With the rapid development of artificial intelligence technology and its advantages in decision-making, smart decision-making is receiving increasing attention from both academics and practitioners. This paper uses a bibliometric approach to analyze 783 works of literature related to smart decision-making from 1965-2022 to understand the research content and development process in the field. Through co-citation analysis, this paper identifies critical publications and research clusters in the field. Combined with keyword analysis, this paper provides a systematic review of the current state of research on smart decision-making. On this basis, the conceptual framework of the field is presented. According to the existing literature and bibliometric analysis, this study provides a list of research questions from three dimensions that deserve further consideration, providing insights into the trend of using AI for decision-making in the future.

Artificial intelligence has transformed marketing and consumer lives. However, despite its multiple beneficial effects, unprecedented artificial intelligence uses pose enormous challenges to consumer privacy protection. This research aims to provide a comprehensive picture of privacy research and enhance privacy protection. We first give more specific definitions of consumer privacy according to data analytic processes. Second, we investigate how AI-related factors, privacy risk, privacy protection, and other factors influence consumer privacy decision-making, and draw a conceptual model based on the extended Antecedents-Privacy Concerns-Outcomes (APCO) model. Third, we examine the potential risks of each data analytic step and the causes of privacy risks from consumer, company, and technology perspectives, and propose privacy protection schemes correspondingly. Lastly, we also provide agenda for future research.

Drug-protein interaction (DPI) prediction in drug discovery and new drug design plays a key role, but the traditional in vitro experiments would incur significant temporal and financial costs, cannot smoothly advance drug-protein interaction research, so many computer prediction models have emerged, and the current commonly used is based on deep learning method. In this paper, a deep learning model Computer-based Drug-Protein Interaction CBSG_DPI is proposed to predict drug-protein interactions. This model uses the protein features extracted by the Computed Tomography CT and Bert method and the drug features extracted by the SMILES2Vec method and input into the graph convolutional neural network (GCN) to complete the prediction of drug-protein interactions. The obtained results show that the proposed model can not only predict drug-protein interactions more accurately but also train hundreds of times faster than the traditional deep learning model by abandoning the traditional grid search algorithm to find the best parameters.

Given the rise of artificial intelligence, big data analytics has emerged as an important tool for processing and assimilating the enormous volume of data available on social media. It is of great theoretical and practical significance to explore the public opinion diffusion process and characteristics, and users' emotions of mega sports events based on big data statistics in the social media environment. This paper takes the Jakarta Asian Games, Russian World Cup and PyeongChang Winter Olympics held in 2018 as cases, uses text mining and social network analysis methods to analyze the dissemination process of social media users' data, presents the semantic words disseminated in sports events through high-frequency word cloud diagrams, and summarizes the general rules of public opinion dissemination. The results show that the more users' participation, the greater diffusion volume, and the diffusion process shows fast increasing, short duration, scattered topics, diversified contents, and strong guidance and weak continuity of attention. The high-frequency words, except for the names of the events, such as "cheer", "win the game" and "must win", have obvious concentration of emotional words.

This article investigates the structural transformation of Ukraine's financial market within the context of innovative metaspace technologies. It presents models outlining the structural changes in financial market sectors amid the rapid development of metaspace technologies, emphasizing the systematic prognostic nonlinear dependence of deposit, credit, and currency flows on time intervals within the FinTech services' payment platform. The article introduces system methods for modeling nonlinear processes and combinatorial procedures for organizing the environment of innovative metaspace technologies within the financial market. It systematically categorizes approaches for measuring the effectiveness signs of financial market structure development in the innovative metaspace. The article argues for the need to structure and evaluate the financial market sectors of Ukraine through non-combinatorial tasks, emphasizing the convenience of describing variables influencing market structure and visually depicting the metaspace environment as a simulated FinTech ecosystem. Standardizing performance indicator measurement algorithms for precise intervals of financial resource movement is advocated. The article establishes that combinatorial tasks for structuring financial market development objects in the metaspace environment should align with configurations influenced by variable factors, such as currency, deposit, and credit flows. An analysis of transactions involving currency exchange, deposits, and loans through FinTech services' payment platform in Ukraine's financial market is presented. Additionally, the article conducts a formalized assessment of changes in currency, deposit, and credit flow transactions influenced by the digital environment of FinTech services in Ukraine's financial market sectors.

This paper proposes a new discrete-time Geo/G/1 queueing model under the control of bi-level randomized (p, N₁, N₂)-policy. That is, the server is closed down immediately when the system is empty. If N₁ (≥1) customers are accumulated in the queue, the server is activated for service with probability p (0≤ p≤1) or still left off with probability (1-p). When the number of customers in the system becomes N₂ (≥ N₁), the server begins serving the waiting customers until the system becomes empty again. For the model, firstly, we obtain the transient solution of the queue size distribution and the explicit recursive formulas of the stationary queue length distribution by employing the total probability decomposition technique. Then, the expressions of its probability generating function of the steady-state queue size and the expected steady-state queue size are presented. Additionally, numerical examples are conducted to discuss the effect of the system parameters on some performance indices. Furthermore, the steady-state distribution of queue length at epochs n^-, n and outside observer's observation epoch are explored, respectively. Finally, we establish a cost function to investigate the cost optimization problem under the constraint of the average waiting time. And the presented model provides a less expected cost as compared to the traditional N-policy.

Connected and autonomous vehicles (CAVs) are expected to coexist alongside human-driven vehicles on roads for the foreseeable future. This study explores the stability and safety of mixed traffic streams, including traditional trucks and cars alongside CAVs. The study utilizes the intelligent driver model and cooperative adaptive cruise control model to characterize human-driven vehicles (including cars and trucks) and CAVs, respectively. It investigates how different ratios of trucks and penetration rates of CAVs impact the linear stability of mixed traffic flows and delineate their stability domains. Additionally, a simulation experiment is conducted using SUMO software to assess the safety implications of traffic congestion at on-ramp bottlenecks, specifically analyzing the safety dynamics of mixed traffic streams. The findings indicate that CAVs enhance both the stability and safety of mixed traffic flows. The presence of trucks is associated with reduced stability values at similar CAVs penetration rates. In scenarios without trucks, CAVs can elevate traffic safety by 58.28%-71.28%, whereas in the presence of trucks, although the enhancement diminishes, safety levels can still improve by 48.67%-65.11%.

This paper investigates the rank reversal issue in the selection of new energy vehicle types, focusing on consumers aged 20 to 30. It employs both the AHP and the PCbHA methods to rank four types of the new energy vehicles — pure electric vehicles, plug-in hybrid electric vehicles, range-extended electric vehicles, and fuel cell vehicles, based on ten influential factors: purchase cost, maintenance cost, fuel and electricity cost, safety, passability, endurance, appearance, brand power, power, and space. To verify the effectiveness of the PCbHA method in addressing the rank reversal problem, one alternative option is removed, and the ranking is recalculated with subsequent analysis of the results. The study finds that rank reversals often stem from the closeness of alternative weights. Through sensitivity analysis, this research reveals the impact of endurance attribute weight on decision outcomes, indicating that when the endurance weight reaches 0.35, the ranking of pure electric vehicles and range-extended electric vehicles reverses.

Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.

The resumption of production after the "suspension" caused by the COVID-19 has emerged as an urgent problem for many enterprises and the government. The resumption of production is actually a dynamic evolution problem from 0 to 1 (100%). This paper constructs a general game model and a dynamic replication system for the resumption of production and government support, and gives theorems for the construction of the model. It analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the "resumption of production" strategy, takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production, and explores systemic countermeasures and suggestions for the rapid convergence of farmers' strategies to the "resumption of work and production" strategy. The study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures, a general regulation model for the resumption ratio from 0 to 1 (100%), and a systematic idea, method and model for exploring the "precise strategy" system to promote the rapid resumption of production.

Based on heterogeneity extraction, this paper analyzes four potential characteristics of the supervisory board, they are Individual Heterogeneity of the Supervisory Member (Internal Heterogeneity), Organization Size of the Supervisory Board (Organization Size), Structural Characteristics of the Supervisory Board (Structural Characteristics) and Identity Background of the Supervisory Board (Identity Background); and verifies the impact and action path of the potential characteristics on irregularities. Then, systematically evaluates the micro enterprise organization construction and corporate governance behavior by using the methods of factor analysis and Heckman two-stage model. Empirical research shows that the scale of corporate assets does have an important impact on corporate irregularities and the governance of the board of supervisors. Under the regulation of the company scale, the three potential characteristics: Organization Size, Identity Background and Structural Characteristics have played a significant inhibitory role on irregularities, and the Internal Heterogeneity has no significant effect. When using violation behavior as an alternative variable of supervision performance, the sample selection deviation will be caused by the lack of information disclosure. This paper suggests that we should pay attention to the team of the board of supervisors scientifically and reasonably, weaken the appropriate personalized differences within the board of supervisors, and comprehensively consider the interaction between the company scale, asset quality and the performance of the board of supervisors when formulating the corporate internal management system.

In recent years, China has witnessed the rapid development in housing finance, and there have emerged constantly real estate finance innovations; however, there exists no relevant index for measuring the innovations of China's real estate finance. Based on the perspectives of the governments, enterprises and the public, this paper constructs the "innovation index of real estate finance" on a quarterly basis from 2009 to 2019, with the method of empowerment which combines the subjective method (analytic hierarchy process) and the objective one (range coefficient method). It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in 2008. The index covers 30 provinces, autonomous regions and municipalities directly under the central government, and analyzes its temporal and spatial characteristics. The findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

The development of digital technology and the construction of smart cities urge service enterprises to seek competitive advantages by building smart service brands. However, there are few studies explore the brand value, brand strategies, and corresponding business strategies of smart service providers from the financial perspective. This paper selects listed property companies from China as the sample and explores the value of the smart community service brand of property enterprises based on the observation data. This research introduces the market value measurement index (Tobin q) and discounted cash flow model (DCF) to explore the influence of diversified brand strategies through combining smart brand strategy with naming strategies and business strategies on brand value. The results show that smart community service brand has a significant impact on firms' market value. Compared with the brand extension strategy, the adoption of brand renewal strategy will significantly affect market value. Further, the development of smart value-added services by enterprises will exert a positive impact on their market value. However, the stakeholders are not optimistic about smart technical services by property companies, which could reduce shareholders' expectations of the market value of enterprises.

Alternating direction method of multipliers (ADMM) receives much attention in the recent years due to various demands from machine learning and big data related optimization. In 2013, Ouyang et al. extend the ADMM to the stochastic setting for solving some stochastic optimization problems, inspired by the structural risk minimization principle. In this paper, we consider a stochastic variant of symmetric ADMM, named symmetric stochastic linearized ADMM (SSL-ADMM). In particular, using the framework of variational inequality, we analyze the convergence properties of SSL-ADMM. Moreover, we show that, with high probability, SSL-ADMM has O((ln N)·N^-1/2) constraint violation bound and objective error bound for convex problems, and has O((ln N)·N^-1/2) constraint violation bound and objective error bound for strongly convex problems, where N is the iteration number. Symmetric ADMM can improve the algorithmic performance compared to classical ADMM, numerical experiments for statistical machine learning show that such an improvement is also present in the stochastic setting.

This research examines the effects of commercial insurance on household financial vulnerability using data from the China Household Finance Survey (CHFS). Data were collected from 39875 households in 29 provinces of China. The probit model was used to test the relationship between the study variables. The results show that commercial insurance participation reduces the likelihood of a householdos financial vulnerability. Heterogeneity analysis found that commercial insurance participation had a more significant dampening effect on the financial vulnerability of households with low personal expenses, low-income, low human capital, rural areas, and the central and western regions, indicating that commercial insurance has a universal effect. This study offers several policy implications for combating household financial vulnerability. First, improving the commercial insurance protection system in both urban and rural areas could improve householdso risk management capacity. Second, establishing tax-rewarding policies to encourage households to participate in commercial insurance. Third, increasing the popularity of commercial insurance, particularly in rural areas, and exploring the rural commercial insurance market.

This paper explores the relationship between CEO age in target firms, earnings management, mergers and acquisitions decision-making, and performance by using a sample of Chinese firms from 2008 to 2017. We found that CEO age is negatively correlated with M&A decision-making and target firms engage in a higher degree accrual-based earnings management (AEM) than non-target firms. In addition, target firms with young CEOs exhibit a greater extent of AEM in the pre-M&A period. We also found that the relationship between CEO age and M&A performance is inverted U-shaped. AEM of pre-M&A is negatively correlated with M&A performance, indicating that M&A performance is affected by AEM of pre-M&A.

In view of the breakthrough progress of the depth learning method in image and other fields, this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions. This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory (LSTM) and convolutional neural network (CNN). The model adopts an end-to-end network structure, using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure. The empirical part makes a comparative experimental analysis based on Shanghai stock index in China. By comparing the experimental prediction results and evaluation indicators, it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.

Considering diseconomies of scale and network externalities in the e-commerce supply chain (ECSC), we construct an e-platform-led benchmark model and derive the optimal decisions. Then, the model is extended by endogenizing the impact of service level on network externalities. Considering service investment that includes fixed and variable investments, the model is further extended. Comparing the extended models with the benchmark model, we found the following conclusions. Although the e-platform dominates the ECSC, its profit is lower than the manufacturer. The corporate profits, service level, and price increase with network externalities. Increases in diseconomies of scale decrease the corporate profits and service level, but increase the price. A high-quality service combined with network externalities can achieve synergy and improve the e-platform's economies of scale, further generating a higher profit. Improving network externalities promotes the fair profit distributionin ECSC and achieves stable development.

Under the background of population aggregation in megacities, some adjustments are made to the urbanization strategy, whose focus is shifted to the development of megacities and megacity clusters. Meanwhile, the housing price differentiation among cities tends to become increasingly serious. This paper, from the perspective of population mobility, takes provincial capitals and municipalities with independent planning status (PCs & MIPSs) as research samples to evaluate the level of housing price differentiation within provincial-level administrative divisions of China, and analyze from the perspective of demand side how the metropolitanization effects regarding the population formed due to population aggregation in megacities affect the housing prices of megacities and the housing price difference between megacities and other cities. The research found that: 1) The increasing net inflow of population boosts the housing prices and accelerate the housing price differentiation; 2) The impact of the increasing net inflow of population on housing price increases and housing price differentiation has regional heterogeneity and city size heterogeneity; 3) The income gap strengthens the effect of population inflow upon the housing price differentiation.

The common models used for grey system predictions include the GM(1, 1), the GM(1, N), the GM(N, 1), and so on, but their whitening equations are all single ordinary differential equations. However, objects and factors generally form a whole through mutual restrictions and connections when the objective world is developing and changing continuously. In other words, variables affect each other. The relationship can't be properly reflected by the single differential equation. Therefore, the paper proposes a novel simultaneous grey model. The paper gives a modeling method of simultaneous grey model SGM(1, 2) with 2 interactive variables. The example proves that the simultaneous grey model has high precision and improves the precision significantly compared with the conventional single grey model. The new method proposed enriches the grey modeling method system and has important significance for the in-depth study, popularization and application of grey models.

In view of the heterogeneity of natural gas consumption in different sectors in China, this paper utilizes Bayesian network (BN) to study the driving factors of natural gas consumption in power generation, chemical and industrial fuel sectors. Combined with Bayesian model averaging (BMA) and scenario analysis, the gas consumption of the three sectors is predicted. The results show that the expansion of urbanization will promote the gas consumption of power generation. The optimization of industrial structure and the increase of industrial gas consumption will enhance the gas consumption of chemical sector. The decrease of energy intensity and the increase of gas consumption for power generation will promote the gas consumption of industrial fuel. Moreover, the direct influencing factors of gas price are urbanization, energy structure and energy intensity. The direct influencing factors of environmental governance intensity are gas price, urbanization, industrial structure, energy intensity and energy structure. In 2025, under the high development scenario, China's gas consumption for power generation, chemical and industrial fuel sectors will be 66.034, 36.552 and 109.414 billion cubic meters respectively. From 2021 to 2025, the average annual growth rates of gas consumption of the three sectors will be 4.82%, 2.18% and 4.43% respectively.

We explore the association between the number of critical audit matters and auditing opinion based on the Chinese capital market data. In addition, we investigate the effect of audit risk on the relationship between the number of critical audit matters and auditing opinion. Using 6, 662 firm-year observations listed in the Chinese capital market from 2017—2019, we find that the number of critical audit matters has significantly positive association with clear audit opinion. Specially, the greater the number of critical audit matters, the more likely getting clear audit opinion. We also find that the audit risk restricts the effect of the critical audit matters on the audit opinion. Lastly, we find that Big4 accounting firms are less likely to be influenced by the number of critical audit matters; the audit complexity restricts the effect of the critical audit matters on the audit opinion; the more board members, the less effect of the number of critical audit matters on the audit opinion. We also use several robustness tests to strengthen the conclusion. Our research may contribute to the understanding of the new audit standard.

The aim of this paper is first to establish a general prediction framework for turning (period) term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice, which allows us to conduct the reliable estimation for the peak period based on the new concept of "${\mathbf{Turning~~ Period}}"$ (instead of the traditional one with the focus on "Turning Point") for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020. By a fact that emergency risk management is necessarily to implement emergency plans quickly, the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible. As applications, the paper also discusses how this "Turning Term (Period) Structure" is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020. Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020, from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020, as verified by the true observation in the practice. The method established in this paper for the prediction of "${\mathbf{ Turning~~ Term ~~(Period) ~~Structures}}"$ by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning, and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called "dynamic zero-COVID-19 policy" ongoing basis in the practice.

The goal of sentiment analysis is to detect the opinion polarities of people towards specific targets. For fine-grained analysis, aspect-based sentiment analysis (ABSA) is a challenging subtask of sentiment analysis. The goals of most literature are to judge sentiment orientation for a single aspect, but the entities aspects belong to are ignored. Sequence-based methods, such as LSTM, or tagging schemas, such as BIO, always rely on relative distances to target words or accurate positions of targets in sentences. It will require more detailed annotations if the target words do not appear in sentences. In this paper, we discuss a scenario where there are multiple entities and shared aspects in multiple sentences. The task is to predict the sentiment polarities of different pairs, i.e., (entity, aspect) in each sample, and the target entities or aspects are not guaranteed to exist in texts. After converting the long sequences to dependency relation-connected graphs, the dependency distances are embedded automatically to generate contextual representations during iterations. We adopt partly densely connected graph convolutional networks with multi-head attention mechanisms to judge the sentiment polarities for pairs of entities and aspects. The experiments conducted on a Chinese dataset demonstrate the effectiveness of the method. We also explore the influences of different attention mechanisms and the connection manners of sentences on the tasks.

The investment in green technology in the process of product design and production is viewed as a powerful tool for sustainable development and carbon emission reduction. However, the substantial cost and pressure of competition weaken incentives for manufacturers to engage in green technology. In this paper, we consider two competitive manufacturer-retailer supply chains, where each manufacturer sells partially substitutable products through the exclusive retailer, study green technology investment selection by manufacturers, and examine the efficacy of retailer cost sharing scheme. Our analysis shows that a dominant equilibrium strategy for both manufacturers is to invest in green technologies, whether cost sharing is in place or not. Retailer sharing the cost of manufacturer green technology investment can avoid firms' preference confliction over the green technology investment and improve social welfare simultaneously when both the cost-sharing rate and the degree of product/channel competition are relatively low. We also find that green technology investment by manufacturers does not necessarily curb total carbon emission, and the cost sharing can either strengthen or weaken the carbon emission reduction of green technology investment.

Accurate knowledge of who knows whom in organizations have important benefits for individual work performance and managerial decision making, but people are not very accurate when recalling connections among others in their social networks. The present study investigates how holisticanalytic thinking styles influence the extent people can accurately perceive network relationships and choose the right persons to form a coalition in a fictious persuasive task. We focused on two dimensions of holistic-analytic thinking style, namely, attention to field (as opposed to parts) and interactionist (as opposed to dispositionist) causal theory. Results from 281 participants reveals that while individuals with greater attention to field were more accurate in recalling relationships in a social network, those inclined toward interactionism in causal theory were less accurate. Furthermore, greater attention to field enhanced the effectiveness of coalition member selection, in part through the mediation of accurate network perception; while interactionism, via the full mediation of network perception, indirectly led to less effective coalition choice.

This study selected 1558 literatures on tourism and environmental management from 1981 to 2020, and systematically analyzed the research hotspots and evolution according to the co-citation network and keyword analysis. We find that these documents focus on the sustainable management patterns in marine and coastal tourism, the environmental management practice of tourism and hotel industry, and the attitude of tourism stakeholders towards environmental management policies. We summarize the status quo, implications and suggestions. In the future, waste management in tourism areas and the competitive advantages established through environmental management would become the new research hotspots.

This paper studies two manufacturer rebate strategies in a green supply chain. The results show that: 1) Irrespective of the type of rebate strategy used, channel members benefit; 2) Rebates for green products improve the green level of green products; 3) The more significant the slippage effects are, the more beneficial the rebate strategies are, for channel members. On this basis, the model is expanded to consider asymmetric potential market demands and asymmetric product substitution rates, respectively. The results show that the product substitution rate does not affect the strategic preference of channel members, but the potential market demand does.

Many studies have shown that energy consumption plays an important role in economic growth. The paper researches the influence of energy consumption on economic growth in China's Yangtze River Economic Zone. The paper divides the energy of Yangtze River Economic Zone into the coal, the oil, the natural gas and the electricity and explores the influences of coal consumption, gas consumption, natural gas consumption and electricity consumption on economic growth quantitatively using an extended production function model. The paper mainly uses two methods. The first method is the output elasticity analysis. The paper calculates the four energy consumption's output elasticity to economic growth to compares the influences of energy consumption in terms of out output elasticity. The second method is the contribution rate analysis. The paper calculates the contribution rates of four energy consumption to economic growth to compare the influences of four energy consumption on economic growth in terms of contribution rate. The paper makes an empirical analysis on the influence of energy consumption on economic growth in China's Yangtze River Economic Zone. Analysis results show that oil consumption has the greatest influence on economic growth in China's Yangtze River Economic Zone, in terms of both output elasticity and contribution rate, followed by natural gas consumption, electricity consumption and coal consumption.

In recent years, China's outward foreign direct investment (OFDI) has risen significantly, arousing considerable interest in the motivations and drivers of Chinese overseas investment. This paper selected 27 host country-related indicators and extracted the common factors using the factor analysis method. This paper discusses the determinants of China's OFDI location choice by using panel data regression method, and focuses on the differences between Belt and Road countries and nonBelt and Road countries. The results show that the favorable institutional environment and strong market demand of host countries have a positive influence on Chinese foreign investment. Besides, China's investment in Belt and Road Initiative countries is more prone to a country with less developed technology and unreasonable energy utilization. China's OFDI can promote technological progress in these countries and making full use of their advantage resources for economic development. As China's economy has entered a "new normal", its global influence has risen, and the relationship between the host countries and China has also mattered more on China's OFDI decisions. This effort provides important supports for optimizing the location choices of Chinese enterprises' foreign investment.

The paper first analyzes price change due to stock splits in Chinese stock markets, which shows stock prices typically go up for stock splits. Then theoretical analyses based on risk theory are presented to explain the reason, where the method comes from a new perspective and obtained theoretical conclusions show that stock splits typically make stock price go up if risk-compensation function is convex, and go down if risk-compensation function is concave. Stock prices typically go up for stock splits because risk-compensation functions are mainly convex. The obtained conclusions are consistent with the known results in the last three decades.

With the development of urbanization in China, tons of municipal solid waste have been produced and disposed Incineration is the best way to deal with municipal solid waste in China but this practice often is opposed and resisted by the public who live nearby. This study systematically analyzed the risk responses of the public, in particular factors affecting the public's resistant behavior We conducted a survey and collected 376 valid questionnaires which we used for the analysis. We used the structural equation model and path analysis for the examination, and the results showed that risk perception was a critical factor predicting the resistant behavior of the public surrounding the wasteto-energy (WTE) plant. Benefit perception had a negative, but insignificant, impact on the public's resistant behavior. We found a negative correlation between benefit perception and risk perception but the relationship was weakened when we added systematic processing to the path analysis. The impact of system processing on risk perception was greater than that of benefit perception; that is, systematic processing was better in explaining the risk judgment of the public than benefit perception. Problem knowledge was a significant indicator in predicting risk perception and systematic processing and technology knowledge was a significant indicator in predicting risk perception and benefit perception. Systematic processing increased the public's risk judgment to the WTE plant. Finally, we discussed practical implications and limitations.

This paper develops a simple trade model of heterogeneous firms, which incorporates the dual heterogeneity of credit constraints at the firm and industry levels and reveals the effects of the interaction mechanisms of trade policy uncertainty and credit constraint heterogeneity on exporters' behaviour. The model confirms that the higher the level of industrial credit constraints, the greater the interaction of trade policy uncertainty and credit constraint heterogeneity, but firms with lower levels of credit constraints within a specific industry are more affected by this interaction. Then, based on the highly dis-aggregated trade data of China's firms from 2000 to 2013, this paper provides empirical evidence for the main predictions and mechanisms of the theoretical model.

The economy of China has turned to the stage of high-quality development. In this sense, the connotation of regional innovation capacity should reflect more aspects, such as better economic effectiveness, people-centered philosophy of development and better living conditions. This study aims at establishing the evaluation index system of regional innovation capacity under high-quality perspective. Then the dynamic evaluation method based on gray correlation degree and TOPSIS is improved. And the improved method is applied to evaluate the regional innovation capacity under high-quality development perspective. The results show that: 1) The regional innovation capacity of Jiangsu, Zhejiang, Shanghai and Guangdong under high-quality development perspective is better than other regions, while the regional innovation capacity of Beijing-Tianjin-Hebei is imbalanced. Regional innovation capability of Fujian, Shandong, and Hainan from a high-quality perspective is at the middle and lower levels. 2) From the perspective of development trends, the gap of regional innovation capacity between Beijing, Jiangsu, Zhejiang, Shanghai, Guangdong and Fujian, Hebei, Shandong is gradually narrowing. 3) An in-depth analysis of the regional innovation capability of the eastern provinces and cities from the perspective of high-quality development through different dimensions shows that Beijing, Guangdong, Jiangsu perform well in all dimensions, while Fujian and Hainan need to pay more attention to innovation input and the creation of a better innovation environment to enhance innovation output and promote innovation effectiveness. At last, based on above analysis, relevant policy recommendations are proposed.

In this paper, we apply the structural vector autoregression (SVAR) model to decompose the international oil price shock into oil supply shocks, aggregate demand shocks and oil-specific demand shocks, and then use the DCC-GARCH model to analyse the dynamic correlations between these three kinds of oil price shocks and the macroeconomic variables of several oil importing and exporting countries. To quantify the intensity of the effect of oil shocks on these variables, we propose a measure, conditional expectation (CoE), to capture the percent change of the economic variable under oil price shocks relative to the median state. The time-varying copula model is employed to estimate the proposed measure through time. The empirical results show that, for instance, the impacts of oil price shocks on macroeconomic variables are different in different periods, showing the time-varying characteristics. Additionally, the impacts of oil price shocks on macroeconomic variables show great differences and some similarities among different countries. Finally, we give some policy suggestions for these countries, in particular for China's special results.

Given consumers' trade-offs between conventional economic and environmental attributes of products, we provide a game-theoretic model to explore the role of GTA strategy in duopoly competition by incorporating two salient features: Two product types-The green product produced by a firm with GTA strategy and the ordinary product produced by a firm without GTA strategy, and two consumer segments, i.e., the green consumers who are willing to pay for green products and the ordinary consumers who are willing to pay for ordinary products. Our analysis shows that GTA strategy may either increase or decrease the green firm's quality provision. The subtle relationship between the green firm's quality strategy and GTA strategy not only affects its own equilibrium performances but its rival's. We also find that two consumer segments may be better off in the presence of a lower GTA intensity. Additionally, although the GTA strategy benefits the environment, the GTA investment is not the more the better. Finally, we find that GTA strategy would lead to higher social welfare only when the GTA efficiency is high enough. Our work not only provides an alternative economic explanation why some firms choose to implement GTA strategy and some do not in reality, but gives managerial insights for firms with different GTA strategies as well as policy insights for the social planner.

We develop a statistical framework to use the data of night-time-lights (DN) from satellite to augment official GDP measures, and a non-linear substitution relationship between DN and GDP is given. In this paper, we take advantage of DN instead of GDP to measure the imbalance of regional development (IRD) in China by using the method of bi-dimensional decomposition under the population-weighted coefficient of variation. The method enables us to analyze the contributions of DN components to within-region and between-regions inequality under the framework which has been proposed, we can get the conclusion that the imbalance between-regions rather than within-region is the main reason for the influence of IRD for the whole country in China.

The topic recognition for dynamic topic number can realize the dynamic update of super parameters, and obtain the probability distribution of dynamic topics in time dimension, which helps to clear the understanding and tracking of convection text data. However, the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge. Therefore, it is impossible to deeply perceive the dynamic change of the topic in the time series. By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model, a topic feature lattice under the dynamic topic number is constructed. In the model, documents, topics and vocabularies are jointly modeled to generate two probability distribution matrices: Documentstopics and topic-feature words. Afterwards, the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix. Finally, the topic feature is induced according to the formal concept analysis (FCA) theory. The topic feature lattice under dynamic topic number (TFL DTN) model is validated on the real dataset by comparing with the mainstream methods. Experiments show that this model is more in line with actual needs, and achieves better results in semi-automatic modeling of topic visualization analysis.