This study fills a gap created by previous environmental investigations by including the impact of agricultural employment and technology on the load capacity factor (LCF) rather than solely focusing on CO₂ or greenhouse gas (GHG) emissions, which only measure from the demand side; LCF provides a complete picture of environmental degradation by evaluating both the demand side and supply side. This connection is moderated further by considering the renewable energy and natural resource rent impacts. In this analysis, panel econometric methods are incorporated, including the cross-sectional dependence test. This study uses the Pooled Mean Group–Autoregressive Distributed Lag (PMG-ARDL) model to evaluate the level of effect independent factors had on the LCF of the BRICS (Brazil, Russia, India, China, and South Africa) nations from 1992 to 2020 in the presence of a heterogeneous integration order. The estimations recognize heterogeneity in the effects of agricultural employment, renewable energy, natural resource rent, and technology on LCF, in the short run and long run. According to the empirical results, agricultural employment significantly enhances the LCF both in the short run and long run, implying that employment in agriculture improves environmental sustainability. However, increasing the use of renewable energy protects the environment from degradation only in the long run; it has no significant impact in the short run. On the contrary, natural resource rent and technology lead to a decline in environmental sustainability in the long run. Hence, this study recommends prioritizing renewable energy intake over other energies, providing proper incentives to motivate agricultural entrepreneurship to ensure a sustainable environment.

Although “green” and “clean” are key terms in sustainable environmental development, the literature on the relationship between green finance, clean environmental technology, and a sustainable environment is lacking. Furthermore, the exploitation of natural resources may provide a distinctive perspective on this interconnection, contributing to more promising policy ramifications for the future planet. Thus, this study examines the impact of sustainable environmental technologies, green finance, natural resource rents, and economic growth on environmental sustainability, proxied by the novel “Environmental Sustainability Index (ESI)”. This analysis utilizes data spanning from 2000 to 2021 for China, applying the cutting-edge “augmented autoregressive distributed lag (AARDL)” model. The findings indicate that sustainable environmental technologies do not effectively enhance long-term environmental sustainability, but rather provide a positive contribution to the environment in the short term. In addition, economic expansion and the exploitation of natural resources have adverse effects on environmental sustainability, both in the near term and over an extended period. Nevertheless, green finance has a significant role in promoting environmental sustainability in China. This study further reveals the causal relationship between the chosen variables. This study highlights the necessity of developing a comprehensive strategy aimed at enhancing environmental sustainability and green finance. This can be achieved by allocating funds towards the advancement of clean environmental technology and the extraction of eco-friendly natural resources.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.