A digital scheduling framework combining machine learning, optimization and blockchain technology could reduce carbon emissions at a lower cost in regional energy systems, according to research in the International Journal of Environment and Pollution. The system cut the marginal emission-reduction cost—the additional expense of removing one more ton of carbon—by about 12% when the carbon quota was restricted to half the system's baseline emissions.
