Demand for critical minerals is projected to double or even triple by 2030 as the global energy and transportation sectors transition away from fossil fuels toward renewable energy. This anticipated demand raises interest in new critical mineral resources, including marine minerals, to supplement future supplies. While the environmental benefits of transitioning to green energy are many, there are inherent environmental costs. In this study, we compiled broad-scale datasets of prominent environmental features and human uses within four deep ocean regions of the U.S. Exclusive Economic Zone where marine minerals are predicted to occur, called prospective regions: they are the Blake Plateau, the Hawaiian Islands, Offshore California, and the Gulf of Alaska. We generated a normalized metric to score regions based on the co-occurrence of either environmental or human use variables within hexagonal grid cells, allowing us to assess the occurrence intensity and spatial patterns of environmental and/or human use variables within each region. Driven by the presence of threatened species and total species richness, the Blake Plateau and the Hawaiian Islands have the highest environmental occurrence scores, followed by Offshore California and the Gulf of Alaska. Offshore California is the busiest shipping region, followed by the Blake Plateau, the Gulf of Alaska and Hawaiian Islands, and subsea cables are densest in Offshore California, followed by Gulf of Alaska and Hawaiian Islands. The Gulf of Alaska has the largest reported fisheries landings. The sensitivities of biota and human uses to mining are highest for benthic or sessile species and infrastructure on or near ferromanganese crusts or manganese nodules that would be directly and physically altered through crust extraction or nodule removal. This study illustrates the usefulness of comprehensive, spatially explicit risk assessments to inform deep sea mining management and minimize ecological harm and human use conflicts. We assert that the relatively straightforward GIS-based methodology tested here can be applied and iterated upon elsewhere.