Carbon Emission Forecasting Using Multi-Scale Temporal Patches

Accurate carbon emission forecasting is essential for China’s dual-carbon targets and mitigation planning. However, many existing models struggle to capture long-range dependencies while remaining sensitive to short-term fluctuations. We evaluate State Space Transformer (SST) on a Rwanda dataset constructed from weekly Sentinel-5P observations. The resulting time series are noisy, weakly periodic, and heterogeneous across monitoring sites. SST forms interrelated temporal patches through Multi-Scale Temporal Patches (MSTP). It models low-frequency trends with a Mamba state space backbone and captures high-frequency disturbances using an enhanced Local Window Transformer (LWT). These design choices explicitly disentangle low-frequency trends from high-frequency perturbations in noisy observations, improving robustness to non-stationary remote-sensing sequences. Across forecasting horizons from 6 to 72 weeks, SST achieves an average MSE of 0.0331. It reduces MSE by approximately 3.5% compared with the strongest baseline, PatchTST, and consistently outperforms other baselines. With short input histories, SST remains stable for one-year-ahead forecasting (about 53 weeks), which is critical when historical records are limited in operational monitoring systems. Ablation studies further show that MSTP, Mamba, and LWT each contribute substantially to accuracy. Overall, SST-style multi-scale modeling is well suited to noisy monitoring data and supports sustainable planning and emission-trend analysis.