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With the diversity of shopping styles, reference prices have become a consideration in consumers’ purchasing decisions. It is critical to understand how manufacturers and retailers make optimal decisions based on consumer behavior. To this end, we develop a manufacturer-led Stackelberg (M-Stackelberg) game model to investigate the impact of consumer proportion, reference price effect (RPE), and the channel preference coefficient on the decision of the remanufacturing supply chain (RSC) in dual-channel structures. Through comparing different game scenario models, we find that when the reference price coefficient is relatively small, the optimal decisions for the manufacturer and the retailer are the same regardless of whether the RPE is considered. The manufacturer and E-tailer always benefit from RPE, while the traditional retailer (T-retailer) is uncertain. The numerical analysis revealed that the profits of supply chain members are inversely proportional to the proportion of consumers. The smaller the proportion of primary consumers, the more favorable the supply chain. In particular, the higher the channel preference coefficient, the higher the profits of the manufacturer and E-tailer, while the lower the benefit of the T-retailer. Further, in the dynamic game, the total supply chain profit is highest when the T-retailer prices earlier than the E-tailer.

Bike-sharing system has become an indispensable element of sustainable urban transportation, effectively resolving the “last mile” transportation challenge for city dwellers. A major daily operational task in these systems is planning a fleet to rebalance the bikes over time, ensuring the optimal availability of bikes and docks to users. Recycling is also a daily work with the an increase in the number of broken bikes. However, rebalancing or recycling operation is always regarded as an independent tasks. They are separately studied in existing papers. Thus, this paper develops an operational strategy for recycling broken bikes during the rebalancing process, and studies the combination of the station inventory and vehicle routing problems. First, an inventory routing model is constructed with the aim of minimizing the total costs including procurement, expected user loss, inventory and transportation costs. Then, a two-stage iterative algorithm is developed with both exact and heuristic algorithms. We use real-world data from Capital Bikeshare to test our proposed model and approach, which shows the two-stage iterative algorithm is efficient and outperforms existing solutions in reducing total costs. Finally, the sensitivity analysis is performed on key parameters such as the vehicle’s capacity, unit penalty costs for customer dissatisfaction events, unit inventory holding costs and the observation period of rebalancing. It shows that enterprises can reduce the total cost by altering vehicle’s capacity, reducing the unit inventory holding costs or changing the observation period of rebalancing.

Understanding the relationship between attribute performance (AP) and customer satisfaction (CS) is crucial for the hospitality industry. However, accurately modeling this relationship remains challenging. To address this issue, we propose an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) approach that leverages interpretable machine learning (IML) to improve traditional relationship analysis methods. The IML-DAA employs extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to construct relationships and explain the significance of each attribute. Following this, an improved version of penalty-reward contrast analysis (PRCA) is used to classify attributes, whereas asymmetric impact-performance analysis (AIPA) is employed to determine the attribute improvement priority order. A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated. The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS, as identified by the dynamic AIPA model. This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.

The authors aim to interpret human and AI interactions from the decision perspective. The authors decompose the interaction analysis into the following main components in the context of interactions: Individual behavior patterns, interaction relationships, and comprehensive analysis. The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study. About the individual behavior patterns, the authors find that human players prefer short-term periods to AI in decision-making. The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run. The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy. The main behavior pattern of players in the game of Go is switching between careful and daring behaviors. The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.

Many countries have cut their corporate tax rates in the past decades to attract foreign investment. To prevent this, a global minimum tax policy was approved by OECD countries in 2021. Global changes in corporate tax rates could reshape production and investment networks while impacting welfare and global emission patterns. Here we develop a theoretical multi-country multi-industry general equilibrium model and show that global corporate tax competition during 2005–2016 would increase global carbon emissions and shift more emissions to developing economies. Implementing a global minimum tax rate of 15% would reduce global carbon emissions and effectively decrease the developing economies’ emissions. The results highlight that corporate tax policies should be coordinated with climate regulations.

The gradual adjustment of fertility and retirement policies in China has social benefits in terms of coping with population aging. However, the environmental consequences of these policies remain ambiguous. Here we compile environmentally extended multiregional input–output tables to estimate household carbon footprints for different population age groups in China. Subsequently, we estimate the age-sex-specific population under different fertility policies up to 2060 and assess the impacts of fertility and retirement policies on household carbon footprints. We find that Chinese young people have relatively higher household carbon footprints than their older counterparts due to differences in income by age group. Relaxing fertility policies and delaying retirement age are associated with an increase in population (and labour supply) and thus increases in household carbon footprints, with the majority of these increases from the fertility side. These results may help policymakers understand interactions among those measures targeting population aging and climate action.

The establishment of a long-term and accurate forecasting approach of urban air pollution is conducive to the implementation of pollution prevention and control policies. However, existing research has not fully taken into account the spatial-temporal pattern characteristics of long-distance air pollution transport. Multistep ahead forecasting faces the challenge of aliasing long-term spatial-temporal correlation and accumulating errors. In this study, a long-term spatial–temporal pattern forecasting model (ASTemCN) of PM$_{2.5}$ air pollutants in Chinese cities was established based on the monitoring sites of the Internet of Things. The model skillfully designs a novel spatial–temporal fusion mechanism to integrate the temporal and spatial characteristics under the spatial–temporal pattern of PM$_{2.5}$. Compared with other learning paradigms, ASTemCN is more suitable for learning long-term spatial–temporal patterns, has the highest forecasting accuracy and stronger generalization ability, and provides a research direction for the spatial–temporal pattern analysis of air pollution.

Statistical analysis of COVID-19 mortality is challenging due to its non-stationarity and cross-sectional instability. In this paper, the authors introduce a unified method to evaluate the fatality rate of COVID-19 across countries, whose method provides more reliable information for cross-country comparison than the traditional case-fatality rate (CFR). It emerges that the new method, the block-wise case-fatality rate (BCFR), varies for different countries and in different periods. The authors also decompose the COVID-19 fatality data by three factors: 1) The virus infection dynamics over population in different countries, 2) pure distribution and evolution of instantaneous death rate attributed to different individual’s physical characteristics such as age and health, and 3) individual countries’ variations affecting interactions between the virus infection and the instantaneous mortality due to individual’s physical characteristics. Based on the new three-factor model, the authors obtain six key findings of the COVID-19 fatality rate. Our study suggests that, on average, the estimated instantaneous fatality rate contributes about 57.0% to the global BCFR while the time-varying weight contributes about 41.5% in December 2020. The country-specific contribution of instantaneous fatality rate is significantly higher than that of the time-varying weight. Besides, the country-specific characteristics in demographical, social, and economic aspects would affect the relative severity of the disease.

The article identifies key principles of the important role played by low-carbon generation for ecosystems, which explores studies from perspectives of technology, strategy, and policy. In doing so, it provides a scientific basis for policymakers to formulate efficient and feasible plans to reduce emissions via low-carbon generation to revive ecosystems. Various situations are designed for the development path of low-carbon power generation via adaptive and systematic policies with advanced technologies and appropriate strategies. The article goes on to introduce and discuss a collection of papers that explore the restoration of ecosystem by low-carbon power generation. They discuss how to use technology to optimize low-carbon power generation system, analyze the impact of policies on low-carbon power generation, and explain the mechanism of strategies on low-carbon development of ecosystems.

ABSTRACTMany researchers have examined the relationship between the original equipment manufacturer (OEM) and the third-party remanufacturer (TPR) and found that is mutually beneficial or competitive. This paper aims to study the competitive strategy and production strategy of the OEM and the TPR. The OEM’s competitive strategy is to change their competitive intensity by investing in pricing power of new components. For this problem, two different competitive strategies are proposed: Investment strategy and Non-investment strategy. Two models are established based on competitive strategies, and their analytical solutions are obtained by Stackelberg game. Comparing the two competitive strategies found that only when the investment cost and cost of new components both are low, can the OEM adopt Investment strategy. The Investment strategy have a threefold effect on the OEM: increasing profit, reducing cannibalization, and blocking the TPR’s entry. It is also found that as the cost of new components increases, the OEM may shift its competitive strategy from Investment to Non-investment. At the moment of competitive strategy shift, the production strategy may change discontinuously. Finally, comparing the environmental effect found that which competitive strategy is environmentally friendly only depend on the discount factor and the environmental advantages of the remanufactured product.

The worldwide trend of decreasing corporate tax in recent years has contributed to an increase in global carbon emissions, but implementing a global minimum tax rate of 15% could partially mitigate this impact. Policymakers should coordinate corporate tax policies with climate regulations.

China is the world’s greatest cement producer, generating significant air pollution and CO2emissions. To combat these impacts, China introduced stricter air pollution standards for the cement industry in 2015, yet no plant-level analysis exists to determine their effectiveness. To analyze the impacts of emission regulations, we coupled 2014–2018 smokestack-level real-time observations with plant-specific information and constructed an hourly based dataset of air pollutants (particulate matter [PM], sulfur dioxide [SO2], nitrogen oxide [NOX]) and CO2emissions. Our analysis shows that regulations introduced in 2015 led to PM, SO2, and NOXreductions of 50.3%, 43.6%, and 34.2%, respectively, but CO2increased by 5%. Interestingly, 9.4% of the plants analyzed reached China’s 2020 ultralow air pollution standards in 2018. Further analysis shows that if small and old plants are phased out and all remaining plants implement advanced equipment and improve fuels and energy efficiency, PM, SO2, NOX, and CO2could be further reduced by 68.8%, 66.1%, 82.2%, and 62.0% by 2060. Our results reveal the co-benefits of clean air and climate policies for cement production.

Purpose: The purpose of this study is to see the energy relation to economic growth and find a way to solve the energy crisis for Bangladesh. Bangladesh is facing a high rate depletion of traditional energy sources. Renewable energy technology may be an alternative solution to meeting Bangladesh’s rising energy demand. Despite huge potential, Bangladesh fails to use renewable energy sources properly due to insufficient information and technical knowledge. The present research studied the current energy condition and potentiality of renewable energy with its influence on economic growth in Bangladesh. Design/methodology/approach: This study analyzes the relationship between renewable energy consumption and economic growth of Bangladesh for the period of 2001–2016, based on yearly data, by using multiple regression model where augmented Dickey–Fuller unit root test has been chosen for testing the viability. Findings: The result of this study showed that economic growth of Bangladesh is influenced positively by the consumption of renewable energy. Practical implications: In addition, SWOT analysis has also done to develop a roadmap, and suggest some policies which will be able to accomplish the country’s climbing energy demands for a short- and long-term solution. Originality/value: This study is an original work for Bangladesh, showing the results of the relationship between renewable energy consumption and economic growth. Therefore, this research will be useful to contribute to the literature review in the near future.

Business survey, which starts from the microeconomic level, is a widely used short-term forecasting tool in practice. In this study, the authors examine whether foreign trade survey data collected by China’s Ministry of Commerce would provide reliable forecasts of China’s foreign trade. The research procedure is designed from three perspectives including forecast information test, turning point forecast, and out-of-sample value forecast. First, Granger causality test detects whether survey data lead exports and imports. Second, business cycle analysis, a non-model based method, is performed. The authors construct composite indexes with business survey data to forecast turning points of foreign trade. Third, model-based numerical forecasting methods, including the Autoregressive Integrated Moving Average Model with Exogenous Variables (ARIMAX) and the artificial neural networks (ANNs) models are estimated. Empirical results show that survey data granger cause imports and exports, the leading composite index provides signal for changes of trade cycles, and quantitative models including survey data generate more accurate forecasts than benchmark models. It is concluded that trade survey data has excellent predictive capabilities for imports and exports, which can offer some priorities for government policy-making and enterprise decision making.

This paper studies the estimation of the partially linear panel data models, allowing for cross-sectional dependence through a common factors structure. This semiparametric additive partial linear framework, including both linear and nonlinear additive components, is more flexible compared to linear models, and is preferred to a fully nonparametric regression because of the ‘curse of dimensionality’. The consistency and asymptotic normality of the proposed estimators are established for the case where both cross-sectional dimension and temporal dimension go to infinity. The theoretical findings are further supported for small samples via a Monte Carlo study. The results suggest that the proposed method is robust to a wide variety of data generation processes.

Relaxing fertility policies and delaying retirement age would increase China’s household carbon footprint mainly by boosting population and labour. Policymakers should synergize policies targeting population ageing and climate change, which are both crucial for sustainable development.

We consider a joint inventory-pricing control problem in a single-product, periodic-review, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide properties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.

Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the real-valued group method of data handling (RGMDH)-type neural network to the complex field and constructs a circular complex-valued group method of data handling (C-CGMDH)-type neural network, which is a white-box model. First, a complex least squares method is proposed for parameter estimation. Second, a new complex-valued symmetric regularity criterion is constructed with a logarithmic function to represent explicitly the magnitude and phase of the actual and predicted complex output to evaluate and select the middle candidate models. Furthermore, the property of this new complex-valued external criterion is proven to be similar to that of the real external criterion. Before training this model, a circular transformation is used to transform the real-valued input features to the complex field. Twenty-five real-valued classification data sets from the UCI Machine Learning Repository are used to conduct the experiments. The results show that both RGMDH and C-CGMDH models can select the most important features from the complete feature space through a self-organizing modeling process. Compared with RGMDH, the C-CGMDH model converges faster and selects fewer features. Furthermore, its classification performance is statistically significantly better than the benchmark complex-valued and real-valued models. Regarding time complexity, the C-CGMDH model is comparable with other models in dealing with the data sets that have few features. Finally, we demonstrate that the GMDH-type neural network can be interpretable.

Some studies on developed economies have revealed that the impacts of oil price shocks have decreased while conclusions about China remain occluded. We investigate the changing effects of oil price shocks on China’s macroeconomy and discuss the causes. A time-varying parameter vector autoregressive (VAR) model reveals that impacts of oil price shocks on China’s economy have shown a downward trend since 1997. The responses of the real output are much greater and last longer than those of inflation. Then a new Keynesian dynamic stochastic general equilibrium model is developed to synthetically explore the causes. The results indicate that decreasing oil intensity and monopoly power reduce the effects of oil price shocks, while increasing capital intensity in production amplifies them.Other factors-such as changing price stickiness, deregulation of refined oil prices, and shifts in monetary policy targets-have limited effects on the relationship between oil price shocks and China’s macroeconomy.

The empirical investigation that examines the dynamics including the interaction between consumption of energy and economic progress has long been assessed. However, the interaction of these two in developing countries in general and Bangladesh, in particular, is a less explored subject. Hence, with this notion, this study examined the causal relationship among economic growth and energy consumption in Bangladesh. For this purpose, the study used energy consumption, gross domestic product (GDP), labor force, and capital data from 1981 to 2017 from different sources and data is analyzed by augmented Dickey-Fuller (ADF) unit root test, Johansen co-integration test and Granger test of causality. Results determine that energy consumption and economic growth have long term bi-directional relationship. The econometric model is estimated using generalized least squares (GLS) model. It is concluded that, consumption of energy and economic growth positively correlated and economic development highly depend on energy consumption in Bangladesh.

Maritime transportation is responsible for the vast majority of global overseas commerce. However, systematic reviews of supply chain resilience from the maritime transportation perspective are limited. This study provides descriptive statistical analysis, bibliometric analysis, and literature review based on relevant papers from 2004 to 2022 to clarify developments in this field. Growth trends, research areas, research regions, influential journals, influential institutions, and highly cited articles are discussed in the statistical analysis. The bibliometric analysis includes collaboration networks, emerging trends, and co-citation analysis. Topical issues and methods in the field are discussed based on papers from 10 highlighted research clusters. Then, studies on resilience measurement, resilience evaluation, and resilience improvement in maritime transportation are reviewed and future directions are proposed. This study summarizes the emerging trends and developments of supply chain resilience from the maritime transportation perspective and supports the establishment of a more dependable maritime transportation system.

By conducting a bibliometric analysis of 1997 scholarly publications on carbon neutrality and zero carbon emissions from 2019 to 2022, it is found that reviews of quantitative socioeconomic modeling research remain limited. To address this issue, a comprehensive review of carbon neutrality research, specifically, a systematic and synergistic review of the literature from a socioeconomic modeling perspective, is needed. The 20 clustering labels in the four largest clusters are summarized as main research streams, that is, carbon emission reduction, carbon sink and carbon capture and storage according to the definition of carbon neutral. Thus, this review revealed that carbon neutrality in the quantitative socioeconomic field is characterized in three main research directions. Sector integration, including integration within and among sectors, and integrated assessment models (IAMs), particularly multimodel analysis, can be regarded as a key node for carbon emission reduction. Carbon sink accounting, trade, and policy evaluated through technological and socioeconomic modeling, which includes both top-down and bottom-up types, are critical issues for carbon sinks. Carbon removal technologies and their application in IAMs are important topics for carbon capture and storage. This review provides a clear understanding of model-driven socioeconomic research on carbon neutrality via a systematic framework in quantitative analyses, outlining the potential gaps and challenges and providing possible insights and implications in a synergistic manner.

A framework based on response-dependence theory is proposed to analyze human and machine interaction patterns. The interaction is an information processing and exchanging system with agents responding to signals from the decision perspective on multiple channels. Agents' pattern differences may emerge via a multiple-input multiple-output model on the sequential machine data. The connectivity with different decision-making factors embodies the interactive narrative features. The magnitude of the frequency response is compared in a parallel analysis. The interaction relationship will emerge in frequency response comparison. In the empirical study, we compare AlphaGo and human professional players' behaviors in the game of Go. We find a similar connectivity structure in vertical analysis and coincidences with the interaction relationship in the game. However, the response magnitude interval differs between AlphaGo and human professional players in several stimulus-response pairs. Integrated with static comparison, we find AlphaGo is more sensitive to long-term payoff changes than human professional players. The framework and empirical studies indicate that theory and techniques from cross-disciplinary can provide a perceptive and objective explanation of behavior patterns in human and machine interaction. The proposed framework might benefit scientists in research on AI ethics and machine behavior interpretation with big data techniques.

Extant studies have suggested that Google search volume data can serve as a new and direct measure of investor attention in various research fields such as economy, financial and energy markets. However, it is not clear that whether investor attention influences prices in the same direction in different market states (prices increase or decrease). In this paper, the authors propose a measure of speculative attention, demonstrate its advantages by comparing it with several existing ones, and then adopt a Markov switching autoregressive model and an EGARCH model to examine its influences on crude oil prices in two market states. It is argued that the responses of crude oil prices to investor attention are asymmetrical in the two states of crude oil prices. The empirical study shows that one increase in searches causes a significant positive increase in crude oil prices during oil price surges, and a more significant reduction of prices during oil price collapses. The authors also conduct robustness checks by limiting the sample periods and using other measures, and the results support the asymmetric effect of web search behaviors on crude oil prices.

In recent years, the relationship between bond spreads and macro economy has been studied extensively by economists in western countries. However, few attentions were paid on this topic in China. This essay regards Chinese bond market as a complex system and constructs bond indices for China with the bottom-up approach. The authors use the data of 3,205 non-financial corporate bonds from February 2010 to October 2017 and construct a new spread noted as the PE_SOE spread. The authors find that the PE_SOE spread has a negative impact on economic activities and has the best predictive ability at short-run forecasting horizons, owing to the institutional superiority of the state-owned enterprises in China. The Treasury bond yields are found to have the best predictive ability at long-run horizons. Both spread shock and Treasury yield shock could lead to deflation and declines in economic activities, and the Treasury yield shock has a more severe and persistent impact on the economy due to the financial accelerator mechanism. PE_SOE spread is proved to be a better indicator for Chinese corporate bond market and can be widely used not only in future Chinese economic studies, but also for Chinese government’s macroeconomic monitoring and warning.

This article addresses whether the promotion of China’s provincial leadership can be explained through economic growth or alternative factors (experience, education or having previously held a central government post). We use promotion definitions, based on official administrative rankings and a more accurate reflection of political-governmental power relationships, and five econometric models. Analysis is also located within specific scholarship about promotion in the Chinese bureaucracy, bureaucratic cultures and transitions from socialism. Findings suggested some support for the idea that economic growth records affected promotion in terms of the second definition and additional evidence to imply impacts from non-economic factors.

Price differentiation or discrimination strategy has been regarded as the best choice for firms with online and offline channels, however, recent years often witnessed the practices of the uniform pricing strategy. This paper aims to address the question whether the uniform pricing strategy may be better for the manufacturer, when the uniform pricing strategy has a positive impact on increasing the customer demand and reducing the operations cost. The research shows that the uniform pricing strategy can be better than the price differentiation strategy when the cost saving and demand increasing are large enough or the consumers’ acceptance of online channel lies in a certain interval. Moreover, the manufacturers or brand owners need a tradeoff between the benefit from online channel and the negative impact from the offline channel when they implement the price differentiation strategy. Finally, the authors obtain some managerial insights and implications based on the numerical analyses, which are in line with the phenomena in practice.

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis (SFA) models and a model average technique. First, considering model uncertainty, a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate.

A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM). First, the exchange rate time series is decomposed into N subcomponents by the VMD method. Second, each subcomponent series is modeled by the KELM. Third, the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers. Finally, each cluster's ensemble weight is estimated by another KELM, and the final forecasting results are obtained by the corresponding clusters' ensemble weights. The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance, and statistically outperform some other benchmark models in directional and level forecasting accuracy.

We examine the relationship between return and volatility of the stock markets and macroeconomic fundamentals for the G-7 countries by using monthly data ranging from July 1985 to June 2015. To meet this end, we apply the spillover index approach based on the generalized VAR framework developed by Diebold and Yilmaz (2012, 2014). The empirical analysis shows strong interactions between the returns and volatilities of the G-7 stock markets and the considered set of corresponding macroeconomic factors including industrial production, money supply, interest rates, inflation, oil prices and exchange rates. The return and volatility spillover transmission/reception dynamics of the relationships between these stock markets and the macroeconomic fundamentals have changed after the global financial crisis of 2008. Our findings provide useful insights for investors and policy makers concerned with the unprecedented swings in the stock markets of G-7 countries.

This paper explores the macroeconomic determinants of non-performing loans (NPL) in 19 Asian countries (low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank. Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is, however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables, such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore, economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.

The development of big data generation, acquisition, storage, processing, and other technologies has greatly enriched our sensory world and fundamentally changed the basis of traditional economic and financial forecasting. Unexpected events in the economic and financial fields challenge our confidence in the performance of forecasting models. Obviously, the big data-driven decision theories and analysis methods are different from the traditional methods. In view of the important role of big data-driven economic and financial forecasting in social stability, innovative development, and sustainability, the research frontiers of big data-driven economic and financial forecasting in the future include: feature mining of complex economic systems with big data representation; accurate real-time correction of theories and methods of dynamic forecasting and early warning; general paradigm of big data forecasting research; formation and process of big data-driven economic and financial system management mechanism, etc. Systematic research on such issues will contribute to the formation of decision-making theories and research systems in the context of big data, thus improving the adaptability and scientificity of management decisions.

This paper combines the perspective of co-evolution with studies in the area of business model, reveals the phenomenon of business model co-evolution and explores the business model coevolution mechanism between firms through a case study. Findings and what other companies can learn from this study are as follows. Firstly, companies, be it leaders or not, can act as enablers for other party’s business model innovation. Secondly, companies can enable each other’s business model innovation by taking the perspective of business model co-evolution; Thirdly, the interdependence of the business model co-evolution process can be explained by the four levers named novelty, lock-in, complementarity and efficiency. Companies can select novelty, lock-in, complementarity, efficiency or the combination of the four as mediators to achieve business model co-evolution.

This article investigates the order problem in a two-stage supply chain consisting of retailers, primary suppliers and backup suppliers. From retailers’ perspectives, the optimal offering strategies in unidirectional transshipments are analyzed on the basis of disruption risks in supply chains. With a focus on retailers’ profits, it develops the decision model of retailers’ orders in the dual sourcing mode and in the mode of capacity options, to maximize the retailers’ expected profits. The simulation shows that retailers’ optimal order decisions are not correlated with the joint disruption probability in the two order modes without service constraints; retailers’ optimal decisions are influenced by the joint disruption probability in the two order modes with service constraints, and the impact of the joint disruption probability of the primary suppliers’ optimal order from retailers differs in the two modes. Retailers’ order decisions in respect of backup suppliers are more sensitive to the changing volumes of transshipments. The contribution of this article is the impact of main parameters on retailers’ decisions, providing some guidance for enterprises to make order decisions.