Province-level fossil fuel CO2 emission estimates for China based on seven inventories

International audienceChina pledges to reach a peak in CO2 emissions by 2030 and to make its best efforts to reach this peak earlier. Previous studies have paid much attention to the total amount of China's CO2 emissions, but usually only one dataset is used in each evaluation. The pledged national reduction target is administratively divided into provincial targets. Accurate interpretation of province-level carbon emissions is essential for making policies and achieving the reduction target. However, the spatiotemporal pattern of provincial emissions and the associated uncertainty are still poorly understood. Thus, an assessment of province-level CO2 emissions considering local statistical data and emission factors is urgently needed. Here, we collected and analyzed 7 published emission datasets to comprehensively evaluate the spatiotemporal distribution of provincial CO2 emissions. We found that the provincial emissions ranged from 20 to 649 Mt CO2 and that the standard deviations (SDs) ranged from 8 to 159 Mt. Furthermore, the emissions estimated from provincial-data-based inventories were more consistent than those from the spatial disaggregation of national energy statistics, with mean SDs of 26 and 65 Mt CO2 in 2012, respectively. Temporally, emissions in most provinces increased from 2000 to approximately 2012 and leveled off afterwards. The interannual variation in provincial CO2 emissions was captured by provincial-data-based inventories but generally missed by national-data-based inventories. When compared with referenced inventories, the discrepancy for provincial estimates could reach −57%–162% for national-data-based inventories but were less than 45% for provincial-data-based inventories. Using comprehensive data sets, the range presented here incorporated more factors and showed potential systematic biases. Our results indicate that it is more suitable to use provincial inventories when making policies for subnational CO2 reductions or when performing atmospheric CO2 simulations. To reduce uncertainties in provincial emission estimates, we suggest the use of local optimized coal emission factors and validations of inventories by direct measurement data and remote sensing results