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Estimates of China’s Provincial Fossil-fuel CO2 Emissions Using Multiple Datasets
韩鹏飞
中国科学院大气物理研究所
China contributed ~30% of global total fossil fuel and industrial processes CO2 emissions, and it pledges to peak its CO2 emissions by 2030. Previous studies often put more attention on the total amount of China’s CO2 emissions based on one dataset. However, the national emission reduction target is usually divided into provincial targets. And the spatiotemporal pattern of provincial CO2 emissions and its uncertainty are still poorly understood. Thus an assessment of provincial-level CO2 emissions by considering activity data sources and local emission factors is urgently needed. Here we collected and analyzed 7 published emission datasets (ODIAC, EDGAR, PKU, MEIC, NJU, CHRED, CEADs) to comprehensively evaluate spatiotemporal distribution of provincial CO2 emissions. We found that emissions estimated from provincial energy statistics were more consistent than that from spatial disaggregation of national energy statistics. The interannual variation of provincial CO2 emissions was captured by provincial-statistics-based datasets, but generally missed by national-statistics-based datasets. The discrepancy for provincial estimates could reach 50%-160% for national inventories, while for provincial inventories they were smaller than 40%. ODIAC and NJU over-estimated provincial carbon emissions by 3%-13%, while PKU under-estimated by 11%-14% compared with CHRED and CEADs. Our results showed that provincial emissions estimates based on national energy statistics have larger uncertainty than provincial inventories. Thus, it is more suitable to use provincial inventories when making policies for sub-national CO2 reductions. For reduction of provincial uncertainties, we suggest using direct measurement data and remote sensing data for inventory validations.