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White Paper: Sample-Based Estimation and Uncertainty Analysis
When undertaking a corporate greenhouse gas (GHG) inventory, the preferred approach is to gather and receive actual activity data (e.g. purchased electricity, purchased natural gas, fleet fuel usage) for all corporate locations and operations. However, many companies, especially those with a significant portion of leased or franchised locations, or other sources of disparate data, view data collection for a full GHG assessment as burdensome, costly and time-consuming. As an alternative, sample-based estimations can be applied on a large scale for companies wishing to conduct a preliminary inventory estimate. ClearCarbon has found that performing sample-based inventory estimates allows our clients to calculate their corporate footprint within a measurable margin of error, while expending a fraction of the resources and effort required for a complete GHG inventory.
A preliminary GHG inventory can serve as a starting point for quantifying corporate carbon impacts, providing companies the opportunity to: begin reporting emissions publicly, share footprint data with management and shareholders; enhance the development of corporate sustainability goals; initiate contact with data suppliers; highlight areas for improving data quality; identify cost- and energy-saving initiatives; and determine efficiencies for future inventories.
This paper outlines ClearCarbon’s method of using statistical analysis to determine the uncertainty associated with sample-based estimations for GHG inventories. The following method provides companies conducting preliminary GHG inventories or those with data gaps in their full inventories a measurable level of accuracy for their calculated emissions. In addition, we discuss two approaches for aggregating the uncertainties associated with sample-based estimations and highlight the benefits of sample-based estimation and uncertainty analysis in a short case study.
