The present study explores the impact of energy natural resource productivity and environmental tax on environmental sustainability in six major CO2-emitting economies: the Euro Area, China, South Korea, Japan, the United Kingdom and the United States, from 1997 to 2019. This analysis aims to reveal novel findings and implications for different energy natural resource productivity types and environmental regulations. We employed data regarding leading national and regional CO2 emitters from 1997 to 2020 to conduct an empirical analysis using the panel non-linear auto-regressive distributed lag (NARDL) and panel quantile ARDL (QARDL) methods. The results show that energy natural resource productivity and environmental tax are crucial components in reducing CO2 emissions by controlling for innovation technology and renewable energy consumption. The main findings demonstrate that the impact is stronger in the presence of increased energy natural resource productivity and vice versa. These findings have novel implications for sustainable development and carbon neutrality.

This paper examines the load capacity curve hypothesis by the tourism and renewable energy from top tourism economies in the World. We employ the data from 2000 to 2020 and applied the panel GMM and panel quantile regression to arrive at our empirical findings. The results of the two models demonstrate the non-validity of the Load Capability Curve (LCC) hypothesis and the significant role of touristic arrival (TRA) and greener energy consumption (GEC) on the load capacity factor (LCP) by contrasting the ecological footprint per capita and bio-capacity. Furthermore, renewable and clean energy is recommended to address air pollution and climatic vulnerability. Thus, the empirical results of the current study provide acumens for policymakers of top tourism economies to consume green innovation technologies to counterbalance the environmental and socio-economic issues induced by the tourism sector without halting economic growth and sustainable tourism development. The study discusses policy-related implications for sustainable development.

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.

The prime objective of this article is to examine the policy-making role of metal markets, gold resources, and clean energy markets in the post-COVID-19 era and the Russia-Ukrainian military conflict. In doing so, we analyze the role of fossil fuels, clean energy, and metals markets, considering the military conflict in Ukraine in 2022. The study employs event study methodology (ESM), Total connectedness index (TCI), and network analyses. The results indicate that natural gas and clean energy prices are less affected by conflict in the aftermath of an invasion than traditional energy and metals markets. In addition, we observe an increase in the TCI in the energy markets during announcement days. The TCI of the metals market is greater than that of the energy market. According to network connectivity, the key asset class transmitters of the shock in Europe are the Geopolitical index (GPR), gold, and the clean energy stock index (ERIX). The U.S. markets are less affected by the situation in Ukraine. The average hedge suggests that the optimal hedge differs from one market to the next, with fossil fuels and renewable energy, respectively, being more hedge effective and reducing risk by an average of around 0.80 and 0.59, given their ability to function as a hedging instrument.

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.