Electricity generation is a significant source of global carbon emissions and a major driver of climate change. In the interest of mitigating climate change impacts, there is substantial interest in minimizing the carbon footprint associated with energy generation through demand-side shifts and supply-side transitions to cleaner power sources. Effective implementation of these measures requires a thorough spatiotemporal understanding of marginal emission factors (MEFs) that are associated with the generators that respond to changes in electricity demand. In this study, we present a hybrid model that estimates MEFs in real-time and forecasts hour-ahead values at a zonal level. The model proposed combines the capabilities of a dispatch model to capture the physical characteristics of the electricity grid with an advanced machine learning algorithm to project these values in the future. This analysis focuses on the NYS electricity grid, based on a publicly available digital twin of the NYISO. The proposed model provides insights into seasonal and diurnal patterns of zonal MEFs and the expected future behavior to support informed decision-making toward reducing carbon emissions.