We explore the connectedness and portfolio implications between Islamic and conventional bonds in global and GCC regions. We also compare which bonds performed better during our sample period. Unlike previous studies, we focus on Islamic bond markets compared to their conventional counterparts and highlight the GCC bonds (Islamic and conventional) in respect of global bonds. We apply the DCC-GJR-GARCH (1,1) method, the Sharpe ratio, and the portfolio implications strategy over the period from September 1, 2013, to February 23, 2022. Our time-varying results suggest that the relationship among all variables varies over time, but most of them are positive, suggesting that there are fewer diversification opportunities between Islamic and conventional bonds. Hedging and diversification benefits are found only in the limited period among these variables, especially between GCC bonds and global bonds, and global Sukuk and GCC Sukuk. The findings of risk-adjusted returns reveal that Islamic bonds outperform their conventional counterparts. Moreover, mixed results are found in the case of hedging costs, and the majority of the fund, based on optimal weights, should be invested in Islamic bonds. Our study endows investors and regulators in the global and GCC markets with new insights on how to shield their investments and the financial system from financial crises through a hedging strategy with Islamic finance.

Generating output growth by adding more inputs into the production process may not be sustainable in the long run for any economy, given the limited resources. On the other hand, if productivity growth dominates the production process, it will generate more output without excessive increase in input use. Hence, this paper examines whether the output growth in Indonesia’s manufacturing sector is excessive inputs driven or productivity driven. Productivity driven growth is measured by Total Factor Productivity (TFP) growth, which is decomposed into its major components of technological progress and technical efficiency within the framework of varying coefficients stochastic frontier analysis (VSFA) using Indonesia’s annual Large and Medium Manufacturing Industries Survey data over the period 2002–2014. The measurement of the components of TFP growth not only provides more insights and better understanding of the dynamic nature of the production processes, but also has important policy implications. The mean TFP growth during the period 2002-2014 was estimated to be 4.3 per cent and was mostly contributed by technological progress experienced by firms. The policy implication is that technical efficiency could still be improved for the selected technology to reap the full benefit of increasing output from the chosen technology.

The paper focuses on younger online viewers’ motivation to watch video game live streaming on live streaming platforms. Unlike existing scholars, it analyses younger online viewers’ watching motivation through a positive perspective and draws on the Play-Others-Downtime theory (P-O-D theory) and the motivation theory to establish the research model. By analysing 397 samples based on the variance-based structural equation modelling and partial least squares path modelling (SEM-PLS), the results present that younger viewers’ entertainment, social and leisure purposes positively affect their watching motivation. Control variables (i.e., gender, education background, and income level) demonstrate insignificant effects across the model. Considering the influence of the COVID-19 pandemic, watching video game live streaming is essential entertainment and social activities for younger adults. Future studies should identify the positive impact of video game live streaming and guide younger viewers to participate appropriately in this activity.

The digital economy based on digital technology is an important force for high-quality economic growth and industrial transformation, and has great potential for value creation. Based on the data of 30 provinces in China from 2007 to 2017, this paper uses entropy method to calculate the industrial green transformation (IGT), and empirically analyzes the impact of Digital economy on IGT. The DE can significantly reduce IGT of local and neighboring areas after excluding the influence of macro system factors and replacing the spatial weight matrix. The DE can indirectly reduce the IGT by accelerating the accumulation of human capital, and green technology innovation. The impact of digital economy on IGT is a non-linear relationship. With the further improvement of environmental regulation, financial development and intellectual property protection, the role of digital economy in IGT is more obvious. To this end, it is necessary to speed up the construction and improvement of digital infrastructure, build an integrated layout of "digital infrastructure", give full play to the radiating role of the digital economy, and implement differentiated development paths based on regional comparative advantages.

In China, the land arrangement behavior of over 160 million rural-urban migrant workers is closely related to the optimal allocation of rural land resources and sustainable development of urban and rural areas. Although previous studies show that social capital affects migrant workers’ land arrangement behavior, few empirical studies reveal the relationship between them, and the corresponding interventions remain unclear. Using survey data collected in Henan Province, China, and a multinomial logit model, this study empirically analyzes the mechanism behind the impact of social capital on migrant workers’ land arrangement behavior from the perspective of social capital. Results illustrate that social capital has a significant impact on the land arrangement behavior of migrant workers. The behavior is significantly correlated with the scale of migrant workers’ urban social networks, the degree of urban social trust, and urban belonging. More social capital in urban areas indicates a higher tendency for migrant workers to abandon their land contracting rights and become permanent urban residents. This study reveals the mechanism of social capital affecting migrant workers’ land arrangement behavior and provides a reference for decision-making with respect to guiding migrant workers’ land management behavior for other countries facing similar social problems.

The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.

The clean energy revolution is a global concern. Cross-border collaboration is crucial for sharing knowledge, pooling resources, and tackling complex challenges on a global scale. International partnerships can accelerate innovation by drawing on diverse knowledge and experience from different regions. Access to capital is a key driver of innovation and entrepreneurship in clean energy. Institutional investors and financial institutions increasingly recognize the potential for high returns from the clean energy sector. This research aimed to understand whether "dirty" energies, such as the Brent Crude Spot, WTI, FTSE 350 Oil, Gas & Coal, EURO STOXX Oil & Gas, influence the formation of sustainable energy prices, namely Geothermal Index, Solar Energy Index, NASDAQ OMX Bio Clean Fuels Index, Wind Energy Index, WilderHill Clean Energy Index, during the 2020 Covid-19 pandemic and the Russian invasion of Ukraine in 2022. When comparing the two sub-periods, the results show that the level of autocorrelation in price formation has increased, i.e., the mean rhoDCCA went from 32 to 45, the weak correlation coefficients without trend decreased from 30 to 19, and the anti-persistence has also decreased from 8 to 4. The strong rhoDCCA increased from 2 to 4; during the Tranquil period, the FTSE 350 / EURO STOXX Oil index pairs had a rhoDCCA of 0.76. While in the Stress subperiod, the NASDAQ OMX Bio / Solar Energy pairs had a slope of 0.70, and the FTSE 350 / EURO STOXX Oil indices had a rhoDCCA of 0.91. Evidence shows a notable dissociation between fossil energy prices and sustainable energy sources. This dissociation is characterized by various degrees of influence between fossil energy prices and sustainable energy indices, from low to moderate. This dissociation between fossil energy prices and sustainable energy prices suggests that investments in sustainable energy can offer diversification opportunities.

This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.

The goal of this paper is to provide an overall presentation of corruption as occupational fraud building on the findings of an empirical study conducted in Greece. As uncertainty leads to increased levels of corruption, Greece was chosen due to the prolonged period of uncertainty the country faces, caused by the 2010 financial crisis and followed by the Covid pandemic crisis. 400 questionnaires reviewing corruption perception were gathered and analyzed by gender, age, marital status, education level, position, occupation, and monthly income, as well as urban vs rural area of residence. The findings of the study were compared with and critically evaluated against those of the previous studies for Greece. Respondents in age group 36-49 believe that if appropriate measures are taken it is possible to tackle corruption in the public sector, in contrast to people aged 18-35 who disagree completely. Residents of rural areas do not consider corruption to be a serious problem and are more willing to whistleblowing (in contrast to residents of urban areas). The study findings have important practical implications in the context of designing effective corruption reduction policies.

Green-biased technological progress takes into account the influence of energy input and pollution emissions, which is of great significance to China's green development. This paper decomposes technological progress into two categories: green input-biased technological progress (IBTC) and green output-biased technological progress (OBTC), using the Slacks-based measure integrating (SBM) model. The factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. The results show that green-biased technological progress exists significantly in the industry, and most of it promotes the growth of green total factor productivity. IBTC first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan. Between labor input and energy input, it is biased towards saving labor and consuming resources. OBTC is biased towards promoting industrial growth and curbing pollution emissions. Medium and light-polluting industries are biased toward promoting industrial growth and curbing pollution emissions, while heavy-polluting industries are biased towards emitting more pollution.

China's carbon emission trading market has been formally established, but few studies have been conducted to analyze the impact of this policy on the regional urbanization level. Therefore, this paper evaluates whether the carbon trading pilot policy can enhance the regional urbanization level in China through the difference-in-differences method and analyzes the mediating role of industrial structure upgrading in this process. The results prove that the carbon trading market policy can accelerate the transformation and upgrading of industrial structure in the region so that it promotes the development of regional urbanization. Moreover, the effects of the policy are concentratedly manifested in the eastern region of China.

Based on provincial panel data from 1998-2018, this paper estimates research and development (R&D) factors, and a stochastic frontier analysis (SFA) model is constructed to examine the effects of R&D factors on regional total factor productivity (TFP). The results show that both R&D capital stock and R&D personnel can significantly promote regional TFP, but the productivity-enhancing effect of R&D factors is different between regions. Specifically, R&D capital and R&D personnel can promote TFP in eastern and central provinces, and the promotion effect is not significant in western provinces. In addition, compared with investment-driven regions, innovation-driven regions are more likely to enhance TFP by R&D factors.

The development of digital economy is becoming an important path to promote the kinetic energy of China's tax revenue development. Based on the panel data from 2011 to 2018, this paper uses Gini coefficient to analyze the regional differences of China's digital economy, and uses fixed effect model, spatial Durbin model, multi-period double difference model and other empirical methods to examine the tax effects of digital economy development. The results show that the regional disparity is the main reason for the regional disparity of digital economy development in China. The development of digital economy directly increases the tax amount of local digital industry, and at the same time it also radiates and drives the increase of local total tax revenue; The tax effect of digital economy has a negative spatial spillover, showing a "siphon effect" to neighboring areas; Mechanism analysis shows that the development of digital economy mainly promotes the increase of local tax revenue through growth effect.

With the advancement of science and technology and the advent of the digital economy, the digital economy has become a new driving force for economic development, and the digital economy industry at home and abroad is facing new opportunities for survival and development. However, due to the special development model and profit mode of the digital economy industry, the traditional performance evaluation method is no longer applicable, so it is particularly important to improve the previous performance evaluation method. This paper first adjusts the customer value assessment (CLV) model according to the characteristics of the digital economy industry, and then analyzes the traditional EVA based on the adjusted user value assessment (ULV) model of the digital economy industry. The performance evaluation system is improved, and the representative enterprises of Alibaba, JD and Pinduoduo in the digital economy industry are selected for performance evaluation and analysis, and the user value performance evaluation model of the digital economy industry is integrated, innovated and standardized, in order to provide reference and reference for the follow-up research and practice of performance evaluation in the digital economy industry.

Green credit policy (GCP) relies on financial means to promote environmental governance. Whether it can achieve the goals of economic development and environmental protection, especially in the context of different institutional supplies, remains to be scientifically tested. Based on the implementation of China’s Green Credit Guidelines in 2012, this study uses panel data of Chinese companies from 2009 to 2019 to explore the influence of GCP on green technology innovation and the role of institutional supply in it. The results show that GCP is instrumental in promoting green innovation in heavily polluting enterprises, and the promotion effect is heterogeneous based on green patent types, firms’ ownership, and regional financial development levels. Further analysis finds that the supply of environmental protection systems by local governments can strengthen the green innovation effect of GCP. However, the institutional supply of innovation has not yet released a promotional effect. This paper finds that green credit can be used as an environmental governance tool and provides inspiration for local governments to issue environmental protection policies scientifically.

This study analyzed whether the level of corporate governance of companies listed on the Brazilian capital market is related to the investment decisions of Brazilian pension funds. We start from the assumption that the corporate governance system mitigates the moral risk, that may arise in the relationship between corporate managers and investors and respective shareholders, Brazilian pension funds. Shareholding dispersion would enable opportunism for corporate managers, making decisions focused on their interests to the detriment of shareholders or investors. The governance system would signal that this behavior would be controlled, signaling safer behavior for investors. Thus, the hypothesis is that the level of corporate governance would attract pension funds that would understand this system as reducing investment risk. To test this, we used panel data, with a sample of 1,582 observations of companies listed on the Brazilian Stock Exchange (B3) from 2010 to 2017. The statistical results show that corporate governance positively correlates with investment in pension funds. This leads to the conclusion that, for Brazilian pension funds, companies' signaling about their corporate governance practices is a risk-mitigating factor. Furthermore, these results allow us to infer that the company's governance level associated with leverage is a differentiating signaling element as the evidence points to a positive relationship.

The global agricultural supply chain grapples with inherent challenges, including opacity, inefficiency, and ethical concerns, necessitating innovative solutions, particularly within Indian Agriculture Supply Chain Management. Despite these challenges, a notable research gap emerges due to the limited exploration of adoption factors specific to the Indian agricultural context. To bridge this gap, a comprehensive investigation is initiated to delve into the nuanced dynamics of blockchain technology adoption in the Indian agricultural supply chain. The primary aim of this study is to provide insights into these adoption dynamics, employing a three-stage methodology. Starting with an extensive literature review to identify key adoption factors, the study then distributed a well-crafted questionnaire to 200 respondents, yielding 150 complete and analyzable responses. The chosen methodologies, including a one-sample t-test and exploratory factor analysis, allow for a quantitative assessment of the significance of various factors. The study's findings reveal crucial dimensions influencing blockchain adoption, such as perceived benefits, ease of use, trust, efficiency, ethical considerations, data security, integrity, regulatory compliance, and collaboration and stakeholder engagement, providing a comprehensive understanding of the adoption landscape. In conclusion, this research significantly contributes to comprehending the nuanced adoption dynamics of blockchain technology in the Indian agricultural supply chain, supported by a substantial sample size and robust methodological choices. The implications extend to stakeholders navigating the evolving agricultural supply chain, offering valuable insights for future research, policy considerations, and practical strategies, with a central focus on fostering transparency, efficiency, and ethical standards to address global agricultural supply chain challenges.