How will arctic shipping emissions evolve? A spatiotemporal topology-aware transformer approach

Abstract

This study proposes a novel spatiotemporal forecasting framework that integrates spatial clustering with a transformer-based model, termed the spatiotemporal topology-aware transformer (SToT), to predict maritime emissions across the Arctic region. Using Arctic Ship Traffic Data (ASTD), node-based representations are constructed through spatial clustering, enabling the joint learning of spatial correlations and temporal patterns using SToT. Experimental results demonstrate that our model outperforms existing methods by 17% in terms of the root mean square error, relative standard error, and node-level Pearson correlation coefficient, validating its predictive accuracy and robustness. According to SToT-based forecasts extending through 2030, total emissions are projected to increase by 30.8% compared with 2022 emissions, with N₂O and CO₂ exhibiting particularly significant growth. Furthermore, this study discusses the environmental, scientific, and political implications of maritime emissions. The proposed framework offers a scalable and accurate solution for climate risk assessments and sustainable policy planning in the Arctic region.