Energy demand is rising day by day, driven mainly by the development of countries. At the same time, uneven economic growth in countries is the prime cause of inequality in energy consumption. Keeping in view the worth of energy in the growth process, this study quantifies the impact of energy inequalities and trade on environmental quality over the period 1995–2018 for 57 countries. The Theil approach is used to quantify inter-and intra-regional disparities in five energy sources; oil, coal, natural gas hydroelectricity, and renewable energy. The results show that North America has the highest oil consumption inequality between the regions while East Asia & Pacific has the highest index value within the regions. Coal consumption inequality is declining in North America, but not in East Asia and the Pacific. Europe & Central Asia, and North America have the highest inequalities in natural gas consumption between the regions. Inequality is shrinking in hydropower consumption between the regions, however, such trend has not loomed within the regions. Europe & Central Asia and East Asia & Pacific have major renewable consumption inequalities within the regions. Generally, there is a decreasing temporal trend in energy consumption inequalities of all energy sources. The GMM technique is applied to investigate the impact of energy inequalities and trade openness on environmental quality. The results reveal that energy inequalities degrade environmental quality. Moreover, trade has a positive impact on environmental quality. However, democratic countries can be advantageous to improve the environmental quality. The study implies that countries should take actions to reduce energy inequalities within and between the regions. Specialization in production through trade can also be an option for improvement in the environment.

The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty (EPU), and bitcoins by employing the Nonlinear Autoregressive Distribution Lag (NARDL) estimation technique recently developed by Shin et al. The outcome of the empirical analysis confirms an asymmetric cointegration between EPU, bitcoins, the clean energy index, oil prices, and green bonds. The NARDL estimation substantiates that positive shock in EPU exerts a negative impact on green bonds, whereas a negative shock in EPU increases the performance of green bonds. It implies, in the long run, a 1 percent increase (decrease) in EPU decreases (increases) the performance of green bonds by 0.22 percent and 0.11 percent, respectively. Likewise, the study also confirms a bidirectional relationship between bitcoins and green bonds. A positive shock in bitcoin increases the performance of green bonds and vice versa. In addition, our study also reveals a direct co-movement between clean energy, oil prices, and green bonds. This outcome implies that green bonds are not a different asset class, and they mirror the performance of other asset classes, such as clean energy, oil prices, and bitcoins. The findings offer several implications to understand the hedging and diversification properties of bitcoins, and assist in understanding the role of U.S. economic policy uncertainty on green bonds.

The advancement in fintech technological development in emerging countries has accelerated the role of digital finance in economic development. Digital finance assists in financial inclusion; however, it may also increase the chances of financial instability due to systematic risks. Emerging countries are also in the clutches of shadow economic growth, which reduces taxable income revenue and creates pressure on financial inclusion prospects. The current study attempts to measure the impact of digital finance on the shadow economic growth and financial stability among the selected South Asian emerging countries. We have used the CUP-FM and CUP-BC estimation methods to measure the above relationship on two model frameworks from 2004 to 2018, with the former measuring the influence of digital finance on the shadow economy and the latter examining the relationship between digital finance and financial stability. In addition, the second-generation unit root test, and the Westerlund cointegration analysis are also employed to confirm the stationarity and cointegration among the variables. The result of the Westerlund’s cointegration confirms a long cointegration between the explanatory and outcome variables. Furthermore, the long-run estimation results conclude that an increase in digital finance helps in reducing the growth of the shadow economy among the selected sample countries. However, it also increases the likelihood of systematic risks and increases financial instability. The study also reveals that the control variables like unemployment and industrial productivity also have a significant influence on financial stability and the shadow economy. The findings will assist readers in comprehending how digital finance influences the shadow economy and promotes financial inclusion and stability in emerging nations.