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The purpose of this article is to explore the impact of the firm's entrepreneurship for the transformation of circular economy (CE). The role of entrepreneurship is thought to be important for the process of four Rs in the CE, and the authors have tried to study the role and impact path of entrepreneurship in CE.

Empirical data from Chinese listed firms are collected, and a measure of digital technology is constructed by text mining method. Mediation analysis method is used to test the proposed hypothesis.

The results show that the innovation entrepreneurship has a significant positive impact upon the CE and digital technology is playing a mediating role in the impact path. However, the business entrepreneurship is negatively affecting the CE adoption. Also, the proportion of shares hold by the institution has a heterogenous influence for the innovation entrepreneurship.

This study guides policy makers about the role of entrepreneurship and the mediating effect of digital technology and to encourage the adoption of CE for firms.

This study reveals the mediation effect of digital technology in the impact of entrepreneurship on CE in the emerging market. The heterogeneity of the proportion of shares hold by the institutions is also analyzed in the empirical study.

This paper aims to put forward an extended lattice hydrodynamic model, explore its effects on alleviating traffic congestion and provide theoretical basis for traffic management departments and traffic engineering implementation departments.

The control method is applied to study the stability of the new model. Through nonlinear analysis, the mKdV equation representing kink-antikink soliton is acquired.

The predictive effect and the control signal can enhance the traffic flow stability and reduce the energy consumption.

The predictive effect and feedback control are first considered in lattice hydrodynamic model simultaneously. Numerical simulations demonstrate that these two factors can enhance the traffic flow stability.

Regenerative braking is an efficient energy saving technology in urban rail system, in which the recovery energy from braking trains is collected by some equipments and released to accelerating trains when needed. However, the high cost and low lifetime of storage devices prevent the widespread use of this technology. The purpose of this paper is to conduct thorough cost-benefit analysis to facilitate China’s urban rail companies to make decisions on the use of such technology.

To evaluate the benefit from regenerative energy storage, the authors formulate an improved integrated scheduling and speed control model to calculate the net energy consumption associated with different energy recovery rates and then define the benefit as the amount of energy saving arising from the usage of storage equipments. With the frequent charge/discharge operations on storage equipments, the energy recovery rate generally decreases which lowers the benefit, but the maintenance cost increases. By trading-off benefit and cost, the authors derive the optimal scrapping time, the maximum profit and the profitability condition for storage devices.

Simulation studies based on the Beijing Metro Yizhuang Line of China are given. The results show that compared with the current timetable and speed profile, the integrated scheduling and speed control approach with energy recovery rate of 0.5 can reduce the net energy consumption by 12.69 per cent; the net energy consumption can be well approximated as a linear function of energy recovery rate; and the maximum profit and the optimal scrapping time on regenerative energy storage devices are both positively related to the electricity price. The allowance proportion and the number of service trains such that busy lines with higher electricity price or allowance proportion have advantages to use the regenerative energy storage devices.

In this work, a linear energy recovery rate and a linear maintenance cost are used in the cost-benefit analysis process. In future research, the more accurate expressions on energy recovery rate and maintenance cost should be considered if more data on recovery rate and maintenance cost can be gathered.

The main values of this paper are to develop the integrated optimization approaches for train scheduling and speed control and, on this basis, make thorough cost-benefit analysis for regenerative energy storage to improve the operations management of urban rail transit.

The power battery is the core of a new energy vehicle and plays a vital role in the rise of the new energy vehicle industry. As the number of waste batteries increases, firms involved in the industry need to properly dispose them, but what party is responsible remains unclear. To reduce environmental impacts, governments introduce two subsidy policies, i.e. collection subsidies, which are provided to the collecting firms, and dismantling subsidies, which are provided to the dismantling firms.

Based on the different characteristics of the subsidies, we develop a stylized model to examine the collection strategies and the preferences over the subsidies.

We derive several insights from analysis. First, the collection strategies depend on the fixed collection cost. Second, the key factor determining the firm's subsidy preference is the efficiency of dismantling. Finally, if the primary target is the collection rate, governments prefer to provide collection subsidies. If consider the environmental impact, the choice of subsidies has to do with the efficiency of dismantling. Moreover, from a social welfare perspective, the raw material cost and the efficiency of dismantling are core indicators of decision.

This work develops the first analytical model to study two power battery subsidies and investigate the optimal collecting strategies and subsidy preferences. The insights are compelling not only for the manufacturer and the third party but also for policymakers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IMDS-08-2019-0450

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Build-operate-transfer (BOT) contracts are widely used in the construction and operation of charging piles for new energy vehicles worldwide and stipulate that governments grant charging pile operators franchises for a certain period of time to invest in the construction and operation of the charging piles. The charging piles are then transferred to governments when the concession expires. To encourage charging pile operators to build and operate charging piles, governments usually provide two kinds of subsidies, namely construction and operating subsidies.

The authors establish a typical game model to study the optimal BOT contract between a government and a charging pile operator and their preferences for the two kinds of subsidies.

First, the authors show that there are substitution and complementarity effects between the concession period and the subsidy level. Second, the operator prefers the construction subsidy (operating subsidy) when the additional operating cost is low (high). The government prefers the operating subsidy (construction subsidy) when consumer sensitivity to the number of charging piles is low (high) and the concession period is short or long (moderate). Finally, the adjusted joint subsidy can not only improve social welfare but also that the charging pile operator can obtain the same profit as under the operating subsidy at a lower subsidy amount.

This work develops the first analytical model to study two subsidies in the construction and operation of charging piles and investigate the optimal BOT contract and subsidy preferences. The insights are compelling not only for the charging pile operator but also for policymakers in practice from a circular economy perspective.

Due to existence of skin effect under rotational excitation, especially to high-frequency motors and power transformers run at the frequency of hundreds or even thousands of hertz, core losses will increase significantly, which may cause local overheating damage, and the efficiency and longevity will be decreased. The purpose of this paper is to accurately calculate the rotational anomalous loss in electrical steel sheets.

The influence of skin effect to rotational anomalous loss coefficient is described in detail. Based on the rotational core losses calculation approach, the transformed coefficient and parameters of rotational anomalous loss are determined in accordance with experimental data obtained by using 3D magnetic properties testing system. Then, a variable loss coefficient calculation model of rotational anomalous loss is built. Meanwhile, a separation of the total 2D elliptical rotation experimental core losses is worked out.

The two methods are analysed and compared qualitatively. It should be noted that the novel calculation model can be more effectively presented anomalous loss features. Moreover, quantitative comparisons between 2D elliptical rotation and alternating core losses have achieved beneficial conclusions.

Transformed rotational anomalous loss coefficient and parameters of electrical steel sheets considering skin effect are determined. Based on that, a novel calculation model evaluating 2D elliptical rotation anomalous loss is presented and verified based on the experimental measurement and the separation of the total 2D elliptical rotation core losses. The 2D elliptical rotation core losses separation method and quantitative comparison with alternating excitation are helpful to engineering application.

The purpose of this study is to examine the role of retail investors’ green attention in promoting corporate environmental investments (EIs) using a communication sample on “Hudongyi” from 2011 to 2022.

In this paper, Python is used to capture data and text analysis techniques to obtain green attention information. In the word-matching process, words are matched in the target document one by one based on the preset dictionary and vocabulary rules. In addition to employing fixed effects, this study also incorporates instrumental variables using two-stage least squares (2SLS) estimation and applies the Heckman two-step method to verify the regression results.

First, this paper empirically examines the positive influence of retail investors’ green attention on EIs. Second, the findings show that retail investors’ green attention promotes EIs through decreasing principal-agent costs and principal-principal costs. Third, the results show that retail investor’s supervision effect is strengthened under the following three circumstances: executives with stronger green conception, corporations with less information asymmetry and areas with higher level of investor protection.

Our findings broaden the scope of prior research by exploring the impact of retail investor activism on nonfinancial outcomes, contributing to understanding the “black box” of how investor attention fosters EIs. Moreover, by leveraging the power of technology, retail investors have evolved from being the “silent majority” to being actively engaged. The internet has empowered retail investors by providing them with access to information and enabling them to exercise “voice” rights by appealing companies to engage in pro-environmental activities. Our study can provide useful suggestions for the green development of listed companies in China, as well as in other emerging countries.

Unlike other studies that focus on the deterrent effect and corporate financial outcomes of retail investors, we focus on the supervisory effect of retail investors and verify its role in driving EIs. This fills the knowledge gap in prior studies and contributes new insights to explain EIs and extends the understanding of retail investor activism.

This study aims to explore the dynamics between the brand manufacturer’s revenue model choice and the platform’s private label product entry strategy, specifically, (1) Under the wholesale and agency models, when should the platform introduce its private label product to compete with the manufacturer? (2) Facing the potential threat of the platform’s entry, how should the manufacturer choose between the wholesale and agency models? (3) How does the platform’s entry strategy affect the manufacturer’s price decisions and demand? (4) What are the implications of the strategic interaction between the manufacturer’s model selection and the platform’s encroachment on social welfare and consumer surplus?

This study develops a multistage game model consisting of a manufacturer, a platform and consumers. The model describes the strategic interaction between the manufacturer and the platform, where the manufacturer first chooses one of the revenue models from the agency model and the wholesale model, followed by the platform’s decision whether to offer its own products to enter the competition, after which the two parties set the price of their products to compete on price according to their strategies, then the consumers make purchase decisions based on the principle of utility maximization.

For the platform, to not hurt its profitability in the manufacturer channel with too much competition, lower social utility sensitivity and lower similarity of product imitation rather favor platform entry. Platform entry affects manufacturers’ retail prices and demand differently across different revenue models. Interestingly, if the social utility sensitivity and imitation similarity are moderate, when the commissions extracted by the platform are low, manufacturers still have the incentive to adopt the wholesale model rather than the agency model. In addition, platform entry into competition increases consumer surplus and social welfare only when consumer sensitivity to social utility is low.

The research model innovatively describes the strategic interaction between the manufacturer and the platform; to more accurately portray the consumer demand model, this model also introduces the parameters of the similarity of the platform’s products to the products of the manufacturer, as well as the social utility sensitivity of consumers. Conclusions are drawn on the choice of platform entry strategy versus the choice of revenue model for manufacturers. Relevant managerial insights are provided for both platforms and manufacturers, which partially explains the existing market situation.

The aim of this study is to investigate the role of Corporate Social Responsibility (CSR) in assisting firms in their response to public emergency crises under the integrated view of government emergency response.

Using event study and survival analysis method, the authors examine whether CSR can act as a stock price stabilizer for companies from China by splitting the stock price fluctuations into two phases – CSR price insurance, which decrease the shock on stock prices during the emergency crisis, and CSR price recovery, which helps stock prices rebound faster during the postcrisis phase.

The authors’ empirical results confirm the stabilizer role of CSR during crisis and that effective government response can strengthen such effect. Furthermore, the authors examine the different aspects of the government’s response and the impact of multiple waves of public emergency.

This study provides empirical evidence on the topic of CSR and the government’s response to public emergency under the emerging context.