Meta-learning applied to a multivariate single-step fusion model for greenhouse gas emission forecasting in Brazil

Abstract

ABSTRACT Climate change, driven by greenhouse gas (GHG) emissions, causes extreme weather events, impacting ecosystems, biodiversity, population health, and the economy. Predicting GHG emissions is crucial for mitigating these impacts and planning sustainable policies. This research proposes a novel machine learning model for GHG emissions forecasting. Our model, Meta-Learning Applied to Multivariate Single-Step Fusion Model, utilizes historical GHG emissions from Brazil over the past 60 years to predict CO2 and CH4 emissions. Additionally, the model employs a unique combination of two techniques in time series forecasting: (i) in the Fusion Model, each substance is individually extracted and trained based on a specific decision task, then integrated into the same feature space; (ii) Meta-Learning allows the model to learn from past prediction tasks, leading to better generalization. Our model was compared with state-of-the-art time series models using the same dataset. The results show that our approach reduces the mean absolute percentage error by 49.06% with 95% confidence compared to the Transformer-based TST model, demonstrating its superior performance and low estimated CO2 emissions of 0.01 kg CO2eq. Furthermore, the model's flexibility allows it to be adapted for various environmental studies and general time series forecasting.