High-Resolution Spatiotemporal Carbon Emission Estimation in Northeast China Based on XGBoost and Multi-Source Data

Accurately characterizing spatiotemporal patterns of carbon emissions and their driving mechanisms is essential for advancing regional low-carbon transitions. Focusing on northeast China, a representative old industrial base, this study develops a spatialized carbon emission estimation framework using an XGBoost model that integrates nighttime light data, land-use information, population, and economic indicators. A 1 km resolution carbon emission dataset spanning 2000 to 2021 is generated, and the SHAP method reveals nonlinear responses and stage-dependent evolutions of key driving factors. The results demonstrate three key findings. Carbon emissions in northeast China increased from 636 Mt in 2000 to 1131 Mt in 2021, exhibiting three distinct phases: rapid expansion (2000–2010, +36%), peak stabilization (2010–2015, +13%), and localized contraction (2015–2021, +3%). Liaoning Province contributed 46% of total emissions in 2021, while Jilin showed the fastest growth rate at 92%. County-level Moran’s I values (0.292–0.349) remain substantially lower than city-level values (0.642–0.700), revealing scale-dependent spatial clustering. High–high clusters concentrated persistently in southern Liaoning, encompassing eight cities by 2010, whereas low–low clusters dominated northern Heilongjiang. Population and GDP exhibited saturating marginal effects after 2015, with SHAP values plateauing beyond thresholds of approximately 450,000 persons and 420 billion CNY respectively, indicating gradual decoupling between economic growth and emissions. Industrial mining land influence declined by 68% from 2005 to 2020, while urban land-use ratio maintained stable contributions. This high-resolution spatiotemporal dataset provides empirical evidence for designing threshold-based emission reduction policies, identifying regional transfer risks, and implementing county-level differentiated strategies in old industrial bases undergoing low-carbon transition. The XGBoost–SHAP framework demonstrates transferability to other heavy industrial regions facing similar structural transformation challenges.