Assessment and Evolution of the Sustainable Development Ability of Human–ocean Systems in Coastal Regions of China

The oceans are a crucial source of natural resources for human development, as productive terrestrial resources increasingly reach their limits of economic and ecological exploitation. With increasing human impact on oceans, it is vital to maintain a sustainable human–ocean relationship. We present an indicator system and information entropy model to assess the evolution of human–ocean systems (HOSs) according to the dissipative structure theory. Sustainable development ability (SDA) scores for HOSs are calculated based on the combination-weighting model. Finally, the Richards model is used to depict the HOSs' evolution states and periods in different coastal regions of China. The assessment indicates that total entropy is undergoing a process of negentropy; and that order degrees of HOSs are gradually improving. The results also suggest that the sustainable development levels of HOSs are continuously improving. The different coastal regions showed notable disparities of SDA and evolutionary processes, due to a differing resource base, environmental carrying capacity, and socioeconomic development. Different limiting factors should determine regional policies for enhancing the SDA process; the key to sustainable development of HOS is achieving a balance between the exploitation of ocean resources for socioeconomic development and conserving ecosystem services that are critical to wellbeing and livelihoods.


Introduction
Ocean ecosystems rank among the most productive ecosystems on Earth [1].Humans-especially in coastal areas-depend on ocean systems for essential and valuable monetary (commercial activity) and non-monetary (climate regulation and food production) goods and services [2,3].Despite their substantial productivity, oceans are extremely sensitive and vulnerable to anthropogenic disturbance.Long-term ocean-and land-based human activities increasingly represent both direct and indirect threats to the oceans.As reported by Antunes and Santos [4], the oceans face severe problems due to: (1) overfishing; (2) dumping and spills in the ocean; (3) coastal ecosystem destruction; (4) land-based contamination; and (5) pressures associated with climate change.These manifold, complex interactions and effects significantly increase levels of risk, exposure, and sensitivity of coastal communities and ocean systems and thus increase their vulnerability to human activities [5]; and even place the goal of "sustainable development"-the balanced socio-economic benefit of the marine environment-out of reach for some regions [6].With these points in mind, sustainable development of the human-ocean systems (HOSs) has long been a focus of research and policy initiatives, despite the difficulties of understanding the relationships between multiple human activities and the status of HOSs [7].
HOSs are complex systems that comprise two relatively independent but interactional subsystems-humans and the ocean-and are understood as "all interactions and linkages between humankind and the entire ocean" [5,8].Mono-disciplinary research can inhibit understanding of the complexity of natural systems (e.g., nonlinearity and openness) [9][10][11], resulting in serious misunderstanding and policy failures [12].Researchers study these interactions and linkages from different perspectives, with the aim of improving human-ocean relationships.Clausen and Clark extended Marx's concept of the metabolic rift, developing a theoretical foundation for understanding the human-ocean relationship and the resulting oceanic crisis as it relates to the depletion of fish stocks and the expansion of aquaculture.This revealed the ecological consequences of ongoing capitalist production in relation to the ocean environment [13].Halpern et al. synthesized 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems, and found that no area is unaffected by human influence and that a large fraction is strongly affected by multiple drivers [2].Parravicini et al. developed a geospatial approach for modeling the complex relationships between multiple human pressures and coastal ecosystems status, which proved effective for modeling complex interactions among multiple pressures and for predicting potential future scenarios [7].In addition, Land-Ocean Interactions in the Coastal Zone (LOCIZ), a core project of both the Global Environmental Change the International Geosphere-Biosphere Programme (IGBP) and the International Human Dimension Programme (IHDP) [14], have more extensive and profound meaning for sustainable solutions to the ecological and environmental problems of the coastal zone created by past, present, and future human populations [15].
The sustainable development of HOSs is bound up with harmonious relationships between human development and sustaining ecosystem services, and addresses the multiple goals of socio-economic development and environmental sustainability in a synergistic manner [16].A number of scholars have recently argued that there are strong links between ecosystem services and sustainable development [17,18].In HOS, the ocean ecosystem services are the linkage between ocean ecosystems and humans, that is, the specific processes that benefit people.Economic activity occurs within a network of social relationships, both of which are constrained by ecological parameters [16].Ocean ecosystem services can be a basis for HOS sustainable development by providing a means for considering how to retain ocean resources for nature and for use by humans in a scenario growing population and, therefore, ever-increasing demand for resources.Rather than condemning societies to poverty by denying human opportunities, the challenge of sustainable development is identifying interventions in ocean ecosystems that offer human possibilities and improve livelihoods over the long term.Modifying ocean ecosystems to facilitate socio-economic development is necessary, but avoiding damaging important ocean ecosystem services is not negligible.The key challenge for HOS sustainable development is to assess trade-offs and find a balance between socio-economic development while sustaining the more important ocean ecosystem services [19].Han and Liu studied the interactions between human societies and oceans from a geographical perspective, aiming to enrich HOS theory and marine sustainable development [20].Halpern et al. provided important information regarding the sustainability of HOS development, by creating an index comprising 10 diverse public goals [21].Using a vulnerability framework, Li established a new paradigm for the study of HOS and its sustainability [22].Qin et al. introduced quantitative models for assessing human-ocean systems' sustainable development from the perspective of metabolic recycling with spatial and temporal analyses and provided important short-and long-term policies that may help enhance sustainability [8].Comparison of those studies demonstrates that management of the sustainable human-ocean relationship calls for interdisciplinary approaches that encompass the three dimensions of sustainable development (economic development, social development and environmental sustainability).

Figure 1.
Growth trend of gross ocean product in China [23].
In China, the development of the marine economy is a concentrated reflection of human-ocean interaction.In the 1990s, a series of policies on ocean exploitation laid the foundation for marine  (1996)).The large-scale extraction and utilization of ocean resources has driven rapid development of the marine economy (Figure 1), which has become a new highlight of the country's economic growth and an important strategic support for socio-economic development in coastal areas.Despite these achievements, the contradiction between humans and oceans has been highlighted by production factors (e.g., industrial labor, capital investment, technology, etc.) constantly agglomerating to oceans [24], since anthropogenic disturbance has increasingly threatened the sustainable use of the oceans.Methods for evaluating the cumulative impacts of human activities and ocean systems, and for grasping the direction, status, and stage of HOSs' evolution, are crucial for promoting integrated coastal zone management (ICZM) and achieving sustainable development of HOSs in coastal regions of China.Marine carrying capacity and marine economic sustainability assessments that synthesize socio-economic and ecological environment indicators are of great significance for the sustainable exploitation of marine resources, environmental protection, and the coordinated development of regional economies and society in coastal areas [25][26][27][28].Along with studies on various perspectives of sustainable development such as human-ocean relationships and marine ecosystems [8,17,19,29,30], related studies have promoted sustainability management in coastal China.However, further to reviewing the existing literature, there is a need for transdisciplinary studies that combine both qualitative and quantitative approaches involved in spatial-temporal analysis; developing methods for improving the sustainable development of HOSs has become crucial, alongside the need for ICZM, and this study therefore aims to develop new methods and perspectives for studying HOSs' evolution.

The Driver-Pressure-State-Impact-Response Framework Analysis of Human-Ocean Systems
The Driver-Pressure-State-Impact-Response (DPSIR) framework, adopted by the European Environment Agency [31], describes a framework for analyzing and assessing the social and ecological problems by establishing cause-effect relationships between anthropogenic activities and their environmental and socio-economic consequences [32].The causal links start with driving forces, pass through pressures to state of the environment and impacts on ecosystem functions and human welfare, eventually leading to societal responses.
In the context of the HOS (Figure 2), the Drivers, defined as the primary sources of external Pressures on coastal ecosystems, refer to the need for food, space for living, recreation, and other basic needs for social and economic development which are delivered through fisheries, recreational sites, bioremediation of waste, and so forth.Particular Pressures created by each of these Drivers, such as the exploitation of fisheries, extraction of the seabed, demands for the conservation of coastal amenity and marine biodiversity, and the discharge of contaminated waters, are exerted on the oceans though human activities.As a result, a State of the ocean ecosystem (e.g., the benthos or the water column) changes and produces Impacts on society (e.g., degraded habitats, removal of species, loss of biodiversity, etc.) that affect human welfare.Where threshold levels are relevant, the Impact of State change may follow accumulative effects over a period of time.Finally, alterations in the provision of ecosystem services affect human well-being which leads to human Responses (social, economic and political) to these changes in the HOS [33].Since the DPSIR framework was devised in the late 1990s, international scholars have applied this framework to the evaluation of sustainable development initiatives, to better understand and overcome barriers to sustainability.Although this framework has been used extensively, it has also been subject to much criticism , based on five main shortcomings: (1) it creates a set of static indicators that serve as a basis for analysis, not taking into account the changing dynamics of the system; (2) it does not capture trends except by repeating the study of the same indicators at regular intervals; (3) DPSIR does not illustrate clear cause-effect relationships for environmental problems; (4) it suggests linear unidirectional causal chains in the context of complex environmental problems; and (5) the question of data relevance remains another concern regarding the credibility of the DPSIR framework [32,34].Therefore, without violating the DPSIR framework, this article develops an information entropy model to assess the dynamic trend and evolution of HOSs according to dissipative structure theory.

Entropy-Based Evolution of Human-Ocean Systems
The concept of entropy has been introduced to many other disciplines, including information theory, bioscience, and environmental science, since it was first proposed by the German physicist Rudolf Clausius, and has exceeded the scope of thermodynamics and statistical physics.Among the applications of entropy, Prigogine established the inner connection between living and inanimate systems on the premise of not violating the second law of thermodynamics [35], and introduced the total entropy formula [36].According to the dissipative structure theory, the total entropy change (dS) of a system can be divided into two parts with different natures: the exchange of system entropy with

Divers (D):
The human activities

Response (P):
The human response

Pressure (P):
The causes of the problem(s)

State (S):
The change of ecosystem services t t

Impact (I):
The impacts on society External environment Human-ocean system the external environment (entropy flow; deS); and internal entropy, produced through the evolutionary process (entropy production; diS).Hence, the total entropy formula can be represented as: As open systems that are far from equilibrium during the process of exchanging matter and energy with outside environments, HOSs comply with the condition of dissipative structure, such that system evolution will follow the total entropy formula.deS is produced by exchanging material, energy, or information with its environment; the value of deS can be positive, negative, or zero.diS is generated from all types of feedback (both negative and positive) between human and ocean subsystems of HOSs.It is an inner, irreversible process of entropy increase; consequently, diS will always be positive.In addition, dS reflects the evolutionary direction and state of HOSs.In an isolated system, the second law of thermodynamics contains an "entropy increase" principle, so that dS is always greater than zero.However, to increase order within an open system, entropy reduction (negentropy) must be established in order to realize dS < 0. deS must be <0 and satisfy the condition |deS| > diS, because diS is positive.Thus, when the entropy of the system is gradually reduced, the system constantly evolves toward a more orderly state; conversely, higher entropy is associated with evolution toward a more disorderly state [37,38].Within HOSs, this article considers four basic metabolic cycle processes of production, consumption, destruction, and reduction as the system evolves (Figure 3).Production and consumption refer to the flows of material, energy, or information between systems, and also represent the productivity and capacity of HOSs, which can be treated as the deS.Destruction and reduction refer to the negative and positive feedbacks in the process of production or consumption, reflecting the degree of destruction and the protective capability of the environment, which can be regarded as diS.HOS is a thermodynamic system but is imperfect, due to the differences between thermodynamic properties of living and nonliving systems [39].In HOSs, human activity is the most dynamic factor preventing its evolution from strictly following the second law of thermodynamics [31].Together with the uncertainty of the system, which is exacerbated by its openness, human activity may cause diS and deS to become positive, negative, or zero [27,31,40,41].Shannon developed information theory to a formal discipline and introduced an accurate and objective quantitative mathematical system [42,43].Information entropy-the calculation of which closely follows Boltzmann's entropy-is a more open or generic measure with a wider range of applications for any existing data set with discrete categories [44].In this regard, information entropy provides a solution for applying thermodynamic entropy concepts and methods to living systems, and for quantitatively describing the evolutionary state of HOSs based on the application of dissipative structure analysis.

Human-Ocean System Sustainable Development Ability Assessment and Its Evolution
The assignment of weightings significantly influences the reliability and accuracy of the environmental assessment and subsequent management decisions.Multi-criteria decision analyses (MCDA), both subjective and objective, have been widely used in social and natural sciences.Many studies have addressed the application of MCDA methods for improving decision making [45].However, no single MCDA method can provide both subjective and objective weighting for sustainable performance criteria that SDA assessment requires [46].Among these subjective MCDA methods, the analytic hierarchy process (AHP) [47] enjoys wide acceptance for criteria weighting through a pairwise comparison method, while EM [48,49] is more appropriate for objective weighting.Therefore, in order to systematically assess the selected scenarios against multiple management objectives, MCDA was conducted by combining two multi-criteria methods-AHP and EM-for SDA assessment.
Socio-economic and biological systems entail complex structure and causality, undergo continuous change, and contain a great deal of diversity [50]; therefore, the biological metaphor is widely used in socio-economic fields.In this context, the biological model is referred to when studying the evolution of HOSs.The Richards model, proposed in 1959, is a mathematical method for describing the process of biological evolution [51,52].It employs a sigmoid curve to represent the three evolutionary stages: slow initial growth (biological germination), rapid growth, and steady growth (biological maturity), stabilizing at a limit state (Figure 4).From the perspective of biological evolution, HOS evolution also undergoes the three evolutionary stages.In the germination stage, humans pay more attention to the extraction of ocean resources for social and economic development than to ecological protection.Restricted by this imbalance along with ocean development ability, degree, and scale, SDA grows at a slow rate.In the growth stage, the degree of HOS sustainability develops rapidly with improvements in the developmental ability of the ocean, increasing degree and scale of ocean utilization, and strengthening emphasis on ecological protection.The maturity stage can be regarded as advancement, but from another point of view, can be considered a bottleneck; the SDA of HOS returns to a slow growth rate, seeking transition and striving for redevelopment.

Design of the Evaluation Indicator System
Indicators have been broadly used in the monitoring, assessment, and management of systems where simplification is required [53,54].A few indicators cannot completely describe the state and evolution of HOSs.Instead, a framework comprising many indices representing different aspects of the processes or system is necessary to depict evolution under the influence of external perturbations and internal fluctuations [31].Based on the principles of sustainable development, an indicator system is founded, referring to previous designs for evaluation indexes [27,31,33,34], and operate according to the scientific principles of comprehensiveness, dynamics, hierarchy, maneuverability, and perceptiveness [55,56].This paper establishes an indicator-based system including the three dimensions of economic development, social development and environmental sustainability, which are grouped into two subsystems (socio-economic and environmental subsystem) for evaluation of sustainable development ability (SDA).Furthermore, to assess the dynamic trend and evolution of HOS, this article divides the two subsystems of specific indicators into four categories based on the four basic processes of system evolution (production, consumption, destruction, and reduction) according to the dissipative structure theory.In order to ensure relative independence between the indicators, Pearson correlation coefficients are calculated using SPSS statistical analysis software (version 19.0) (the correlation coefficient matrixes of the four sub-criteria are reported in Appendix A).The hierarchical structure of the evaluation index system is described in Table 1.

Socio-Economic Subsystem
(1) Supportive entropy is the material basis for system evolution and embodies the economic development level.It represents the productivity of the HOSs, which is reflected by indexes of marine products (e.g., seawater aquatic products, sea salt, marine mining) and income (e.g., per capita gross ocean product).In order to make a comprehensive assessment, indicators were selected from different marine industries according to data availability.For example, S2, S4, and S9 represent the primary, secondary, and tertiary marine industries, respectively (marine industries are classified by AQSIQ and SAC (2007)).

Table 1.
Indicator system for assessing the sustainable development ability of the human-ocean system; two types of entropy (deS and diS) were selected as criteria, in addition to two entropy aspects for each type of sub-criterion layer (ΔeS1, ΔeS2, ΔiS2, and ΔiS1); in total, 32 representative indexes were selected; S1, Per capita gross ocean product; S2, Seawater aquatic products; S3, Sea salt production per unit area; S4, Output of offshore crude oil; S5, Output of offshore natural gas; S6, Output of marine mining industry; S7, Length of quay line per unit coastline; S8, Per capita import-export volume; S9, Per capita foreign exchange earnings from international tourism; C1, Natural population growth rate; C2 (2) Consumptive entropy embodies the consumption and social development levels of HOS in some degree, and expresses-from a different perspective-the potential pressure and disturbance within the system, caused by human activity.These pressures include population pressure, energy utilization efficiency, and anthropogenic ecological stress, which generate negative effects on the evolution and sustainable development of the system.It is worth noting that moderate consumption can generate positive effects and promote the evolution of the system.This paper selects C1 and C2 to denote population pressure, C4 for energy utilization efficiency of resources, and C5 for anthropogenic ecological stress.

Environmental Subsystem
(1) Destructive entropy represents the degree of environmental destruction and hazard interaction within the HOSs, which hinders sustainable development.The indexes selected for destructive entropy are wastewater, sewage, waste gas, solid wastes, sulfur dioxide, and soot (dust) emissions.D7 denotes the hazards of the ocean environment to human society.In addition, given that the HOSs are open systems, gas and water flow into and out of the systems and cannot be controlled in specific locations or regions (such as coastal zones).This is one of the reasons why HOS evolution does not strictly follow the second law of thermodynamics.
(2) Reductive entropy concerns environmental protection and regeneration capacity; it represents the governance capacity for promoting the sustainable development of HOSs.R1 was selected to denote the recyclability of waste; R2 for environmental protection input intensity; R3 and R7 for the foundations of human resource of environmental reduction; and R5 and R6 for the ecological foundation of the system.

Assessment of Human-Ocean System Evolution based on the Information Entropy Model
The information entropy model can be described as follows: in a system with uncertainty, if a random variable (X) represents the state of the system, set X = {x1, x2,…, xn} (n ≥ 2); the corresponding probability for each value of X is P = {p1, p2,…, pn} (0 ≤ Pi ≤ 1, i = 1,2,…,n), and ∑ = 1.The information entropy can be described as: where S is the information entropy of an uncertain system.When evaluating n indicators of HOS in m years, year-based values of S, which are calculated using annual statistics, are mainly used to calculate deS and diS.The formula can be expressed as: where ΔS represents the four types of entropy: supportive entropy (ΔeS1), consumptive entropy (ΔeS2), destructive entropy (ΔiS2), and reductive entropy (ΔiS1).The parameter i (i = 1,2,…,n) represents an indicator, j (j = 1,2,…,m) represents a year.Year-based S can be expressed where xij is the value of the indicator i for year j, qij is the standardized value of xij calculated from raw data, and = ∑ .The value size of entropy can express the changes in system affordability.For the four aspects, ΔeS1 and ΔiS1 are positive indicators, with larger values indicating greater coordination in the system; ΔeS2 and ΔiS2 are negative indicators, with larger values denoting less coordination in the system [34].
The formulae for deS, diS, and ΔS can be formed as: ΔeS is generated from the discrepancy between the output and consumption levels, representing the level of harmony within the HOSs.ΔiS is generated from the difference between the degree of environmental destruction and protection capability, representing the vitality of the HOSs.ΔS is the total entropy change in each year of the study period, signifying the health status of the HOSs.

Integrated Weighting Model of Human-Ocean System Sustainable Development Ability Assessment
Vector w1i = (w11, w12, …, w1n) contains weightings determined by AHP.For the AHP weight of each indicator, given that all types of entropy are indispensable, the four types of entropy are equally important.Specific indicator weights are determined by means of expert consultation, according to their importance for the sustainable development of the HOSs (Table 1) (the pairwise comparison matrixes of the four sub-criteria are reported in Appendix B).Vector w2i = (w21, w22, …, w2n) contains those determined by EM.Vector Wi = (W1, W2, …, Wn) is the combined weight where i = 1,2,…,n.In order to ensure Wi is as close as possible to w1i and w2i, according to the principle of minimum relative information entropy, the optimization function is expressed as: ) ln (ln ) ln (ln min (7) where ∑ = 1 , > 0, = (1,2, ⋯ , ).Using the Lagrange multiplier method [59], the optimal solution is given by: Thus, the SDA scores of HOSs can be calculated as the weighted sum of different indicators Xij in different samples: where the range of Zij is [0-1], Xij is the standardized value of indicator j for sample i, and Wij is the weighting of the indicator.

Analysis of Human-Ocean System Evolution based on the Richards Model
The Richards model can be expressed in differential form: If initial conditions are given by X(t0) = X0, then the general form of the Richards equation can be expressed as: where K is the development index threshold of the system, representing the maximum capacity of the system, and K > 0; r is the growth rate of the system development index, and r > 0; λ is the comprehensive influence index; B = λC; and finally, C is the integration constant.In the Richards model, the shape of the curve changes with λ (Figure 5).Due to the dissimilarity between resource endowment and socio-economic development, HOSs have different regional evolutionary characteristics.This paper maintains that the development index of the system K = 1, because the SDA values were between 0 and 1 following data standardization; the growth rate r was the average growth rate of the SDA values, which was calculated using = ( ⁄ ) ( ) ⁄ − 1 and λ was an estimated parameter.SPSS (version 19.0) was used to fit the nonlinear Richards equation.The characteristics of the Richards curve as a function λ value are as follows: (1) When λ = 1, the Richards curve is a logistic curve.The development index curve is centrosymmetrical, whose center is point A2.The development speed curve is symmetrical, showing that the speed of development is the same in the earlier and later periods.However, the condition of λ = 1 is theoretical and is not observed in practice.(2) When λ < 1, the speed of system development peaks earlier in the evolutionary process.
Development speed is faster in the earlier period than in the later period, whereas the later period is of longer duration.The entire evolutionary process shows an initially quick then slow trend.(3) When λ > 1, initial development is slow and becomes faster toward the end, peaking relatively late in the evolutionary process, contrary to that observed when λ < 1.
The turning points A2, A′2, and A′′2, where the second derivative of the Richards equation is zero (Figure 5a), divide the curve into two segments that respectively represent the earlier and later periods in the HOS evolutionary process.The turning points in Figure 5b denote the changes of development speed [60].
HOS evolution is divided into three stages of germination, growth, and maturity according to the turning points where the second and third derivatives of the Richards equation are zero (Table 2) [51,61].

Data Processing
The reliability of the assessment was improved by using the Min-Max method to normalize each indicator (range 0-1) in order to eliminate the effects of magnitude (units of measurement) and attributes (positive or negative).The following data processing methods were used: (a) information entropy model for HOS evolution assessment used the four types of entropy for vector quantization, avoiding the need to distinguish between positive and negative indicators for standardization; (b) the SDA assessment model did not use vector quantization for different types of indicators, such that the positive and negative indicators must be distinguished for data processing.To assess the n indicators in m samples, the standardizing equations are as follows: For a positive indicator: For a negative indicator:

Study Area
The coastal region of China (Figure 6), which refers to all regions with coastlines (both continental and island coastlines), is located in the east and south of the Chinese mainland, comprising provinces, autonomous regions, and municipalities that are directly administered by the central government [23], from north to south: Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, and Hainan.Taiwan Province, Hong Kong, and Macao special administrative regions are excluded from this study, as data are unavailable.The total length of coastline is about 32,000 km (18,000 km continental and 14,000 km of island coastline).In 2012, the total population was 584.63 million, accounting for 43.18% of total national population; GDP was 31,589.42 million yuan, accounting for 60.87% of total national GDP.The rapid growth of the Chinese economy has been increasingly concentrated in the coastal regions [62].These regions have become some of the most developed areas of the economy, with the highest degree of international exposure and the highest population densities.The contribution of the marine economy was 15.84% in 2012 [23].However, population growth and the continual exploitation of ocean resources are having increasingly negative effects on coastal systems.

Four Types of Entropy
After each indicator was standardized using the Min-Max normalization method, Equation ( 3) was used in the information entropy model and the four types of entropy were calculated for the HOSs in different regions.The results from 1996-2012 and mean values are listed in Figure 7.
The four types of entropy showed different trends during the study period, as shown in Figure 7.There were significant upward trends for ΔeS1 and ΔiS1 in most provinces, except ΔeS1 of Shanghai and Guangdong, and ΔiS1 of Tianjin and Jiangsu, which rose initially and then dropped.This suggests that ocean development and environmental protection were improving in most coastal provinces.ΔeS2 in most provinces increased in the first stage and then decreased, except in Guangxi and Hainan where there was an uptrend.The entropy value of ΔiS2 fluctuated initially and then decreased rapidly.It can be inferred from these trends that the ecological pressures of human activity on the HOSs are displaying predictable decline; pollution emission levels are falling in the coastal regions of China.

Entropy Flow, Entropy Production, and Total Entropy Change
Entropy changes can reflect the evolution and state of HOSs.For further analysis, regional deS, diS, and dS were calculated using Equations ( 4)-( 6) from 1996-2012, as shown in Figure 8.
Figure 8 shows that most of the provinces have positive ΔeS.Overall, although the processes influencing ΔeS vary, a clear trend of initial increase was followed by a decrease.The magnitude of the decrease was greater than that of the increase.Some exceptions are the ΔeS of Zhejiang, which was negative in 2011, and ΔeS of Guangdong, which fluctuated between positive and negative values.Shanghai and Guangdong experienced fluctuations, resulting in an indistinct trend.The main reason for the trend of ΔeS in most of the provinces is that ΔeS1 and ΔeS2 showed concurrent increases, whereas ΔeS2 initially increased then decreased.Improved energy efficiency plays an important role in the ΔeS2 trend, which can be explained from the downtrend of the unit GDP energy consumption index.Moreover, mariculture and marine saltpan areas in most provinces follow the same overall trend (initial increase followed by a decrease).The findings confirm that ecological pressures on the HOSs have recently become less severe.
With the exception of some individual provinces (e.g., Hebei), ΔiS showed an overall declining trend in most provinces.The ΔiS values fluctuated from positive to negative and those of Shanghai and Guangdong were lower than in other provinces.The ΔiS2 trend was similar to that of ΔiS, which suggests that a decrease in ΔiS2 significantly affects ΔiS.For example, the decrease in ΔiS2 in Shanghai and Guangdong is much larger than that in other provinces, whose ΔiS values are lower.These phenomena are caused by reduced discharge of pollutants such as wastewater and gas.The coastal provinces show declining ΔS values, typically with slow initial decline becoming more rapid, but with differences between the trends observed in each region.ΔS values for Hebei, Tianjin, and Liaoning provinces initially increased and then decreased.ΔS values of Guangdong and Shanghai were lower than those of other provinces, which was attributed to high energy-efficiency, low pollution emission, and powerful environmental protection.The ΔS of most provinces reached the negative phase in 2012, except for Hebei, Liaoning, Shandong, Guangxi, and Hainan (which approached the negative phase).This indicates that the total entropy of each of these HOSs is decelerating, and that the degrees of order within the system are gradually improving.

Sustainable Development Ability of the Human-Ocean System
The SDA scores represent the level and stage of HOSs sustainable development, where higher values denote greater sustainability.To calculate the SDA scores, the weight of each indicator was decided by the integrated weighting model of AHP-EM.
Generally, the SDA scores for the HOSs of various coastal regions improved during the study period.The sustainable development capacities of the HOSs were enhanced.This supports the results presented in Section 4.1.2,which detail the trend of declining entropy among coastal province HOSs: increasing order and enhanced SDA.Moreover, notable differences are seen between the different regions (Figure 9).Due to the high levels of ocean development and environmental protection capacity, SDA values for Shanghai, Guangdong, and Shandong increased during the research period, from 0.1823, 0.2433, and 0.2076, respectively, in 1996, to 0.5150, 0.4592, and 0.4480 in 2012.Hebei, Guangxi, and Hainan were restricted by limited ocean resources and development capacity; their SDA scores were low and development was slow, with SDA scores increasing from 0.0874, 0.0931, and 0.1287, respectively in 1996, to 0.1529, 0.2337, and 0.2141 in 2012.Although the five remaining provinces showed strong ocean development potential, their corresponding capacity for environmental protection could not match the intensity of development, which resulted in intermediate SDA scores.
Among the coastal regions of China, Tianjin, Shanghai, and Guangdong all show comparatively high potential for developing their ocean economies.Per capita gross ocean products of Tianjin and Shanghai grew to 27,875.31 and 24,979.94yuan by 2012.However, the SDA of Tianjin is much lower than that of Shanghai, due to differing environmental protection ability together with weak ocean resource base; furthermore, the investment in pollution treatment projects and the number of workers employed in the environmental protection agency are lower than the averages for coastal China.As to Shandong, the ocean economy development ability is inferior to that of Tianjin, whereas environmental protection ability is much higher.Shandong invests five times more than Tianjin in pollution treatment projects, and by 2012 had the largest number of staff employed in marine scientific research in coastal China.Therefore, the SDA of Shandong is higher than that of Tianjin.The SDA of Hebei and Guangxi are restricted by both ocean resources base and ocean development capacity.Hainan Province has advantages in terms of resources and environmental conditions, but its potential to develop ocean resources is lower and the strength of its ocean scientific research is obviously weaker than other provinces.In 2012, Hainan only had 192 persons employed in marine scientific research, which is far lower than the average number of 2024 in coastal China.

Analysis of Human-Ocean System Evolution based on the Richards Model
Based on the hypothesis that HOS evolution follows the laws of biological evolution, this paper used the Richards model to depict HOSs evolution states and periods for the different regions using the calculated SDA scores.The Richards equation includes three parameters.The results are listed in Table 3.   3 shows that most of the λ values are less than 1, except for Guangxi and Hainan.Shanghai had the smallest λ value, while Hainan had the largest.There were three numerical ranges of λ in the 11 regions: those of Shanghai, Shandong, and Guangdong were less than 0.5.Their HOSs' evolution curves were the steepest initially and the smoothest later.The sustainable development level of the HOSs in these provinces enhanced very rapidly in the early stages, and then very slowly in the mature stages.Values of λ in Guangxi and Hainan were larger than 1.Their HOSs evolution curves were the smoothest initially and the steepest later.The sustainable development level of HOSs in these provinces were enhanced smoothly in the early stages, and quickly in the mature stages.The evolutionary processes were quite different.λ in the other provinces ranged between 0.5 and 1.
The HOS evolution stage for each province can be determined using the turning points during the period 1996-2012 (Table 2).The results are summarized in Table 4.There are five important points listed in Table 4: At1996 and At2012 are SDA values at the beginning and end of the evolutionary period for different regions; At1 is the turning point from the germination stage to growth; At2 is peak development speed; and At3 is the turning point from the growth stage to maturity.The HOS evolutionary period for each province can be determined by comparing the numerical size at the beginning and end with the two turning points.For example, the initial point At1996 of Tianjin is 0.1549, which is greater than the turning point At1 = 0.1548, but smaller than the turning point At3 = 0.7515.This suggests that, at the beginning of the study period, Tianjin was in its growth stage.The end point At2012 is 0.3999, which is much less than At3 = 0.7515.Thus, the Tianjin HOS underwent a period of growth during the entire evolutionary period.According to these principles, four HOS evolutionary periods were recognized in the 11 provinces.
Comparing Tables 3 and 4, it was found that the smaller the size of λ, the closer was the HOS to the mature evolutionary stage; conversely, larger λ is associated with closer proximity to the germination stage.For example, λ values for Shanghai, Shandong, and Guangdong were less than 0.5; their HOSs evolution reached maturity in 2012, especially for Shanghai, which has the smallest λ value of 0.2571.The λ values for Guangxi and Hainan are greater than 1; their HOSs evolution is still in the germination stage, with a slow growth rate.

Discussion and Conclusions
Anthropogenic effects on ocean systems are now widely recognized but are often difficult to quantify [63].Coupled with the complex and unpredictable characteristics of these interactions, an accurate method of assessment can be a useful decision-making tool for sustainable development management [64].With reference to ecological studies, an information entropy model was developed in this study to assess the evolutionary development of HOSs.We found that the ΔS of most coastal provinces in China had reached (or approached) the negative phase in 2012.This indicates a deceleration of the total entropy of the HOSs, gradually increasing order of the systems, and evolution in a healthy and orderly direction.Furthermore, the SDA scores of various coastal provinces showed constant improvement throughout the study period, indicating increasing capacity for sustainable development.Regardless of the differences in study area, scale, and perspective, these findings are broadly similar to those of Yu et al. [65], Li et al. [19], Li [66], Di et al. [26], and Qin et al. [8], who reported increasing SDA of HOSs in coastal regions of China.
The broad concept of sustainable development [67] is characterized by three dimensions: economic development, social development, and environmental sustainability [16].Different resource and environmental carrying capacities and socio-economic development levels will have appreciable impact on SDA in different regions.Shanghai, Guangdong, and Shandong have the most promising scores, as a result of their relatively high levels of socio-economic development.By contrast, Hebei, Guangxi, and Hainan had low SDA scores and developed slowly, which was attributed to limited resources and lower socio-economic development.
Finally, the Richards model was used to depict the state and period of HOSs evolution in different regions.The magnitudes of λ varied between the provinces.Most λ values were less than 1, with the exception of Guangxi and Hainan.Shanghai had the lowest λ and Hainan had the highest.λ was the key indicator of differing regional SDA values and evolutionary processes, and showed positive correlation with system entropy and negative correlation with SDA score.For example, the ΔS values of Guangdong and Shanghai were lower than those of other provinces, as were their λ values; however, their SDA scores were higher.
Regional policies for enhancing the SDA of HOSs are influenced by different limiting factors.Non-integrated ocean protection or development is inappropriate for the sustainable development of HOSs.For example, Hainan Province has the largest protected marine area in coastal China but its ocean development ability is below average, which is reflected by the comparatively low gross ocean product.Therefore, the SDA of the HOS in Hainan is comparatively weak, and it is in the germination stage of HOS evolution.In this regard, policies for sustainable development of the HOSs in provinces like Hainan must pay more attention to developing the oceans on the premise of ensuring the ecological quality of the marine environment.However, for provinces like Tianjin, policies for sustainable development of the HOSs should focus on environmental protection.
Sustainable development depends on maintaining ecosystem services.The key to sustainable development of HOS is achieving a balance between the exploitation of ocean resources for socio-economic development while conserving ecosystem services that are critical to societal wellbeing and livelihoods.For example, ecosystem-based approaches (EBAs) that consider socio-economic development in the context of ecosystem dynamics could effectively improve the sustainable development of ocean ecosystems [68].Holistic, integrated responses have the potential to effectively address issues related to ecosystem services and human well-being simultaneously [16].For policy planning, a better understanding of the resource and environment exploration levels, energy efficiency, pollution levels, and environmental protection capacity are needed in order to enhance the SDA of HOSs in different regions.ICZM can promote the overall sustainability of China's coastal areas [69].Nevertheless, sustainable development is the responsibility of all parts of society, i.e., governments, public interest groups, consumers, and the private sector [70].Unlike other coastal states, where public participation or community-based engagement was key to the success of ICZM, stakeholder involvement in ICZM programs in China has usually been quite weak and received insufficient attention, due to the current top-down management approaches [63].Principles such as stakeholder consultation should inform sustainable development strategies for the HOSs of coastal regions of China.
The present study has several limitations: Sustainable development itself is a multi-dimensional concept and demands consideration of trade-offs among environmental, social, and economic impacts [58].Restrictions on data availability make it difficult to choose indicators that cover all aspects of the HOS.Moreover, sustainability requires a long-term perspective, whereas the currently available data are short-term and incomplete.The number of studies on complex systems has increased recently.We believe that new tools and techniques for analyzing complexity will be created in the near future, and intend to focus on such improvements in our future research.

Appendix B
Public consultation was an important aspect of the AHP weighting for each indicator in this study.Experts from different fields, including ecology, economics, economic geography, and ocean governance, were consulted extensively on the weighting of each indicator shown in the following tables (Tables B1-B4).For a better understanding of the table, consider the following example.The value in the first row and fourth column is 3, which means that S1 is three times as important as S4.The consistency ratio of the pairwise comparison matrix is found to be 0.0362, and as the value is under 0.10, we conclude that the comparison matrix is consistent.
economic development in China (e.g., National Marine Development Planning (1995) and China Ocean Agenda in the 21st Century

Figure 2 .
Figure 2. A visual representation of the DPSIR framework for human-ocean systems.

Figure 3 .
Figure 3. Basic metabolic processes in human-ocean system evolution.

Figure 5 .
Figure 5. Human-ocean system development index and speed based on the Richards model.(a) development index; (b) development speed.

Figure 7 .
Figure 7. Four types of entropy value of human-ocean systems in coastal regions of China, 1996-2012.

Figure 8 .
Figure 8. Entropy flow, entropy production, and total entropy change of the human-ocean systems in coastal regions of China, 1996-2012.

Figure 9 .
Figure 9. Sustainable development ability of human-ocean systems in different regions from 1996-2012.
The numerical size of λ varies between provinces.Table

Table 1 .
, Population density; C3, Resident Consumption Level; C4, Unit GDP energy consumption; C5, Mariculture area; C6, Sea salt pan area; C7, Marine goods turnover; C8, Marine passenger turnover; C9, Number of coastal travel agencies; D1, Intensity of industrial wastewater discharged into sea; D2, Domestic sewage emissions per capita; D3, Intensity of industrial waste gas emissions; D4, Sulfur dioxide emission per unit of GDP; D5, Industrial soot (dust) emission per unit of GDP; D6, Industrial solid wastes generated per unit of GDP; D7, Direct economic loss of storm surges; R1, Output value of products made from the wastewater, waste gas, and solid wastes; R2, Investment completed in pollution treatment projects; R3, Number of environmental workers in environmental protection agency; R4, Number of coastal observation stations; R5, Per capita coastal wetland area; R6, Per capita construction of marine protect area; R7, Number of employed population of marine scientific research; negative indicators are marked by "(−)".Cont.

Table 2 .
Division of changes in human-ocean system evolution based on the Richards model.

Table 3 .
The Richards equation and evolution of HOSs in coastal regions of China.As shown in Table3, the R-squared values of the nonlinear fitting of the Richards equation are adequate for determining the statistical significance of the estimated λ and HOSs evolution trends in

Table 4 .
Turning points and evolutionary stage of HOSs in coastal regions of China.

Table A1 .
Coefficient calculation matrix of supportive entropy indicators.

Table A2 .
Coefficient calculation matrix of consumptive entropy indicators.

Table A3 .
Coefficient calculation matrix of destructive entropy indicators.

Table A4 .
Coefficient calculation matrix of reductive entropy indicators.

Table B1 .
Pairwise comparison matrix of supportive entropy indicators.

Table B2 .
Pairwise comparison matrix of consumptive entropy indicators.

Table B3 .
Pairwise comparison matrix of destructive entropy indicators.