While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has remarkable practical effects^[1]. Against this background, we present and discuss a case study at length on the impact of this technology in the concrete setting of small short-term loans in retail banking. We propose to banks a robust and scalable blockchain technology with proof of stake and limited energy consumption used to streamline their processes, resulting in lower transaction and administration costs. This is made possible by smart contracts. Thereby, we facilitate small scale lending at high frequencies and short-term duration as well as an easier and more efficient way to connect small borrowers and lenders.

Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China's e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

The construction industry is one of the largest energy consumers in China. It not only uses energy directly but also consumes a large amount of embodied energy hidden in intermediate goods and services from other industries. This paper utilizes the multi-regional input-output (MRIO) model to measure the embodied energy consumption in China's construction industry at the province level. To quantify the backward and forward linkages between the construction industry and other industries, the energy-driven and energy-driving abilities of the construction industry are investigated for 30 provinces. The results show that the values of the energy-driven coefficients are significantly larger than the energy-driving coefficients, indicating that the construction industry is highly dependent on products from other industries and other regions, not vice versa. The construction industries in the southwest and northwest regions of China have the highest energy-driven coefficients, showing that the construction industries in west China heavily rely on indirect energy embodied in goods and services. This particularly applies in Guangxi and Hainan provinces, which have the highest energy-driven abilities and the lowest energy-driving abilities. The findings can facilitate the policy-makers to examine the critical energy-saving paths.

With the continuous development of economy, the bottleneck problem of environmental resources has become increasingly prominent. Enterprise environmental governance technology innovation incentive has become an important issue for the development of government, society and enterprises. Under the control of enterprise's expected economic target, this paper discusses the synergistic incentive effect of environmental policy and green finance on enterprise's environmental governance technology innovation decision by using nonlinear programming model. The results show that when the funds for environmental governance technological innovation are insufficient, there is an optimal decision space to use green financial loans to implement technological innovation and upgrade, and then achieve the expected economic goals; Under a given level of environmental governance technology, environmental policies affect whether enterprises can make decisions on technological innovation and upgrading of environmental governance; Green financial mechanism will not. However, when the enterprise makes the decision of environmental governance technology upgrading, it will affect the enterprise's decision on green financial loan amount. The results of the study have guiding significance for the formulation of environmental policy and green financial policy, as well as the decision-making of enterprise environmental governance technology innovation and upgrading.

Scholars have a variety of theoretical explanations for housing price growth. However, few scholars have studied the internal influence mechanism among urbanization, land finance, and housing price. Based on the data of 182 prefecture-level cities from 2009 to 2016, this paper studies the influence of land finance on housing price under different urbanization rate levels. The study finds that with the increase of urbanization rate, the effect of land finance on housing price presents a "U" shape. Specifically, an increase in land finance by 1% results in a corresponding increase in average housing price by 0.18%, with relatively low urbanization rate, 0.06% with medium level of urbanization rate, and 0.38% with high level of urbanization rate.

In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n^2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.

This paper builds a theoretical framework of two-period general equilibrium model to explore whether 1) the restrictions of the Islamic financial system (RIFS) limit economic development in Xinjiang and 2) counterpart financial support for Xinjiang (CFSX) promotes economic development and social stability. First, we introduce above mentioned restrictions caused by Islamic beliefs into a general equilibrium model and modify Islamic agents' budget constraints to define the benchmark equilibrium. Comparing the benchmark equilibrium with the perfect equilibrium, in which these restrictions are removed, we discover the RIFS paradox that RIFS undermine the social welfare and income of Muslims. Second, the financial support is introduced into the pattern of benchmark equilibrium as an exogenous variable to model its impact and hence we define the CFSX equilibrium. A series of policy analyses implies that the CFSX strategy improves living standards and social welfare in Xinjiang.

This paper analyzes the perceptions that workers in the Ivorian industrial sector have as the interface between their professional and personal life, and the factors that facilitate or make it difficult. Data collected from 4 Ivorian industrial companies which represent a sample of 187 respondents according to the selection criteria. These collected data make it possible to identify the factors which act on the labor conflict towards the family as well as the factors which act on the family conflict towards the work within the industrial sector in C?te d'Ivoire. The results of the correlation coefficients show that the relationships between the proposed variables are significant and in the direction suggested for the most part, thus providing support for nine of the ten hypotheses affecting these relationships. According to some results obtained, it was discovered that certain factors could be at the same time, factors of conflict and satisfaction, such as the commitment to work or the nature of the work. These variables can simultaneously increase stress, and increase the self-esteem of the worker. The results of the analysis of the proposed model recommend the possibility that the data fit the model well, explaining the impacts of work, non-work and stressors on work-family conflict. This indicates that the proposed model is adapted to the Ivorian context.

Uncertainty is a main source of opportunistic behaviors in research and development cooperations, which challenges strategic decisions ranging from investments to operations. To avoid opportunism arising in cooperations, commitments (either formal contracts or informal communications) are generally made in advance by members of an alliance. However, how does a commitment encourage R&D investments and ultimately enhance the total performance, without loss of flexibility in ex post operations? This study attempts to answer this by investigating a bounded commitment in the context of vertical collaborations in a supply chain. In this exploratory study, a 3-stage game model is used (by backward induction) to examine a two-echelon supply chain under a bounded commitment in NPD. Our analysis shows that upstream R&D investment is stimulated more and that both members are better off under the bounded commitment. At the same time, when the relative bargaining power between the supplier and the manufacturer falls into an appropriate area, it is possible to reach a bounded commitment, and the manufacturer is more sensitive to this relationship. Finally, the bounded commitment restrains opportunistic behaviors, but there is no strong sign that an increase in the ratio of the order outside the chain to the inside one can make firms better.

In this study, the key drivers of sustainability commitment, green supply chain management, big data integration and green human resource practice are explored, and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated. In addition, the influence of green management practices on integrating big data technology into operations is presented. As for the concept of dynamic ability, it has been used to recommend and empirically test conceptual models. Data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries. Research suggests that big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks. Besides, it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals. Furthermore, our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR. Finally, from our study, it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks, thus ensuring that the bank of Association of Southeast Asian Nations (ASEAN) mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.

The time-dependent solution of a kind of supply chain system with the multi-suppliers and single demander is investigated in this paper. By choosing state space and defining operator of system, we transfer model into an abstract Cauchy problem. We are devoted to studying the unique existence of the system solution and its exponential stability by using the theory of C₀-semigroup. We prove that the system operator generates C₀-semigroup by the theory of cofinal operator and resolvent positive operator. We derive that the system has a unique nonnegative dynamic solution exponentially converging to its steady-state one which is the eigenfunction corresponding eigenvalue 0 of the system operator.

Blockchain has been receiving growing attention from both academia and practices. This paper aims to investigate the research status of blockchain-related studies and to analyze the development and evolution of this latest hot area via bibliometric analysis. We selected and explored 2451 papers published between 2013 and 2019 from the Web of Science Core Collection database. The analysis considers different dimensions, including annual publications and citation trends, author distribution, popular research themes, collaboration of countries (regions) and institutions, top papers, major publication journals (conferences), supportive funding agencies, and emerging research trends. The results show that the number of blockchain literature is still increasing, and the research priorities in blockchain-related research shift during the observation period from bitcoin, cryptocurrency, blockchain, smart contract, internet of thing, to the distributed ledger, and challenge and the inefficiency of blockchain. The findings of this research deliver a holistic picture of blockchain research, which illuminates the future direction of research, and provides implications for both academic research and enterprise practice.

Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

Miners in various blockchain-backed cryptocurrency networks compete to maintain the validity of the underlying distributed ledgers to earn the bootstrapped cryptocurrencies. With limited hashing power, each miner needs to decide how to allocate their resource to different cryptocurrencies so as to achieve the best overall payoff. Together all the miners form a hashing power allocation game. We consider two settings of the game, depending on whether each miner can allocate their fund to a risk-free asset or not. We show that this game admits unique pure Nash equilibrium in closed-form for both settings.

Bitcoin is the most famous and the most used cryptocurrency in the world, such that it has received extreme popularity in recent years. However the Bitcoin system is accompanied by different attacks, including the block withholding (BWH) attack. When a miner plays the BWH attack, it will withhold all the blocks newly discovered in the attack pool, damaging the honest miners' right to obtain the fair reward. In this paper, we consider a setting in which two miners may honestly mine or perform the BWH attack in a mining pool. Different strategy profiles will bring different payoffs, in addition influence the selection of the strategies. Therefore, we establish an evolutionary game model to study the behavior tendency of the miners and the evolutionary stable strategies under different conditions, by formulating the replicator dynamic equations. Through numerical simulations, we further verify the theoretical results on evolutionary stable solutions and discuss the impact of the factors on miners' strategic choice. Based on these simulation results, we also make some recommendations for the manager and the miners to mitigate the BWH attack and to promote the cooperation between miners in a mining pool.

Blockchain technology plays a very positive role in promoting the development of the port supply chain. Although there are some practical examples of blockchain in the port supply chain (eg. Trade Len or Cargo Smart), there are few application scenarios. Therefore, blockchain technology has yet to be widely used in the port industry. This may be related to the construction of blockchain. This paper analyzes the impact of blockchain technology on the port supply chain and the technology sharing on the shipping market. It is found that the investment of blockchain technology in a competitive environment has different characteristics. When the efficiency of technology investment and the proportion of market expansion reach a certain combination, there are two different equilibrium strategy choices: Only technology investment or technology sharing. Based on the above research results, this paper further investigates the results of the comprehensive influence of different factors such as competition intensity and market expansion proportion.

The traditional human resource management system is faced with such problems as the quality of staff recruitment can not be guaranteed, the training performance is not consistent with the actual performance, the enterprise performance appraisal is unfair, and the salary distribution is unfair, which seriously affects the enthusiasm and loyalty of employees and threatens the survival and development of enterprises. In view of the current situation and problems of human resource management system, combined with the technical characteristics of blockchain, this paper proposes a human resource management mechanism based on blockchain technology, in order to achieve the company's human resource management system accurate, efficient, open and transparent. It is found that there is a good coupling between blockchain technology and human resource management system. This paper constructs a human resource management mechanism based on blockchain, innovates the application scenarios of blockchain technology, and provides certain guiding significance for the establishment of human resource management system and process improvement in the future.

This paper studies the benefit of the blockchain food traceability system (BFTS). Based on game theory and the willingness-to-pay model, pricing models are formulated considering important factors like the proportion of consumer with high expertise in traceability, risk attitude to doubtful traceability information and perceived convenience of traceability information checking. By compared the optimal total welfare under the BFTS and that under the traditional food traceability system in valuation analysis, conditions where applying the BFTS is more valuable than applying the TFTS are figured out. Finally, insightful management implications are given.

The influence of taxation on the economy and society is everywhere. Given the fact that CO₂ emission is closely connected with human activities, the question naturally arises whether the tax burden affects the carbon emissions. To address this issue, based on the data of 21 OECD countries over the period from 1991-2014, we use the ratio of tax revenue to GDP as a proxy of the tax burden and employ a panel quantile model with the non-additive fixed effects for analysis. The results suggest that the impact of the tax burden on carbon emissions is heterogeneous across countries based on the level of carbon emissions. Moreover, we find a stable U-shaped relationship between the tax burden and carbon emissions whether for countries with a high or low level of carbon emissions. Our findings suggest that moderate taxes could help reduce carbon emissions, but the effect of excessive taxation is the opposite.

With the rapid development of e-Commerce and takeaway platforms, retailers have gradually developed multi-channel operations. However, limited empirical studies explored the effects of an online channel offered by takeaway platforms on the store performance. Does an online channel addition have a synergy effect or a cannibalization effect on store performance? We empirically investigate these effects by analyzing a large dataset including diverse samples collected from multiple retailers across various regions. The dataset includes 2115 stores across 25 retailers for 10 months that includes two types of retail formats and covers 16 provinces and 21 cities of China. We study the impacts of the newly introduced online channel on the incumbent offline channel and the overall store performance. The empirical results reveal that the online channel addition mainly has a synergy effect. Specifically, for the overall store, it has a positive effect on the sales and product variety, whereas it has a negative effect on the basket size. Surprisingly, an online channel addition also has a positive effect on the offline sales. Our study adds novel values to multi-channel retailing literature by empirically researching the cannibalization and synergy effect of a new type of online channel, offered by takeaway platforms. It can provide insights for retail enterprises who are interested in introducing O2O model.

DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China's regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

According to behavioral finance theory, investor sentiment generally exists in investors' trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a "disruption problem" caused by the non-linear performance of the wavelet function. It didn't improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the "disruption problem" of applying the wavelet function. These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

Crowdsourcing task assignment has become an important task assignment model in the Internet economy era. In this paper, we study the crowdsourcing task assignment problem based on employer net profit and employee satisfaction. First, the reliability and interest of employees are modeled, based on which the mathematical expressions for employer net profit and employee satisfaction are given. Then, a multi-objective optimization problem is formulated to maximize employer net profit and employee satisfaction by jointly optimizing the task assignment matrix and task offer vector. Since the considered problem contains discrete variables, it cannot be solved directly by traditional optimization methods. Therefore, two low-complexity high-performance algorithms are proposed. The first algorithm is based on a fast non-dominated ranking genetic algorithm with an elite, which is able to explore the Pareto bound of the considered problem. The second algorithm is based on a reinforcement learning framework, which is able to maximize the weighted sum of employer net profit and employee satisfaction. Numerical results show that the number of tasks assigned to employees affects both employee satisfaction and employer net profit. The Pareto bounds and Pareto optimal solutions based on the solutions of the two proposed algorithms are also presented numerically, which quantitatively characterize the tradeoff between employer net profit and employee satisfaction.

Understanding the causation of accidents is essential to promote metro operation safety. In terms of 243 reported metro operation accident cases in China, a directed weighted network was constructed based on complex network theory, where nodes and directed edges denotes factors and event chains respectively. To reveal the key causal factors, the topological characteristics of metro operation accident network (MOAN) were analyzed from both global and local views. The results show that facility-type factors are more closely related to the occurrence of the accidents from the perspectives of average path length and cascading effects. Accident types like train delay and train suspension are the great risk recipients. Key causal factors with large out-degree, out-strength, betweenness centrality and cluster coefficient, such as communication and signal failure, vehicle failure and piling into the train should be noticed. The research framework proposed in the paper is not only applicable to China's metro operation system, but also appropriate for other transportation system safety studies.

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.

This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-Ⅰ hybrid censoring scheme is employed to improve the efficiency and controllability of the test. Firstly, the MLEs for parameters are established based on the cumulative exposure model (CEM). Then the conditional moment generating function (MGF) for unknown parameters is set up using conditional expectation and multiple integral techniques. Thirdly, confidence intervals (CIs) are constructed by the exact MGF-based method, the approximate normality-based method, and the bias-corrected and accelerated (BCa) percentile bootstrap method. Finally, we present simulation studies and an illustrative example to compare the performances of different methods.

Pollution cost control is key to solve pollution problem. The paper takes pollution control cost of pollution control contract between management authority and pollutant discharge enterprise as research object, considers pollution control quality level, pollution control quality inspection and pollution control cost model, and establishes pollution control cost model of management authority and pollutant discharge enterprise, including rational constraints of pollutant discharge enterprise. And it analyzes principal-agent relationship between the two under condition of asymmetric information, and un-observability of pollution control level is shown as hiding information of sewage enterprises. In essence, it is problem of adverse selection in principal-agent. Pollution control cost of management is objective function. The first order condition of pollution control cost of sewage enterprise is transformed into state space equation, and optimal control of problem is solved by using maximum principle. In particular, management authority, as principal, uses pollution control provisions to reward, punish and encourage pollutant discharge enterprises as agents.

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.

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.

Mergers and acquisitions (M & A) are important parts of banking reform, which can increase the synergies and reduce the costs of the banks. To analyze the effect and importance of the M & A in the reform period, we measure the productivity change of China's banks who completed M & A during 2004--2018, by using a two-stage data envelopment analysis (DEA) method. First, we incorporate the process of deposits producing and the process of profit earning as a two-stage structure of bank's system. Then, we construct a slacks-based measure (SBM) model considering the weak disposability of undesirable outputs to measure the productivity of 14 M & A banks in China. Particularly, we adopt the global Malmquist index (GMI) to evaluate the productivity change of the banks, and analyze the efficiency change (EC) and technical change (TC) for the whole system and individual stages. Additionally, to facilitate making M & A plans, we classify the M & A banks to obtain the process they need to improve and the trend they could adopted: (i) We classify them into four categories by the productivity of two individual stages; (ii) We also classify them into four categories by EC and TC. Finally, the policy recommendations for M & A banks are given.

China is a country with the most water consumption, so it is lack of water resources. Industry has brought serious water pollution while driving economic development, which leads to the destruction of ecological environment. With the improvement of environmental awareness, many scholars have shifted their research direction to how to improve the ecological environment. Most studies consider the whole system as a "black box", regardless of its internal structure. Therefore, a method to identify inefficiency is necessary and some suggestions for optimization are given. In this paper, a two-stage undesirable fixed-sum output data envelopment analysis (DEA) model is proposed. The industrial chemical oxygen demand (COD) emission during 2011–2015 are adjusted, and the efficiency values are calculated by heuristic search algorithm. The efficiency of 30 provinces and cities is divided into eastern, central and western regions. The model can identify the inefficient stage in industrial system, and find the source of low efficiency in the system. The analysis shows that the efficiency of eastern region is the highest, while the overall efficiency is inclined to the pollutant treatment stage. Finally, the paper puts forward some suggestions for the low efficiency areas, which can save water while ensuring economic benefits, and provide new direction for water pollution reduction and improve the ecological environment.

In recent years, China's industrialization and urbanization have deepened, and the economy has grown considerably. But at the same time, they have also brought about many environmental problems. As a pillar of the national economy, the iron and steel (IS) industry is one of the most emitting and energy-consuming sub-sectors of the industrial sector. It is also one of the industries with the most severe overcapacity problem in China. In this paper, we explore the impact of capacity utilization on carbon dioxide emission based on panel data of China's iron and steel industry from 2005 to 2014. We also tested the heterogeneity in different regions and different sub-samples. Results show that capacity utilization and carbon dioxide emission are positively correlated. However, the impact of capacity utilization on carbon dioxide emission differs when considering regional heterogeneity. Results in all three regions show a positive relationship between capacity utilization and carbon dioxide emission, but the impact intensity is strongest in the western region, followed by the eastern and central regions. Moreover, capacity utilization impacts carbon dioxide emission by influencing firm numbers in the iron and steel industry and energy consumption. Further analysis shows that there exists a threshold effect in different stages of energy consumption and energy structure. Finally, some findings and practical policy recommendations are provided.

Based on China's real estate investment (REI), commercial housing sales (CHS), and GDP data from 2001 to 2020, we studied the dependence, asymmetry, and transmission direction of China's real estate and economy. Their cross-correlation is demonstrated through the deep cross-correlation analysis (DCCA) algorithm, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) algorithm and DCCA algorithm based on a time delay. Both REI and CHS have persistence with GDP. The real estate industry and economy have asymmetric multifractal characteristics. Meanwhile, real estate and the economy have different trends, and they can affect each other with the same length of time delay. This effect diminishes in a short period of time, and there are unsteady fluctuations in cross-correlation persistence in the long term.

The article selects the panel data of 289 prefecture-level and above cities in China from 2003 to 2017, establishes simultaneous equations and uses the 3SLS estimation method to study the relationship between air pollution and economic growth. On the whole, industrial sulfur dioxide and soot emissions have an inverted U-shaped relationship with per capita GDP, and the increase in sulfur dioxide emissions has played a significant role in hindering economic growth. From a regional perspective, the sulfur dioxide emissions in the eastern and western regions conform to the environmental Kuznets curve, and the per capita GDP at the turning point in the western region is significantly lower than the overall national level. The smoke and dust emissions in the eastern, central and western regions all conform to the environmental Kuznets curve, while the per capita GDP at the turning point in the western region is significantly lower than that in the eastern and central regions. In the eastern region, both sulfur dioxide and smoke and dust emissions significantly hinder economic growth, and this hindrance is mainly caused by the spatial transfer of labor. Through further analysis, it is found that environmental regulations can significantly suppress the negative impact of air pollution on economic growth. Finally, the article puts forward some suggestions, such as environmental governance according to local conditions and strengthening environmental regulations.