An investigation of dynamic connectedness between robotic, artificial intelligence development, and carbon risk by quantile spillovers

Climate change is causing issues in all areas of the economy, and robotic and artificial intelligence development have the potential to prevent these risks. We investigate quantile spillovers using quantile vector autoregression (QVAR), robotics, artificial intelligence development, and carbon risk. Using global data on a global daily dataset from carbon emission future (CEF), Global X Robotics & artificial intelligence (BOTZ), iShares Robotics and Artificial Intelligence Multisector (IRBO), First Trust Nasdaq Artificial Intelligence and Robotics (ROBT) from April 1, 2019, to October 28, 2022, our study illustrates that there exists considerable dynamic connectedness between robotic, artificial intelligence development, and carbon risk. The COVID-19 epidemic and the Russia-Ukraine war caused a brief change in direct connectivity over quantiles. CEF consistently receives shocks from the system, while IRBO and ROBT transmit significant shocks to systems. The dynamic net pairwise directional connectivity in the context of quantiles demonstrates that the common eigen factor (CEF) mainly absorbs the influences of the three other variables, especially during COVID-19. This dynamic net pairwise directional connectivity across quantiles demonstrates how unknown events, such as the Ukraine-Russia conflict and the COVID-19 epidemic, affect investor mood as well as both environmentally unfriendly and environmentally beneficial cryptocurrencies. Hence, our paper calls for policy designs that maximize the positive effects of robotic and AI development on the carbon emission market.