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The key to achieving the high-quality development of the marine economy (HDME) is to facilitate synergy through the coordination of marine elements. By adopting a perspective concerning complex systems, this paper presents a comprehensive framework—the composite system for high-quality development of the marine economy (CSHDME)—that synergistically integrates the five dimensions of "economy—technology—ecological—society—culture" in the marine domain. Building upon this framework, in this paper, we construct a CSHDME synergy evaluation index system using relevant data from 11 coastal provinces in China from 2010 to 2020. We utilize the subsystem orderliness degree model and composite system synergy degree model to calculate the degree of subsystem orderliness and degree of composite system synergy for the CSHDME as well as analyze the development status of the CSHDME subsystem and the degree of CSHDME synergy. The results indicate that: (1) the five-dimensional subsystem of the CSHDME exhibits an overall trend of year-on-year growth, with significant development disparities; (2) the degree of the synergy of the CSHDME is characterized by strong regional differences, strong fluctuations, instability, and low synergy; (3) the degree of the synergy of the CSHDME demonstrates the spatial evolution characteristics of low synergy, strong differences, strong fluctuations, and high agglomeration; and (4) the degree of orderliness and development speed of subsystems affect the degree of the synergy of the CSHDME. Therefore, measures such as the rational allocation of marine resources, accurate improvement of shortcomings, optimization of the spatial layout, and strengthening of regional cooperation should be considered to further promote the synergistic development of the CSHDME. [ABSTRACT FROM AUTHOR]

The marine fishing industry has a huge carbon sink potential and is also an important source of carbon emissions. The low-carbon development of the marine fishing industry is particularly important. Based on the perspective of carbon neutrality, this study analyzed the trend of net carbon emissions, carbon emissions and carbon sinks in the offshore fishing industry in China and 11 coastal provinces from 2010 to 2019 and decomposed the driving factors of the net carbon emissions of the offshore fishing industry with the LMDI decomposition method. The results show the following: (1) China's offshore fishing industry is in a partially carbon-neutral state. Overall, the net carbon emissions have decreased, and the carbon neutrality capacity has improved. However, the net carbon emissions have increased since 2016. From 2010 to 2019, both the carbon emissions and carbon sinks of China's offshore fishing industry declined. Carbon emissions fluctuated at first and then declined rapidly, while carbon sinks rose slowly and then showed a significant downward trend. (2) The offshore fishing industry in coastal provinces is also in a state of partial carbon neutrality, and the trends of carbon emissions, carbon sinks and net carbon emissions in most provinces are consistent with the national trends, but there are large differences between regions. (3) For the whole country, among the driving factors of net carbon emissions in the offshore fishing industry, industrial development is the main positive driving factor, and population size is the main negative driving factor. The net carbon coefficient and energy intensity also play a certain role in driving net carbon emissions. (4) Population size is an important inhibitory factor for the net carbon emissions of the offshore fishing industry in most coastal provinces, and the driving direction of the net carbon coefficient, energy intensity and industrial development is inconsistent. Based on the above research, relevant suggestions are put forward for the green development of the marine fishing industry. [ABSTRACT FROM AUTHOR]

Based on the micro-data of 79 listed companies in the Chinese marine ship industry chain from 2015 to 2019, this paper calculates the comprehensive technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of the upstream, midstream, and downstream of China's marine ship industry chain by using a three-stage super-efficiency slacks-based model (SBM), and further analyzes the weak links in industrial chain efficiency and their influencing factors. It is shown that (i) the TE and PTE of the upstream, midstream, and downstream of China's marine ship industry chain are in a "V"-shaped distribution, high at both ends and low in the middle, but that the SE is ranked as follows: upstream > midstream > downstream. In addition, the PTE is the main factor which hinders the improvement of TE in the industrial chain. (ii) The environmental variables have significant impacts on industrial chain efficiency. When the influences of environmental variables and random error terms are excluded, the industrial chain efficiency changes significantly. The values of SE and TE decrease significantly, and the distribution characteristic of TE changes. However, the PTE is still in a "V"-shaped distribution and appears to be the main driving force for the progress of TE. (iii) China's marine ship industry chain has obvious weak links in terms of efficiency, and the midstream and downstream areas need to focus on development. Each link of the industry chain has high coupling and low coordination, and they are all closely related to each other, but the coordination ability is insufficient. The industrial chain in terms of efficiency and coordinated development can still be improved. [ABSTRACT FROM AUTHOR]

While artificial intelligence (AI) technology can enhance social wellbeing and progress, it also generates ethical decision-making dilemmas such as algorithmic discrimination, data bias, and unclear accountability. In this paper, we identify the ethical risk factors of AI decision making from the perspective of qualitative research, construct a risk-factor model of AI decision making ethical risks using rooting theory, and explore the mechanisms of interaction between risks through system dynamics, based on which risk management strategies are proposed. We find that technological uncertainty, incomplete data, and management errors are the main sources of ethical risks in AI decision making and that the intervention of risk governance elements can effectively block the social risks arising from algorithmic, technological, and data risks. Accordingly, we propose strategies for the governance of ethical risks in AI decision making from the perspectives of management, research, and development. [ABSTRACT FROM AUTHOR]

The high-quality economic and social development of the Yellow River Basin is a combined system comprising the coordinated development of "economy–resources–environment–society", with resources and the ecological environment bearing capacity as the constraints, and green innovative development as the driving force. Based on the systematic analysis of the structural dimensions of the composite system, this paper uses the balanced indicators and their coordinated development effectiveness to describe the development quality of the macro-composite system. In order to reveal the mechanism of the evolutionary path of the macro system, the resource- and environment-bearing capacity, regional high-quality development potential, regional innovation capacity, and high-quality development guarantee capacity are adopted as the main attributes and decision-making basis of the autonomous agents. The simulation results show that, under the existing development model, the economic development of all of the provinces in the Yellow River Basin will be constrained by resources and the environment. However, different policy scenarios significantly affect the evolutionary trends of economic development, resource consumption, and the environmental pollution situation. The mechanisms to overcome the bottleneck of the resource and ecological constraints are different for these policies, and the effects of the same policy in different provinces are also not the same. [ABSTRACT FROM AUTHOR]

Based on the panel data of 250 prefecture-level cities from 2006 to 2019, we adopted the idea and method of a "quasi-natural experiment" with difference-in-difference to assess the industrial-structure-upgrading effect of FTZ construction, analyze the factors and mechanisms influencing the effect, and further compare and analyze the regional heterogeneity and differences in spatial and temporal characteristics. We also propose policy recommendations to promote the orderly development of the FTZ. Empirical results show that, (i) compared with non-FTZ cities, the level of rationalization for industrial structures in FTZ cities increased by 9.69%, and the level of the advanced industrial structures increased by 7.01%. (ii) The innovation effect and the foreign investment effect enhance the effect of FTZ policy on industrial structure upgrading. (iii) Heterogeneity analysis found that the eastern and central FTZ have more significant roles in promoting industrial structure upgrading. (iv) Further spatial and temporal comparison analysis found that the promotion effect of the FTZ on industrial upgrading is temporally sustainable but with a certain lag. From the spatial perspective, the FTZ inhibits the level of advanced industrial structure in neighboring cities. [ABSTRACT FROM AUTHOR]

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality. [ABSTRACT FROM AUTHOR]

In China, new generation entrepreneurs are gradually becoming the main force of intergenerational inheritance. New generation entrepreneurs have different educational background, growth experience and personality characteristics from the old generation entrepreneurs. They are endowed with the historical mission of an innovative generation. Therefore, it is of great significance to reveal the key factors influencing their innovative behavior and find out how to inspire their innovative behavior. Based on grounded theory and in-depth interviews, this study used NVivo 11 to deal with the recording materials. After a series of steps including open coding, spindle coding, selective coding and theoretical saturation test, it constructed a theoretical model of innovative behavior and innovative performance for new generation entrepreneurs. The purpose of this study was to improve the theory of entrepreneur innovation behavior and provide guidance for government to cultivate innovative spirit and innovative ability of new generation entrepreneurs. The research showed that: (1) New generation entrepreneurs' personal traits and educational background affect their perceptions of innovation. (2) Risk awareness, understanding of innovation content and social capital would influence innovation behaviors. (3) Innovation behaviors further lead to changes in organization performance. Therefore, government should provide more opportunities for new generation entrepreneurs to widen their knowledge, social resources and innovation environment. [ABSTRACT FROM AUTHOR]

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality. [ABSTRACT FROM AUTHOR]

Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality. [ABSTRACT FROM AUTHOR]

Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well. [ABSTRACT FROM AUTHOR]