In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ ‘s the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption.

In evaluating the influence of greenhouse gases (GHGs) on climate change, the effectiveness of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) is intricately tied to their atmospheric turnover rates, which play a crucial role in their heat-trapping capacity. Understanding the dynamics of how these gases cycle through the atmosphere is essential for assessing their respective contributions to the greenhouse effect and, consequently, their impact on global warming and climate change. The prime objective of this research is to examine the role of climatic change, agriculture output, and fertilizer use on the agriculture soil's greenhouse gas emissions. In doing so, the present study has focused on the temperature of land, fertilizer consumption, crop and livestock production, and energy used in agriculture soils on the pollution level of agricultural soils. The study further delineates the intricate interdependencies between climate change factors and GHG emissions using novel econometric methodologies, specifically the PMG-ARDL, SC-ARDL, and Dumitrescu Hurlin Panel Causality frameworks. In doing so, we use a large panel dataset spanning 1990 to 2020. The estimations show that climate change, as measured by variations in terrestrial temperature, has a discernible and positive impact on GHG emissions over the short and long term. Energy consumption and livestock production positively correlate with GHG emissions, with the former having a more pronounced effect. The implications of fertilizer usage and overall crop yield become noticeably significant in the long term. It emphasizes the importance of using a diachronic perspective when assessing GHG emissions in the agricultural sector. It is also worth noting that agricultural land use appears to negatively impact GHG emissions, emphasizing the importance of implementing sustainable land management practices to mitigate adverse environmental consequences. The study also explores the causality between climate change, agricultural practices, and GHG emissions, revealing a bidirectional association between climatic change and soil emissions. Additionally, unidirectional causation is observed from fertilizer consumption and crop production to emissions, underscoring the importance of adopting sustainable agricultural practices to reduce emissions. The findings offer valuable insights for governments and researchers to create sustainability-related strategies for dealing with climate change issues, safeguarding natural resources, and ensuring a sustainable future for agriculture.

This study investigates the dynamic connectedness between the USA's corporate bond market (CB) and various factors, including WTI, financial uncertainty, and geopolitical risks. We employ two advanced techniques to analyse these relationships: TVP-Vector autoregressive (TVP-VAR) and VAR connectedness. Specifically, we focus on two significant events, the Russian-Ukrainian conflict (RUC) and the COVID-19 pandemic (C19P), to provide insights into the behaviour of the CB during these critical periods against the oil prices and uncertainties. The empirical analysis reveals compelling findings, particularly concerning the extreme events and the magnitude of effects observed. We find a significant increase in interconnections over the time impacts during these two events, lending support to using an asymmetric and heterogeneous product over the time-varying. Furthermore, we observe that the influence of the GPR and the VIX factors is more robust when uncertainty rises rather than decreases, indicating temporary events. Policymakers and macroprudential authorities can benefit from these findings, as they emphasise the need to adapt to a changing monetary policy and reduce reliance on energy volatility to make informed decisions in a rapidly evolving financial landscape.