Publicly accessible data has been used to construct a county-scale supply chain model of United States gasoline consumption and quantify the scope 3 CO2; emissions from gasoline consumption. Our model tracks the movement of refined fuels from county of refinement to county of blending and eventually to county of consumption via multiple infrastructure networks – pipelines, tankers, trains, and trucks. Where quantities of the fuel moved across different linkages and different transportation modes are known, they are used as is. However, for the vast majority of the country, the exact quantities of fuel moved between county of refining and county of blending or county of blending and county of consumption, as well as the mode of transportation, is not known with certainty. Linear optimization is used to model those links with constraints related to total supply and demand at lower spatial resolutions (State-level and Petroleum Administration for Defense (PAD) Districts). This is the first real attempt at a spatially-resolved scope 3 style CO2 emissions data product specific to United States gasoline consumption. This model can improve understanding of the complex liquid fuel supply chain, and has significant implications for local policy. With a complete model of scope 3 CO2 emissions, it is also possible to analyze how the differences between scope 1 and scope 3 emissions vary across the country. Finally, this model lays the foundation to model the evolution of the U.S. gasoline supply chain – its dependencies, critical linkages, and pinch points – and the evolution of scope 1 and scope 3 CO2 emissions using the full extent of available public data.

The carbon cycle displays strong sensitivity to short term variations in environmental conditions, and it is key to understand how these variations are linked with variations in CO2 fluxes. Previously, atmospheric observations of CO2 have been sparse in many regions of the globe, making it challenging to evaluate these relationships. However, the OCO-2 satellite, launched in July 2014, provides new insight into global CO2 fluxes, particularly in regions that were previously difficult to monitor. In this study, we combine OCO-2 observations with a geostatistical inverse model to explore data-driven relationships between inferred CO2 flux patterns and environmental drivers. We further use year 2016 as an initial case study to explore the applicability of the geostatistical approach to large satellite-based inverse problems. We estimate daily, global CO2 fluxes at the model grid scale and find that a combination of air temperature, daily precipitation, and photosynthetically active radiation (PAR) best describe patterns in CO2 fluxes in most biomes across the globe. PAR is an adept predictor of fluxes across mid-to-high latitudes, whereas a combined set of daily air temperature and precipitation shows strong explanatory power across tropical biomes. However, we are unable to quantify a larger number of relationships between environmental drivers and CO2 fluxes using OCO-2 due to the limited sensitivity of total column satellite observations to detailed surface processes. Overall, we estimate a global net biospheric flux of -1.73 ± 0.53 GtC in year 2016, in close agreement with recent inverse modeling studies using OCO-2 retrievals as observational constraints.