Multi-Scale Temporal Integration for Enhanced Greenhouse Gas Forecasting: Advancing Climate Sustainability

Greenhouse gases (GHGs) significantly shape global climate systems by driving temperature rises, disrupting weather patterns, and intensifying environmental imbalances, with direct consequences for human life, including rising sea levels, extreme weather, and threats to food security. Accurate forecasting of GHG concentrations is crucial for crafting effective climate policies, curbing carbon emissions, and fostering sustainable development. However, current models often struggle to capture multi-scale temporal patterns and demand substantial computational resources, limiting their practicality. This study presents MST-GHF (Multi-Scale Temporal Greenhouse Gas Forecasting), an innovative framework that integrates daily and monthly CO 2 data through a multi-encoder architecture to address these challenges. It leverages an Input Attention encoder to manage short-term daily fluctuations, an Autoformer encoder to capture long-term monthly trends, and a Temporal Attention mechanism to ensure stability across scales. Evaluated on a fifty-year NOAA dataset from Mauna Loa, Barrow, American Samoa, and Antarctica, MST-GHF surpasses 14 baseline models, achieving a Test_R 2 of 0.9627 and a Test_MAPE of 1.47%, with notable stability in long-term forecasting. By providing precise GHG predictions, MST-GHF empowers policymakers with reliable data for crafting targeted climate policies and conducting scenario simulations enabling proactive adjustments to emission reduction strategies and enhancing sustainability by aligning interventions with long-term environmental goals. Its optimized computational efficiency, reducing resource demands compared to Transformer-based models, further strengthens sustainability in climate modeling, making it deployable in resource-limited settings. Ultimately, MST-GHF serves as a robust tool to mitigate GHG impacts on climate and human life, advancing sustainability across environmental and societal domains.