Environmental Challenges (Dec 2023)
Network interlinkages between artificial intelligence and green energy dynamics during the War in a Pandemic: An application of Bayesian vector heterogeneous autoregressions
Abstract
We investigate time-varying network interlinkages between artificial intelligence development and green energy market dynamics during the War in a Pandemic. While regarding AI development, we use First Trust NASDAQ Artificial Intelligence and Robotics ETF, Global X Robotics and Artificial Intelligence and ishares Robotics and Artificial Intelligence, and the green energy market, including green bonds, clean energy, wind energy, solar energy, natural gas, and crude oil using seven Bayesian vector heterogeneous autoregression fashions. During the short, medium, and long run, this paper differentiates dynamically between network interlinkages between these markets. We found some noteworthy results in our study. We show that network interlinkages exhibit remarkable differences over time. Interlinkages between networks are increased in the short, medium, and long term due to transient events occurring in markets during the studied period. As a result of the ongoing COVID-19 epidemic and the Russia-Ukraine conflict, the long-term ties within the system are significantly impacted. Additionally, based on net directional linkages, market directional links indicate a shift in roles (from shock receiver to shock transmitter) during the epidemic and at the beginning of the Russia-Ukraine conflict. Observations of short- and medium-term trends reveal that all three indexes for artificial intelligence development are shock receivers from outside, which is transmitted to the green energy sector. The results indicate that artificial intelligence development persists as shock receivers in terms of long-horizon measures but become shocking transmitters during the illness crisis of COVID-19 (from January 2019 to January 2020) and the Russia-Ukraine conflict (from early 2022).
