CEDUP: Using incremental learning modeling to explore Spatio-temporal carbon emission distribution and unearthed patterns at the municipal level

Carbon emissions reduction has become a world consensus. Cities have an essential role to play in addressing emission reductions. However, previous studies have estimated China's municipal-level carbon emissions based on provincial-level emission patterns, and such a top-down carbon emission accounting approach has led to biased results. Therefore, this study employed an incremental learning ensemble model and a Savitzky-Golay algorithm tomeasure carbon emission distribution and unearth patterns at the municipal level based on nighttime light (NTL) and regional development characteristics (GDP, population, patents, industry structure). The performance of the proposed method is substantially better than its counterparts in terms of municipal-level estimation (R-square boosted by 20.64%). This research shows significant difference in carbon emission mechanisms between provinces and cities and demonstrates that carbon emissions are time-continuous. It also shows that per-capita carbon emissions are peaking in many China's cities, except in some heavy industrial cities. Our approach provides accurate and dynamic monitoring of municipal emissions in China.