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

This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is proposed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China’s digital economy, with an annual growth rate of 7.87%, however, there are notable regional development disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.

Circular economy (CE) is a sustainable alternative to tackle global challenges like climate change, waste, and pollution. The relations, perceptions and behaviors of stakeholders in circular economic activities may form barriers that hinder the circular transition. The promotion of CE requires investigating the interactions and information flow between CE stakeholders from a network perspective. This study revisits the applications of social network analysis (SNA) in promoting CE. Related concepts of CE and the research contents of SNA are reviewed. A bibliometric analysis is conducted to provide a bird’s eye on the research status and trend. On this basis, we summarize the challenges of promoting CE and refine specific problems, around which we review the research status of network modeling methods and statistical measures, information diffusion models, mining methods of perceptions, and social influence analysis. This study outlines the pathways through which SNA contributes to promoting CE, such as through revealing the relational structure and characteristics of stakeholders, forming and changing perceptions of stakeholders, improving behaviors of stakeholders, and examining the development of CE. The lessons learned from the review and future prospects are extensively discussed in combination with the features of the information age from both theoretical and practical perspectives.

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

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

Most countries have suffered from prolonged budget deficits over the past two decades. This situation has made researchers and policymakers aware of the challenges to debt sustainability. This study investigates the threshold effect of institutional quality on debt sustainability in a panel of 82 countries, focussing on various threshold effects at different income levels. All the countries selected for this study fell under the categories of high income (HI), upper middle income (UMI), and lower-middle and low income (LMLI) based on the World Bank classifications. The dynamic threshold panel regression results indicate the presence of a threshold effect of institutional quality on the fiscal reaction function (including debt sustainability and cyclical fiscal policy) in all the countries with different income levels. In HI countries, fiscal adjustment weakens if institutional quality surpasses the threshold value of institutional quality. The fiscal adjustment in UMI countries is similar to that in HI countries but statistically insignificant. By contrast, governments in LMLI countries can promote sustainable debt if their institutional quality exceeds the threshold value. Thus, policymakers in LMLI countries need to prioritise their efforts to raise the level of institutional quality to promote debt sustainability.

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

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

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

The unprecedented downward pressure of China’s economic growth trend raises several questions, including what the current level of China’s long-term economic growth trend is, and what drives and how to inhibit the downward trend. Therefore, we develop a time-varying mixedfrequency dynamic factor model using data with different start dates to measure the trend, and perform a real-time decomposition of changes in the trend. We find that the trend has entered a downward stage since 2007, left a high-speed phase since 2012, and stepped in an accelerated downward stage since 2018. The current level of the trend is about 4%. However, the lower limit of the 90% confidence interval is below 2%, which is lower than natural rate level. Additionally, decelerated capital deepening, diminishing demographic dividend and technological recession all drive the downward trend. Compared to the relatively weak push-down effects of capital deepening and demographic dividend that are less than two percentage points, the downward trend is mainly driven by technological recession. Given that technological progress is unlikely to improve significantly in the short run, mitigating the mismatch between technological progress and obsolete capital, revitalizing existing capital stock, and increasing the efficiency of technology utilization become more feasible means.