, then the consistency index ofthe judgment matrix is CI:

CI ¼ lmax�mm�1

: (13)

22

MAEM2,1

If multiple experts are required to perform consistency check on the assignment of multipleindicators, it is necessary to perform the mean processing on the expert assignment of eachindicator, such as arithmetic mean, geometric mean and then refer to the assignment ofmultiple indicators by a single expert. The consistency check process can be tested.

7.1.2 Random consistency ratio. As k increases, the error of the judgment matrixconsistency test index increases, so the random consistency ratio CR is introduced to correct CI:

CR ¼ CIRI; (14)

where RI is the average random consistency index, which is the average of the consistencyindicators calculated by a large number of experimental experts of the National Oak RidgeNational Laboratory. The value RI of the 1–15th order is shown in Table VII, where the orderrepresents the number of indicators in the judgment matrix, and when the order exceeds 3, theconsistency test is often required.

When CR o 0.1, the judgment matrix is considered to have reasonable consistency;otherwise, the expert assignment of the pairwise comparison needs to be adjusted untilsufficient consistency is achieved.

7.2 Coordination test of multi-index expert assignmentFor multiple indicators or even for each index, there will always be differences in theevaluation scores of different experts. Some experts’ evaluation is generally high, someexperts’ evaluation is generally low and some experts’ evaluation is generally different. Howto judge whether the overall evaluation scores of multiple indicators are stable and effective,and whether experts have the same understanding of the importance of each index?Coordination test of expert assignment is needed.

Coordination coefficient W reflects the consistency of all experts’ evaluation of allindicators, and it is also an indicator of the credibility and stability of expert evaluationresults. In this paper, Kendall synergy coefficient is used to test the overall consistency of allexperts’ evaluation scores. The consistency test of Kendall’s synergistic coefficient (Dong et al.,1997) requires ranking transformation after each expert ranks the evaluation results of eachindex. The specific method refers to the non-parametric test of 4.4 expert assignment.

Kendall’s synergistic coefficient formula is divided into two forms on the basis ofwhether there is the same rank or not:

(1) If each expert has different rating for each index, that is no same rank, the Kendallsynergy coefficient W is given as follows:

W ¼ 12Sn2 m3�mð Þ: (15)

(2) If an expert has the same rank in the evaluation of m indexes, the Kendall synergycoefficient W is given as follows:

W ¼ 12Sn2 m3�mð Þ�n

Pg t3k�tk� �; (16)

where n is the number of experts,m is the number of indicators, Rj is the rank (or rank) sumof each expert evaluation grade of the index j, τk is the length of k equal-order columns and

Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.52 1.54 1.56 1.58 1.59Table VII.

RI value table

23


system design

g is the number of all equal-order columns and S is the sum of squares of deviations betweenthe total rank and the average rank of the index:

S ¼Xmj¼1

Rj�n mþ1ð Þ

2

� �2

:

The range of Kendall’s synergy coefficient is [0, 1]. When all the experts’ opinions on index jare completely consistent, the value of Rj is jn( j¼ 1, 2,…,m), the sum of squares ofdeviations S is the largest and the Kendall’s synergy coefficient W is close to 1.

The specific steps (Linquan, 2017) of Kendall synergistic coefficient significance test areas follows:

(1) Establishing original hypothesis and alternative hypothesis:

• Original hypothesis H0. Expert evaluation score is random.

• Alternative hypothesis H1. The expert evaluation score is stable.

(2) Constructing χ2-statistic K:

K ¼ n m�1ð ÞW-w2a m�1ð Þ: (17)

Under the original assumption that H0 holds, the statistic K approximately obeys the chi-square distribution with degree of freedom m−1:

(3) Conducting significance test.

At a given saliency level α, the critical value table w2a m�1ð Þ of χ2 is consulted and theoriginal hypothesis is compared and judged.

When K4w2a m�1ð Þ, the original hypothesis H0 is rejected and the alternativehypothesis is accepted, thus considering that the expert score has overall stability under thesignificant level α.

For example, in order to study the operation status of enterprises, three indicators arepreliminarily screened: corporate social responsibility, customer satisfaction and customerloyalty. Through in-depth interviews with enterprises, experts and entrepreneurs areinvited to evaluate the importance of these three indicators (Tables VIII–X).Because of the same rank, the Kendall synergistic coefficient is calculated by formula (16). Theresults show the following:

W ¼ 12Sn2 m3�mð Þ�n

Pg t3k�tk� � ¼X3

j¼1

12 Rj� 6 3þ1ð Þ=2� �� 262 33�3� ��6

P5k¼1 t3k�tk� � ¼ 0:2321:

Significance test results show that statistic K¼ n(m−1)W¼ 6× (3−1)×0.2321¼ 2.7852. Thescore of six entrepreneurs is random because it is less than the critical value w20:05 ¼ 5:991.

Corporate social responsibility Customer satisfaction Customer loyalty

Entrepreneur 1 4 4 5Entrepreneur 2 6 6 6Entrepreneur 3 3 2 4Entrepreneur 4 1 1 1Entrepreneur 5 4 3 3Entrepreneur 6 4 4 4

Table VIII.Indicators of expertappointment scores

24

MAEM2,1

7.3 Distribution of importance of all indicatorsWhen the quality diagnosis of the single indicator expert assignment is passed, it indicatesthat the expert assignment is consistent and reliable; and the expert assignment indicates thatthe expert’s recognition of the indicator is greater, indicating that the expert believes that theindicator is more important. However, how to use the value of the indicator to measurethe importance of the indicator in the whole indicator system, especially in the same level orless important but need to be retained, does not stipulate the standard of inspection, so it isnecessary to test the importance of all indicators to achieve the purpose of preliminaryscreening indicators.

First, the average value of the experts for each index is averaged.When the mean sequenceapproximates the normal distribution, the test is used. When the normal distribution is notfollowed, the distribution can be performed using the method in Section 5.3.The specific steps of all indicators based on the importance distribution test of the F-test areas follows:

First, calculating the mean- xj of the expert assignments for each indicator and arrangingthe mean values from small to large. The ascending mean sequence is obtained asXj

0 ¼ x10 ; x20 ; . . .; xm0f g.Second, establishing hypothesis:

• H0. An indicator of the minimum assigned mean m0 is important.

• H1. The indicator of the minimum assigned mean m0 is not important.

Third, constructing F statistics:

F ¼ SS1�SS2ð Þ=m0

SS1= m�m0�1ð Þ � Fa m0;m�m0�1ð Þ; (18)

where SS1 ¼Pm

j¼1 ðxj�xjÞ2 is the sum of squared deviations on behalf of all indicatorexperts and SS2 ¼

Pm�1j¼2 ðxj0�xj0 Þ2 represents the sum of squared deviations after removing

the maximum and minimum values assigned by the expert.Fourth, checking the F distribution table to get the critical value Fα(m0, m−m0−1), given

a level of significance α.

Sequence 1 Sequence 2 Sequence 3 Sequence 4 Sequence 5

τk 2 3 3 2 3

t3k�tk 6 24 24 6 24

Table X.Serial modified

calculating table withthe same rank

for index convertedfrom expert

appointment grade

Corporate social responsibility Customer satisfaction Customer loyalty

Entrepreneur 1 1.5 1.5 3Entrepreneur 2 2 2 2Entrepreneur 3 2 1 3Entrepreneur 4 2 2 2Entrepreneur 5 3 1.5 1.5Entrepreneur 6 2 2 2Entrepreneur R 12.5 10 13.5

Table IX.Rank of grade

conversion of expertappointment scores

25


system design

Fifth, comparing and judging:

• If F ⩽ Fα(m0, m−m0−1), H0 is accepted and H1 is rejected. It is considered that theindicator corresponding to the minimum m0 assignment mean values is importantand should be retained.

• If FWFα(m0, m−m0−1), H0 is rejected and H1 is accepted. It is considered that theassignment of the indicator corresponding to the smallest m0 of the assigned meanvalues is too small and can be filtered out.

m0¼ j−1, j−2,…, 2, 1 should be taken separately. The above F-test should be performeduntil the one F-statistic is less than the critical value.

8. ConclusionIn this paper, through the scientific process that from “analysis of theory basic, analysis ofrelated factors, description of process mechanism” to “analysis of system structure,decomposition of system hierarchical, identification of the complete set of indicators” toprimary selection the indicators system, using system engineering and hierarchical structuredecomposition theory, optimize the index system construction process and analysis method;aim at the expert assessment quality test, judge the authoritative degree of expert opinion andclarify the index attribute value range, based on Delphi assignment method and other fourmethods for expert assignment, expert evaluation results for individual indicators assignmentquality test, reliability test and validity test, and multi-index expert assignment quality test.

Through the above-mentioned series of tests for the evaluation of experts in the indicatorsystem, the paper completes the optimization of the index system design. In the optimizationprocess, the overall inspection process is detailed and clear. Compared with the traditionalindex system construction method, the process standardizes the process of indexestablishment, reduces subjectivity and randomness, and enhances objectivity andscientificity. In the future, the focus of optimization and testing of the design of theindicator system should be on the importance test and classification based on the indicatordata, that is how to use the principal component analysis method, factor analysis method andorthogonal design method to reduce the dimension of the index system, how to combine thegray comprehensive evaluation, fuzzy comprehensive evaluation andAHP to comprehensivelyevaluate and how to innovate the golden section method to effectively classify.

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Corresponding authorYin Kedong can be contacted at: [email protected]

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MAEM2,1

Evaluation of the marineeconomic development quality inQingdao based on entropy and

grey relational analysisPeide Liu, Xiaoxiao Liu and Hongyu YangSchool of Management Science and Engineering,

Shandong University of Finance and Economics, Jinan, China

AbstractPurpose – Accurately judging the quality of marine economic development is the premise of grasping thelevel and status of marine economic development. In order to scientifically evaluate the development qualityof regional marine economy, the purpose of this paper is to select the marine area of Qingdao as the researchobject, and construct a marine economic development quality evaluation index system with 16 indicators.Design/methodology/approach – The raw data is normalized by the range conversion method, and theweight of the index is determined by the information entropy model. Further, the grey relational analysis(GRA) method is used to evaluate the quality of marine economic development of Qingdao from 2012 to 2017.Findings – The results show that the marine economic development capacity of Qingdao is with thegenerally increasing trend, the total marine economy is with on the rising trend, the marine storage andtransportation capacity, and marine ecological environment are first decreased, and then increased. Theutilization of marine resources is generally decreasing, and the comprehensive management of oceans varieswith the changes of environment and economy. Therefore, in view of the development capacity of marineeconomy, the coordinated development of economy and environment should be carried out.Originality/value – This paper uses the GRA to evaluate the quality of marine economic development andprovides a reference for the development of marine economy in Qingdao.Keywords GRA, Entropy weight method, Economic development quality, Marine economiesPaper type Technical paper

1. IntroductionIn recent years, the total value of marine economic production has occupied an increasingproportion in the national economy, and the marine economy has become a new long-termpoint of economic development. However, there are a series of problems in the rapiddevelopment of the marine economy. In order to ensure the sustainable use of marine resources,the harmonious marine environment and the sustainable development of the marine economy,it is necessary to improve the quality of marine economic development. So, accurately judgingthe quality of marine economic development is the prerequisite for accurately grasping thelevel and status of marine economic development. The research related to the quality of marineeconomic development conducted by various scholars mainly includes the following aspects:first, research on the development of marine industry and the utilization of marine resources.Sheng et al. (2016) used 16 index data from 2006 to 2013 such as gross ocean product (GOP),output value of the three industries and so onto study the impact of three marine industries.Wang et al. (2017) introduced the VAR model to explore the relationship between marine


Vol. 2 No. 1, 2019pp. 29-38


DOI 10.1108/MAEM-08-2019-0005

Received 5 August 2019Revised 10 August 2019

Accepted 14 August 2019


© Peide Liu, Xiaoxiao Liu and Hongyu Yang. Published in Marine Economics and Management.Published by Emerald Publishing Limited. This article is published under the Creative CommonsAttribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivativeworks of this article (for both commercial and non-commercial purposes), subject to full attribution tothe original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

29

Entropyand GRA


resources development and marine economic growth. Cao and Ning (2019) adopted the entropyweigh and TOPSIS model to dynamically evaluate the carrying capacity of marine resourcesand environment in Zhanjiang from 2007 to 2016. Second, research on the structure and layoutof the development of the marine industry. Ma et al. (2013) discussed the increase of Chineseresearched on maritime industries structure and layout, offered suggestion on future researchexploration in the frontline field and theoretical system building, and scientific guidance forbuilding state-level maritime economy development model district. Based on map of Cite Spaceknowledge and Excel data statistics for 31 years in China, Han et al. (2016) summarized theoverall characteristics of the marine industry research. Third, research on the comprehensiveevaluation of the development of the marine economy. Jiang (2018) proposed the evaluationindex in 11 coastal provinces and cities in China to comprehensively evaluate the marineeconomy overall development and regional differences. Guo (2014) analyzed the developmentstatus of the coastal regions with stronger development capacity. Fourth, research on thesustainable development of the marine economy. Yu et al. (2019) evaluated the sustainabledeveloping ability of marine economy about Shanghai. Niu (2015) studied coordinativemechanism for marine economic sustainable development in Shandong province.

At present, multi-attribute evaluation methods, such as TOPISIS, ELECTRE,QUALIFLEX, grey relational analysis (GRA), VIKOR, etc. (Dai and Qi, 2018; Liu and Qin,2018; Liu and You, 2017), have been widely used in engineering, social, economic and otherfields, and have become the focus of today’s stduies. The GRA method (Liu, 2010) is animportant part of the grey system theory. Based on the generalized GRA method, Li (Li andXu, 2016) analyzed the influential factors of the development of marine fishery. Liu and Chen(2016) analyzed the relationship between coastal household consumption and marineeconomic growth by the GRA method. However, there are few studies on the GRA method toanalyze the quality of marine economic development. In this paper, the grey relational relativecloseness is used as the evaluation standard, which can make full use of the obtained datainformation and effectively avoid the loss of information in the calculation process.

In view of the development of marine economy in Qingdao, this paper establishes theevaluation index system of Qingdao’s marine economic development quality, determines theindex weight by the entropy weight method and then uses GRA method to evaluate the qualityof marine economic development in Qingdao city from 2012 to 2017. The analysis of developmenttrends provides a certain reference for the development of marine economy in Qingdao.

2. Research methods2.1 The standardized methodDifferent criteria can be adopted when conducting evaluations. There are differences inmeasurement units, dimensions, etc., between indicators, and there are also different typesof indicators, both benefit and cost. Therefore, the indicators need to be standardized beforethe calculations are performed. This paper uses the range conversion method (Liu, 2010):

(1) For benefit indicators:

yij ¼xij� min xij

max xij� min xij: (1)

(2) For cost indicators:

yij ¼max xij�xij

max xij� min xij; (2)

where yij is the normalized value, xij is the original value, max xij is the maximumvalue of the index and min xij is the minimum value of the index.

30

MAEM2,1

2.2 Entropy weight methodThis paper uses the more objective entropy weight method (Liu, 2010) to determine the weightof each indicator. Entropy can measure uncertainty. The larger the amount of information, thesmaller the uncertainty; the smaller the entropy, the smaller the amount of information; andthe greater the uncertainty, the greater the entropy. The calculation steps are as follows:

(1) Calculate the proportion Dij of the ith alternative under the jth indicator:

Dij ¼yijPmi¼1 yij

; ðj ¼ 1; 2; . . .; nÞ; (3)

where m is the number of evaluation objects and n is the number of evaluation indicators:

(2) Calculate the entropy weight Ej of the jth indicator:

Ej ¼ �KXmi¼1

Dij ln Dij� �

; (4)

where K¼ 1/ln(m), 0ln0¼ 0:

(3) Calculate weight:

wj ¼1�Ej

n�Pnj¼1 Ej

: (5)

2.3 GRA methodThe GRA method (Liu, 2010) is an important part of the grey system theory, which canquantitatively describe the relative changes of each attribute in the system with time. Thesteps of the GRA method are:

(1) Determining the ideal solution and negative ideal solution as follows:

V þj ¼ max

iyij� �

V�j ¼ min

iyij� �

8><>: j ¼ 1; 2; . . . ; n: (6)

(2) Calculate the grey correlation coefficient between the ith and ideal solution on thejth index:

rþij ¼ mþxMDþij þxM

; Dþij ¼ vþj �vij

�� ; m ¼ min|{z}i

min|{z}j

Dþij ; M ¼ max|ffl{zffl}

i

max|ffl{zffl}j

Dþij ;

x ¼ 0:5:

Calculate the grey correlation degree between the ith and ideal solution:

Rþi ¼

Xnj¼1

wjrþij ; ði ¼ 1; 2; � � � ;mÞ: (7)

Calculate the grey correlation coefficient between the ith and negative ideal solutionon the jth index:

r�ij ¼mþxMD�ij þxM

; D�ij ¼ v�j �vij

�� ; m ¼ min|{z}i

min|{z}j

D�ij ; M ¼ max|ffl{zffl}

i

max|ffl{zffl}j

D�ij ; x ¼ 0:5:

31

Entropyand GRA

Calculate the grey correlation degree between the ith and negative ideal solution:

R�i ¼

Xnj¼1

wjr�ij ; ði ¼ 1; 2; � � � ;mÞ: (8)

(3) Calculate the relative closeness of each alternative:

Ci ¼Rþi

Rþi þR�

i

; ði ¼ 1; 2; � � � ;mÞ: (9)

3. Evaluation of the marine economic development quality in Qingdao3.1 Construction of evaluation index systemBased on the existing studies from Di and Gao (2015) and Gao (2016), and we build amarine economic development quality evaluation index system from five aspects: totalmarine economy, marine storage capacity, marine ecological environment, marine resourceutilization and integrated marine management. Finally, the Qingdao Ocean EconomicDevelopment Quality Evaluation Index System with 16 indicators including the target layer,the standard layer and the indicator layer was determined, and shown in Table I.

3.2 Data processing3.2.1 Data normalization. The data used in this study are derived from the QingdaoStatistical Yearbook (2012–2017), the Qingdao Marine Environment Bulletin (2012–2017) andthe Qingdao Municipal Statistical Report on National Economic and Social Development(2012–2017). The raw data obtained are shown in Table II, and the standardization results ofthe evaluation indicators are shown in Table III.

3.2.2 Calculated indicator weights. According to the normalized data, the entropy weightmethod is used to calculate the weights of each indicator shown as follows in Table IV.

4. Result and analysis4.1 Evaluation resultsUsing the GRA method, we get the evaluation results shown in Table V.

Project Indicator Indicator attributes

Total marine economy Gross ocean product (GOP) BenefitGOP’s share of GDP BenefitThe proportion of tertiary industrial output-value BenefitTotal import and export volume of Qingdao Port Benefit

Marine storagecapacity

Coastal passenger throughput BenefitCoastal cargo throughput BenefitWaterborne passenger turnover BenefitWaterborne cargo turnover Benefit

Marine ecologicalenvironment

Beach garbage density CostIndustrial wastewater discharge Cost

Marine resourceutilization

Fishing volume BenefitAquaculture area BenefitPer capita coastline length Benefit

Integrated marinemanagement

Environmental protection expenditure CostWater conservancy and environmental fixed assets investment CostTerritorial marine meteorological expenditures Cost

Table I.Qingdao city marineeconomic developmentquality comprehensiveevaluation indexsystem

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As seen from Table VI, the quality of marine economic development in Qingdao hasexperienced a process of decreasing first and then rising.

4.2 Results analysisAs can be seen from Figure 1, the quality of marine economic development in Qingdao from 2012to 2017 shows an increasing trend. The total marine economy has been rising steadily. Marinestorage and transportation capacity is declining in the first three years, and is rising since 2014.Analyzing the value of ocean storage and transportation capacity index, the passenger turnoverof water transport is declining in recent years, which indicates that other modes of transportationwill also restrict passenger turnover of water transport. The change trend of marine ecologicalenvironment is unstable in the first three years, and it is rising steadily since 2014. The utilizationof marine resources shows a downward trend, which indicates that marine environmentalproblems will restrict the development of marine economy. The unstable trend of integratedmarine management indicates that government departments will adjust their expenditure onenvironmental governance according to the annual environmental changes (Figure 2).

From the analysis of the changes in the quality of Qingdao marine economicdevelopment, it can be concluded that from 2012 to 2013, the marine ecological developmentquality index has declined, and has been steadily rising from 2013 to 2017. From the grey

Indicator 2012 2013 2014 2015 2016 2017

Total marine economyGross oceanproduct(GOP)/100m yuan 1,114 1,317 1,751 2,093 2,515 2,909GOP’s share of GDP% 15.3 16.4 20.2 22.5 25.1 26.4The proportion of tertiary industrial output-value% 48.5 49.8 50.7 52.2 54.1 55.4Total import and export volumeof Qingdao Port/100m yuan 1,489 1,567 1,651 8,973 9,495 11,102

Marine storage capacityCoastal passengerthroughput/10,000 people 12.56 14.07 12.32 10.9 16.4 16Coastal cargothroughput/10,000 tons 41,465 45,782 47,701 49,749 51,463 51,314Waterborne passengerturnover/100m person-km 0.64 0.55 0.34 0.3 0.2 0.22Waterborne cargoturnover/100m ton-km 1,426 428 427 556 727 808

Marine ecological environmentBeach garbage density/kg km−2 905 296 1,071 360.1 392.9 177.6Industrial wastewaterdischarge/10,000 tons 11,145 10,660 10,989 10,566 6,865 5,613

Marine resource utilizationFishing volume/10,000 tons 27.21 26.7 26.78 26.07 26.21 23.83Aquaculture area/10,000 acres 5.23 5.1 5.01 4.9 4.5 4.3Per capita coastline length/cm 8.79 8.88 8.98 9.03 9.11 9.21

Integrated marine managementEnvironmental protection expenditure/10,000 yuan 191,713 642,809 126,356 72,970 213,621 204,246Water conservancy and environmental fixed assetsinvestment/100m yuan 232.5 273.7 254.6 354 283.3 452.9Territorial marine meteorologicalexpenditures/10,000 yuan 85,121 140,768 124,146 153,023 144,448 129,040

Table II.Raw evaluation data

for all indicators

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Indicator 2012 2013 2014 2015 2016 2017

Total marine economyGross ocean product (GOP)/100m yuan 0 0.113 0.355 0.545 0.781 1GOP’s share of GDP% 0 0.099 0.441 0.649 0.883 1The proportion of tertiary industrial output-value% 0 0.188 0.319 0.536 0.812 1Total import and export volume of Qingdao Port/100m yuan 0 0.008 0.017 0.779 0.833 1

Marine storage capacityCoastal passenger throughput/10,000 people 0.3 0.576 0.258 0 1 0.927Coastal cargo throughput/10,000 tons 0 0.432 0.624 0.829 1 0.985Waterborne passenger turnover/100m person-km 1 0.795 0.318 0.227 0 0.045Waterborne cargo turnover/100m ton-km 1 0.001 0 0.129 0.3 0.381

Marine ecological environmentBeach garbage density/kg km−2 0.19 0.867 0 0.796 0.759 1Industrial wastewater discharge/10,000 tons 0 0.088 0.028 0.105 0.774 1

Marine resource utilizationFishing volume/10,000 tons 1 0.849 0.873 0.663 0.704 0Aquaculture area/10,000 acres 1 0.86 0.763 0.645 0.215 0Per capita coastline length/cm 0 0.214 0.452 0.571 0.762 1

Integrated marine managementEnvironmental protection expenditure/10,000 yuan 0.79 0 0.906 1 0.753 0.77Water conservancy and environmental fixed assetsinvestment/100m yuan 1 0.813 0.9 0.449 0.77 0Territorial marine meteorological expenditures/10,000 yuan 1 0.18 0.425 0 0.126 0.353

Table III.Normalizationevaluation datafor all indexes

Project Indicator Weights

Total marine economy Gross ocean product (GOP) 0.0599GOP’s share of GDP 0.0588the proportion of tertiary industrial output-value 0.0552Total import and export volume of Qingdao Port 0.1091

Marine storage capacity Coastal passenger throughput 0.0527Coastal cargo throughput 0.0378Waterborne passenger turnover 0.0828Waterborne cargo turnover 0.1084

Marine ecological environment Beach garbage density 0.0469Industrial wastewater discharge 0.1219

Marine resource utilization Fishing volume 0.0325Aquaculture area 0.0456Per capita coastline length 0.0483

Integrated marine management Environmental protection expenditure 0.0317Water conservancy and environmental fixed assets investment 0.0358Territorial marine meteorological expenditures 0.0727

Table IV.Qingdao city economicdevelopment qualityevaluation indexweight

2012 2013 2014 2015 2016 2017

Total marine economy 0.25 0.29 0.357 0.579 0.664 0.75Marine storage capacity 0.627 0.433 0.351 0.359 0.475 0.486Marine ecological environment 0.273 0.399 0.26 0.393 0.635 0.75Marine resource utilization 0.559 0.559 0.591 0.561 0.524 0.441Integrated marine management 0.73 0.394 0.588 0.417 0.46 0.418

Table V.Evaluation results ofQingdao marineeconomic developmentproject

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correlation, it can be concluded that from 2016, the grey correlation degree between eachyear and the negative ideal solution is significantly greater than the grey correlation degreewith the positive ideal solution. In 2016, there was a turning point. Then in 2017, the greyrelational degree with positive ideal scheme was also greater than that with negative idealscheme, so the evaluation result of Qingdao marine economic development quality after2016 is more than 0.5. Therefore, although the marine economic development quality indexhas been rising from 2013 to 2015, the development of the marine economy has been slowdue to the decline of the ecological environment and storage and transportation capacity.The economy has risen sharply from 2015 to 2017. From the trend line, we can predict thatthe marine economy will also rise in the future.

4.3 Comparative analysisIn this part, we will compare and analyze with the other two papers. The first one is theevaluation on the sustainable developing ability of Shanghai marine economy written by

2012 2013 2014 2015 2016 2017

Rþi 0.6036 0.465 0.4552 0.5099 0.633 0.7511

R�i 0.6963 0.7125 0.697 0.6109 0.503 0.5042

Ci 0.4643 0.3949 0.3951 0.4549 0.5572 0.5983

Table VI.Comprehensive

evaluation results ofQingdao marine

economic developmentquality

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

2012 2013 2014 2015 2016 2017

Ri+

Ri–

Ci

Trend line (Ci)Figure 2.

Comprehensiveevaluation results of

Qingdao marineeconomic development

quality

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2012 2013 2014 2015 2016 2017

M1

M2

M3

M4

M5 Figure 1.Evaluation results of

Qingdao marineeconomic development

project

35

Entropyand GRA

Yu et al. (2019). And the second is an empirical study about the carrying capacity evaluationof marine resources and environment based on the entropy-weight TOPSIS model writtenby Gou et al. (2018). The comparison results are shown in Table VII.

It can be found from Table VII that the cities and contents evaluated by Yu et al. (2019) andthis paper are different, but the method of normalizing indicators and the method of calculatingweights are the same. In calculating the final comprehensive index, Yu et al. (2019) use a simpleweighted average, and we use the GRA. Gou et al.’s (2018) paper and our paper all evaluateQingdao. The normalization methods both are range conversion method and the method forcalculating weights both are entropy weight method. However, the content of the evaluation andthe final evaluation method are different. We use GRA to evaluate the quality of marineeconomic development, while Gou et al. (2018) use the TOPSIS to evaluate the environmentalcarrying capacity of marine resources. In order to better prove the validity and superiority of theevaluation results obtained by the GRA, we use the other two methods to calculate theindicators in this paper. The results obtained are shown in Table VIII and Figure 3.

As shown in Table VIII and Figure 3, the evaluation values calculated by the weightedaverage and TOPSIS are different from the values obtained by our method, but the trends ofthe quality of Qingdao’s marine economic development reflected are the same. The quality of

AuthorsEvaluationcity Content of the evaluation

Standardizedmethod

Weightdeterminationmethod

Evaluationmethod

Yu et al.(2019)

Shanghai Sustainable developingability of marine economy

Range conversionmethod

Entropy weightmethod

Weightedaverage

Gou et al.(2018)

Qingdao Carrying capacity evaluationof marine resources andenvironment



TOPSIS

Liu et al.(this paper)

Qingdao Marine economicdevelopment quality



GRATable VII.Comparisons over theabove papers

2012 2013 2014 2015 2016 2017

Weighted average (Yu et al., 2019) 0.0884 0.0628 0.0609 0.0956 0.1402 0.1587TOPSIS (Gou et al., 2018) 0.4506 0.3166 0.2861 0.4053 0.5669 0.6243GRA 0.4643 0.3949 0.3951 0.4549 0.5572 0.5983

Table VIII.Evaluation resultsobtained by threedifferent methods

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2012 2013 2014 2015 2016 2017

Weighted average

TOPSIS

GRAFigure 3.Comprehensiveevaluation results ofQingdao marineeconomic developmentquality obtained bydifferent methods

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economic development in 2013 and 2014 is at a low level, lower than the quality developmentlevel of 2012. After 2014, the quality of marine economic development increase year by year.Therefore, we believe that our evaluation results are valid. For a given decision problem, it isnot easy to determine which method is more reasonable and reliable. A more reasonable andreliable method of evaluation is to apply several different methods to the same problem,compare their results, and then make the final decision (Wu, 2002). GRA is an important partof the grey system theory and has been proven to be useful for dealing with poor, incomplete,and uncertain information. Therefore, we choose GRA to evaluate the quality of the marineeconomy and we believe that GRA is effective and superior for the evaluation.

5. ConclusionsThis paper constructs a regional marine economic development quality evaluation index systemfrom the five dimensions, including total marine economy, marine storage capacity, marineecological environment, marine resource utilization and integrated marine management. Theentropy weight method is used to calculate the index weight, and the GRA method is used toevaluate the quality of marine economic development. Through the analysis of the researchresults, it can be concluded that the quality of marine economic development in Qingdao hasexperienced a process of decreasing first and then rising. Through the comparative analysiswith the other two methods, the rationality of the evaluation research in this paper is proved.

Through the analysis of the research results, it can be concluded that the marineeconomic development capacity of Qingdao is generally on the increase trend, the totalmarine economy has been on the rise trend, the marine storage and transportation capacity,marine ecological environment first declined and then increased. The utilization of marineresources is generally declining, and the comprehensive management of oceans varies withthe changes of environment and economy. Therefore, we should rationally exploit andutilize marine resources, give full play to marine regional advantages and pay attention tothe development of marine tertiary industry. At the same time, we should pay attention tomarine environmental protection and promote the green development of marine economy.

References

Cao, Y. and Ning, L. (2019), “Evaluation of environmental carrying capacity of marine resources basedon entropy weight TOPSIS model: taking Zhanjiang as an example”, Marine Science Bulletin,Vol. 38 No. 3, pp. 266-272.

Dai, W. and Qi, C. (2018), “Multi-attribute group decision making method based on interval 2-tuplelinguistic VIKOR”, Decision Making, Vol. 34 No. 9, pp. 41-45.

Di, Q. and Gao, Q. (2015), “Comprehensive evaluation of marine economic development quality inliaoning province”, Marine Development and Management, Vol. 32 No. 11, pp. 74-78.

Gao, Q. (2016), Comprehensive Evaluation of the Quality of Marine Economic Development in 11 China’sCoastal Provinces and Cities, Liaoning Normal University, Dalian.

Gou, L., Wang, Y. and Jin, W. (2018), “Empirical study about the carrying capacity evaluation of marineresources and environment based on the entropy-weight TOPSIS model”, MarineEnvironmental Science, Vol. 37 No. 4, pp. 586-594.

Guo, Y. (2014), “Evaluation of the comprehensive development capacity of marine economy”, MarineEconomy, Vol. 4 No. 4, pp. 1-8.

Han, Z., Hu, W., Li, B., Liu, T. and Hu, Y. (2016), “Progress and prospect on the research of marineindustry in China”, Economic Geography, Vol. 36 No. 1, pp. 89-96.

Jiang, L. (2018), “Study on the evaluation of comprehensive development strength of regional marineeconomy in China”, Feature, Vol. 350 No. 3, pp. 38-42.

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Li, Y. and Xu, Z. (2016), “Evaluation on the influential factors of the development of marine fishery:based on the generalized grey relational grade analysis”,Marine Development and Management,Vol. 33 No. 7, pp. 37-40.

Liu, H. and Chen, D. (2016), “Grey relational analysis of coastal households consumption and marineeconomy growth”, Journal of Qingdao University (Natural Science edition), Vol. 29 No. 4, pp. 105-109.

Liu, P. (2010), Study on the Evaluation Methods and Application of Enterprise Information Level Basedon Fuzzy Multi-attribute Decision Making, Beijing Jiaotong University, Beijing.

Liu, P. and Qin, X. (2018), “An extended TOPSIS method based on interval-valued linguistic intuitionisticfuzzy numbers and information entropy”,Economic andManagement Review, Vol. 34 No. 3, pp. 87-94.

Liu, P. and You, X. (2017), “Research on selection of the offshore platform based on entropy weight andextended ELECTRE method”, Economic and Management Review, Vol. 33 No. 3, pp. 53-59.

Ma, R., Li, J., Zhao, J. and Zhuang, P. (2013), “Progress on the research of maritime industry structureand layout in China”, Geographical Research, Vol. 32 No. 5, pp. 902-914.

Niu, D. (2015), “Coordinative mechanism for marine economic sustainable development in Shandongprovince”, Journal of Ocean University of China, No. 1, pp. 44-49, doi: 10.16497/j.cnki.1672-335x.2015.01.007.

Sheng, N., Gao, J. and Liu, Y. (2016), “Incidence research between marine industry and resource factorsin China”, Marine Economy, Vol. 6 No. 5, pp. 19-25.

Wang, Z., Lu, H. and Sun, C. (2017), “The relationship between marine resources development andmarine economic growth in China”, Economic Geography, Vol. 37 No. 11, pp. 117-126.

Wu, H. (2002), “A comparative study of using grey relational analysis in multiple attribute decisionmaking problems”, Quality Engineering, Vol. 15 No. 2, pp. 209-217.

Yu, D., Yang, T. and Yang, G. (2019), “Evaluation on the sustainable developing ability of marineeconomy about Shanghai”, Marine Development and Management, Vol. 36 No. 6, pp. 55-58, 66.

About the authorsPeide Liu received the BS and MS Degrees in Signal and Information Processing fromSoutheast University, Nanjing, China, in 1988 and 1991, respectively, and the PhDDegree in information management from Beijng Jiaotong University, Beijing, China, in2010. He is currently Professor at the School of Management Science and Engineering,Shandong University of Finance and Economics, Shandong, China. He is AssociateEditor of the Journal of Intelligent and Fuzzy Systems, the editorial board of the journalTechnological and Economic Development of Economy, and the member of editorial

board of the other 12 journals. He has authored or coauthored more than 200 publications. His researchinterests include aggregation operators, fuzzy logic, fuzzy decision making, three-way decision andtheir applications. Peide Liu is the corresponding author and can be contacted at: [email protected]

Xiaoxiao Liu received the BS Degree in Mathematics, Shandong University of Financeand Economics, Jinan, China, in 2017. Now she is pursuing a Master’s Degree inManagement Science and Engineering, Shandong University of Finance andEconomics, Shandong, China. She has coauthored four publications. Her researchinterests include aggregation operators, three-way decision, fuzzy decision makingand their applications.

Hongyu Yang received the BS Degree in Industrial Engineering, Shandong University ofTechnology, Zibo, China, in 2017. Now she is pursuing a Master’s Degree in ManagementScience and Engineering, Shandong University of Finance and Economics, Shandong,China. She has coauthored four publications. Her research interests include aggregationoperators, three-way decision, fuzzy decision making and their applications.


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Analysis of China’s coastal zonemanagement reform based on

land-sea integrationDahai Liu and Wenxiu Xing

First Institute of Oceanography, Qingdao, China

AbstractPurpose –After the 19th CPC national congress, Chinese Communist Party and the government put forwardhigher requirements for the development of coastal zones, and it is urgent to establish an integrated coastalzone management system, so as to better guarantee the construction of maritime powers and regionalcoordinated development. The purpose of this paper is to aim at re-examining and positioning China’sintegrated coastal zone management.Design/methodology/approach – This paper sorts out the current situation of coastal zone resources andenvironment, summarizes prominent problems and clarifies the path of comprehensive management ofcoastal zone based on the typicality and comprehensiveness of coastal ecosystem.Findings – Coastal zone is a typical area of “life community shared among mountains, rivers, forests, fields,lakes and grass.” However, there are three prominent problems at present, namely, separation between landand sea, separation among industry sectors and separation among administrative jurisdictions. Coastal zoneplanning and legislation are important measures to realize the comprehensive management of coastal zone.Originality/value – This paper puts forward some suggestions on the reform of coastal zone managementfrom the perspective of planning and legislation.Keywords Legislation, Planning, Coastal zone management, Current situation, Prominent problemPaper type Viewpoint

China’s coastal zone is an arc-shaped ribbon protruding to the southeast, with 18,000 km ofmainland coastline and 14,000 km of island coastline, from the mouth of the Yalu River inLiaoning province in the north to the mouth of the Beilun River in Guangxi ZhuangAutonomousRegion in the south. At the forefront of reform and opening up in the past 40 years, the coastalzone is not only the “golden coastal zone” where China’s population, capital and technology aremost concentrated, but also an “ecologically fragile zone” where there is intense land-seainteraction. The coastal zone has become China’s economic zone with the highest economicdensity, the strongest comprehensive strength and the greatest strategic support. In 2017, 11provinces along China’s coastal zone, which occupy about 13.5 percent of the national territory,nurtured 43 percent of China’s population and contributed 57 percent of its GDP. In particular,coastal cities which cover an area of only 4.6 percent of China’s national land, nurtured 18.4percent of the country’s population and contributed 34 percent of the country’s GDP.

However, the coastal zone is strongly influenced by the process of population growth,urbanization and the rapid development of the marine economy (Igumnova et al., 2009).The resources and environment of the coastal zone are undergoing unprecedented abnormalchanges, and many common problems hindering sustainable utilization have emerged:continuous reduction of natural coastal wetlands, severe contamination in some coastal


Vol. 2 No. 1, 2019pp. 39-49


DOI 10.1108/MAEM-03-2019-0001

Received 19 March 2019Accepted 19 April 2019


© Dahai Liu and Wenxiu Xing. Published in Marine Economics and Management. Published byEmerald Publishing Limited. This article is published under the Creative Commons Attribution(CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of thisarticle (for both commercial and non-commercial purposes), subject to full attribution to the originalpublication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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China’scoastal zonemanagement

reform


waters, frequent environmental disasters in coastal zone (Turner, 2000), unbalancedregional development, convergent industrial structure and extensive utilization of resources(Dauvin, 2008). The coordinated development of coastal zones has become an importantfocus for society at large.

From the perspective of national strategy, higher requirements are required for thedevelopment of coastal areas, which should not only achieve their own high-qualitydevelopment, but also coordinate with the Yangtze River Economic Zone and the YellowRiver Eco-economic Zone so as to form a grand national strategic pattern of “jointlyconnecting south and north, east and west.” The report of the 19th National Congress of theCPC puts forward the ideas of “implementing the strategy of coordinated regionaldevelopment,” “establishing a more effective new mechanism for coordinated regionaldevelopment,” and “pursuing coordinated land and marine development, and stepping upefforts to build China into a strong maritime country.” On November 18, 2018, the CentralCommittee of the CPC and the State Council put forward suggestions on the establishmentof a more effective new mechanism for coordinated regional development. On the one hand,planning should be regarded as the guide to promote the comprehensive coordinateddevelopment of land and sea in spatial distribution, industrial development, infrastructureconstruction, resource development and environmental protection. On the other hand,comprehensive plans for coastal zone protection and utilization should be formulated andimplemented to strictly control reclamation and promote integrated land and sea ecologicalprotection and rehabilitation in coastal areas.

In light of the above, it is urgent to establish a comprehensive coastal zone managementsystem for the coordinated development of land and sea, so as to better guarantee amaritime power and the coordinated regional development. Based on analysis of the currentsituation of coastal resources and environment, this paper summarizes the prominentproblems in the process of coastal zone management and then discusses the ways ofimplementation and reform suggestions on coastal zone management for coordinateddevelopment of land-sea integration.

1. Current situation of coastal resources and environmentCoastal zone is the material foundation for sustainable economic development and plays animportant role in China’s economy. China’s coastal zones span three climatic regions fromnorth to south, namely warm temperate zone, subtropical zone and tropical zone, and theyare abundant in space resources and material resources.

1.1 Abundant space resources in coastal zoneChina’s coastal zone has important special land resources and provides strategic space tosupport the country’s future social and economic development. Such resources and spacemainly include coastal waters, coastline, estuaries, bays, islands, coastal wetlands and otherspace resources.

In accordance with international law and the relevant provisions of the United NationsConvention on the Law of the Sea, the area of the sea under China’s jurisdictions can reach3m sq. km, of which 380,000 sq. km is the territorial sea with the same legal status as theland territory. The mainland coastline is more than 18,000 km and according to the statisticsfrom 908 Special Surveys on coastal zones in coastal provinces, the proportion of artificialcoastlines in the mainland of China has reached 61.55 percent, and some cities do not havenatural coastline at all. China’s coastline is dotted with more than 1,800 estuaries of varioussizes and types, including more than 60 estuaries with rivers over 100 km in length.In addition, it has more than 160 bays covering more than 10 sq. km (State OceanicAdministration, 2012). According to the results of the national census regarding the namesof sea waters and islands, there are more than 11,000 islands in China, of which 6,961 islands

40

MAEM2,1

have an area of more than 500 sq. km (excluding Hainan Island and the islands belonging toTaiwan, Hong Kong and Macao), accounting for about 0.8 percent of the country’s landterritory. A large number of offshore islands could pave the way for island development inChina. Coastal wetlands are widely distributed in China. According to statistics, in 2015, thetotal area of offshore and coastal wetlands was about 5.796 million hectares, of which thearea of coastal wetlands in Jiangsu and Guangdong was the largest with 1,087.5 thousandhectares and 815 thousand hectares, respectively (State Oceanic Administration, 2016).

1.2 Sharply increasing demand for material resources in the coastal zoneWith the rapid development of the national economy and the growing scarcity of landresources, the development of economy and society has a desperate demand for materialresources including marine biological resources, water resources, and mineral resources inthe coastal zone and the ocean. There are about 20,278 species of marine aquatic life inChina, accounting for about 10 percent of the world’s total marine species. In terms ofdevelopment and utilization, China’s marine products mainly rely on marine aquacultureand marine fishing. In 2016, the output of national marine products was 349,014m tons, with196,313m tons of marine aquaculture production and 132,827m tons of marine fishingproduction, accounting for 56.25 and 38.06 percent, respectively. More than one-fifth ofprotein food in China is supplied by these marine products (Research group, 2013), and thecoastal zone has become strategic base of China’s food security. In recent years, the scaleand level of seawater resources utilization in China have been continuously expanding.Among the various uses of seawater, industrial cooling water accounts for more than90 percent of the total amount of utilized seawater in China, followed by seawaterdesalination. By the end of 2015,121 seawater desalination projects were completed in ninecoastal provinces excluding Shanghai and Guangxi (Ministry of Natural Resources, 2016a, b).Oil and gas resources and coastal placer resources are buried in China’s coastal zone, whichplay an important role in promoting economic development. According to the results of TheThird National Assessment of Oil and Gas Resources, China’s potential petroleum resourcesare more than 107bn tons, of which 24.6bn tons are offshore oil resources, accounting for23 percent of China’s total petroleum resources. The potential natural gas resources are54.54 trillion cubic meters, among which the marine natural gas is 16 trillion cubic meters,accounting for 30 percent of the total national resources. Coastal placer mine is one of the mostimportant mines in the coastal zone next only to the submarine oil, which is mainly distributedin Zhejiang, Fujian, Guangdong, Shandong, Hainan and other places, most of which arelocated near the shore in small and medium size with annual output within several thousandtons to ten thousand tons. However, in recent years the amount of exploitation has beenincreasing year by year, especially regarding the rapid increase in the amount of sandexplored for construction.

1.3 The great pressure on ecological environment in coastal zoneWith the tremendous increase in the population of the coastal zone and the acceleratingurbanization process, there has been greater pressure on the development of the coastalresource environment. While bringing great economic and social benefits to the coastal zone,the environment and resources of the coastal zone are experiencing unprecedented changes.Many serious problems that hinder sustainable utilization have occurred.

First, the condition of the typical ecosystem is poor. As one of the typical coastalecosystem, coastal wetlands are located in the transitional zone between terrestrialecosystem and marine ecosystem. According to the available statistics, since the 1950s,more than 2m hectares of China’s coastal wetlands have been lost, which are equivalent to50 percent of the total coastal wetlands (Zhang et al., 2005). The natural wetlandsthat experienced greatest reduction include beach, mangrove wetland and delta wetland.

41


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In recent years, although China has redoubled efforts on the restoration and protection ofwetlands, most of the wetland ecosystems are still in a sub-optimal state.

Second, pollution remains severe in some waters. Since the introduction of “the 10thFive-year Plan,” four types of inshore water with inferior quality have increased from about32,000 sq. km in 2001 to 35,000 sq. km in 2016 (Ministry of Environmental Protection China,2017), with a growth rate of 16 percent. Part of the inshore waters are influenced bycompound pollution problems like the eutrophication, poisonous and harmful pollutants,which are mainly concentrated in Liaodong Bay, Bohai Gulf and Laizhou Bay, JiangsuCoast, the Yangtze Estuary, Hangzhou Bay, Pearl River Estuary and other major estuaries.

Third, coastal environmental disasters frequently hit. In 2016, a total of 68 red tideswere detected in the whole area, with cumulative areas reaching about 7,484 sq. km(Ministry of Natural Resources, 2017). Compared with the average value of the past fiveyears, the number of red tides detected increased by 18 percent and the cumulative areasincreased by 15 percent. Enteromorpha tide has gained momentum, covering a maximumaverage area of 41,900 sq. km in the past five years and increasing by 33 percentcompared with “the 11th Five-year Plan” period. Seawater intrusion and soil salinizationare also expanding in some areas. The most serious seawater intrusion areas are mainlydistributed in the coastal plain of the Bohai Sea. The seawater intrusion’s distance alongthe coasts of Hebei and Shandong is generally 13~25 km from the shore. The areas withserious soil salinization are mainly distributed in the Bohai coastal plain whichbelongs to the monitoring areas of Hebei, Tianjin and Shandong, and the salinizationspots are generally 10~25 km away from the shore. Storm surges and huge wavedisaster losses have increased. From 1980 to 2015, the rate of sea level rise in China’scoastal areas was 3 mm/year, higher than the global average (Ministry of NaturalResources, 2016a, b). Accidents such as oil spills and dangerous goods leakages werefrequent. In the past four decades, there have been about 3,000 oil spill accidents fromships in China’s coastal areas, with an average pollution accident occurring every four orfive days (Lan et al., 2014).

Fourth, coastal resources are excessively exploited and utilized. Ways of developmentand utilization of coastal zone resources in China mainly include the salt pans, cultivation,fishing, ports, oil fields, oil chemicals, electric power, industrial parks, tourism and naturereserves. For a long time, the development and utilization of the coastal zone in ourcountry have generally been following a resource-based model, which is characterized bysingle-type industry on a small scale. Coastal exploitation and utilization have theprominent issues of exclusivity, lack of comprehensiveness and optimization and thecombination of different uses of coastal resources, resulting in low economic efficiency,waste of resources and insufficient use of the comprehensive benefits of coastal resources.In addition, the industrial structure in the coastal zone is unbalanced, characterized bymore traditional industries and fewer emerging industries, and more energy-consumingenterprises and fewer low-emission industries. The upstream and downstream industrialchains are insufficiently extended and the industrial homogenization is serious. Someindustries with high risks, high emissions and high pollution are located in the vicinity ofimportant marine ecological areas so that the risks on marine environment are increased,and the contradictions between the industrial layout and the bearing capacity of resourcesand environment of land and sea still exist (Zheng, 2019).

2. Prominent problems in coastal zone managementThe prominent problems in coastal zone management can be summarized into three types –separation between land and sea, separation among industry sectors and separation amongadministrative jurisdictions. They are also the main reasons for the unbalanced development, thedepletion of natural resources and the deterioration of ecological environment in coastal zones.

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2.1 Separation between land and seaCoastal zone is the interlaced zone where the land and the sea interact. Due to the complexityof natural elements and ecological processes, the coastal zone has become a uniqueecosystem which is different from both the general terrestrial ecosystem and the typicalmarine ecosystem (Han and Du, 2007). In China, planning and legal system application rulesare shown in Table I.

There is no effective and overall coordination mechanism between the legal and planningsystems of the land and sea, making it impossible to utilize and protect coastal zones in thebest way, especially the intertidal zone where the interaction between the land and the sea isthe strongest. Such a lack of institutional design and management system makes the overalldevelopment with land and sea as a whole of the coastal zone in a low level. The functionallayout of the land and the sea space, the infrastructure construction and the resourceallocation are unbalanced and inadequate (He, 2018). The inadequate coordination betweenthe environmental improvement and disaster prevention among the regions, watershedsand sea areas goes against the national strategy to implement coordinated developmentplans for land and sea.

2.2 Separation among industry sectorsCoastal zone is not only an area with the most intensive production activities and thehighest degree of development, but also an area managed and planned by variouscompetent departments. Before institutional reform in 2018, China’s coastal zone was underthe management of various departments, such as Ministry of Land and Resources, Ministryof Housing and Urban-Rural Construction, Ministry of Environmental Protection, StateForestry Administration, State Oceanic Administration, Ministry of Water Resources andother departments, with each department carrying out their own duties, respectively, tomanage land or sea, resulting in competing policies from different departments and theabsence of actual effective management. The overlapping of different spatial planning of thecoastal areas, the decentralization of the supervision and law enforcement responsibilitiesand the fact that no department took responsibility for all people-owned natural resourcesand asset, made it difficult to carry out comprehensive management in coastal zone forwardeffectively. The marine-related work has been impeded by the problems of land-seaintegration, river-sea connection and coastal zone regional coordination for a long time.The problems are especially prominent in the land source pollution prevention and control,marine economic regulation and control and intensive use of sea reclamation.

After the institutional reform, although the spatial planning has been largely handedover to the Ministry of Natural Resources, the lack of comprehensive managementlaws, regulations and policies will cause conflicts of interest and competition amongdifferent departments, leading to accumulated contradictions and problems in practice.This negatively affects the new pattern of all-round coordinated development topromote the space layout of the coastal zone, industrial development, infrastructureconstruction, resource development and environmental protection (CPC Central Committeeand State Council, 2018).

Areas Applications

Land side of the coastalzone

Land-use planning, urban planning, Land Management Law of the People’sRepublic of China, etc.

Ocean side of thecoastal zone

Planning of marine functional zones, marine functional zoning and the Law of thePeople’s Republic of China on the Use and Management of Sea, etc.

Table I.Comparison ofland and Ocean

management policies

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2.3 Separation among administrative jurisdictionsChina has a long coastline, and the coastal zone is composed of administrative jurisdictionsat different levels. Over the years, based on the strategic deployment with the eastern regiontaking the lead in development, several national coastal development strategies have beendeployed successively, such as Regional Cooperation Development of Tumen River in JilinProvince, Liaoning Coastal Economic Belt, Tianjin Binhai New area, Blue economic zone inShandong Peninsula, Jiangsu Coastal Area, Pudong New Area in Shanghai, Zhejiang marineeconomic development demonstration region, Western Taiwan Straits Economic Zone inFujian Province and Pearl River Delta Region in Guangdong Province, Beibu Gulf EconomicZone in Guangxi Province, International Tourism Island in Hainan Province and so on.However, with the improvement of marine development capability and the gradualupgrading of marine industry, the competition of marine developments among differentprovinces has intensified (Li et al., 2011). Meanwhile, the industrial structure has obviousproblems of convergence and low quality even with the phenomenon of mutual conflicts andcontradictions. Even in the same coastal province, each sub-jurisdiction has difficulty informing the overall planning of marine development and utilization. The main reason is thatthere is no adequate and effective overall coordination mechanism across jurisdictions.Moreover, among administrative jurisdictions, there is no adequate and effective overallcoordination mechanism in the industrial layout, construction planning and environmentalprotection. In such a situation, it is difficult to promote complementary and coordinateddevelopment of the whole coastal zone and implement the regional coordinated developmentof national major strategy (Figure 1).

3. Path choice of coastal zone management based on the coordinateddevelopment of land and seaIn order to better implement the strategic deployment of the national coordinateddevelopment of coastal zones, adhere to the overall planning of land and sea, and acceleratethe construction of maritime power, it is essential to fully understand the regionalcharacteristics, ecological features, land and sea attributes and industrial developmentstatus of coastal zones in order to coordinate the development of coastal zones with targetedlegal systems and planning tools.

3.1 Ecological features of land and sea in coastal zonesCoastal zone is a typical region of “life community shared among mountains, rivers, forests,fields, lakes and grass” (Huang, 2018). The land and sea resources in the coastal zoneinterrelate with each other, and the ecosystems are interconnected with its functions. Thereis ecological connection among “mountains, rivers, forests, fields, lakes and grass,” andthe land and the sea form an interdependent life community:

(1) The ecosystems of land and sea are mutually integrated. The land and the sea forma cyclic ecosphere, in which water, soil, organisms and other ecological elements inthe coastal zone are generally connected. There is ecological connection among“mountains, rivers, forests, fields, lakes and grass,” and the land and the seaecosystems are integrated with each other. For example, the input of terrestrialnutrients contributes to the growth of marine biomass, and the moisture from thesea makes the land vegetation flourish. The transport of silt by rivers promotes thedevelopment of coastal wetlands. Moreover, fish migrate, spawn and reproducebetween rivers and oceans, and birds roost and forage in mountain forests andcoastal beaches.

(2) There exists spatial relation between ecological damages to the land and the sea(de Groot et al., 2002). The water, soil, organisms and other ecological elements in the

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coastal zone interact with each other, and the ecological damages of “mountains,water, forests, fields, lakes and grass” are also related to each other. Therefore, thereis a spatial relation between ecological damages to the land and the sea. Forexample, the pollution of river water may lead to the damage of marineenvironmental quality and cause an algal bloom disaster. Excessive exploitation ofgroundwater might cause seawater intrusion and land salinization. And the flowcutoff of rivers and the consequent decrease of silt transported can result in coastalwetland degradation. What’s more, coastal wetlands and mangrove destructioncause coastal geological disasters. Dam construction blocks the fish migrationchannel, and land reclamation destroys foraging and habitat of birds.

Therefore, it is necessary to take an overall perspective to keep balance between the currentand long-term, land and sea, demand and supply, development and protection and tocoordinate all kinds of utilization demands in coastal zones. And in the meantime, thefunctional development and elements allocation in adjacent land and sea areas should befully connected. According to the internal and compatible relations between the naturalecological functions and the spatial influencing scope, the systematic and integratedsolutions to see the land and the sea as a whole can be found.

Figure 1.Regional strategic

distribution map ofChina’s coastal areas

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3.2 Path to achieving comprehensive management in coastal zonesThe root cause of the three “separations” is the lack of an overall coordination mechanismbetween departments and regions in coastal zone. Due to the lack of an institutionalized andlong-term mechanism, the overall coordination of the coastal zone across naturalgeographical units, industry sectors and administrative jurisdictions in a “life communityshared among mountains, water, forests, fields, lakes and grass” is not only inefficient andcostly in administration, but also leads to the overlapping of planning and conflicts betweenfunctions (Powell et al., 2009). A comprehensive coastal zone management system should beestablished from the top down at the national level. This could deal with the threemanagement “separations,” achieve coordinated overall planning and the coordinatedregional development of land and sea in the coastal zone, control the development andutilization of land and sea scientifically and rationally, and restore the deterioratingecological environment in the coastal zone. Based on the national development and planninggoals and national spatial planning pattern, and directed by the important philosophy of“life community shared among mountains, water, forests, fields and grass,” the planningand legislation work in coastal zones can be promoted as the key issue in the overalldevelopment of the land and the sea. It is a significant measure to remove the barrier ofmanagement, set up the management chain, strengthen the legal basis and promote thecoordinated development between land and sea from “isolated development” to“complementary symbiosis”:

(1) Coastal zone planning is the key to establishing a comprehensive coastal zonemanagement system. Coastal zone planning is comprehensive and coordinated,targeted at various kinds of problems in coastal zones. It is also a unification of“strategic planning” and “workplanning,” and a unification of “spatial planning” and“development planning.” The ecological protection, industry development, urbanconstruction and environmental governance factors are included comprehensively inthis planning which not only directs the establishment of the overall development andprotection framework in coastal zones, but also includes the specific managementsuggestions. From the perspective of the process of overall management in the coastalzone, the coastal zone planning is the key step to comprehensive management ofcoastal zone, which includes preliminary investigation, analysis and research at earlystage and implementation, supervision and feedback in the later stage (Huang andHuang, 2010). It also formulates the implementation plan and procedure of theoverall coastal zone management and is the key step of comprehensive managementof the coastal zone.

According to the coastal zone planning, the key area is within 10 km of theintertidal zone facing toward the land and the sea. Based on the resource andenvironment carrying capacity and the development situation and potential of thecoastal zones, overall considerations should be given to the population distribution,industrial structure and layout so as to coordinate the relationship between the naturalresources protection and the social and economic development in the coastal zone in acomprehensive way. A series of scientific and reasonable planning and zoning shouldbe established and the measures of use classification and control should be taken so asto strictly control the access of industries on the negative list to the coastal zone.High-quality development of regional economy should be encouraged to set up thenew pattern of land-sea integration in space development and protection in the coastalzone. It can be said that as the key to establishing a comprehensive coastal zonemanagement system, coastal zone planning cannot only provide institutional supportfor comprehensive management of coastal zones, but also provide a preliminary basisfor the legislation of coastal zone management and the formulation of varioustechnical norms and standards for coastal zone management.

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(2) Coastal zone legislation is the legal safeguard for the establishment of acomprehensive management system in coastal zones. Developed ocean countriesaround the world attach great importance to coastal zone management, and theyhave introduced a series of legal systems successively, such as the US CoastalZone Management Act, the UK Coastal Protection Act, Japan’s Coastal Act,Australia’s Coastal Protection and Management Act, and South Korea’s Coastal ZoneManagement Act (Han, 2013). For China, the key content of coastal zone planning andcomprehensive management system is the multi-sectoral and multi-field planningand regulatory policies as well as the comprehensive coordination mechanism. Suchcross-field, cross-sectoral and cross-regional regulatory policies, and coordinationmechanism established from the top down must be safeguarded by normativedocuments at the legal level. Without the legal “escort,” it is difficult for thecomprehensive coastal zone management system to give full play to its expectedeffect. Only in the form of laws can a macro and comprehensive policy systemand coordination mechanism be built from the top level, including the establishmentand distribution of rights and obligations of overall coordination, the establishment ofoverall objectives and basic principles of the comprehensive management of coastalzones and the evaluation and review system.

4. Suggestions on promoting the reform of the overall management of landand sea in coastal zones

(1) Preliminary studies should be strengthened and coastal zone planning should bepromoted continuously. Coastal zones are key areas and strategic platforms to promotethe coordinated development of land and sea, green coordinated development, andhigh-quality economic development. Coastal zone planning is an important part ofimproving the land-sea overall spatial planning system, and it is the “main battlefield”to optimize the land space layout of offshore area, expand the space for marineeconomic development, and realize the “integration of several plans.” It is suggestedthat the preliminary studies of coastal zone planning should be strengthened and thatthe internal and logical relations among coastal zone planning and market mechanismshould be improved. Moreover, land-sea coordination, natural resources, ecologicalenvironment, spatial usage control and other aspects should be understood fully. Fullconsideration should be given to the particularity of marine territorial space and itsdevelopment and protection. Meanwhile, marine spatial planning and zoning, useclassification and regulatory methods should be unified with the land in guidingprinciples, technical routes and regulatory principles so as to achieve coordination andconnection in the comprehensive management of the coastal zone. If this isimplemented then a solid foundation will be laid for the unified execution of theresponsibilities for all people-owned natural resources and assets, land and sea useregulation and ecological conservation and restoration (Huang, 2018). With the “highquality” as the aim, the “opening” pattern as the foundation, the “large area” as theyardstick to deal with the relation between protection and utilization and guided by theprinciple of promoting the coordinated development of society, economy and nature,the coastal zone planning should be comprehensively promoted and upgraded tonational mid-and long-term development strategy for coastal zones, forming the newpattern of national spatial strategy with the Yangtze River Economic Belt and theYellow River Ecological Economic Belt.

(2) Comprehensively review the current relevant laws and regulations and promote theintroduction of the “Coastal Zone Management Act.” Coastal zone management

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involves different fields, departments and disciplines of the land and sea economy.It is necessary to formulate special laws for coastal zone management andstrengthen the overall coordination of the coastal zone so as to ensure the balancebetween the sustainable utilization of coastal resources and the level ofcomprehensive benefits. Therefore, it is recommended that the related legislativeactivities should be carried out with Xi Jingping’s thought on Socialism withChinese Characteristics for a new era as the guiding ideology. This will go togetheron the basis of the, unified execution of the responsibilities for all people-ownednatural resources and assets, and land and sea use regulation, and ecologicalconservation and restoration (Huang, 2018). The fulfillment of the responsibility forthe natural resources assets in the coastal zone and the spatial planning reform arealso an important foundation. On the one hand, adequate preparatory work shouldbe done for Coastal Zone Legislation by investigating the existing laws and othernormative documents and sorting out the clauses that are conflicted and overlappedand the blank fields, especially those that can be addressed by institutional reform.On the other hand, by adhering to the principle of having protection andconservation as the top priority, the legislation work of Coastal Zone ManagementAct can be initiated in a timely way, focusing on the overall coordination mechanismand aiming at the coordination between the land and the sea by differentdepartments and regions.

References

CPC Central Committee And State Council (2018), “Opinions of the CPC central committee and the statecouncil on establishing a more effective new mechanism for coordinated regional development”,available at: www.sohu.com/a/279317340_100088635 (accessed November 29, 2018).

Dauvin, J.C. (2008), “The main characteristics, problems, and prospects for Western European coastalseas”, Marine Pollution Bulletin, Vol. 57 Nos 1-5, pp. 22-40.

de Groot, R.S., Wilson, M.A. and Boumans, R.M.J. (2002), “A typology for the classification,description, and valuation of ecosystem functions, goods, and services”, Ecological Economics,Vol. 41 No. 3, pp. 393-408.

Han, L.M. and Du, X.Y. (2007), Ocean Economics Review of China, Economic Science Press, Beijing.

Han, T.P. (2013), Legislation Research on China’s Coastal Zone Management, Ocean University ofChina, Qingdao.

He, L.F. (2018), “Report on the work of developing the Marine economy and accelerating thebuilding of a maritime power”, available at: www.sohu.com/a/284503926_99917889(accessed December 26, 2018).

Huang, K.N. and Huang, S.L. (2010), “An exploratory study of integrated coastal zone management inChina”, Journal of Shanghai Ocean University, Vol. 19 No. 2, pp. 246-251.

Huang, X.J. (2018), “Land ecological economics and territorial space use control: the academiccontribution of the work ‘land ecological economics’ ”, China Land Science, Vol. 32 No. 2, pp. 1-5.

Igumnova, E.M., Solodova, S.M. and Timchenko, I.E. (2009), “Management of the coastal-sea-zone-ecosystem model”, Physical Oceanography, Vol. 19 No. 1, pp. 32-44.

Lan, D.D., Sui, W.N., Wang, Z.Z., Liang, B., Xu, Y., Li, M. and Ma, M.H. (2014), “Study on prevention ofmarine oil spill risk zoning”, Ocean Development and Management, Vol. 31 No. 12, pp. 12-18.

Li, J.Y., Li, J.X., Ren, J.Y. and Chen, H. (2011), “Liaoning province should strengthen the national pilotarea for overall land-sea development”, Decision-Making & Consultancy, No. 5, pp. 25-30.

Ministry of Environmental Protection China (2017), “Offshore sea environmental quality bulletin”,available at: www.cnemc.cn/jcbg/zgjahyhjzlgb/ (accessed May 22, 2018).

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Ministry of Natural Resources (2016a), “China sea level bulletin 2015”, available at: http://gc.mnr.gov.cn/201806/t20180619_1798296.html (accessed September 17, 2018).

Ministry of Natural Resources (2016b), “National seawater utilization report 2015”, available at: http://gc.mnr.gov.cn/201806/t20180614_1794750.html (accessed October 10, 2018).

Ministry of Natural Resources (2017), “China’s Marine environment bulletin 2016”, available at: http://gc.mnr.gov.cn/201806/t20180619_1797645.html (accessed January 13, 2019).

Powell, R.B., Cuschnir, A. and Peiris, P. (2009), “Overcoming governance and institutionalbarriers to integrated coastal zone, marine protected area, and tourism management in SriLanka”, Coastal Management, Vol. 37 No. 6, pp. 633-655.

Research group (2013), Ecosystem Issues and Policy Options Addressing the Sustainable Development ofChina’s Ocean and Coasts, China environment press, Beijing.

State Oceanic Administration (2012), “National marine functional zoning 2011-2020”, available at:https://wenku.baidu.com/view/7a6bde96783e0912a3162a31.html (accessed March 18, 2019).

State Oceanic Administration (2016), China Ocean Statistics Yearbook 2015, China Ocean Press, Beijing.Turner, R.K. (2000), “Integrating natural and socio-economic science in coastal management”, Journal

of Marine Systems, Vol. 25 Nos 3-4, pp. 447-460.Zhang, X.L., Li, P.Y., Li, P. and Xu, X.Y. (2005), “Present conditions and prospects of study on coastal

wetlands in China”, Advances in Marine Science, Vol. 23 No. 1, pp. 87-95.Zheng, M.Z. (2019), “Take multiple measures to strengthen the protection of marine resources and

environment”, available at: www.sohu.com/a/287145701_100122948 (accessed January 8, 2019).

Corresponding authorWenxiu Xing can be contacted at: [email protected]


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Inter-basin water transfersupply chain coordinationwith the fairness concern

under capacity constraint andrandom precipitation

Zhisong ChenBusiness School, Nanjing Normal University, Nanjing, China, and

Huimin WangState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,

Hohai University, Nanjing, China

AbstractPurpose – The purpose of this paper is to explore the impact of supply capacity constraint, waterdelivery loss and fairness concern on the operational decisions/efficiency of the IBWT supply chain under therandom precipitation.Design/methodology/approach – Two game-theoretic decision models for the IBWT supply chaincoordination considering water delivery loss without/with fairness concern under the supply capacityconstraint and random precipitation are developed, analyzed and compared. On this basis, the correspondingnumerical analyses are conducted and compared to derive the corresponding management insights andpolicy implications.Findings – The research results indicate that the two-part tariff contract could effectively coordinate theIBWT supply chain and achieve operational performance improvement; the binding supply capacityconstraint makes the water capacity to be allocated among IBWT distributors in accordance with fairshortage allocation rule and reduces the profit (or utility) of the IBWT supply chain and its members; theexistence of fairness concern reduces the utility of the IBWT supply chain and its members; a lowerprecipitation utilization factor in the case with non-binding capacity constraint is beneficial for improving theprofit/utility of the IBWT supply chain while a higher precipitation utilization factor in the case with bindingcapacity constraint is beneficial for improving the profit/utility of the IBWT supply chain; and reducing thewater delivery loss rate, the mainline transfer cost, the branch-line transfer cost, the holding cost and theshortage cost and setting a higher retail price are beneficial for improving the profit/utility of the IBWTsupply chain.Originality/value – Two innovative coordination decision models under random precipitation are developed,analyzed and compared through game-theoretic approaches to investigate the impact of supply capacityconstraint, water delivery loss and fairness concern on the operational decisions/efficiency of the IBWT supplychain, which have enhanced the optimization decision theory for the operations management of IBWT projectsand provided a better decision support for the IBWT stakeholders to make better operations strategies.Keywords Supply chain coordination, Capacity constraint, Fairness concern,Inter-basin water transfer (IBWT), Random precipitation, Water delivery lossPaper type Research paper

Marine Economics andManagementVol. 2 No. 1, 2019pp. 50-72Emerald Publishing Limited2516-158XDOI 10.1108/MAEM-06-2019-0003

Received 29 June 2019Revised 19 July 2019Accepted 19 July 2019


© Zhisong Chen and Huimin Wang. Published in Marine Economics and Management. Published byEmerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0)licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for bothcommercial and non-commercial purposes), subject to full attribution to the original publication andauthors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

This work is supported by the National Natural Science Foundation of China (Grant Nos 71603125,71433003), China Scholarship Council (Grant No. 201706865020), the National Key R&D Program of China(Grant No. 2017YFC0404600), the Natural Science Research Project of Colleges and Universities in JiangsuProvince (Grant No. 15KJB110012), Young Leading Talent Program of Nanjing Normal University.

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1. IntroductionThe inter-basin water transfer (IBWT) project is to use a large-scale artificial method totransfer a large amount of water from the water abundant basin to the water shortage basin,so as to promote the economic and social development and alleviate the contradiction ofwater shortage in the water-scarce region. Many large-scale IBWT projects have been builtand operated in major river basins around the world, such as, the Central Valley Project inthe USA (SWP, 2017; Yang, 2003) and the South-to-North Water Diversion (SNWD) Projectin China (Wang et al., 2009).

In the operations management of IBWT projects, several key factors have importantimpacts on the operational performance of projects. First, the terminal water marketdemand is affected by local precipitation: the more the regional precipitation is, the less theterminal water market demand is. Obviously, this random precipitation has an importantimpact on the operations decision and operational efficiency of the IBWT project. Second,owing to the existence of supply capacity constraint in the IBWT project, a situation that thetotal order quantity exceeds the supply capacity may occur. Thus, how to allocate scarcewater resources to distributors fairly, to pursue economic benefit and social welfare, is stillan urgent problem need to be solved in the IBWT projects. Third, there is generally a certainwater loss in the water transfer process of the IBWT project. This water loss has animportant impact on the operational decision making and operational efficiency of theIBWT project. Fourth, there are multiple operating entities in the IBWT project, including:local supplier, external supplier and multiple distributors. Thus, how to effectivelycoordinate multiple entities in the operations management of IBWT project to achieveoperational performance improvement is also an urgent problem for IBWT projects. Finally,the operational entities of IBWT project usually have a certain fairness concern: inequityaversion, which is the preference for fairness and resistance to incidental inequalities.Apparently, this kind of fairness concern has an important impact on the operationsdecision and operational efficiency of the IBWT project.

Owing to the advantage of considering both the collective rationality and individualrationality simultaneously, supply chain management (SCM) theory has been applied in theoperations management of IBWT project to investigate the interactions among the multiplestakeholders and develop cooperative/coordination operations mechanisms (Wang et al.,2012). However, the interactions among the multiple stakeholders and operationsmanagement mechanisms in an IBWT supply chain considering fairness concern underthe capacity constraint and the random precipitation are rarely investigated in the currentliteratures and practices.

Therefore, this paper will try to explore the issues of IBWT supply chain coordinationwithout/with fairness concern under the supply capacity constraint and random precipitation.In the following sections, the corresponding literatures are reviewed first in Section 2; thetheoretical modeling notation and overview for a generic IBWT supply chain are defined inSection 3; the IBWT supply chain coordination models consideringwater delivery loss without/with fairness concern under the supply capacity constraint and random precipitation aredeveloped and analyzed in Sections 4.1 and 4.2; the corresponding numerical and sensitivityanalysis for all models is conducted and the results and comparisons are summarized inSection 5; the management insights and policy implications are then discussed in Section 6;and, finally, the research contributions and foresights are summarized and concluded.

2. Literature reviewCurrently, game theory is applied to identify the interaction relationships among stakeholdersin the operations management of IBWT projects are investigated through game theory, forexample, game theory model for the water conflicts in the SNWD project (Wei et al., 2010),game-theoretic model for the IBWT system considering both the quantity and quality

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Inter-basinwater transfersupply chain

(Manshadi et al., 2015), innovative option contract for allocating water in the IBWT projects(Rey et al., 2016), and the incentive-compatible payments in the SNWD project (Sheng andWebber, 2017). Furthermore, cooperative game theory is applied to balance the individualrationality and the collective rationality in the operations management of IBWT projects; forexample, optimal water allocation in the IBWT system using the crisp and fuzzy Shapleygames (Sadegh et al., 2010), water resources allocation considering the water quality in theIBWT system using cooperative game (Nikoo et al., 2012), IBWT water resources allocationusing least core game ( Jafarzadegan et al., 2013), IBWT water-resource allocation using arobust multi-objective bargaining methodology (Nasiri-Gheidari et al., 2018).

Currently, the theories, methods and techniques of SCM have been applied to the study ofthe operations management of IBWT projects (especially the SNWD project in China), suchas, Wang et al. (2012) studied the pricing and coordinating schemes of the eastern route ofSNWD project and discussed the analytical results and their policy implications for theeastern route of SNWD water-resource supply chain. Chen and Wang (2012a) developed adecentralized decision model and a centralized decision model with strategic customerbehavior using a floating pricing mechanism to construct a coordination mechanism via arevenue-sharing contract. Chen and Wang (2012b) further used several game-theoreticalmodels such as Stackelberg game, asymmetric Nash bargaining et al. in studying the SNWDsupply chain. A finite-horizon periodic-review inventory model with inflow forecastingupdates following the Martingale Model of Forecast Evolution was developed to study two-echelon reservoirs in an IBWT project (Xu et al., 2012). Chen et al. (2013) applied a two-tierpricing scheme to balance the water allocation by using a Stackelberg game model for theeastern route of SNWD project and they concluded that the two-tier pricing scheme is aneffective way that can integrate the government control and market powers to ensure boththe public interest and the economic benefit. Chen and Pei (2018) explored the interactionsbetween multiple stakeholders of an IBWT green supply chain through the game-theoreticand coordination research approaches considering the government’s subsidy to the water-green-level improvement under the social welfare maximization.

Nevertheless, these existing literatures regarding operations management of IBWTsupply chain, neither explored the coordination strategies of IBWT supply chain under thesupply capacity constraint and random precipitation, nor investigated the impact of supplycapacity constraint, water delivery loss and fairness concern on the operational performanceof IBWT supply chain. This paper intends to address the literature shortage issues andexplore the coordination strategies for an IBWT supply chain without/with fairness concernunder supply capacity constraint and random precipitation. A coordination decision modelwithout fairness concern and a coordination decision model with fairness concern for theIBWT supply chain under capacity constraint and random precipitation are developed,solved and compared to explore the optimal operations strategies for the IBWT supplychain and the optimal pricing regulation policy for the government.

3. Theoretical modeling notations and overviewAn IBWT distribution system is a typical “embedded” supply chain structure. In this supplychain system, a horizontal water supply system is embedded in a vertical water distributionsystem (see Figure 1). The horizontal water supply system is comprised of a local supplier and anexternal supplier and they serve as a joint IBWT supplier via an efficient cooperationmechanism,and the vertical water distribution system distributes water by the joint IBWT supplier throughthe multiple water distributors to many water consumers in the service region. Specifically, thewater resources are transferred and supplied by the local supplier from the water source to theexternal supplier within the trunk channel and then distributed to water resources distributors ofall water-intakes via river channels and artificial canal. Finally, the water resources are sold byeach distributor to the water resources consumers in his service region.What needs to be noted is

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that the water consumers can only buy water from their regional water distributors due to thefixed physical structure of the water transferring channel and the corresponding facilities andequipment. This feature determines that there is no competition among water distributors.

In Figure 1, the water distributors and the corresponding consumers are indexed byi¼ 1, 2,…, n. We assume there are m distributors supplied by the local supplier andn−m distributors supplied by the external supplier. The branch-line water transfer costfrom the ith water-intake to the ith distributor is cdi, the mainline water transfer cost fromthe (k−1)th water-intake to the kth water-intake within the horizontal green supplychain is ck, and the water delivery loss from the (k−1)th water-intake to the kthwater-intake within the horizontal green supply chain is δk, k¼ 1, 2,…, n. The orderquantity of the ith water-intake is qi, which is delivered from the water source with theoriginal pumping quantity Qi. Obviously, the relationship between the water demand of theith water-intake qi and the pumping quantity from the water source Qi isqi ¼ Qi

Qik¼1 1�dkð Þ, and the total transfer cost of the pumping quantity from the water

source Qi is TCi Qið Þ ¼ QiPi

k¼1½ckQk�1

j¼0 ð1�djÞ�, hereinto, δ0¼ 0. Therefore, the totaltransfer cost of the water demand (order quantity) of the ith water-intake is

TCi qið Þ ¼ Pik¼1½ck

Qk�1j¼0 ð1�djÞ�=

Qik¼1 1�dkð Þqi . Define Ci ¼

Pik¼1½ck

Qk�1j¼0 ð1�djÞ�=

Qik¼1

1�dkð Þ, then TCi(qi)¼Ciqi. It is assumed that the IBWT water supply capacity in the watersource is Q and satisfies

Pni¼1 QipQ. The fixed cost of water delivery for the ith water-

intake of the IBWT supplier is cfi, the fixed cost for the local supplier is cfl and cf l ¼Pm

i¼1 cf i ;the fixed cost for the external supplier is cfe and cf e ¼

Pni¼mþ 1 cf i ; then the fixed cost for the

IBWT supplier is cf ¼ cf lþcf e ¼Pn

i¼1 cf i . The local supplier sells and transfers waterresources to the external supplier with the wholesale price w (per m3). The IBWT suppliersells water resources to the ith distributor with a two-part tariff system, i.e. an entry price (alump-sum fee) wei and a usage price (charge per-use or per-unit) wi. The ith distributor sellswater resources to the consumers in his service region with a retail price pi. The waterdemand for the ith water distributor is di(xi)¼ di−ϑxi, di is the basic water demand, ϑ is theprecipitation utilization factor, xi is the precipitation in the ith water distributor’s serviceregion defined in the range [A,B] with BWA⩾ 0, and xi is a random variable with thecumulative distribution function Fi(⋅) and probability density function) fi(⋅), and the meanvalue and standard deviation of xi are μi and σi. The unit cost of holding water inventory forthe ith distributor is hi, while the shortage cost of unmet demand for the ith distributor is ri.The benchmark profit of the IBWT supplier’s ith water-intake arePb

Siin the case with non-

binding capacity constraint (CNB) andPbSiin the case with binding capacity constraint (CB),

the benchmark profit of the ith distributor are PbDi

in the CNB and PbDiin the CB.

… …

Local Supplier

Distributor n

Distributor m+2

Distributor m

Distributor 2

Water Source

Distributor i

Distributor m+1

Distributor m+k

Horizontal Supply Chain: IBWT Water Resource Joint Supplier

External SupplierPump

Reservoirs 2

Pump

Reservoirs J

Pump

Reservoirs j

Pump

Reservoirs 1

Distributor 1

1stMarket

2ndMarket

ithMarket

mthMarket

(m+1)thMarket

(m+1)thMarket

(m+k)thMarket

nthMarket

Vertical Supply Chain: IB

WT W

ater Distribution

water-intake

… …

Figure 1.A generic inter-basinwater transfer supply

chain system

53


Based on the foregoing parameters setting and model assumption, the profit function ofthe IBWT supply chain is as follows:

PSC q1; . . .; qi; . . .; qnð Þ ¼Xni¼1

piE min qi; di xið Þ� �� hiE qi�di xið Þ� �þ�riE di xið Þ�qi

� �þ� Ciþcdið Þqi�cf i

8<:

9=;:

In the IBWT vertical supply chain coordination model, the IBWT supplier offers thedistributors a two-part tariff contract in which the IBWT supplier charges a usage price wifrom the ith distributor. The distributors either accept or reject the contract. If thedistributors accept, they have to pay an entry price wc

ei to the IBWT supplier, whichare determined by the negotiation between the IBWT supplier and distributors. Under thetwo-part tariff contract, the profit functions of the ith water-intake of the IBWT supplier, theIBWT supplier and the ith distributor are as follows:Y

Siwið Þ ¼ wi�Cið Þqiþwei�cf i;

YSw1; . . .;wi; . . .;wnð Þ ¼

Xni¼1

YSiwið Þ ¼

Xni¼1

wi�Cið Þqiþwei�cf i� �

;

YDi

qið Þ ¼ piE min qi; di xið Þ� �� hiE qi�di xið Þ� �þ�riE di xið Þ�qi� �þ� wiþcdið Þqi�wei:

On this basis, the profit functions of the local supplier and the external supplier are as follows:

YLS

w1; . . .;wm;wð Þ ¼Xmi¼1

wi�Cið ÞqiþXni¼1

wei�cf lþwXn

i¼mþ 1

qi;

YES

wmþ 1; . . .;wn;wð Þ ¼Xn

i¼mþ 1

wi�Cið ÞqiþXn

i¼mþ 1

wei�cf e�wXn

i¼mþ 1

qi:

Due to the quasi-public-goods characteristics of the water resources and the quasi-public-welfare characteristics of the IBWT projects, the operations management of the IBWTprojects should take both the economic benefit and the social welfare into account. However,the operations management of the IBWT project typically pursues only the economic benefitmaximization, if the government does implement any regulation measures to pursue socialwelfare improvement. Therefore, the government’s regulations (such as shortage allocationrule, etc.) are essential for guaranteeing social welfare in the operations management ofIBWT project. Owing to the existence of supply capacity constraint, when the total orderquantity exceeds the supply capacity, it is inevitable that the allocation of scarce waterresources among IBWT distributors should be conducted. If the shortage allocation rule ismade by the IBWT supplier, the water resources would be preferentially allocated to thehigh-value distributor (the distributor who can contribute more profit) by the IBWTsupplier to pursue more profits without considering allocating fairness and social welfare.A fair shortage allocation rule is made by the government as follows: once the total orderquantity exceeds the supply capacity, i.e.

Pni¼1 Qi ¼

Pni¼1 qi=

Qik¼1 1�dkð Þ4Q, the ith

distributor could be allocated with a certain ratio of initial order quantity, this ratio is setbased on the overall order fulfillment ratio, i.e. l ¼ Q=

Pni¼1 qi=

Qik¼1 1�dkð Þ.

54

MAEM2,1

4. IBWT supply chain coordination with fairness concern under capacityconstraint and random precipitationBased on modeling notations and assumptions in Section 3, the theoretical models of IBWTsupply chain coordination without/with fairness concern under capacity constraint andrandom precipitation are developed, analyzed and compared in this section.

4.1 IBWT supply chain coordination without fairness concern under capacity constraintand random precipitation4.1.1 IBWT supply chain centralized decision. Under the fair shortage allocation rule madeby the government, the optimal problem for the centralized IBWT supply chain undercapacity constraints can be formulated as follows:

maxq1 ;...;qi ;...;qn

QSC q1; . . .; qi; . . .; qnð Þ

s:t:Xni¼1

qiQik¼1 1�dkð Þ

pQ

8>><>>: :

Solving the first-order condition and the second-order derivative of the optimal problemw.r.t. the order quantity qi, we can obtain the optimal order quantity of the water resourcesfor the ith water-intake as follows:

qci ¼ min qni ; qn

i

� �; i ¼ 1; 2; . . .; n:

Hereinto:

qni ¼ di�WF�1i

Ciþcdiþhipiþhiþri

� �; qni ¼ lnqni ; l

n ¼ Q=Xn

i¼1

qniQik¼1 1�dkð Þ

:

Plugging the optimal order quantity of the water resources into the profit function of theIBWT supply chain, we can obtain the optimal profit of the IBWT supply chain as follows:

PcSC ¼

Xni¼1

pi� Ciþcdið Þ� �di�Li qni

� � ��cf ; if lnX1

Xni¼1


� �Hi qni� � ��cf ; if lno1

8>>>>><>>>>>:

;

where:

Li zið Þ ¼ W piþhið ÞZ B

Axif i xið Þdxi�W piþhiþrið Þ

Z 1W di�zið Þ

Axif i xið Þdxi;

Hi qni� ¼ piþhiþrið ÞFi

1W

di�qni� �

� Ciþcdiþhið Þ�

di�qni�

:

4.1.2 IBWT vertical supply chain coordination. In the IBWT vertical supply chaincoordination model, the IBWT supplier offers the distributors a two-part tariff contract in

55


which the IBWT supplier charges a usage price wi from the ith distributor. The distributorseither accept or reject the contract. If the distributors accept, they have to pay an entry price,wcei in the CNB or lnwc

ei in the CB, to the IBWT supplier, which are determined by thenegotiation between the IBWT supplier and distributors. Under the fair shortage allocationrule made by the government, the ith distributor’s optimal problem under the two-part tariffcontract is formulated as follows:

maxq1

PD1 q1ð Þ^

maxqi

PDi qið Þ^

maxqn

PDn qnð Þ

s:t:Xni¼1


pQ

8>>>>>>>>>>>>>><>>>>>>>>>>>>>>:

:

Solving the first-order condition and the second-order derivative of the optimal problemw.r.t. the order quantity qi, respectively, and we can obtain the reaction function of the orderquantity qi w.r.t. the water usage price wi under the two-part tariff contract as follows:

qdi wið Þ ¼ min qnni wið Þ; qnni wið Þ� �; i ¼ 1; 2; . . .; n:

Hereinto:

qnni wið Þ ¼ di�WF�1i

wiþcdiþhipiþhiþri

� �; qnni wið Þ ¼ lnn wið Þqnni wið Þ; lnn wið Þ

¼ Q=Xn

i¼1

qnni wið ÞQik¼1 1�dkð Þ

:

Under the two-part tariff contract, to achieve the IBWT supply chain coordination, it isnecessary to achieve the coordinated condition: qci ¼ qdi wið Þ: Then, we have the coordinatedusage price for the ith water-intake of the IBWT supplier as follows:

wci ¼ Ci; i ¼ 1; 2; . . .; n:

Therefore, the coordinated profit of the distributors PcDi

and the IBWT supplier PcS under

the two-part tariff contract are shown below:

PcDi¼


� �wcei; if lnX1


� �Hi qni� �lnwc

ei; if lno1

(; i ¼ 1; 2; . . .; n;

56

MAEM2,1

PcS ¼

Xni¼1

PcSi¼

Xni¼1

wcei�cf i

� ; if lnX1

Xni¼1

lnwcei�cf i

� ; if lno1

:

8>>>>><>>>>>:

4.1.3 IBWT horizontal supply chain cooperation. Plugging wci and q

ci into the profit functions of

the local supplier and the external supplier in the IBWT horizontal supply chain, we can get:

PcLS wð Þ ¼

Xmi¼1

wcei�cf i

� þwXn

i¼mþ 1

qni ; if lnX1

Xmi¼1

lnwcei�cf i

� þwXn

i¼mþ 1

qni ; if lno1

8>>>>><>>>>>:

;

PcES wð Þ ¼

Xni¼mþ 1

wcei�cf i

� �wXn

i¼mþ 1

; qni ; if lnX1

Xni¼mþ 1

lnwcei�cf i

� �wXn

i¼mþ 1qni ; if lno1

8>>>>><>>>>>:

:

According to the Nash bargaining theory (Nash, 1950; Kalai and Smorodinsky, 1975; Binmoreet al., 1986; Muthoo, 1999), the asymmetric Nash bargaining problem for bargaining over thewholesale price w can be expressed as follows:

maxw

y wð Þ ¼ PcLS wð Þ� �t

PcES wð Þ� �1�t

; s:t: PcLS wð ÞþPc

ES wð Þ ¼ PcS :

Hereinto, τ is the bargaining power of the local supplier.Solving the first-order condition and the second-order derivative of the optimal problem

w.r.t. the wholesale price w respectively, we can obtain the bargaining wholesale price wcas follows:

wc ¼

tPn

i¼1wcei�cf

� �Pm

i¼1wcei�cf l

� Pn

i¼mþ 1qni

; if lnX1

tPn

i¼1lnwc

ei�cf�

�Pm

i¼1lnwc

ei�cf l� Pn

i¼mþ 1qni

; if lno1

8>>><>>>:

:

Hence, we can get the bargaining profit of the local supplier and the external supplier in theIBWT horizontal supply chain as follows:

PcLS ¼ tPc

S ;

PcES ¼ 1�tð ÞPc

S :

57


Remark 1. Only when the following conditions hold: PcSiXPb

Si, Pc

DiXPb

Di, the IBWT

supply chain members would have the economic motivation to coordinate, thatis, the reasonable interval of the entry price is: wc

eiA ½wcei ;w

cei �; i ¼ 1; 2; :::; n.

Hereinto:

wcei ¼

PbSiþcf i; if lnX1

1ln P

bSiþcf i

� �; if lno1

8<: ;

wcei ¼


� �PbDi; if lnX1

1ln pi� Ciþcdið Þ� �

di�Li qni� �Hi qni

� �PbDi

n o; if lno1

8<: :

4.2 IBWT supply chain coordination with fairness concern under capacity constraint andrandom precipitationUnder the scenario with fairness concern, owing to the distributors’ weak position in theIBWT supply chain, the IBWT supplier is fair neutral, the distributors have inequity aversion,the utility functions of the IBWT supplier and the distributors are defined as follows:

US ¼ PS ¼Xni¼1

PSi;

UDi ¼ PDi�ki PSi�PDi

� ¼ 1þkið ÞPDi�kiPSi; i ¼ 1; 2; . . .; n;

where κi is the ith distributor’s coefficient of fairness concern.Thus, the utility function of the IBWT supply chain is as follows:

USC ¼ USþXni¼1

UDi ¼ PSC�Xni¼1

ki PSi�PDi

� :

Likewise, the benchmark utility of the IBWT supplier’s ith water-intake are UfbSi¼ Pb

Siin

the CNB and UfbSi¼ P

bSiin the CB, and the benchmark utility of the ith distributor are

UfbDi

¼ 1þkið ÞPbDi�kiPb

Siin the CNB and U

fbDi

¼ 1þkið ÞPbDi�kiP

bSiin the CB.

4.2.1 IBWT supply chain centralized decision. Under the fair shortage allocation rulemade by the government, the optimal problem for the centralized IBWT supply chain undercapacity constraints can be formulated as follows:

maxq1 ;...;qi ;...;qn

USC q1; . . .; qi; . . .; qnð Þ

s:t:Xni¼1


pQ

8>><>>: :

Solving the first-order condition and the second-order derivative of the optimal problemw.r.t. the order quantity qi, we can obtain the optimal order quantity function of the waterresources qi w.r.t. the usage price wi as follows:

qf ci wið Þ ¼ min qfni wið Þ; qfni wið Þn o

; i ¼ 1; 2; . . .; n:

58

MAEM2,1

Hereinto:

qfni wið Þ ¼ di�WF�1i

1þkið Þ Ciþcdiþhð Þþ2ki wi�Cið Þ1þkið Þ piþhiþrið Þ

�; qfni wið Þ ¼ lnf wið Þqfni wið Þ; lnf wið Þ

¼ Q=Xn

i¼1

qfni wið ÞQik¼1 1�dkð Þ

:

4.2.2 IBWT vertical supply chain coordination. In the IBWT vertical supply chaincoordination model, the IBWT supplier offers the distributors a two-part tariff contract inwhich the IBWT supplier charges a usage price wi from the ith distributor. The distributorseither accept or reject the contract. If the distributors accept, they have to pay an entry price,wfcei in the CNB or lnf w

f cei in the CB, to the IBWT supplier, which are determined by the

negotiation between the IBWT supplier and distributors. Under the fair shortage allocationrule made by the government, the ith distributor’s optimal problem under the two-part tariffcontract is formulated as follows:

maxq1

UD1 q1ð Þ^

maxqi

UDi qið Þ^

maxqn

UDn qnð Þ

s:t:Xni¼1


pQ

8>>>>>>>>>>>>>><>>>>>>>>>>>>>>:

:

Solving the first-order condition and the second-order derivative of the optimal problemw.r.t. the order quantity qi, respectively, and we can obtain the reaction function of the orderquantity qi w.r.t. the water usage price wi under the two-part tariff contract as follows:

qfdi wið Þ ¼ min qfnni wið Þ; qfnni wið Þn o

; i ¼ 1; 2; . . .; n:

Hereinto:

qfnni wið Þ ¼ di�WF�1i

1þkið Þ wiþcdiþhið Þþki wi�Cið Þ1þkið Þ piþhiþrið Þð Þ

�;

qfnni wið Þ ¼ lnnf wið Þqfnni wið Þ; lnnf wið Þ ¼ Q=Xn

i¼1

qfnni wið ÞQik¼1 1�dkð Þ

:

Under the two-part tariff contract, to achieve the IBWT supply chain coordination, it is necessaryto achieve the coordinated condition: qf ci wið Þ ¼ qfdi wið Þ. Then, we have the coordinated usageprice for the ith water-intake of the IBWT supplier as follows:

wfci ¼ Ci; i ¼ 1; 2; . . .; n:

59


Thus, we can obtain the optimal order quantity of the water resources for the ith water-intakeas follows:

qf ci ¼ min qfni ; qfnin o

; i ¼ 1; 2; . . .; n:

Hereinto:

qfni ¼ di�WF�1i

Ciþcdiþhipiþriþhi

� �; qfni ¼ lnf q

fni ; lnf ¼ Q=

Xni¼1

qfniQik¼1 1�dkð Þ

:

Therefore, the optimal utility of the IBWT supply chain UfcSC , the coordinated utility of the

IBWT supplierUfcS and the distributorsUfc

Diunder the two-part tariff contract are shown below:

UfcSC ¼

Xni¼1

1þkið Þ pi� Ciþcdið Þ� �di�Li qni

� � �þkicf i�2kiwf cei

n o�cf ; if lnf X1

Xni¼1


� �Hi qni� � �þkicf i�2kil

n

f wf cei

n o�cf ; if lnf o1

8>>>>><>>>>>:

;

UfcDi¼


� � �þkicf i� 1þ2kið Þwfcei ; if lnf X1


� �Hi qni� � �þkicf i� 1þ2kið Þlnf wf c

ei ; if lnf o1

8<: ;

UfcS ¼ Pf c

S ¼Xni¼1

Pf cSi¼

Xni¼1

wfcei�cf i

� �; if lnf X1

Xni¼1

lnf wf cei�cf i

� �; if lnf o1

8>>>>><>>>>>:

:

4.2.3 IBWT horizontal supply chain cooperation. Plugging wfci and qf ci into the profit functions

of the local supplier and the external supplier in the IBWT horizontal supply chain, we can get:

UfcLS wð Þ ¼

Xmi¼1

wfcei�cf i

� �þw

Xni¼mþ 1

qfni ; if lnf X1

Xmi¼1

lnwfcei�cf i

� �þw

Xni¼mþ 1

qfni ; if lnf o1

8>>>>><>>>>>:

;

UfcES wð Þ ¼

Xni¼mþ 1

wfcei�cf i

� ��w

Xni¼mþ 1

qfni ; if lnf X1

Xni¼mþ 1

lnwfcei�cf i

� ��w

Xni¼mþ 1

qfni ; if lnf o1

8>>>>><>>>>>:

:

The asymmetric Nash bargaining problem for bargaining over the wholesale price w can beexpressed as follows:

maxw

y wð Þ ¼ UfcLS wð Þ

h itUfc

ES wð Þh i1�t

s:t: UfcLS wð ÞþUfc

ES wð Þ ¼ UfcS ;

where τ is the bargaining power of the local supplier.

60

MAEM2,1

Solving the first-order condition and the second-order derivative of the optimal problemw.r.t. the wholesale price w, respectively, we can obtain the bargaining wholesale price wf

cas follows:

wfc ¼

tPn

i¼1wfcei �cf

� �Pm

i¼1wfcei �cf l

� Pn

i¼mþ 1qfni

; if lnf X1

tPn

i¼1lnf w

f cei �cf

� �Pm

i¼1lnf w

f cei �cf l

� Pn

i¼mþ 1qfni

; if lnf o1

8>>><>>>:

:

Hence, we can get the bargaining profit of the local supplier and the external supplier in theIBWT horizontal supply chain as follows:

UfcLS ¼ tUfc

S ;

UfcES ¼ 1�tð ÞUfc

S :

Remark 2. Only when the following conditions hold: UfcSiXUfb

Si, Ufc

DiXUfb

Di, the IBWT

supply chain members would have the economic motivation to coordinate, thatis, the reasonable interval of the entry price is: wfc

ei A ½wfcei ;w

fcei �; i ¼ 1; 2; :::; n.

Hereinto:

wfcei ¼

UbSiþcf i; if lnf X1

1lnf

UbSiþcf i

� �; if lnf o1

8><>: ;

wfcei ¼

11þ 2ki


� � �þkicf i�UfbDi

n o; if lnf X1

11þ 2kið Þlnf


� �Hi qni� � �þkicf i�U

fbDi

n o; if lnf o1

8><>: :

5. Numerical and sensitivity analysisBased on the real characteristics of IBWT project, an IBWT supply chain with one IBWTsupplier and six water distributors is developed for the numerical analysis of theIBWT supply chain models developed and analyzed in Section 4. Since there are sixwater-intakes and six water distributors in the IBWT supply chain, i.e. n¼ 6. We assumethat three water distributors are supplied by the local supplier (i.e. m¼ 3) and three waterdistributors are supplied by the external supplier (i.e. n–m¼ 3). The random precipitationxi obeys normal distribution, i.e. xi∼N ðmi;s2i Þ. A is set at 0 and B is set at 1.0E+ 10. Thelocal supplier’s bargaining power τ is 0.6. The precipitation utilization factor ϑ is 0.01.The water delivery loss from the (i−1)th water-intake to the ith water-intake within thehorizontal green supply chain δi is 5 percent. The fixed cost of water delivery for the ithwater-intake of the IBWT supplier cfi is 20,000. The ith distributor’s coefficient of fairnessconcern κi is 0.8. To simplify the analysis, the supply capacity Q is set as 1,500,000,000

61


and 1,200,000,000. Table I lists the parameters mainly relating to the IBWT supply chainand their values for the numerical analysis.

5.1 Numerical analysisThe numerical analysis assesses and compares the quantity decisions and the resultingprofits for the IBWT supply chain coordination models under capacity constraint andrandom precipitation considering fairness concern or not. The numerical analysis results ofIBWT supply chain coordination without fairness concern are shown in Table II (CNB) andTable III (CB), and the numerical analysis results of IBWT supply chain coordination with

Waterintake i

Mainlinewater transfer

cost ci

Actual watertransfercost Ci

Branch-linewater transfer

cost cdi

Retail pricepi

Holdingcost hi

Shortagecost ri

Basic waterdemand di

1 0.25 0.26 0.05 0.94 0.05 0.42 50,000,0002 0.30 0.59 0.06 1.96 0.12 0.95 100,000,0003 0.35 0.99 0.07 3.19 0.20 1.59 150,000,0004 0.40 1.47 0.08 4.64 0.29 2.35 200,000,0005 0.45 2.02 0.09 6.32 0.40 3.23 250,000,0006 0.50 2.65 0.10 8.25 0.53 4.24 300,000,000

Waterintake i

Mean value ofprecipitation μi

Standard deviationDof precipitation σi

Benchmarkprofit Pb

Si

Benchmarkprofit Pb

Di

Benchmarkprofit P

bSi

Benchmarkprofit P

bDi

1 3.00E+ 08 1.00E+ 07 12,000,000 9,000,000 10,800,000 8,100,0002 2.50E+ 08 8.00E+ 06 60,000,000 40,000,000 54,000,000 36,000,0003 2.00E+ 08 6.00E+ 06 150,000,000 100,000,000 135,000,000 90,000,0004 1.50E+ 08 4.00E+ 06 300,000,000 200,000,000 270,000,000 180,000,0005 1.00E+ 08 2.00E+ 06 400,000,000 300,000,000 360,000,000 270,000,0006 5.00E+ 07 1.00E+ 06 600,000,000 500,000,000 540,000,000 450,000,000

Table I.Parameters in theIBWT supply chainfor the numericalanalysis

i wcei Range of wc

ei wci qci Pc

DiPc

S

1 15,000,000 [12,020,000, 20,420,988] 0.26 47,064,721 14,420,988 1,939,880,0002 65,000,000 [60,020,000, 87,372,701] 0.59 97,552,751 62,372,701 Pc

LS3 160,000,000 [150,020,000, 214,785,202] 0.99 148,039,834 154,785,202 1,163,928,0004 320,000,000 [300,020,000, 414,130,097] 1.47 198,526,647 294,130,097 Pc

ES5 530,000,000 [400,020,000, 749,093,822] 2.02 249,013,348 519,093,822 775,952,0006 850,000,000 [600,020,000, 1,147,514,348] 2.65 299,506,686 797,514,348 Pc

SCNote – wc¼ 1.24 Total 1,039,703,988 1,842,317,157 3,782,197,157

Table II.Numerical analysisresults of IBWTsupply chaincoordination withoutfairness concern (CNB)

i lnwcei Range of lnwc

ei wci qci Pc

DiPc

S

1 13,811,165 [11,751,362, 19,028,779] 0.26 43,334,574 11,809,475 1,786,123,9532 59,848,380 [58,669,926, 80,512,238] 0.59 89,821,140 50,282,805 Pc

LS3 147,319,089 [146,642,232, 197,054,274] 0.99 136,306,834 124,117,512 1,071,674,3724 294,638,178 [293,262,742, 378,798,823] 1.47 182,792,280 234,138,681 Pc

ES5 487,994,482 [391,009,749, 686,858,439] 2.02 229,277,623 414,426,515 714,449,5816 782,632,660 [586,503,763, 1,049,619,324] 2.65 275,769,076 633,798,356 Pc

SCNote – wc¼ 1.24 Total 957,301,526 1,468,573,345 3,254,697,297

Table III.Numerical analysisresults of IBWTsupply chaincoordination withoutfairness concern (CB)

62

MAEM2,1

fairness concern are shown in Table IV (CNB) and Table V (CB). The findings from thenumerical analysis results are summarized below:

(1) Comparing the numerical analysis results between the CNB (Table II) and the CB(Table III) under the scenario without fairness concern: the coordinated usage pricesare the same between the CNB and the CB; the entry prices in the CNB are higherthan those in the CB; the actual received quantities of water resources in the CNB arehigher than those in the CB; and the profits of the IBWT supply chain and itsmembers in the CNB are higher than those in the CB.

(2) Comparing the numerical analysis results between the CNB (Table IV ) and the CB(Table V) under the scenario with fairness concern: the coordinated usage prices arethe same between the CNB and the CB; the entry prices in the CNB are higher thanthose in the CB; the actual received quantities of water resources in the CNB arehigher than those in the CB; and the utilities of the IBWT supply chain and itsmembers in the CNB are higher than those in the CB.

(3) Comparing the numerical analysis results between the scenario with fairnessconcern (Table IV) and the scenario without fairness concern (Table II) in the CNB:the coordinated usage prices are the same between the scenario with fairnessconcern and the scenario without fairness concern; the entry prices are set the samebetween the scenario with fairness concern and the scenario without fairnessconcern; the actual received quantities of water resources are the same between thescenario with fairness concern and the scenario without fairness concern; and theutilities of the IBWT supply chain and its members under the scenario with fairnessconcern are no more than those under the scenario without fairness concern.

(4) Comparing the numerical analysis results between the scenario with fairnessconcern (Table V ) and the scenario without fairness concern (Table III) in the CB: thecoordinated usage prices are the same between the scenario with fairness concernand the scenario without fairness concern; the entry prices are set the same between

i lnf wf cei Range of lnf w

f cei wf c

i qf ci U f cDi

Uf cS

1 13,811,165 [11,751,362, 16,789,574] 0.26 43,334,574 10,224,124 1,786,123,9532 59,848,380 [58,669,926, 73,791,526] 0.59 89,821,140 42,646,345 Ufc

LS3 147,319,089 [146,642,232, 181,542,877] 0.99 136,306,834 105,572,251 1,071,674,3724 294,638,178 [293,262,742, 352,480,029] 1.47 182,792,280 185,755,084 Ufc

ES5 487,994,482 [391,009,749, 595,828,073] 2.02 229,277,623 355,588,141 714,449,5816 782,632,660 [586,503,763, 907,122,229] 2.65 275,769,076 514,746,913 Ufc

SCNote – wf

c ¼ 1:24 Total 957,301,526 1,214,532,858 3,000,656,811

Table V.Numerical analysis

results of IBWTsupply chain

coordination withfairness concern (CB)

i wf cei Range of wfc

ei wf ci qf ci Uf c

DiUf c

S

1 15,000,000 [12,020,000, 17,836,068] 0.26 47,064,721 13,973,778 1,939,880,0002 65,000,000 [60,020,000, 78,956,485] 0.59 97,552,751 60,286,861 Ufc

LS3 160,000,000 [150,020,000, 194,857,447] 0.99 148,039,834 150,629,363 1,163,928,0004 320,000,000 [300,020,000, 379,019,298] 1.47 198,526,647 273,450,175 Ufc

ES5 530,000,000 [400,020,000, 641,686,492] 2.02 249,013,348 510,384,880 775,952,0006 850,000,000 [600,020,000, 979,054,548] 2.65 299,506,686 755,541,826 Ufc

SCNote – wf

c ¼ 1:24 Total 1,039,703,988 1,764,266,882 3,704,146,882

Table IV.Numerical analysis

results of IBWTSupply chain

coordination withfairness concern (CNB)

63


the scenario with fairness concern and the scenario without fairness concern; theactual received quantities of water resources are the same between the scenario withfairness concern and the scenario without fairness concern; and the utilities of theIBWT supply chain and its members under the scenario with fairness concern are nomore than those under the scenario without fairness concern.

5.2 Sensitivity analysisThe sensitivity analysis assesses and compares the impacts of the changes of thewater delivery loss rate, precipitation utilization factor, retail price, mainline transfer cost,branch-line transfer cost, holding cost, shortage cost and coefficient of fairness concern forthe IBWT supply chain coordination models under the capacity constraints consideringfairness concern or not.

To capture the impact of the change of key parameters, we only select the parametersfrom the 1st distributor and the 1st water intake to conduct sensitivity analysis, including:the retail price, the mainline transfer cost, the branch-line transfer cost, the holding cost andthe shortage cost. The findings from the sensitivity analysis results are summarized below:

(1) The sensitivity analysis results of the water delivery loss rate for the IBWT supplychain coordination decision under the supply capacity constraints without/withfairness concern are shown in Figure 2. The results show that: no matter under thescenario without fairness concern or under the scenario with fairness concern, no

Supply chain

Supply chain

Supply chain

Supply chain

3.98

3.96

3.94

3.92

3.9

3.88

3.86

3.84

3.82

3.8

3.780.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

Delivery loss rate

Delivery loss rate

Delivery loss rate

Delivery loss rate

Pro

fitP

rofit

Pro

fitP

rofit

×109×109

×109×109

3.8

3.6

3.4

3.2

2.8

2.6

2.40.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0.075

3

4.1

4.05

3.95

3.9

3.85

3.75

3.70.01 0.015 0.0250.02 0.0350.03 0.045 0.050.04

3.8

4

4

3.5

2.5

1.50.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0.075

3

2

(a) (b)

(c) (d)

Notes: (a) CNB without FC; (b) CB without FC; (c) CNB with FC; (d) CB with FC

Figure 2.The impact of waterdelivery loss ratechange on the profitof IBWT supply chainwithout/with fairnessconcern (FC)

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matter in the CNB or in the CB, the profit (or utility) of IBWT supply chain decreasesas the water delivery loss rate increases.

(2) The sensitivity analysis results of the precipitation utilization factor for the IBWTsupply chain coordination decision under the supply capacity constraintswithout/with fairness concern are shown in Figure 3. The results show that: in theCNB, no matter under the scenario without fairness concern or under the scenario withfairness concern, the profit (or utility) of IBWT supply chain decreases as theprecipitation utilization factor increases; in the CB, no matter under the scenariowithout fairness concern or under the scenario with fairness concern, the profit (orutility) of IBWT supply chain increases as the precipitation utilization factor increases.

(3) The sensitivity analysis results of the retail price for the IBWT supply chain coordinationdecision under the supply capacity constraints without/with fairness concern are shownin Figure 4. The results show that: no matter under the scenario without fairness concernor under the scenario with fairness concern, no matter in the CNB or in the CB, the profit(or utility) of IBWT supply chain increases as the retail price increases.

(4) The sensitivity analysis results of the mainline transfer cost for the IBWT supplychain coordination decision under the supply capacity constraints without/withfairness concern are shown in Figure 5. The results show that: no matter under thescenario without fairness concern or under the scenario with fairness concern, nomatter in the CNB or in the CB, the profit (or utility) of IBWT supply chain decreasesas the mainline transfer cost increases.

Supply chain

Supply chain Supply chain

Supply chain

Pro

fit

Pro

fit

Pro

fit

Pro

fit

×109 ×109

×109 ×109

3.79

3.78

3.77

3.42

3.4

3.38

3.36

3.34

3.32

3.3

3.28

3.26

3.240.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

3.76

3.75

3.74

3.73

3.72

3.72

3.7

3.68

3.66

3.64

3.62

3.6

3.58

3.56

3.540.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

3.71

3.7

3.690.01 0.015 0.02 0.025 0.03 0.035 0.04 0.050.045

Precipitation utilization factor Precipitation utilization factor

Precipitation utilization factor

3.18

3.16

3.14

3.12

3.1

3.08

3.06

3.04

3.02

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.053

Precipitation utilization factor

(a) (b)

(c) (d)


Figure 3.The impact ofprecipitation

utilization factorchange on the profit

of IBWT supply chainwithout/with fairness

concern (FC)

65


(5) The sensitivity analysis results of the branch-line transfer cost for the IBWT supplychain coordination decision under the supply capacity constraints without/withfairness concern are shown in Figure 6. The results show that: no matter under thescenario without fairness concern or under the scenario with fairness concern, nomatter in the CNB,or in the CB, the profit (or utility) of IBWT supply chain decreasesas the branch-line transfer cost increases.

(6) The sensitivity analysis results of the holding cost for the IBWT supply chaincoordination decision under the supply capacity constraints without/with fairnessconcern are shown in Figure 7. The results show that: no matter under the scenariowithout fairness concern or under the scenario with fairness concern, no matter inthe CNB or in the CB, the profit (or utility) of IBWT supply chain decreases as theholding cost increases.

(7) The sensitivity analysis results of the shortage cost for the IBWT supply chaincoordination decision under the supply capacity constraints without/with fairnessconcern are shown in Figure 8. The results show that: no matter under the scenariowithout fairness concern or under the scenario with fairness concern, no matter inthe CNB or in the CB, the profit (or utility) of IBWT supply chain decreases as theshortage cost increases.

(8) The sensitivity analysis results of the coefficient of fairness concern for the IBWTsupply chain coordination decision under the supply capacity constraints with

3.81×109

×109

×109 ×109

3.805

3.795

3.785

3.780.94 0.99 1.04 1.09 1.14 1.19 1.24 1.29 1.34 1.39 1.44

3.79

3.8

Retail price

3.28

3.275

3.27

3.265

3.26

3.255

3.250.94 0.99 1.04 1.09 1.14 1.19 1.24 1.34 1.441.29 1.39

Retail price

3.75

3.745

3.735

3.725

3.715

3.705

3.72

3.71

3.7

3.73

3.74

0.94 0.99 1.04 1.09 1.14 1.19 1.24 1.29 1.341.39 1.44

Retail price

0.94 0.99 1.04 1.09 1.14 1.19 1.24 1.29 1.34 1.39 1.44

Retail price

3.04

3.03

3.035

3.025

3.02

3.015

3.01

3

3.005

Pro

fit

Pro

fit

Pro

fit

Pro

fit

(a) (b)

(c) (d)




Figure 4.The impact of retailprice change on theprofit of IBWT supplychain without/withfairness concern (FC)

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fairness concern are shown in Figure 9. The results show that: no matter in the CNBor in the CB, the utility of IBWT supply chain decreases as the coefficient of fairnessconcern increases.

6. Managerial insights and policy implicationsBased on the modeling and numerical analytical results of Sections 4 and 5, thecorresponding management insights and policy implications can be summarized as follows:

(1) No matter under the scenario without fairness concern or under the scenario withfairness concern, no matter in the CNB (case with non-binding capacity constraint)or in the CB (case with binding capacity constraint), the two-part tariff contractcould effectively coordinate the IBWT supply chain and achieve operationalperformance improvement.

(2) No matter under the scenario without fairness concern or under the scenario withfairness concern, the actual received quantity in the CB is lower than CNB, and so dothe profits of the IBWT supply chain and its members. Thus, once the total orderquantity touch upon the supply capacity constraint, the received quantities areallocated by the IBWT supplier according to the overall order fulfillment ratio, andthe profits of all the stakeholders are also restricted.

(3) No matter in the CNB or in the CB, owing to the existence of inequity aversion, theIBWT distributors suffer from negative utilities of inequity aversion, the IBWT

3.8×109 ×109

×109 ×109

3.75

3.7

3.65

3.6

3.65

3.5

3.450.25 0.3 0.35 0.4 0.45 0.5

Mainline transfer cost

0.25 0.3 0.35 0.4 0.45 0.5


3.3

3.25

3.2

3.15

3.05

3.1

2.95

2.90.25 0.3 0.35 0.4 0.45 0.5

3


0.25 0.3 0.35 0.4 0.45 0.5


3.8

3.7

3.6

3.5

3.4

3.3

3.2

3.1

Pro

fit

Pro

fit

Pro

fit

Pro

fit

3.1

2.9

2.7

2.6

2.5

2.4

2.3

2.8

3

(a) (b)

(c) (d)




Figure 5.The impact of

mainline transfer costchange on the profit

of IBWT supply chainwithout/with fairness

concern (FC)

67


supply chain and its members could gain less utilities under the scenario withfairness concern than those under the scenario without fairness concern.

(4) No matter under the scenario without fairness concern or under the scenario withfairness concern, no matter in the CNB or in the CB, reducing the water delivery lossrate, the mainline transfer cost, the branch-line transfer cost, the holding cost and theshortage cost are beneficial for improving the profit/utility of the IBWT supplychain. Setting a higher retail price is beneficial for improving the profit/utility of theIBWT supply chain.

(5) No matter under the scenario without fairness concern or under the scenario withfairness concern, a lower precipitation utilization factor in the CNB is beneficial forimproving the profit/utility of the IBWT supply chain while a higher precipitationutilization factor in the CB is beneficial for improving the profit/utility of the IBWTsupply chain.

(6) No matter in the CNB or in the CB, a lower coefficient of fairness concern (inequityaversion) is beneficial for improving the utility of the IBWT supply chain under thescenario with fairness concern.

In sum, the government should make fair shortage allocation rule for the IBWT supplychain, set suitable retail prices of water resources to promote consumer’s water saving andguarantee a certain profit of IBWT supply chain and encourage improving the precipitationutilization to reduce unnecessary water transfer and waste. The decision maker of the IBWTsupply chain should design a suitable water supply capacity to avoid the shortage of

3.784×109

×109 ×109

×109

3.782

3.78

3.778

3.776

3.774

3.7720.05 0.1 0.15 0.2 0.25

Pro

fit

Pro

fit

Pro

fit

Pro

fit

Branchline transfer cost




3.255

3.254

3.253

3.252

3.251

3.25

3.249

3.248

3.247

3.2460.05 0.1 0.15 0.2 0.25

3.706

3.704

3.702

3.7

3.698

3.696

3.694

3.692

3.69

3.688

3.6860.05 0.1 0.15 0.2 0.25

3.002

3

2.998

2.996

2.994

2.992

2.99

2.988

2.986

2.9840.05 0.1 0.15 0.2 0.25

(a) (b)

(c) (d)




Figure 6.The impact of branch-line transfer costchange on the profitof IBWT supply chainwithout/with fairnessconcern (FC)

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necessary water demand to reduce total water shortage cost and improve the operationalperformance. Besides, the IBWT supply chain should make a lot effort to reduce the waterdelivery loss rate, the mainline and branch-line transfer cost, holding cost and shortage costand inequity aversion to improve the operational performance. Finally, two-part tariffcontract is recommended to coordinate the IBWT supply chain and improve the operationalperformance under the capacity constraint.

7. ConclusionIn the operations management of IBWT project, the supply capacity constraint, the waterdelivery loss and the fairness concern have important impacts on the operations decisionand operational efficiency of the IBWT project under the random precipitation. From asupply chain perspective, this paper tries to explore the issues of the operationsmanagement mechanism of IBWT project considering the water delivery loss without/withfairness concern under the supply capacity constraint and random precipitation. The IBWTdistribution system is defined as an IBWT supply chain system first; and then a fairshortage allocation rule is made by the government; on this basis, the IBWT supply chaincoordination models considering water delivery loss without/with fairness concern underthe supply capacity constraint and random precipitation are developed, analyzed andcompared through the game-theoretic and coordination research approaches, and thecorresponding numerical and sensitivity analysis for all models is conducted and compared;finally, the corresponding management insights and policy implications are summarized inthis paper. The research results indicate that: the two-part tariff contract could effectivelycoordinate the IBWT supply chain and achieve operational performance improvement;

×109 ×109

×109 ×109

Pro

fitP

rofit

Pro

fitP

rofit

3.78219

3.782185

3.78218

3.782175

3.78217

3.7821650.05

0.05 0.05

0.06

0.06 0.06

0.07

0.07 0.07

0.08

0.08 0.08

0.09

0.09 0.09

0.1

0.1 0.1

0.11

0.11 0.11

0.12

0.12 0.12

0.13

0.13 0.13

0.14

0.14 0.14

0.15

0.15 0.15

Holding cost

Holding cost Holding cost

Holding cost

3.255

3.2545

3.254

3.2535

3.253

3.2525

3.252

3.25150.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15

3.70413

3.704125

3.70412

3.704115

3.70411

3.704105

3.7041

3.704095

3.70409

3.001

3

2.999

2.998

2.997

2.996

2.995

(a) (b)

(c) (d)


Supply chainSupply chain


Figure 7.The impact of holding

cost change on theprofit of IBWT supply

chain without/withfairness concern (FC)

69


the binding supply capacity constraint makes the water capacity to be allocated amongIBWT distributors in accordance with fair shortage allocation rule and reduces the profit (orutility) of the IBWT supply chain and its members; the existence of fairness concern reducesthe utility of the IBWT supply chain and its members; a lower precipitation utilization factorin CNB is beneficial for improving the profit/utility of the IBWT supply chain while a higherprecipitation utilization factor in CB is beneficial for improving the profit/utility of theIBWT supply chain; and reducing the water delivery loss rate, the mainline transfer cost, the

3.7821864×109 ×109

×109 ×109

3.7821862

3.782186

3.7821858

3.7821856

3.7821854

3.7821852

3.78218480.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5

3.782185

Shortage cost

0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5

Shortage cost

0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5Shortage cost

0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5

Shortage cost

3.2548

3.25475

3.25465

3.2546

3.2545

3.2544

3.25455

3.25445

3.25435

3.2547

3.7041275

3.704127

3.7041265

3.704126

3.7041255

3.7041245

3.704125

3.0009

3.0008

3.0007

3.0006

3.0006

3.0004

3.0003

3.0002

3.0001

Pro

fitP

rofit

Pro

fitP

rofit

(a) (b)

(c) (d)




Figure 8.The impact ofshortage costchange on the profitof IBWT supply chainwithout/with fairnessconcern (FC)

3.74

3.72

3.68

3.64

3.620.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

3.66

3.7

Coefficient of fairness concern

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

Coefficient of fairness concern

Pro

fit

Pro

fit

3.1

3.05

2.95

2.9

2.8

2.85

2.75

3

(a) (b)×109×109


Notes: (a) CNB with FC; (b) CB with FC

Figure 9.The impact ofcoefficient of fairnessconcern change on theprofit of IBWT supplychain with fairnessconcern (FC)

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branch-line transfer cost, the holding cost and the shortage cost and setting a higher retailprice are beneficial for improving the profit/utility of the IBWT supply chain.

In the theoretical modeling, based on the theories and methods of Nash bargaining gameand two-part tariff contract, the coordination decision models considering water deliveryloss without/with fairness concern under the capacity constraint and random precipitationare developed, analyzed and compared for the IBWT supply chain, respectively, which haveenhanced the optimization decision theory for the operations management of IBWTprojects. In practice, the modeling and corresponding numerical analysis results provide abetter decision support to the governments to make appropriate shortage allocationregulations and the IBWT stakeholders to make better operations strategies.

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Corresponding authorZhisong Chen can be contacted at: [email protected]


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EDITOR-IN-CHIEFYin KedongOcean University of China, China Email [email protected]

HONORARY EDITOR-IN-CHIEF(S)Keith W. HipelUniversity of Waterloo, CanadaLi JingwenBeijing University of Technology, China

ASSOCIATE EDITORSFang LipingRyerson University, CanadaKevin LiUniversity of Windsor, CanadaLiu PeideShandong University of Finance and Economics, ChinaGan jianpingHong Kong University of Science and Technology, Hong Kong, ChinaWu KejianOcean University of China, ChinaLi XuemeiOcean University of China, China

ISSN 2516-158X © Ocean University of China

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Marine Economics and Management (MAEM) is an international, transdisciplinary journal focusing on marine economics and management research and practice. Through published articles, the journal aims to help societies become more sustainable and harmonious.MAEM covers research in integrated marine systems, marine modeling and prediction, marine planning and marine management, presenting theoretical insights and developments, as well as real-world case studies. Major articles are contributed by specialists in marine affairs, including marine economists and marine resource managers, and marine scientists. Articles from all relevant disciplines are invited, but all contributions must make clear the link between fundamental concepts and the central improvement of marine economics and management.



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1 Research on optimization of index system design and its inspection method: indicator design and expert assessment quality inspectionYin Kedong, Shiwei Zhou and Tongtong Xu

29 Evaluation of the marine economic development quality in Qingdao based on entropy and grey relational analysisPeide Liu, Xiaoxiao Liu and Hongyu Yang

39 Analysis of China’s coastal zone management reform based on land-sea integrationDahai Liu and Wenxiu Xing

50 Inter-basin water transfer supply chain coordination with the fairness concern under capacity constraint and random precipitationZhisong Chen and Huimin Wang




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