A multi-factor carbon price prediction method MEEMD-LSTM is proposed based on traditional Long Short-term Memory (LSTM) neural network. Multi-factor used in carbon price prediction method included the historical carbon price and other factors which affect carbon price fluctuation. The change...

Background: Global carbon stocks in forest biomass are decreasing by 1.1 Gt of carbon annually, owing to continued deforestation and forest degradation. Deforestation emissions are partly offset by forest expansion and increases in growing stock primarily in the extra-tropical north. Innovative...

This paper proposes two dimension-reduction and forecasting quantile methods (i.e., the quantile group lasso and the quantile group SCAD models) to predict carbon futures returns and investigate the predictability of a comprehensive group of factors including market fundamental variables and...