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Coal is the primary energy source in China, and its life cycle inventory (LCI) is widely used as background data for life cycle assessment studies. Previous research indicates that the inventory of coal production varies regionally. However, the development of complete regionalized LCIs for coal production is quite limited. Here, we establish the first provincial-level LCIs of local coal production and market for coal in China, based on a database of 6,122 coal mines and developed models. In the inventory results of local coal production, the coefficients of variation (CVs) of nine indicators exceed 0.5, especially SO 2 and particulate matter emission factors (CVs > 1). Compared with that, the interprovincial coal trade homogenizes the provincial production inventory of market for coal relatively, despite four indicators with CVs exceeding 0.5. Therefore, the regionalized inventory with remarkable spatial differentiation can provide more accurate fundamental data for future research such as electricity production. Furthermore, CH 4 emissions from coal production account for 24% of China's total methane emissions, highlighting its significance in mitigating global warming. Moreover, through the increasing coal trade, the significant and implicit plunder of water resources from the three coal net-exporting provinces, located in water-scarce areas, should be noted.

Supply chain information is invaluable to further regionalize product life cycle assessments (LCAs), but detailed information linking production and consumption centers is not always available. We introduce the commodity supply mix (CSM) defined as the trade-volume-weighted average representing the combined geographic areas for the production of a commodity exported to a given market with the goal of (1) enhancing the relevance of inventory and impact regionalization and (2) allocating these impacts to specific markets. We apply the CSM to the Brazilian soybean supply chain mapped by Trase to obtain the mix of ecoregions and river basins linked to domestic consumption and exports to China, EU, France, and the rest of the world, before quantifying damage to biodiversity, and water scarcity footprints. The EU had the lowest potential biodiversity damage but the largest water scarcity footprint following respective sourcing patterns in 12 ecoregions and 18 river basins. These results differed from the average impact scores obtained from Brazilian soybean production information alone. The CSM can be derived at different scales (subnationally, internationally) using existing supply chain information and constitutes an additional step toward greater regionalization in LCAs, particularly for impacts with greater spatial variability such as biodiversity and water scarcity.