The volatility of the energy market, particularly crude oil, significantly impacts macroeconomic indices, such as inflation, economic growth, currency exchange rates, and trade balances. Accurate crude oil price forecasting is crucial to risk management and global economic stability. This study examines various models, including GARCH (1,1), Vanilla LSTM, GARCH (1,1) LSTM, and GARCH (1,1) GRU, to predict Brent crude oil prices using different time frequencies and sample periods. The LSTM and GARCH (1,1)-GRU hybrid models showed superior performance, with LSTM slightly better in predictive accuracy and GARCH (1,1)-GRU in minimizing squared errors. These findings emphasize the importance of precise crude oil price forecasting for the global energy market and manufacturing sectors that rely on crude oil prices. Accurate forecasting helps ensure economic sustainability and stability and prevents disruptions to production and distribution chains in both developed and emerging economies. Policymakers may choose to implement energy security measures in response to the significant impact of crude oil price volatility on the macroeconomic indicators. These measures could include maintaining strategic reserves, diversifying energy sources, and decreasing the dependence on volatile oil markets. By doing so, a country's ability to handle oil price fluctuations and ensure a stable energy supply can be enhanced.

This study delves into the intricate relationship between financial digitization and green innovation, aiming to shed light on their dynamic interplay within a global context. Spanning from 2003 to 2020, the study encompasses 15 diverse countries, encompassing both developed and emerging economies, including Australia, Brazil, Canada, China, France, Germany, India, Italy, Japan, Mexico, Russian Federation, South Africa, Turkey, the United Kingdom, and the USA. It not only explores the direct connection between financial digitization and green innovation but also takes into account various controlling factors such as economic growth, industrial value addition, research and development expenditure, and gross national expenditure. The key findings from quantile regression reveal financial digitization have a significant positive effect, indicating that in countries with lower green innovation levels, an increase in digital financial services significantly boosts green innovation. This positive impact persists across quantiles, even in countries with higher green innovation levels, albeit to a lesser degree. Economic growth consistently shows a negative association with green innovation across all quantiles. Research and development expenditure consistently demonstrate a positive relationship with green innovation across all quantiles, emphasizing that countries allocating a higher percentage of their economic growth to research and development expenditure activities experience substantial increases in green innovation. This underscores that countries allocating a higher percentage of their economic growth to research and development expenditure activities experience substantial increases not only in green innovation but also in the facilitation of green finance initiatives.

This research presents an in-depth investigation into the dynamic correlation between geopolitical conflicts and carbon markets utilizing the Time-Varying Parameter Vector Autoregression (TVP-VAR) technique. The analysis focuses on the interconnectedness between the Geopolitical Risk Index Daily (GPRD) and vital carbon pricing instruments, specifically the Intercontinental Exchange Endex European Union Allowance (ECEFDC), KraneShares California Carbon Allowance Strat ETF (KCCAK), Shanghai Environment and Energy Exchange China Emission Allowances Online Transactions (SAXCEA), and S&P Global Ex-Japan LargeMidCap Carbon Efficient Index (SPGJ). The daily fluctuations were traced from May 2021 to July 2023. The analysis is divided into short- and long-term connectedness, with particular emphasis on the impact of the Russia-Ukraine conflict on the GPRD's spillover on carbon markets. The short-term connectedness (1–5 days) between GPRD and ECEFDC shows variability, fluctuating between 10% and 40%. Conversely, long-term connectedness exhibited a significant increase during the conflict, peaking at approximately 34% by mid-2022. The analysis of the Total Dynamic Connectedness (TCI) between the GPRD and the KCCAK indicates comparable magnitudes, although with minor initial discrepancies. The short-term connectedness of GPRD and KCCAK decreases from its peak of approximately 10% to approximately 1%. Conversely, long-term connectedness varies between approximately 32% and 2% from May 2022 onwards. The long-term connectedness between GPRD and SAXCEA revealed variable patterns, peaking at around 18% at the beginning of the sample period and rapidly reducing to around 1% within two months. The analysis of the connectedness between GPRD and the SPG) identifies intense fluctuations in both TCI and long-term connectedness. After an initial increase and decrease, these patterns rebound and experience another increase. This research provides significant insights into the complex dynamics of geopolitical conflicts and carbon markets, particularly the impact of the Russia-Ukraine conflict on carbon market behavior.