The CFR is performance-based, yet requires the use of a single value for the CI of crop production for many feedstocks, regardless of the location where production took place or the methods employed (and despite data that underly the CFR illustrating that the CI of crop production varies widely across Canada for many crops ). The LCFS also does not allow fuel producers complete flexibility to determine a unique field-level feedstock CI by accounting for field-specific biogeochemistry. However, yield and soil carbon sequestration by miscanthus, a perennial grass energy crop that can produce significant biomass with few chemical inputs, have been estimated to vary from approximately 7 to 22 megagrams per hectare and -1.0 to 1.5 megagrams of carbon per hectare-year, respectively, across the rainfed U.S. Life cycle assessment (LCA) is a quantitative methodology regularly used to evaluate the environmental impacts of biofuel production, including as part of low carbon fuel policies and tax credits. In short, LCA entails combining quantities of material and energy flows in a product or process system (e.g., fertilizers, electricity, and biofuels in the case of biorefineries) with information on their likely environmental impacts to determine the overall environmental impacts of the product or process (e.g., according to ISO Standards ). Depending on the goal and scope of a LCA and data availability, the assessment may utilize emissions data from different scales (e.g., national averages vs. regional vs. state-level). There is, however, a recognition that assessments conducted using low-granularity (i.e., not site-specific) data may not properly elucidate differences in environmental impacts that inevitably occur across individual production sites. Thus, there has been movement toward using more site-specific (e.g., "spatially explicit", "location-specific", "field-level") data in LCAs, particularly for analyses of bioenergy crops, biorefining technologies, and bioproducts. Simultaneously, improved technologies to facilitate field-level measurements of important parameters are being developed to support these analyses (i.e., improved measurement, monitoring, reporting, and verification [MMRV]). For example, a proposed combination of field-specific modeling and sensing and big data analytics has the potential to more accurately quantify agricultural greenhouse gas (GHG) outcomes. Given the continued interest in emerging fuel production pathways (e.g., miscanthus to sustainable aviation fuel [SAF] as a means to decarbonize medium-and long-haul aviation travel ), there exists a need to evaluate the implications of such field-specific data for biofuel CI quantification and, in particular, a comparison to CIs calculated based on existing policy guidelines. Understanding the flexibility of policy-specified CI quantifications such as those utilized by the Clean Fuel Production Credit (a tax credit also known as the 45Z credit because it is established in 26 U.S. Code § 45Z) is critical to capitalize on the benefits of biofuels and field-specific monitoring.