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With the emergence of digital technologies, we have witnessed a rapid growth of the sharing economy in recent years. In fashion industry, sharing economy concept is also widely applied. Instances can be found all over the world, like Rent the Runway, Golden Tote, Stitch Fix, and Le Tote in the US, Air Closet in Japan, Y Closet, Ms Paris, and Dora's Dream in China, etc. In this paper, motivated by the industrial practices of fashion sharing platforms, we first conduct a literature review on business operations of sharing platforms, including the sharing platform operations, digital and information technologies employed in sharing economy, business models, and sustainability and circularity in business operations. Afterwards, we analyze the challenges faced by fashion sharing platforms by exploring the two leading fashion sharing platforms in the US and in China, respectively. At last, we propose future research directions on fashion sharing platform operations and conclude the paper. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper examines the impact of data-driven innovation (DDI) on firm performance, based on an exploratory case study of a manufacturing firm in China's textile and apparel industry. It explores the influence of various contextual variables on the firm's DDI and suggests ways to enhance DDI and thereby firm performance. Extending the literature on DDI, the paper proposes and validates a theoretical framework that incorporates the influence of various contextual factors on firms' DDI. The findings show that (1) individual context is associated with DDI; (2) organizational context is associated with DDI; and (3) DDI is associated with firm performance. This paper extends our understanding of how firm performance can be improved through DDI and shows that DDI should match a firm's contextual environment. [ABSTRACT FROM AUTHOR]

This paper explores the mispricing relative to the capital asset pricing model through an equilibrium model. We find that both the strong risk preference dependent on good market states and strong risk aversion dependent on bad market states can produce high mispricing. Choosing the China stock market, the largest emerging market dominated by individual investors and known for its volatile nature in a short history as our sample, the empirical results also support our theoretical findings. Overall, our paper sheds light on the mispricing caused by the investor's risk preference reference-dependent on market states. [ABSTRACT FROM AUTHOR]

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis. [ABSTRACT FROM AUTHOR]

The carbon quota allocation rules of China's pilot carbon emissions trading (CET) regions are various, which mainly include benchmarking, historical carbon intensity reduction and auctioning. When the allocation rules change, it is unresolved how to achieve the optimal product prices and effectively allocate the carbon emissions reduction profits of CET-covered enterprises in the cooperative supply chain. Thus, this paper uses the Stackelberg game, Nash equilibrium and the Shapley value based on cost modification to investigate these issues. The results indicate that: (1) The increasing carbon prices can always improve the retail prices only under the auctioning rule. Meanwhile, the growing low-carbon awareness of consumer cannot be always conducive to improving the wholesale and retail prices, and the similar product prices of non-CET-covered enterprises have greater impact on the wholesale prices than that on the retail prices. (2) Only under the free carbon quota allocation rules, can the optimal wholesale and retail prices under the Stackelberg game be always higher than those under the Nash equilibrium. Meanwhile, the auctioning rule can better reduce carbon emissions than the free allocation rules. (3) Improving carbon emissions reduction contribution and emission reduction costs can be conducive to increasing the carbon emission reduction profits of the supplier and retailer, while the impact of carbon emission reduction contribution on improving the carbon emission reduction profits is not always greater than that of the carbon emission reduction costs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 is important for China to achieve industrial upgrading and promoting the quality of manufacturing development. This paper investigates the driving force of the Industry 4.0 in China's manufacture industry, and evaluates the impact of Industry 4.0 on firm's performance. First, a textual mining is conducted to identify 460 companies that are implementing Industry 4.0 strategy, and then a Probit model is adopted to examine the driving forces of Industry 4.0. Through the propensity scores matching difference-in-difference method, the impacts of Industry 4.0 on firm's performance are evaluated. The results reveal that private and large companies show a higher motivation to promote the Industry 4.0 strategy, and government subsidies have no significant impact on firm's Industry 4.0 decision. The implementation of Industry 4.0 can significant improves firm's financial performance, innovation activities and stock returns, but has no significant impact on supply chain efficiency. In addition, the adoption of Industry 4.0 has positive impact on firm's information transparency grade. [ABSTRACT FROM AUTHOR]

Organization's sustainability performance is influenced by its suppliers' sustainability performance. This relationship makes sustainable supplier development a strategic competitive option for a buyer or focal organization. When considering sustainable supplier development practices (SSDPs) adoption, organizations have to balance and consider their limited financial resources and operational constraints. It becomes necessary to both select the best SSDPs set and investment allocation among the selected SSDP set such that the organization can maximize overall sustainability performance level. In this paper, an integrated formal modeling methodology using DEMATEL, the NK model, and multi-objective linear programming model is used support this objective. The proposed methodology is evaluated in a practical sustainable supply chain field study of an equipment manufacturing company in China. Through case study, we found that the interdependency among SSDPs must be considered in SSDPs selection and investment allocation problem. Theoretical, managerial and methodology implications, conclusions, and directions for future research are also presented. [ABSTRACT FROM AUTHOR]

The classical multi-period inventory routing problem (MIRP) combines inventory management and vehicle routing problems, attempting to maintain customer inventory levels via autonomous deliveries, with the assumption that stock-out never occurs at the supply side. While this is no more the truth when a common third-party logistics company operates a central warehouse for a group of small and medium sized retailing companies. Procurements have to be made to refill inventory at the central warehouse. This study thus extends the traditional MIRP by incorporating procurement decisions (MIRP-PD) and tries to minimize the total cost of procurement, inventory holding and transportation. To our knowledge, this paper is among the first to formally describe MIRP-PD and model it as a mixed integer linear program. A hybrid two-level heuristic is proposed to address large-scale instances. The upper level of the algorithm determines the visiting schedule while the lower level works out procurement and routing decisions accordingly. Components from tabu search and adaptive threshold acceptance are also embedded to help escape from local optima. Extensive numerical instances are generated from a real case in Chengdu (China), based on which the computational results reveal the effectiveness and efficiency of the proposed algorithm compared with the commercial solver CPLEX. The comparison between MIRP and MIRP-PD has demonstrated the cost advantage of incorporating procurement decisions into inventory and routing plans. Impacts on costs of key factors including delivery frequency and buffer size of stores have also been examined to provide managerial implications to practitioners. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the green efficiency performance of the logistics industry in China's 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) in general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China's logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China. [ABSTRACT FROM AUTHOR]

This paper examines the performance of Chinese commercial banks before, during and after the 2007–2008 global financial crisis and China's 4 trillion renminbi stimulus plan during 2008–2010. Fully nonparametric methods are used to estimate technical efficiencies. Recently-developed statistical results are used to test for changes in efficiencies as well as productivity over time, and to test for changes in technology over time. We also test for differences in efficiency and productivity between big and small banks, and between domestic and foreign banks. We find evidence of non-convexity of banks' production set. Our results suggest that technical efficiency declined at the start of the global financial crisis and again during 2011–2012 after enactment of the stimulus plan. Although efficiency recovered somewhat during 2013–2014, China's banks ended with lower technical efficiency on average in 2014 than in 2007. We find that productivity declined during 2008–2009, increased during 2013–2014, but as with efficiency ended lower on average in 2014 than in 2007. We find compelling evidence that big banks were more efficient than small banks throughout 2007–2014, and in most years also were more productive. Finally, domestic banks were on average more efficient than foreign banks in 2007, but foreign banks were more efficient than domestic banks in 2014. Similarly, foreign banks were on average more productive than domestic banks in 2007, but less productive than domestic banks in 2012–2014. [ABSTRACT FROM AUTHOR]

Though interfirm collaboration on carbon emission reduction, the cross-enterprise flow of emission reduction resources and improved efficiency in greenhouse gas reduction can be realized. Especially in the context of big data, enterprises can find suitable partners for emission reduction faster and more accurately through interfirm collaboration. However, similar to other cooperative modes, revenue allocation is the key to ensuring the stability of the collaborative emission reduction system. Based on the premise of carbon trading, this paper discusses revenue allocation among enterprises participating in the collaborative emission reduction process under complete information in a big data context. Specifically, we constructed a Shapley value analysis model of revenue allocation for interfirm collaboration on carbon emission reduction, and amended this model with investment cost and risk-bearing. Consequently, this research provides not only a theoretical basis for solving the problem of revenue distribution in the process of collaborative emission reductions among enterprises but also a theoretical guide for enterprises countermeasures following the completion of China's future carbon trading mechanism. [ABSTRACT FROM AUTHOR]

The validity of performance evaluation is determined by, and therefore greatly influenced by, the accuracy of data set. To address such imprecise and negative data problems widely spread in the real world, this paper proposes a second-order cone based robust data envelopment analysis (SOCPR-DEA) model, which is more robust to data variety. Further, this new computational tractable model is applied to analyze 13 new-energy vehicle (NEV) manufacturers from China. The findings support that the SOCPR-DEA model could well mitigate the deficiency caused by data variety, and the evidence from Chinese NEV industry shows that a focus strategy is more likely to enhance a firm's efficiency especially at its emerging stage, and the efficiency is more sensitive with production cost than other factors such as research and development, sales income, earnings per share, and predicted income. In addition, this paper also gives some industrial implications and policy suggestions based on these interesting findings. [ABSTRACT FROM AUTHOR]

The current study proposes a new DEA model to evaluate the efficiency of 39 Chinese commercial banks over the period 2010–2018. The paper also, in the second stage, investigates the inter-relationships between efficiency and some bank-specific variables (i.e. bank profitability, bank size, expenses management, traditional business and non-traditional business) under the Robust Endogenous Neural Network Analysis. The findings suggest that the sample of Chinese banks experiences a consistent increase in the level of bank efficiency up to 2015; the efficiency score is 0.915, after which the efficiency level declines and then experiences a slight volatility, while finally ending up with an efficiency score of 0.746 by the end of 2018. We also find that among different bank ownership types, the state-owned banks have the highest efficiency, the rural commercial banks are found to be least efficient and the foreign banks experience the strongest volatility over the examined period. The second-stage analysis shows that bank size exerts a positive influence on the development of non-traditional banking business and a proactive expense management, bank size and non-traditional businesses have a positive impact on efficiency levels, while bank profitability, traditional businesses and expenses management have negative influences on bank efficiency. [ABSTRACT FROM AUTHOR]

This paper aims to discuss the fragilities of hierarchical structures of Chinese financial market, and find out the possible faults. We propose an analysis approach to explore the structure characteristics of financial market based on the manifold structure of financial data. First, we extract the underlying manifold embedded in financial time series data by manifold learning, which governs the dynamics of financial system. Second, the surface curvature of manifold is used to serve the quantitative analysis of manifold structure, in which the rate of curvature change is employed to measure the structure fragility. Finally, the dynamics of manifold structures are discussed by Lyapunov exponents, which further explore the fragilities of markets and confirm the conclusions of curvature analysis. In empirical research, we select CSI 300 index as the overall trend indicator of China's financial market, and nine industry indexes as hierarchical market indicators. Our research results indicate that Chinese financial market has less chaotic than the real economy industries, in spite of its high fragility. Some real economic industries are most likely to crash first in the event of a crisis. The findings contribute to present the states of financial markets and provide decision support for investors. [ABSTRACT FROM AUTHOR]

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases. [ABSTRACT FROM AUTHOR]

Technological progress is considered an important means of decreasing energy consumption. However, rebound effects caused by energy efficiency improvements directly affect the realization of energy savings and emission reduction. This paper focuses on the main theory and methodology of direct and indirect rebound effects. Using 30 sets of provincial panel data and national input-output data for China from 2007, this paper builds a co-integrating equation, a panel error correction model, and an 8-sector energy-input-output model. We subsequently estimate the direct and indirect rebound effects of urban residential electricity use. The results indicate that in the long term the direct plus indirect partial rebound effect is 0.79; in the short term it is 0.78. Thus, the majority of the expected electricity reduction in Chinese urban residential energy consumption arising from efficiency improvement may be offset. These rebound effects impair the functioning of energy efficiency policies. Therefore, the Chinese government should not improve energy efficiency alone-they must also take into consideration the relevant energy-pricing reforms when formulating energy policies. [ABSTRACT FROM AUTHOR]

Over the years, foreign direct investment (FDI) has not only promoted rapid economic growth in China, but also affected the country's environmental quality through technology spillover. This paper tests the variables that may affect the ability of green innovation by using the Granger causality test. It extracts the variables passed the test as input variables, selects the number of patents as output variable, and evaluates the efficiency of various provinces in mainland China by examining their yearly technological progress variables. At the same time, technological progress is defined and divided into capital and environmental factors, and then panel data using the variable coefficients model was used to fit influencing factors to obtain impact coefficients of capital and environment. On this basis, this paper makes the determination of membership to replace the general sense of 'threshold' value by using fuzzy theory and proposes the concept of the 'comprehensive threshold' of economic development and environmental protection. The results show that less than a quarter of China's provinces have crossed the comprehensive threshold. Finally, based on the conclusions of quantitative analysis, some suggestions are proposed that the Chinese government ought to enact different strategies for the introduction of FDI according to different development situations of different provinces. [ABSTRACT FROM AUTHOR]

In emergency management, it is important to arrange the medical resources according to emergency needs. This paper proposes a multi-criteria decision analysis model that combines grey theory and multi-criteria decision making theory to assess the current medical resource situation, find satisfactory solutions, and help the emergency decision makers to take appropriate responses in a timely manner. In addition, the best alternative is identified by the global optimal solution which can provide a theoretical guidance for decision makers to optimize the allocation of medical resources. To validate the proposed model, this paper conducts a case study of medical service assessment in provinces of East China. The results demonstrate that the proposed model can provide objective and comprehensive assessment of medical resources. [ABSTRACT FROM AUTHOR]

This paper discusses an allocation issue of promotion budget and traffic volume, faced by VIP.com that is the largest online flash-sales platform in China. Through flash sales mode, each day VIP.com provides consumers one hundred authorized brands of products on consignment, which last for a short period of time (frequently 3–5 days), called by VIP.com 'Dangqi'. As a consignee, VIP.com has no pricing rights of products but can allocate the promotion budget and traffic volume to each brand. Due to different categories of products and different brands of products having different responses to the same promotion budget and different profit margins, it is very necessary for VIP.com to allocate promotion budget and traffic volume among all brands of products offered in each 'Dangqi' reasonably in order to improve the usage efficiency of limited resources. Based on the real historical data of VIP.com, we first find main elements that influence the sales revenues of different brands of products through machine learning. We then predict the sales of all brands of products in each 'Dangqi' by multiplicative regression model, which has better accuracy of forecasting than other forecast models and obtain the function relationship between the total sales of each brand and its main impact elements. Finally, considering VIP.com's actual concerns, we develop allocation optimization models with objectives of maximizing VIP.com's total sales and total sales profit, respectively. The results from VIP.com's real data tests show that under the same resource investment the presented allocation optimization approach can yield a significant increase in VIP.com's total sales and sales profit in each 'Dangqi'. [ABSTRACT FROM AUTHOR]

The rapid economic development of China has intensified the country's many problems. Among them, energy shortage and environmental pollution are two main problems, which highly affects the economic growth and sustainable development. To achieve more rapid green growth, the innovative technology by reusing the environmental wastes has been widely used since doing so not only decreases the environment pollution, but also further brings more natural resource. The present paper establishes a two-stage structure for evaluating the regional green growth and sustainable development in China by calculating the efficiency of "energy saving" and "pollution treatment" separately. Specifically, a set of models based on slack-based measure approach are constructed in which non-discretionary inputs can be calculated in both resource utilization stage and pollution treatment stage. Comparing with the traditional models, the new proposed models can measure the performance of resources saving and pollution treatment with considering the influence of non-discretionary inputs. An empirical application on Chinese 30 regions during 2011–2015 have been done to illustrate the use of our framework and the performance of regional green growth and sustainable development. Based on the efficiency results, we find that the efficiency scores of the provinces in central and northeast area are lower, which is mostly caused by their poor performance on "pollution treatment". Both the environmental efficiency scores and target values for performance improvement are obtained in this paper to enlighten the corresponding decision-makers. [ABSTRACT FROM AUTHOR]

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China's 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development. [ABSTRACT FROM AUTHOR]

This paper proposes a lexicographic decomposition strategy for the two-stage network DEA model. Instead of assigning a priority over the two component stages, our proposed approach employs a lexicographic algorithm in such a way that the system efficiencies of the DMUs are lexicographically computed for each of their possible sequences while maintaining the efficiencies of those units already investigated unchanged. In particular, the system efficiency and the two component efficiencies can be uniquely determined under our proposed decomposition approach. Besides, prior to implement the proposed lexicographic evaluation procedure, we also highlight a potential infeasibility problem arising from the normalization constraint, and develop a modified iterative approach that facilitates us to guarantee a feasible search procedure. Finally, we use a numerical example to illustrate the effectiveness of the proposed approach, and conduct an empirical study to analyze China's regional high-tech innovation systems for the period of 12th Five-Year Plan (2011–2015). [ABSTRACT FROM AUTHOR]

The weight and reliability of an individual assessment are two important concepts considered in the evidential reasoning (ER) approach. Through analyzing the existing studies on the combination of individual assessments with both their weights and reliabilities considered in the ER context, their deficiencies are identified in accordance with two principles. One principle is developed in the situation where a specific individual assessment is fully unreliable and the other is developed in the situation where all individual assessments are fully reliable. To address the deficiencies, this paper proposes a new method. In the method, a combination process that takes into account both the weights and reliabilities of individual assessments simultaneously is developed to generate the overall assessment. It is theoretically proven that the combination process satisfies the two principles. Three ways are designed to help a decision maker to flexibly provide individual assessments and determine their reliabilities. A strategic project evaluation problem for an enterprise located in Changzhou, Jiangsu, China is analyzed using the proposed method as a case study to demonstrate its validity and applicability. These are highlighted by its comparison with two existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a behavioral DEA model to evaluate Chinese provincial carrying states. To introduce the behavioral DEA model, we take the individual decision maker’s preference into account, including fairness concern, reference dependence and loss aversion. By considering those decision preferences, the proposed models can help to make fair planning with accounting for decision maker’s utilities. Our proposed model provides the detailed technique to demonstrate the fairness concern, reference dependence and loss aversion quantificationally. An empirical study in evaluating Chinese provincial carrying states is used to demonstrate our methods. We also provide comparative analysis and correlation analysis to discuss the results and point out the managerial implications of this study. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most serious threats to human health and is an issue causing growing public concern. Air quality forecasts play a fundamental role in providing decision-making support for environmental governance and emergency management, and there is an imperative need for more accurate forecasts. In this paper, we propose a novel spatial–temporal deep multitask learning (ST-DMTL) framework for air quality forecasting based on dynamic spatial panels of multiple data sources. Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records that were generated on an hourly basis from January 2013 to September 2017 in China. An application check is also conducted to verify the practical value of our proposed ST-DMTL framework. Our empirical results indicate the efficacy of the framework as a viable approach for air quality forecasts. [ABSTRACT FROM AUTHOR]

This paper evaluates the efficiency of Chinese vehicle manufacturing with different products for the period January to December 31st, 1993. It utilizes actual data rather than data modified by utilizing methods that can deal with zeros in the outputs. The paper thus highlights the fundamental role of zeros in DEA analysis, and derives a special calculation method which can decrease the dimension for computer implementation. The paper also illustrates the importance of the use of Assurance Regions (AR) and their application. [ABSTRACT FROM AUTHOR]

A fresh agricultural food online exchange system is gradually becoming an emerging e-business model in China. However, how and when to make an investment in this new business model is risky due to the unique characteristics of fresh agricultural food. In this paper, from a third party decision maker's perspective, we propose an evolutionary discounted cash flow model to investigate the optimum time point for investment. Based on the assumptions of base demand, the derived e-demand occurring on the pendent e-business system can be characterized by non-stationary stochastic processes. Then our new model can further be innovatively analyzed by considering the dynamic scheme of the cash flow and its evolution. The analytical results suggest the optimal investment time point depends upon the consumers' switch rate from the physical store to e-store and on the urbanization rate. A Monte Carlo simulation is further presented to compare the effects of multiple uncertainties embedded into the system. Our uncertainty analysis reveals that when government financial support fluctuates greatly, the optimal investment time could be either in the very beginning or in the end. This optimal time strategy also holds for the random demand factor after the coefficient variation of the demand reaches a certain threshold. [ABSTRACT FROM AUTHOR]

A dilemma between economic growth and environmental deterioration has never been recognized so seriously until the environmental problem has been impacting everyone's daily life cogently (e.g., the serious fog and haze in China). Simultaneously, the rising environment awareness of consumers leads to the close attention to the product's carbon performance reflected in the carbon concerned demand, which provides opportunity to rebuild our business ecosystem. The paper expands the environment view to supply chain operations. A Stackelberg-like model is developed to game-theoretically analyze the decentralized decisions of the manufacturer and retailer. Low-carbon effort is brought into decision by both sides. The centralized decision-making is also investigated as a benchmark to evaluate the supply chain performance. Additionally,we propose a carbon-related price-discount sharing-like scheme to achieve the channel coordination and discuss the possibility of Pareto improvement. Several interesting managerial insights on low-carbon factors are concluded. [ABSTRACT FROM AUTHOR]

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results. [ABSTRACT FROM AUTHOR]

Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals' characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed. [ABSTRACT FROM AUTHOR]

In the era of the Internet and big data, data permeate the entire process of multiple criteria decision making (MCDM). Therefore, generation of rational solutions from current observations and historical data has become an important and interesting issue. To address this issue, this paper proposes a data-driven MCDM framework in the context of the evidential reasoning approach. Three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data-driven solution. The proposed framework is then used to model the diagnosis of thyroid cancer and generate data-driven diagnostic results. The three challenges in the application are met to aid radiologists in improving the diagnostic accuracy of thyroid cancers. To examine whether the application of the proposed data-driven MCDM framework to the diagnosis of thyroid cancer can help improve diagnostic accuracy, we conduct a case study by using the examination reports of three radiologists from July 2015 to October 2017 in the ultrasonic department of a tertiary hospital located in Hefei, Anhui Province, China. [ABSTRACT FROM AUTHOR]

Mass customization (MC), which is popular in the fashion industry, is proposed to provide customers with products with a high degree of adaptation and a price comparable to a mass product. Both the manufacturer and customers can benefit from it. However, how to properly apply the MC strategy when facing challenges in the real world needs more exploration. In this paper, we first review the related operations research literature. Then, we conduct a real case study of a fashion company adopting MC in China. We highlight the challenges and critical successful factors for implementing apparel MC. We identify important technologies in fashion MC systems. We also propose future research opportunities on MC operations in the fashion industry. [ABSTRACT FROM AUTHOR]

Renewable energy is playing an increasingly important role in energy security and environmental protection. As China has a huge demand for renewable energy and also has abundant wind resources, it is vital that government, investors and operators work together to ensure the sustainable development of the wind energy industry. Even though China is already a leader in wind power generation with the largest installed wind power capacity in the world, it has continued to build new wind power facilities. However, due to industrial immaturity and the need for significant investment, wind power investors and operators are faced with uncertainty about the attendant risks. To achieve risk mitigation and sustainability, this paper proposes an investment risk evaluation and optimization process for Chinese wind power projects. The Monte Carlo method is first used to evaluate the investment risks, after which a multi-objective programming model is built for the optimization. A specific case in western China is examined to demonstrate the proposed methodology, with the evaluation results indicating that the project has high investment risk. Based on the case study, the key risk factors are identified and optimization suggestions given for China's wind power projects. The proposed methodology and findings contribute to research on the planning, investment and sustainable operation of renewable energy power generation projects in other areas in China. [ABSTRACT FROM AUTHOR]

As policies and regulations related to environmental protection and resource constraints are becoming increasingly tougher, corporations may face the difficulty of determining the optimal trade-offs between economic performance and environmental concerns when selecting product technology and designing supply chain networks. This paper considers weight reduction technology selection and network design problem in a real-world corporation in China which produces, sells and recycles polyethylene terephthalate (PET) bottles used for soft drinks. The problem is addressed while taking consideration of future regulations of carbon emissions restrictions. First, a deterministic mixed-integer linear programming model is developed to analyze the influence of economic cost and carbon emissions for different selections in terms of the weight of PET bottle, raw material purchasing, vehicle routing, facility location, manufacturing and recycling plans, etc. Then, the robust counterpart of the proposed mixed-integer linear programming model is used to deal with the uncertainty in supply chain network resulting from the weight reduction. Finally, results show that though weight reduction is both cost-effective and environmentally beneficial, the increased cost due to the switching of the filling procedure from hot-filling to aseptic cold-filling and the incumbent uncertainties have impacts on the location of the Pareto frontier. Besides, we observe that the feasible range between economic cost and carbon emission shrinks with weightreduction; and the threshold of restricted volume of carbon emission decreases with the increase of uncertainty in the supply chain network. [ABSTRACT FROM AUTHOR]

The Malmquist–Luenberger productivity index would cause infeasible problem when measuring mixed period change of total factor productivity. The former research focuses on the infeasible issue under the hypothesis of constant return to scale (CRS). There are many meaningful solutions which could avoid this infeasible problem of Malmquist–Luenberger productivity index. However, these solutions couldn't avoid infeasible problem under condition of variable returns to scale (VRS). The new solution is proposed in this paper which could solve this problem under VRS based on super-efficiency issue. The empirical results of environmental total factor productivity (ETFP) change of thirty regions in China indicate that China isn't efficient from 1997 to 2014. Among eight economic regions northwest and southern coastal decline, the ETFP decrease 15%, 10% respectively. From the perspective of provinces in China, Hainan, Qinghai and Ningxia have the lowest environmental total factor productivity and their environmental technical efficiency is also the lowest. These non-efficient provinces all have lower gross domestic product and they should improve technique change efficiency through adopting advanced technology in the future. The fixed effects regression model illustrates that energy intensity, research and development, foreign direct investment are factors of ETFP in China. Both research and development and foreign direct investment could improve total factor productivity and this indicates that Pollutant Heaven Hypothesis doesn't exist in China. [ABSTRACT FROM AUTHOR]

Online customer segmentation is a significant research topic of customer relationship management. Previous literatures mainly studied the differences between non-purchasers and purchasers, lacking further segmentation of online purchasers. There is still existing significant heterogeneity within purchaser-groups. This paper focuses on Chinese online purchaser segmentation based on large volume of real transaction data on Taobao.com, we firstly extracted and investigated Chinese online purchaser behavior indicators and classified them into six types by cluster analysis, these six categories are: economical purchasers, active-star purchasers, direct purchasers, high-loyalty purchasers, risk-averse purchasers and credibility-first purchasers; then we built an empirical model to estimate the sensitivity of each type of online purchasers to three mainstream promotion strategies (discount, advertising and word-of-mouth), and found that economical purchasers are the most sensitive to discount promotion; direct purchasers are the most sensitive to advertising promotion; active-star purchasers are the most sensitive to word-of-mouth promotion; finally, the implications of online purchaser classification for marketing strategies were discussed. [ABSTRACT FROM AUTHOR]

The study of multiobjective optimization (MOO) in China began at the beginning of the seventies, when some researchers in Beijing and Shanghai adopted techniques in this field to deal with practical problems arising from industrial designs and management sciences for achieving good results. Subsequently, theoretical studies began. A seminar on MOO was organized by members of the Institute of Mathematics of the Chinese Academy of Sciences in 1977. Meanwhile, studies of the theory and applications of MOO were developed in many universities. Related seminars were held and courses were offered to graduate students at these universities. At the same time, many managers and industrial designers had tried to use the techniques of this branch of OR to solve practical problems arising in a variety of fields. In 1981, 1984, 1987 and 1989, national symposiums on MOO were held, each of which had approximately 100 participants; about 300 papers and reports were presented. Up to the present time, about 200 papers on MOO by Chinese scholars have been published in China or abroad. The sphere of application becomes broader and the scale of problems being solved becomes larger. In China today, there are groups of experienced researchers working in this field, in particular at the Institute of System Sciences of the Chinese Academy of Sciences, the Jiangxi University, the Jilin Technical University, the People's University, and the Shanghai Jiao Tong University. In this paper, we will give a sketch of the results obtained by our colleagues since 1979. The paper consists of seven parts. [ABSTRACT FROM AUTHOR]

China is the largest emerging capital market with a unique setup: it issues simultaneously both (i) Class A shares addressed to Chinese domestic investors, and (ii) Class B Shares addressed to foreign investors. After Chinese stock market resumed the operation, they feature dramatic fluctuations due to policy changes and over-speculative activity of individual investors. This paper aims to analyse the evolution of both the Shanghai A and B Markets through a Markov-switching asymmetric GARCH in four different time frames. [ABSTRACT FROM AUTHOR]

As the imported cross-border e-commerce market is getting more competitive nowadays, the free shipping strategy, as a unique service model, plays an important role in the market share competition. Formulating a free shipping strategy to enable companies to obtain more orders and make more profits has become one of the imperative concerns of cross-border e-commerce companies. We will analyze the impact of increasing the free shipping threshold on consumer behavior and corporate profits, combine the free shipping solutions of four large cross-border e-commerce companies in the China, consider more realistic factors (i.e., commodity prices, order distribution, market competition, consumer characteristics), build a consumer purchase decision model and a consumer increase purchase model based on consumer decision theory, and provide decision support for its selection of a suitable free shipping strategy based on the real operating data of the cross-border e-commerce platform. The results indicate that raising the free shipping threshold within a certain range can help increase a company's average order price and gross profit margin as well as reduce corporate costs. However, an excessively high free shipping threshold weakens the stimulus for consumers to increase purchases and decreases the customer conversion rate. The closer the order price is to the free shipping threshold, the more likely it is that consumers will increase their purchases; the further the order price is from the free shipping threshold, the more likely it is that consumers will pay the shipping fee. This study provides a practical reference for decisions about cross-border e-commerce free shipping policies. [ABSTRACT FROM AUTHOR]

This study uses two empirical approaches to explore the asymmetric effects of oil and coal prices on renewable energy consumption (REC) in China from 1970 to 2019. As a conventional approach, we used the nonlinear autoregressive distributed lags (NARDL) model, while machine learning was used as a non-conventional approach. The empirical findings of the NARDL indicate that oil and coal price fluctuations have a significant effect on REC for both the short and long term. The results of the non-conventional approaches based on machine learning indicated that the SVM model was more efficient than the KNN model in terms of accuracy, performance, and convergence. Referring to the SVM model findings, the results show that an increase in the coal price has a higher ability to predict REC than the oil price. As a robustness check, we also find that an increase in Brent prices significantly decreases REC. The findings of this study support the view that there is a substitution effect from oil to coal before initiating the use of renewable energy in China. [ABSTRACT FROM AUTHOR]

We revisit in this article the Two-Fund Separation Theorem as a simple technique for the Mean–Variance optimization of large portfolios. The proposed approach is fast and scalable and provides equivalent results of commonly used ML techniques but, with computing time differences counted in hours (1 min vs. several hours). In the empirical application, we consider three geographic areas (China, US, and French stock markets) and show that the Two-Fund Separation Theorem holds exactly when no constraints are imposed and is approximately true with (realistic) positive constraints on weights. This technique is shown to be of interest to both scholars and practitioners involved in portfolio optimization tasks. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China's film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model. [ABSTRACT FROM AUTHOR]

The present paper proposes two weighted goal-programming models for optimizing crude oil production in the middle and later periods of oilfield development. These models divide the oil yield optimization problem into two stages according to the characteristics of Chinese oil companies' yield compositions. In the first stage, the total planned crude oil yield will be optimized by four sub-yields, while each sub-yield will be allocated optimally among different oil extract plants in the later stage. A case study is conducted to validate the proposed models. The results show that the model can address the annual crude oil yield planning subjectively. Moreover, the proposed model can rationally allocate resources among various extract oil plants and effectively reduce production costs in the context of ensuring planned oil yields. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China. [ABSTRACT FROM AUTHOR]

Chinese industry has developed greatly since China implemented its 'reform and opening-up' policy in 1978. With the rapid development of industry, the problems of growing energy consumption and environmental pollution are drawing increasing attention from government managers and scholars. This paper divides industrial systems into two stages, an energy utilization stage and a pollution treatment stage, for accurately evaluating the total-factor energy efficiency as well as the overall efficiency. We build a new two-stage data envelopment analysis model with shared inputs to open the 'black box' of efficiency measurement in traditional energy efficiency methods. Applying the model to data for Chinese regions, we can display the advantages and disadvantages of these two stages of industry. The results show that (1) the performance of Chinese industry improved during the years 2006-2010; (2) the energy utilization stage performance was better than that of the pollution treatment stage, but the gaps reduced year by year; and (3) energy efficiency increased during this period. Based on these results, some policy recommendations are given. [ABSTRACT FROM AUTHOR]

The carbon emission of China's industry accounts for more than 70 % of the total in the nation, thus the implementation of carbon emission quota trading in industry is of great importance to realize China's national carbon emission reduction targets. Meanwhile, the allocation of carbon emission quota among sectors or enterprises proves the first and critical step. For this reason, this paper constructs a comprehensive index combined with the subjective, objective and linear combination weighting methods to allocate carbon emission quotas among the 39 sectors of China's industry in 2020 based on the level of 2015, and employs the input-oriented ZSG-DEA model to examine the efficiency of allocation solutions in 2020. The results indicate that, first, when carbon emission reduction capacity, responsibility and potential are considered for the comprehensive index of carbon emission quota allocation, the mitigation responsibility plays a relatively higher role than other two indicators. Second, all of the subjective, objective and linear combination weighting methods can be used for effective allocation of carbon emission quotas, and the former two methods have less advantage in light of efficiency. Third, six key industrial sectors are respectively allocated over 500 million tonnes of carbon emission quotas in 2020, which together account for 91.77 % of the total in the industry. Finally, the final carbon emission quota allocation solution reflects both the equity and efficiency principles and achieve the Pareto optimal state. [ABSTRACT FROM AUTHOR]

Recovery energy has been one of important components of energy consumption structure in China. Recovery energy is a type of secondary energy, which is generated from various sources. Since the estimation of recovery energy from some sources such as coke oven gas and other coal gas contains some uncertainties, recovery energy can be represented as an interval variable. The extant studies in the literature ignore recovery energy in the evaluation of energy efficiency or carbon dioxide ( $$\text {CO}_{2}$$ ) emissions efficiency. As a part of energy supply in China, recovery energy may influence regional primary energy consumption and $$\text {CO}_{2}$$ emissions significantly. In this context, this paper proposes an interval slacks-based measure approach to evaluate energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in the presence of recovery energy in China. In the described approach, the indicator of $$\text {CO}_{2}$$ emissions is incorporated based on the weak disposability assumption, and recovery energy is modeled as a dual factor. The optimal exact data on recovery energy of each region can be obtained. Based on the proposed approach, the measures of energy saving potential and $$\text {CO}_{2}$$ emissions reduction potential are derived. Findings resulting from the model application show that there are great disparities in regional efficiencies, and the inefficiency of Chinese regions is largely driven by those inefficient regions in the central and western areas. Notably, the development of recovery energy can help to improve regional energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in China. [ABSTRACT FROM AUTHOR]

The environmental issue has attracted ever-increasing attention from governments and academics in recent years. Environmental performance analysis is widely considered a crucial and useful tool for effectively protecting the environment and developing a sustainable society. Many analytical techniques have been used to assess carbon emission performance, among which data envelopment analysis (DEA) is prominent. However, few previous DEA-related carbon emission performance studies recognize that the total amount of carbon dioxide emissions is limited to a specific level by authorities; ignoring this fixed-sum requirement may lead to distortions and deviations in empirical results. This paper proposes an alternative Malmquist DEA approach for evaluating the carbon emission performance while considering fixed-sum undesirable outputs. For this purpose, we develop a generalized equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and combine the model with the Malmquist productivity index (MPI). The proposed approach is applied to assess the carbon emission performance of 30 provincial regions in China from 2009 to 2015. Then we provide analytical results and policy suggestions regarding the provincial carbon emission performance in China. [ABSTRACT FROM AUTHOR]