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Our study is the first to empirically analyze the nonlinear relationship between digitalization and export value and diversification. This paper measures the digital transformation process in terms of digital connectivity, uses of the internet, e-business, e-commerce, and e-government. The various econometrics techniques are applied for the database of 23 European countries during the period 2015-2020. The vital findings should be conveyed here. First, the bottlenecks of export values could be resolved by promoting digital transformation. However, the non-linear reverted U-shaped relationship between digitalization and export diversification suggests that positive effects only appear when the digital activities, especially in digital connectivity, humans with digital skills, use of internet services, or digital public services reaches a certain threshold. Second, the positive influence of digitalization on exports stems from a reduction in export cost and export time to deal with documentary and border compliance as well as improvements in competence and quality of logistic services and quality of trade and transport-related infrastructure, thus enhancing exports. Third, the role of digital connectivity and the integration of digital technology into business and commerce become especially important for export diversification. [ABSTRACT FROM AUTHOR]

This paper examines the digital economy's impact on rural household consumption upgrading. Existing studies remain mainly at the level of rural consumption scale and rarely address the consumption structure. The specific impact of the development of the digital economy on rural consumption upgrading and its mechanisms of action deserves in-depth study and consideration. We analyzed the related content using the fixed-effect model, the instrumental variable method (IV), the mediation effect model, and three-year panel data from the China Family Panel Studies (CFPS). This study found that, at the scale level, the digital economy contributes to expanding rural household consumption. At the structural level, the digital economy significantly increases the share of enjoyment-oriented household consumption but has no significant effect on development-oriented consumption. Income is an important transmission mechanism for the digital economy to improve rural household consumption. Besides, the impact of the digital economy on the upgrading of rural consumption varies according to income level and age. [ABSTRACT FROM AUTHOR]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

The widespread and substantial effect of the global financial crisis in history underlines the importance of forecasting financial crisis effectively. In this paper, we propose temporal convolutional network (TCN), which based on a convolutional neural network, to construct an early warning system for financial crises. The proposed TCN is compared with logit model and other deep learning models. The Shapley value decomposition is calculated for the interpretability of the early warning system. Experimental results show that the proposed TCN outperforms other models, and the stock price and the real GDP growth have the largest contributions in the crises prediction. [ABSTRACT FROM AUTHOR]

The Circular Economy (CE) is receiving more attention, especially in Industry 4.0 (I4.0). In the face of several ambiguous and uncertain information, fuzzy techniques based on Fuzzy Set Theory (FST) are essential for developing CE strategies. This paper uses bibliometric methods to analyze the characteristics of the authors, nations/regions, institutions of the literature of FST and CE, and the collaborations relations between them, and then summarize the literature on fuzzy techniques in the CE and identify the specific role that FST can play in each stage of CE, its primary effects on the CE's pre-preparation stage, design and production stage, and recycling and reuse stage. Meanwhile, the paper explores the advantages of I4.0 technologies for CE and analyzes the research on the role of fuzzy techniques based on FST for CE and I4.0 technologies. Last but not least, this paper is concluded by summarizing the knowledge gained from the bibliometric and content analyses of the literature and suggesting further research directions of investigation. This research will draw attention to FST's contribution and encourage its advancement in CE and I4.0 technologies. [ABSTRACT FROM AUTHOR]

Since the fuzzy techniques in supplier selection, industrial procurement and inventory management form the important parts of the application in supply chain management (SCM). Considering that, the fuzzy techniques involved in the eight papers are all based on the fuzzy theory proposed by Zadeh, and they all well integrate the original fuzzy theory into various decision-making method and further properly be applied into the most popular fields of supply chain management (SCM) and finance economics. Supplier selection, procurement containing supplier and order allocation and inventory management are important parts of SCM. [Extracted from the article]

Reverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years). [ABSTRACT FROM AUTHOR]

The issue of pensions has become increasingly topical. This paper presents the ordered weighted averaging real average pension (OWARAP) operator. The OWARAP operator is based on the ordered weighted averaging (OWA) operator and calculates the future average retirement benefit taking into account price changes. Moreover, this work extends the OWARAP operator by using order-inducing variables, generalized means, and probabilities. This paper ends by analyzing the applicability of the OWARAP operator and its extensions in forecasting the real average Social Security benefits for retired workers in each state of the United States (U.S.). The results demonstrate the usefulness of the proposed approach in retirement decision making. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the impact of a country's entry into the monetary union on its economic variables growth rate of real GDP as well as on GDP per capita growth for the period from 2010 to 2020. The clustering method and the ANFIS method were used in the data analysis. A total of two cluster analyses were performed. The first cluster includes countries that joined the EU in 2004 and became EZ members by 2010. The second cluster refers to those countries that joined the EU in 2004 but are not yet members of the EZ. For the first individual cluster analysis two models were analysed and for the second individual cluster three models were analysed using the ANFIS method. As expected, the results showed that GDP growth is connected with trade, inflation and gross investments in fixed capital in the observed countries, while GDP per capita is connected with unemployment, interest rates and public debt. With regard to GDP growth, the difference between countries that are in the eurozone and those that are not is not significant, which is in line with other studies. [ABSTRACT FROM AUTHOR]

China's constantly rapid economic growth accompanying by a low overall inflation has long been mysterious in macroeconomics. The core purpose of this paper is to solve this puzzle. Therefore, we integrate overdetermined set of equations into a MUCSVO model to explore the volatility mechanism of the overall inflation from a sectoral perspective. Our key findings include: 1) the hedging effect of sectoral inflation fluctuations principally accounts for China's long-run stable overall inflation; 2) the main contradiction of China's inflation has been shifting from high price levels in the traditional food and residence categories to rising prices in the health care category; 3) as the proportions of inflation in the food and residence categories fall steadily, sectoral inflation weights become more evenly distributed. In conclusion, China's overall inflation and deflation will be much less likely to occur, while inflation is still of sectoral imbalance. Unusual price fluctuations in the food and health care categories, which are highly relevant to basic living standards of the low-income group, deserve close attention in particular. Overall, besides solving the puzzle of China's low inflation, our model is applicable to economies that do not publish inflation weights, which is a useful extension of core inflation measurement. [ABSTRACT FROM AUTHOR]

The current situation of the Romanian economy must be understood beginning from the analysis of the main measures of fiscal-budgetary policy applied over the last years by the public authority. In general, the Romanian fiscal policy (before and after accession) was procyclical. However, we continue by presenting some of its characteristics for the past years when we underwent the last ascending phase of the economic cycle. Actually, Romania's GDP exceeded constantly the potential level, and the demand surplus became predominant, generating inflationary pressures. Maintaining the expansionist level of the fiscal policy, in the conditions of a positive deviation of GDP, as of 2017, and opting-out regarding the structural deficit target contributed to affecting the stability of public finances, on short-and medium-term. Romania entered into an extremely difficult economic context, generated by the pandemic, with an extremely narrow fiscal space which limited a lot the possibilities of combating the effects of the pandemic. In this paper we analyzed a period limited to the year 2020, because we consider this time as marking the end of an economic cycle in a period of peace and economic calm, as another is about to begin based on the new realities. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) epidemic has deferred global progress in energy efficiency to a decade-long low, posing a threat to the achievement of international climate goals, and also profoundly affected the development of economics. To gain insight into the research frontiers and hotspots in energy efficiency and COVID-19, a systematic literature review and bibliometric analysis on economic effects are performed with the help of the bibliometric tools VOSviewer and Bibliometrix. This paper selects all the publications retrieved based on the subject terms in the Web of Science core collection. Firstly, this article performs a performance analysis of related publications to present the development and distribution of energy efficiency and COVID-19 from research areas, relevant sources, and influential articles. Afterward, a visual analysis of the literature called science mapping analysis is implemented to display the structural and dynamic organization of knowledge in energy efficiency and COVID-19 research. In the end, detailed discussions of two research hotspots and some theoretical and practical implications are concluded in the systematic literature review and bibliometric analysis findings, which may contribute to further development for researchers in the field of energy efficiency and eventually propel the progress of society and economy in an all-round way. [ABSTRACT FROM AUTHOR]

High-quality innovation is an inevitable requirement for high-quality economic development, and an accurate grasp of the regional distribution characteristics of the level of highquality innovation development (HQID) can better understand and simulate high-quality innovation. This paper constructs a dataset of high-value inventions based on the Chinese patent database using textual analysis methods and the criteria for High-Quality Inventions proposed by the State Intellectual Property Office, and explores inter-provincial variability and convergence by using the Theil index, α convergence, and β convergence. This paper finds that High-Quality Inventions are significantly and positively correlated with various indicators measuring the quality and value of patents. In general, the overall level of HQID is on an upward trend with clear differences among provinces, which shows a regional unbalanced but overall rapid development. From the regional differences of HQID, the regional differences of HQID have been narrowed, mainly within the region. In terms of the convergence of HQID, α convergence, absolute β convergence, conditional β convergence, and club convergence exist nationally and within each region, which indicates that the gap between the national and regional levels of HQID has been narrowing over time. HQID has a positive promotion effect on the relationship between HQID and economic growth, economic growth rate, and total factor productivity. High-quality innovation can improve the quality of input factors, cultivate new dynamic energy for economic development, and then promote high-quality economic development. In the context of the shift of IP work from pursuing quantity to improving quality, the scientific understanding of high-quality innovation, the development status, and the differences of regional innovation high-quality development is of great significance for improving innovation quality and efficiency, and effectively supporting high-quality economic development. [ABSTRACT FROM AUTHOR]

In recent years, knowledge management (KM) has consistently attained considerably growing research attention. Consequently, several literature reviews have been performed addressing different topic areas of KM. This paper seeks to present a comprehensive bibliometric and network analysis on KM to understand its development from the perspective of academic communities. Subsequently, it seeks to identify the structure of associations between prior and current themes, predict emerging trends and offer a longitudinal perspective on KM research. This study used web of science database and the initial sample was trimmed down by considering only the articles contributing to KM literature, and further 8,721 KM papers published in the last 30 years were systematically evaluated. The descriptive statistics and science mapping methods employing co-citation analysis were performed with VOSviewer software. In the descriptive analysis, we have analysed publication trends over time, geographical localization of the contributing institutions, journals, most prolific authors, top-performing institutions and most cited articles. Science mapping analysis is based on co-word analysis and co-citations analysis, namely articles' co-citations and authors' co-citations. The main findings of this paper will help researchers and academicians to develop knowledge in a specific sub-field by analysing the research outcomes of the papers included in the body of literature. [ABSTRACT FROM AUTHOR]

Fintech is driving the revolution of finance and profoundly affecting the development of the financial sector. However, few studies examined how commercial banks' fintech innovation affects total factor productivity (TFP). To build up the fintech index of commercial banks, we use web crawler technology to accumulate news related to the fintech innovation of commercial banks in Baidu news. We use the panel data of 72 banks in China from 2010 to 2020 to explore the impacts and mechanisms of fintech on commercial banks' TFP. The results show that fintech innovation effectively improves TFP after a series of robustness tests. Further, we find that fintech innovation can improve commercial banks' TFP by promoting innovations of financial products, increasing risk control capability, reducing cost, and improving profit. Also, the utility of fintech is more significant in banks with more assets, facilities, and human capital, which means that fintech innovation creates a "bigger is better" mindset. Meanwhile, the result of quantile regression shows that the higher the fintech innovation, the more significant the increase in TFP, which further reveals that there is 'too big to fail' among commercial banks under digitalization. [ABSTRACT FROM AUTHOR]

This article explores the concept of the circular economy and its connection to emerging technologies like the Internet of Things (IoT) and Industry 4.0. It emphasizes the significance of the circular economy in promoting sustainability and the reuse of materials. The article also discusses the potential benefits of IoT technologies in implementing circular economy practices. It presents various research papers that delve into the challenges, risks, and advantages of utilizing the IoT for the circular economy. The overall aim of the research is to contribute to the existing knowledge on the circular economy and inspire further progress in the field. [Extracted from the article]

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to present a methodology for measuring contemporary protectionism - based on data from Poland. In light of the difficulties in assessing all trade barriers, an approximation was proposed: the regional barometer of protectionism (RBP). Recognizing that 21st century protectionism is observable only to a certain extent, the research is based on data that reflect the level of this phenomenon - i.e., media releases as well as Global Trade Alert data. In constructing this barometer, the TOPSIS method (a Technique for Order Preference by Similarity to an Ideal Solution) was used. The RBP constructed (for 2009-2020) is the first regional barometer of protectionism. The results of the research reveal the level and dynamics of protectionism in the case of Poland. This RBP improves the methodology of foreign trade analysis by providing a thorough basis for further research into both the effects and causes of protectionism; this paper presents initial explanations for the latter. Furthermore, after the barometer of protectionism is constructed, trade barriers may eventually be included in further research for models designed to explain Polish trade. [ABSTRACT FROM AUTHOR]

This paper analyses the recent evolution of the electricity price of one of the East-European countries' Balancing Markets (BM) - Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants' trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players. [ABSTRACT FROM AUTHOR]

Recently, sustainable economic performance (SEP) has been the foremost requirement due to uncertainty of the economy and environment. This issue needs best practices and governance and also needs researchers' focus. Thereby, the paper aims to assess green supply chain (SC) practices, green SC governance and their role in the achievement of SEP in the manufacturing sector of China. The paper also attempts to explore the moderating effect of organizational support in proposed framework. PLS-SEM model was employed to test model's reliability and validity. The very same method has applied to test the linkage among outlined constructs. Obtained results confirms the moderating impact of organizational support among traditional governance, relational governance and the achievement of SEP in the manufacturing industry in China. This article provides guidelines to the regulators in developing policies related to the achievement of SEP using green SC practices and governance. [ABSTRACT FROM AUTHOR]

Social sustainability is a global necessity because of environmental and economic uncertainty. This issue needs the foremost solution, and for this purpose, researchers' and policymakers' emphasis is required. Thereby, the present paper investigates sustainable energy technologies such as solar and hydroelectric, eco-innovation and EG and their impact on social sustainability in China. The study also used industrialization and inflation as the control variables in the time span of 1981 to 2020. The present study also applied the Dynamic Auto-regressive Distributed Lags (DARDL) model to evaluate the association between the outlined variables. The results indicated that sustainable energy technologies such as solar and hydroelectric, eco-innovation, economic growth, industrialization and inflation are significantly associated with social sustainability in China. The present paper offers standard policies to regulators in making regulations related to maintaining social sustainability by using effective sustainable energy technologies and eco-innovation. [ABSTRACT FROM AUTHOR]

Using the policy package pilot implemented in Hubei Province, China, in April 2020 as a natural experiment, we use the synthetic control (SC) and synthetic difference in differences (SDID) methods to estimate the impact of the Chinese government's support policy on the economic resilience and to analyze the mechanisms by which it impacts. This study finds that the policy package has contributed to the growth of economic resilience in the pilot provinces, with the policy package increasing the average economic resilience of the pilot provinces by 0.062 compared to their potential resilience. The validity and robustness of the above conclusions are objectively confirmed by multidimensional quantitative outcomes such as placebo tests, ranking tests, and replacements in calculating resilience. The mechanism analysis shows that the investment in real estate development, the stimulus for consumption, and the core industry development are virtual channels for the policy package to promote economic resilience growth in the pilot provinces. Moreover, traditional investment in transportation fixed assets plays a minor role. This paper quantitatively corroborates the academic idea that government governance capacity affects regional economic resilience (RER), and research can provide empirical support for regional economic recovery and policy support under a major crisis. [ABSTRACT FROM AUTHOR]

Scanner data mean electronic transaction data that specify product prices and their expenditures obtained from supermarkets’ IT systems by scanning bar codes (i.e. GTIN or SKU). Scanner data are a relatively new and cheap data source for the calculation of the Consumer Price Index (CPI) and the biggest advantage of scanner data is the full product information they provide already at the lowest level of aggregation. Thus, the digitization of the public sector becomes not only something that is needed but an actual necessity resulting from organisational and economic premises (e.g.: reduction of costs or time related to obtaining data). One of main challenges while using scanner data is the choice of the right price index. The list of potential price indices, which could be used in the scanner data case, is quite wide, i.e. bilateral and multilateral indices are used in practice. One of the most important criterion in selecting index formula for scanner data case is the potential reduction of the chain drift bias. The chain drift occurs if the index differs from unity when prices and quantities revert back to their base level. In the paper we present situations on the market leading to the serious chain drift bias. Our main hypothesis is that lagging consumers’ reaction to price changes is the cause of the chain drift effect. Moreover, the article is an attempt to answer the question whether the correlation of prices and quantities may have an influence on the scale and sign of the bias of the measurement of price dynamics. The study focuses also on the scale of over- and underestimation the target full-window multilateral indices by their corresponding splicing extensions. Finally, the paper verifies a hypothesis that the identity test is a key property in reducing chain drift bias. In order to verify the above research problems, both empirical and simulation studies were carried out. Our main result is the confirmation of earlier suspicions that delayed consumer response and price-quantity correlation are determinants of chain drift bias. [ABSTRACT FROM AUTHOR]

Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model. [ABSTRACT FROM AUTHOR]

According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning. We first adopt the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition technology to separate the short-term noise, cycle transformation and long-term trend from the original series, and use the CSI 500 Baidu index series to reflect the investors' attention, which provides data support for establishing a more effective forecasting model. Then, the CEEMDAN-BP neural network model is designed based on the obtained effective information of low-frequency trend series, investor attention index and CSI 500 stock index futures market transaction data. Finally, an Attention-based Dual Thrust quantitative trading strategy is proposed and optimized. The optimized Attention-based Dual Thrust strategy solves the core problem of breakout interval determination, effectively avoids the risk of subjective selection, and can meet investors' different risk preferences. The quantitative investment decision-making model based on CEEMDAN-BP neural network utilizes the advantages of different algorithms, avoids some defects of a single algorithm, and can make corresponding adjustments according to changes in investors' attention and the occurrence of emergencies. The results show that considering investor attention can not only improve the predictive ability of the model, but also reduce the cognitive bias of the market, effectively control risks and obtain higher returns. [ABSTRACT FROM AUTHOR]

Open innovation (OI) has received significant attention from practices and theories over the past decades. This paper investigates the role of OI and personalized patterns in firms at home and abroad, and then measures the risks involved. Firstly, this paper reviews the definition of OI, the business model innovation characteristics, and the facing problems in practice. Based on the existing literature, we illustrate the openness and challenges of business OI. By introducing bibliometrics, this paper presents a whole research framework. Based on keywords cooccurrence analysis and clustering analysis, we locate the current research hotspots and potential research opportunities from a comprehensive perspective. According to the analysis results, five clusters are obtained, including resource management and value creation; collective innovation and form sustainability; innovation management, intellectual property management, and crossborder cooperation; knowledge management and knowledge sharing; innovation ecosystem, big data, and policy-level innovation. Taking Huawei as an example, its typical business OI model is studied from the perspectives of organizational, project-related, marketing and consumer-based, and summaries the facing challenges and risks. We illustrate its financial performance, innovation performance, and development prospects. We found that, during the implementation of OI practical activities and theoretical exploration, the risks and opportunities facing small and medium-sized enterprises (SMEs) are multiple dimensional. [ABSTRACT FROM AUTHOR]

Ecological efficiency (EE) provides much reference for formulating appropriate regional economic, social and environmental policies to promote sustainable development. Interactive subsystems of economy, society and environment within EE system have been considered in this paper. By innovatively integrating the merits of two advanced economic research methods (global super efficiency network data envelopment analysis (GSE-NDEA) and panel vector autoregression (PVAR) and updating the EE evaluation indicator system by following the new features of sustainable development in the recent China, this paper comprehensively evaluates EE by drawing evidence from 3 regions in China during the period of 2011–2020, and further reveals how the three subsystems within EE system interact to achieve EE enhancement. The findings show EE and its three subsystems’ trend, the major constrains of EE development, the regional discrepancies in EE progress, and the interactions among the subsystems of economy-society-environment within the EE system in different regions of China. The policy implications are proposed accordingly. [ABSTRACT FROM AUTHOR]

Digital economy is a great route to promote the efficient utilization of natural resources and promote sustainability due to its high-tech, rapid growth, extensive penetration, deep integration and other characteristics. Existing study on the influencing factors of the digital economy is not deep enough and lacks the analysis on the relationship structure of factors influencing the digital economy, which is not conducive for an overall grasp of the digital economy. To correctly understand how to better develop the digital economy, this paper studies its influencing factors and the relationships between them. Based on the time-series data of China from 2002 to 2018, grey correlation analysis was applied to calculate the correlation between these influencing factors and the digital economy, and determine the major influencing factors of digital economy development in China. The Granger causality test and a review of existing research were used to judge the interrelationship of various factors. The interpretative structure model was utilized to determine the relationship structure of the main factors affecting the development of China's digital economy. The results show that the number of digital talents, state of the technology market, and degree of digitalization are direct influencing factors of the digital economy. The results help to better understand the development of the digital economy and will enable the implementation of policies to improve towards more sustainable cities. [ABSTRACT FROM AUTHOR]

Modern "Supply Chains (SCs)" have recently been introduced as value networks of high complexity, and firms have focused on its efficiency as an important support for staying competitive in the market. Firms are currently capable of observing, tracking, and monitoring their products, activities, and processes throughout their value chain networks using new technologies, namely the "Internet of Things (IoT)". Though, the influencing factors of IoT are highly complex and diverse, which result in the information-intensiveness of the SCs processes. This, in turn, leads to lots of barriers to SCs. In this paper, we evaluate and rank the IoT risks for "Supply Chain Management (SCM)" by utilizing "Stepwise Weight Assessment Ratio Analysis (SWARA)" and "Additive Ratio Assessment (ARAS)" under "q-Rung Orthopair Fuzzy Sets (q-ROFSs)". A case study is presented for investigating the IoT risks for SCM in the q-ROFSs setting. Moreover, the obtained results were compared to those of some methods currently used in the literature. The outcomes of the study show that the security and privacy risks with a weight value of 0.0572 is the main IoT risk factor for the SCM and the organization-I with the utility degree 0.8208 is the best option with diverse IoT risks for SCM. [ABSTRACT FROM AUTHOR]

This paper focuses on calculating resilience index of 282 cities in China from 2012 to 2019, to analysis the regional differences and random convergence. We use the entropy method to calculate the urban resilience index, adopt the Dagum Gini coefficient method to analyze the regional differences and the sources, explore the variation coefficients method and beta convergence model to diagnose the convergence mechanism. The conclusions are: (1) The urban resilience in China is at a medium and low level with a stable growth tendency, with a significant regional unbalance of "higher in east, and lower in other regions". As the sub-resilience, there is a big gap in the regional difference of the resilience structure with good performance in social resilience and economic resilience, poor in ecological resilience and infrastructure resilience. (2) The Gini coefficient of urban resilience continuously decreases with the regional unbalance narrowing accordingly. The Gini coefficients in different regions have a phased convergence tendency, and the hypervariable density contribution and intra-regional differences contribution are the main sources of differences in urban resilience. (3) The urban resilience in China and eastern region has s convergence, while China and all regions have significant absolute ß and conditional ß convergence. Therefore, this paper proposes to continuously accelerate the urban resilient construction, make up for the shortcomings, and narrow the regional development gap, to promote the healthy and orderly development of cities. [ABSTRACT FROM AUTHOR]

The construction sector plays a significant role in national economic development and accounts for a dominant portion of national economic growth. The international construction industry's development has been explored from the perspective of the economy. Internationalization improves awareness about participation in international trade activities. This paper aims to quantify the internationalization of the construction industry in the global value chain. The sectoral participation index and interaction index are conducted to evaluate the internationalized degree of the construction industries. In the results, the distribution of the backward participation indices shows nearly all countries' internationalization processes were still at the stage of benefiting from primary resources. The forward support index is easily influenced by the scale effect. The backward independence index accounts for a small percentage of national imports measured by value added. The main source of value-added supply was the local construction sector. This study proposes a comprehensive indicator to quantify the internationalization of the construction industries in the sight of national and global points. The references have a positive value for policymakers in the management of value-added flows in the international market and adjustment of the foreign trade environment. [ABSTRACT FROM AUTHOR]

This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios. [ABSTRACT FROM AUTHOR]

The role of the digital economy in economic transformation is increasingly prominent. This paper empirically examines how the digital economy affects TFP and its mechanism using Chinese provincial panel data. The results show that the impact of the digital economy on regional TFP is U-shaped. Further studies show that digital economy development may improve regional TFP through two channels: stimulating innovation and promoting entrepreneurship. In addition, we also discuss the role of active government, effective market, and high-level judicial protection in digital economic policies improving economic growth. Based on our research results, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

Our study bridges the gap between in previous research on the synchronization between financial and business cycles over a long period. Using the data for the UK from 1270 to 2016 we analyze the synchronization between financial and business cycles using spectral Granger causality (Breitung & Candelon, 2006). Our paper brings several important findings to the discussion on the financial and business cycle link. Our paper is the first one (to the best of our knowledge) that use data over a long period spanning several centuries. We use spectral analysis and advanced spectral analysis (SSA) and (MSSA) to study the relationship between financial and business cycles in the long run. Paper results show financial and business cycles series moves along over the medium-term spectrum. We find a strong link between the cyclical component in the output (real GDP series) and the cyclical component in the financial series (housing price, credit). [ABSTRACT FROM AUTHOR]

Poverty is a challenge faced by all countries worldwide. This paper focuses on a factor that has been less well documented: the consumption loss of farmer households caused by the fluctuation of rural public expenditure. Based on large-scale micro household data and climate data, the instrumental variable estimation results show that every 1% fluctuation of rural public expenditure will lead to a 0.113% decrease in farm household consumption. In addition, the fluctuation of rural public expenditure is also a main cause of long-term consumption loss of farmer households. Furthermore, it was found that the impact of rural public expenditure fluctuation on consumption loss is of certain spatial heterogeneity. The worse the spatial environment is, the more serious the consumption loss will be. The policy suggestion of this paper is to ensure a stable scale of rural public expenditure through the central transfer payment, so as to improve the ability of local governments to implement counter cyclical public policies, and transform local finance (industrial investment) into public finance (infrastructure and education) to improve the local space environment. Overall, this study reveals the impact of spatial externality on rural poverty from the perspective of public expenditure fluctuation, and at the same time provides empirical evidence for a better evaluation of the relationship between development and poverty and support for rational regional anti-poverty policies. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added. [ABSTRACT FROM AUTHOR]

The projections of UN indicate that by the year 2030, natural resource demand will become threefold as of today. This for sure surpasses existing capacity due to which many countries are facing depletion issue or might face depletion issue in coming years. Realizing this challenge, economies all over the globe are shifting towards circular economy development in order to address the environmental sustainability issue. In Vietnam, economic activities are normally based on the principle of linear economy. However, this traditional approach not only creates shortage of natural resources but also becomes the reason of environmental destruction. Thus, the study is an attempt to find out the harmonious relationship of economic and non-economic determinants with circular economy in the presence of sustainable supply chain management as a moderator. By employing structural equation modelling, findings indicate that circular economy is the right direction as it ensures job security and bring quality education and political stability. Results also confirm that sustainable supply chain management increases the strength of positive relationship of said variables. In the light of the evidences, the paper proposes few suggestions and implications to promote the development of circular economy so that economic growth and environmental quality can be achieved at the same time. [ABSTRACT FROM AUTHOR]

Poverty reduction is considered as the top priority of governments and international institutions. At the same time income inequality is a far-reaching concern especially in emerging nations. US former president even labelled income inequality and poverty are the challenges of our times. Theoretically there are different views on the impact of microfinancing and financial inclusion on income inequality and poverty, hence, it is interesting to evaluate these estimations in Vietnamese context from 1986 to 2020. The researchers have applied the Bayesian Auto-regressive Distributed Lags (BARDL) model and exposed that microfinancing, financial inclusion, educational loans increase income equality and reduces poverty. Based on the evidences, the paper implies that government institutions must focus on microfinancing and financial inclusion aspects to facilitate poor and boost prosperity which ultimately brings income equality. [ABSTRACT FROM AUTHOR]

This paper examines whether capital flow management (CFM) and monetary policies effectively reduce lending growth in emerging market economies (EMEs) in the presence of conventional and unconventional monetary policy actions undertaken by advanced economies. We apply a dynamic panel model with fixed effects to a sample of 24 emerging market economies for 2000–2021 using quarterly data and more continuous variables than in other studies rather than limiting the variability using proxies. Capital controls and macroprudential regulation, as CFM policy tools, moderate lending growth. This effect is particularly shown in countries with tighter monetary conditions. Our main findings highlight the useful role of coordinating CFM and monetary policies. This role stands for both fixed and flexible exchange rate regimes. Lastly, we find capital flow management and monetary policies manage to control lending in normal periods, but their coordination is less effective during crises and high volatility periods. [ABSTRACT FROM AUTHOR]

Creative hubs are institutional organization of newly emerging creative industries. Economic activities associated with creative industries are very often business-oriented and strongly independent. However, there are often needs for public sector. The aim of paper is to analyze the situation in the Czech Republic and assess the involvement and benefits of the public sector towards the support of these business activities. Methodologically, the work is based on several approaches. Primarily, a database of Czech creative hubs was created as part of desk research. This was followed by quantitative questionnaire survey with representatives of creative hubs and qualitative research in the form of structured interviews with representatives of hubs and the public sector. It was found that without public finances and interest, Czech creative hubs cannot function in the long term. Financial support is therefore the most common form of public support. The main strength of hubs lies in the built community of members, offered services and networking. Conversely, the main weakness is the inability to secure sufficient funds and sponsors. Although public sector cooperation in the Czech Republic is still limited, many public representatives are beginning to realize the benefits of the creative economy and gradually implement it into policies. [ABSTRACT FROM AUTHOR]

Tourism exerts a great effect on the modern economy and relies largely on the flow of people facilitated by high-quality transportation infrastructure. Applying a spatial econometric method, this paper investigates the effect of high-speed rail (HSR) on tourism expansion in China from the view of the spatial spillover effect. Based on a 276 Chinese cities’ panel dataset over 2005–2019, a positive role of HSR in tourism expansion is observed. Compared with cities unconnected to the HSR network, cities accessible by HSR experienced a 22% increase in tourism revenue and a 38% rise in tourist arrivals. In addition, the connection of a city to the HSR network also exerts a great spatial spillover role in the increase of tourism revenue and arrivals in peripheral cities which are not directly connected by HSR. The research findings offer important insights on the relationship between transportation infrastructure and tourism with significant policy implications regarding tourism development. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the effects of the macroeconomic factors to the movements of the asset returns of the banks in Turkey in terms of systemic risk from 2005 to 2018. In the study, Independent Component Analysis is applied for extracting driving factors of the asset returns of Turkish banks by decomposing the returns into its components. After examining the relationship between the independent components and the macroeconomic variables, the results conclude that one component shows strong similarities with the well-known stock market index of Turkey, namely the BIST100. Besides, the BIST100 is observed as the most important macroeconomic indicator affecting the movements of the asset returns. From systemic risk perspective, the BIST100 and the exchange rate from US dollar to Turkish lira are interpreted as two macro factors that contribute to the systemic risk of Turkish banks. When it is reviewed the regression results of the estimated independent components with the macroeconomic variables, it is found that while the BIST100 affects the asset returns of Turkish banks on its own, three macroeconomic factors (the credit default swap spreads of Turkey, the exchange rate and volatility) jointly affect the banks by creating a chain effect. [ABSTRACT FROM AUTHOR]

In recent years, a series of serious attacks against industrial control system (ICS), such as Stuxnet, Duqu, Flame and Havex, have sounded the alarm for industrial enterprises. For many industrial enterprises, it is a very important part of enterprise management decisions to evaluate ICS security suppliers and choose the appropriate one as a partner. The purpose of this paper is to build the TODIM method based on cumulative prospect theory (CPT-TODIM) to solve multiattribute group decision making problem under interval-valued bipolar fuzzy environment. This extraordinary model not only uses CPT to supplement the traditional TODIM method, but also introduces the entropy weight method to determine the attribute weight so as to avoid the negative influence of subjective weight on the decision result. In addition, in this model, all attribute evaluation information will be interval-valued bipolar fuzzy number (IVBFN) to cope with complex and fuzzy decision environment. Then, the most important part of this paper, we elaborate on the logical structure of the model. What's more, in the last two sections, we apply this newly constructed model to the selection of ICS security suppliers, and verify the acceptability of this method by sensitivity analysis and comparing with IVBFWA and IVBFWG operator. [ABSTRACT FROM AUTHOR]

The perpetual need for developing a sustainable economic environment places the education policies at the foundation of social adaptability. Creating and maintaining curriculum content that meets the demands of a continuously changing society, and the challenges that such a rapid evolution put on the labour market, is one of the top priorities for any education system and institution involved in education at any level. This paper proposes a cognitive computing solution for assessing, in a programmatic manner, large corpora of curriculum content created by teachers from lower secondary education environment for Informatics instruction in Romanian schools. The result of this initiative at the national level is corpora of curricular content that must be evaluated to verify the degree to which the material meets the requirements of the national curriculum. We addressed this crucial yet tedious process by designing and implementing a solution for automating curriculum assessment through cognitive computing. The paper outlines a sustainable framework to evaluate curriculum content in an automated fashion, and for providing critical feedback timely to both content creators, and to policy makers responsible for creating economically viable and future adaptable education strategies. [ABSTRACT FROM AUTHOR]

Despite being rich in cultural heritage, traditional handicrafts require innovation to achieve competitiveness. This study addresses the understanding of innovation in traditional handicrafts for sustainable development. The paper emphasizes the importance and potential advantages of innovation and its synergistic effect with cultural traditions leading to sustainable development. Apart of the explanation of most important issues regarding this topic, publications containing the following keywords selected for the study were identified in the WOS database. A total of 500 different publications from 1975 to 2021 were identified. The database was used for text-mining analyses. The clustering method (data mining) was used. The systematic literature review was carried out with the use of VOSviewer software. This tool was used to identify and analyse clusters and dominant research areas and to identify potential new research directions. This has never been done by any author before. We show that future studies should focus on the issue of measuring incremental innovation in cultural creative industries, especially handicraft since this topic is not enough analysed in the literature. The findings can help academics and practitioners to improve the knowledge about the topic and concentrate on identified priority areas to fulfil the assumptions of sustainable development. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic put further pressure on the economies, both at individual and global level and, amid already existing vulnerabilities, worsened the economic prospects. As a result of the negative effects of these tensions in tandem with global value chains disruptions, commodities' prices increased, leading to strong inflationary pressures around the globe. Given possible permanent effects on inflation expectations, it is therefore questionable how fast those prices are to be stabilized. In this context, this paper focuses on a group of 10 European countries, namely Czechia, Hungary, Poland, Romania, France, Germany, Italy, Spain, the Netherlands and the United Kingdom, in the period spanning from 2005 to 2020. By implementing a GVAR model, the study analyses the pass-through of the external shocks stemming mainly from the Euro Area and the US to the CEE region, comparing the responses obtained for these countries with the ones of the developed economies. Considering the strong trade relationships between the analysed countries, the considered transmission channel is the commercial one. The results indicate that a supply shock in the Euro Area has a significant negative impact on the selected CEE countries' economic growth, the offsetting factors not being strong enough to diminish the response. [ABSTRACT FROM AUTHOR]

Assessing the performance of the Research and development (R&D) organizations to achieve higher productivity, growth, and development is always a critical necessity. Therefore, developing a more accurate model to evaluate the performance is always required. For this purpose, this study is aimed at developing a decision-making model for evaluating R&D performance. The model comes up with determining the most proper evaluative criteria for assessing R&D organizations. Then, it integrates Data Envelopment Analysis (DEA) with Analytical Network Process (ANP) to assess R&D performance. This paper is aimed to develop an integrated model for evaluating R&D performance. The findings of the study show that the DEA-ANP model is an accurate and acceptable model for evaluating R&D organizations' performance. [ABSTRACT FROM AUTHOR]

The aim of the paper is to find out whether euro is a convenient substitution for U.S. dollar as an anchor currency for Iranian rial and whether this replacement would affect Iran's international trade positively. We explore these effects via Optimum Currency Area (OCA) theories using generalized least square from 2000 to 2018. Based on OCA index, euro would be a good substitution for U.S. dollar as an anchor for Iranian rial. In addition, gravity model and Generalized Method of Moments estimation confirm that substitution of U.S. dollar by euro would improve bilateral trade between Iran and its major trade partners especially the European Economic and Monetary Union (EMU). Furthermore, we confirm that a basket containing main currencies (euro, U.S. dollar, yuan, Russian rubble) would be more efficient than a single currency anchor however euro should be prominent in the basket. Such a change of anchor could positively contribute to reduction of transaction costs, diversification of external risk, rise of mutual trade exchanges between Iran and the EMU or the EU and consequent economic growth of trade partners. The paper contributes to the existing literature by comprehensive methodological approach how to identify an appropriate anchor currency. [ABSTRACT FROM AUTHOR]

The combination of knowledge innovation and technology innovation provides vitality for social science and technology innovation. China leaps into the front ranks of the world in the 2021 Global Innovation Index (GII). Therefore, this research takes China's theoretical-application innovation as the research object and empirically analyzes measure the innovation efficiency of knowledge innovation dominated by universities and technological innovation dominated by enterprises in China, as well as the gravity-center migration trajectory. The results show that the ranking of overall efficiency of theoretical innovation-application innovation is eastern region > central region > western region. Knowledge innovation presents a drag on overall efficiency, while technology innovation offers a contribution to overall efficiency. In the analysis of PIE (R&D personnel of industrial enterprises above a designated size) variables, the efficiency value is relatively low. The peak value of kernel density increases in the eastern, central and western regions, namely the concentration degree of theoretical innovation-application innovation effi- ciency in China has risen. The gravity center of each stage migrates to the eastern region, meaning the efficiency value of China's theoretical innovation and application innovation increases more significantly in the eastern region. From the perspective of knowledge innovation and technology innovation, this paper puts forward suggestions for China and provides some references for other developing countries. [ABSTRACT FROM AUTHOR]