PurposeThe discussion on energy efficiency has been increasing due to the increasing population, emissions of degradable and harmful pollutants, and clean energy substitutes are being developed in ...order to manage and control the energy requirements all over the world. Against this backdrop, the factors of technological innovation and environmental regulations have been determined as key indicators for the evaluation of sustainable developments and practices in the energy efficiency evaluation studies.Design/methodology/approachA two-stage analysis process has been configured for evaluation of the energy efficiency. The first stage includes the estimation of the Total factor energy efficiency scores using the data envelopment Multiplier input-oriented methodology, while the second stage includes the exploration of the impact of technological innovation and government environmental regulations on the Total factor energy efficiency scores obtained in the first step through the application of a spatial regression model.FindingsThis paper highlights the link between the need for and impact of energy efficiency innovations and shows that the energy efficiency goal can be fulfilled by incorporating laws on sustainability and incorporating strict regulations that allow for the use of clean energy, low carbon energy technologies.Originality/valueThe present study, furthermore, provides evidence from 15 countries, five from three different continents, i.e. Asia, Europe and Africa so that a cross-country performance of these factors can be evaluated. The main contribution of the present study is the evaluation of the technological innovation on energy efficiency. There have been studies evaluating various factors on the development of energy-efficient practices; however, the focus on the role of technological innovation and governmental regulations has been scarce.
Unpredictable stock market factors make it difficult to predict stock index futures. Although efforts to develop an effective prediction method have a long history, recent developments in artificial ...intelligence and the use of artificial neural networks have increased our success in nonlinear approximation. When we study financial markets, we can now extract features from a big data environment without prior predictive information. We here propose to further improve this predictive performance using a combination of a deep-learning-based stock index futures prediction model, an autoencoder, and a restricted Boltzmann machine. We use high-frequency data to examine the predictive performance of deep learning, and we compare three traditional artificial neural networks: 1) the back propagation neural network; 2) the extreme learning machine; and 3) the radial basis function neural network. We use all of the 1-min high-frequency transaction data of the CSI 300 futures contract (IF1704) in our empirical analysis, and we test three groups of different volume samples to validate our observations. We find that the deep learning method of predicting stock index futures outperforms the back propagation, the extreme learning machine, and the radial basis function neural network in its fitting degree and directional predictive accuracy. We also find that increasing the amount of data increases predictive performance. This indicates that deep learning captures the nonlinear features of transaction data and can serve as a powerful stock index futures prediction tool for financial market investors.
There is a growing utilisation of information and communication technologies (ICT) in the recent digital era. Trade and tourism have also attained attention as determinants of environmental ...sustainability. Therefore, this study investigates linkages between ICT, tourism, trade, economic growth, and environmental sustainability in BRICS economies. Advanced panel estimation entitled cross-sectionally augmented autoregressive distributed lags (CS-ARDL) was applied from 1990 to 2019. Findings suggest the adverse effect of tourism, trade, and growth factors on environmental sustainability, whereas ICT helps promote a sustainable environment among the targeted economies. Likewise, the short-run results prove that economic growth and tourism are prone to ecological health, while trade possesses an insignificant influence on ecological sustainability. These results suggest the integration of ICT in trade and tourism sectors to mitigate their negative ecological consequences.
This paper studies the role of digital finance in shaping corporate resilience to the COVID-19 pandemic by analyzing the stock prices of Chinese listed firms. We find that firms located in regions ...with higher levels of digital finance experience fewer losses and recover more quickly from the COVID-19 pandemic. Further analysis shows that digital finance helps build corporate resilience by facilitating firms' access to external financing and reducing financing costs. We further document that the positive effects of digital finance on corporate resilience are more pronounced for small firms, non-state-owned enterprises, and low cash holding firms. Overall, these findings suggest that digital finance improves corporate resilience by mitigating financing frictions.
•This paper examines how digital finance affects corporate resilience during the COVID-19 pandemic andits mechanism.•Digital finance reduces the severity of the COVID-19 crisis experienced by firms and decreases their time to recovery.•These effects are more pronounced for small firms, non-state-owned enterprises, and low cash holding firms.•Digital finance improves corporate resilience by facilitating firms' access to finance and reducing financing costs.
•Switching behavior involves push, pull, and mooring factors.•Privacy concerns push users to switch.•Monetary rewards of alternatives pull users to switch.•Inertia moors users to stay with incumbent ...mobile payment apps.
Switching behaviors of mobile payment application received scarce attention. This study investigates the key factors influencing the switching behaviors of mobile payment application through the perspective of the push–pull–mooring framework. Privacy concerns, alternative rewards, and inertia are identified as push, pull, and mooring factors, respectively. The model was tested with 3785 valid responses among Alipay users. Inertia was found to attenuate the relationship between alternative rewards and switching behavior. This study sheds new light on the switching behavior of mobile payment application users. Our findings inform service providers to retain existing users as well as attract potential users.
The systemic risk of financial systems can affect the entirety of stock markets around the world. As the time series of stock markets contain rich information of the corresponding complex systems, a ...novel method using the complex network theory is proposed to measure the systemic risk in stock markets. Through the correlation analysis the time series of stock market financial indicators can be converted to the series of the complex networks. The dynamic topological indices of the networks can be used to analyze the network transmission characteristics and calculate the systemic risk. Based on the network dynamics we construct and test the systemic risk measurement model from the perspectives of regional, financial and global stock indices respectively. The topological parameter model is introduced to measure the systemic risk and the comparison is made with the traditional measurement model. The results show that the new model can provide more detailed and accurate information on the systemic risk of stock markets. This method can be applied in giving suggestions on investment decisions and early warnings of systemic risk.
•The method of converting multivariate time series into complex networks is proposed for the analysis of financial time series.•The dynamic topological indices are used to analyze the network transmission characteristics.•A new systemic risk measurement model based on the dynamic topological indices is proposed.•The proposed model is tested from the perspectives of regional, financial and global stock indices respectively.
EVA and FGV are new tools for performance measurement and engineering management. The paper tests the value relevance of EVA, FGV and traditional performance measurements such as ROE, EPS, and CFOPS. ...Ohlson model, relative information content test and incremental information content test are used to do the work. The result of relative information test shows that FGV's relative information content is largest. It is more highly associated with corporate value than EVA, EPS, ROE, and CFOPS. Although EVA is also associated with corporate value, it is not as remarkable as EVA advocator's proposed. The result of incremental information test reveals that EVA and FGV all have significant incremental information content. It also suggests that EVA and FGV have significant incremental explanatory ability to corporate value. And FGV has more incremental explanation than EVA.
Economic value added (EVA) is a good method to measure the company's true value. This paper discussed on how to improve traditional performance measurement using EVA with neural network. It presented ...the integrated EVA performance measurement (IEPM) model and analyzed its superiority empirically with BP neural network. All the data came from China's listed companies. The final results showed that both the measurement ability and prediction ability of IEPM model were superior to those of traditional performance measurement. It suggests that introducing EVA to performance measurement well reflects the company's real economic profit and neural network model will be well applied to economic area, especially to the company's performance measurement.
PDF
