Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry

Abstract

Aiming to serve as a preliminary study for South Korea’s national GHG emission factor development, this study reviewed data treatment and sample size determination approaches to establishing the destruction and removal efficiency (DRE) of the semiconductor and display industry. We used field-measured DRE data to identify the optimal sample size that can secure representativeness by employing the coefficient of variation and stratified sampling. Although outlier removal is often a key process in the development of field-based coefficients, it has been underexplored how different outlier treatment options could be useful when data availability is limited. In our analysis, three possible outlier treatment cases were considered: no treatment (using data with outliers as they are) (Case 1), outlier removal (Case 2), and adjustment of outliers to extreme values (Case 3). The results of the sample size calculation showed that a minimum of 17 and a maximum of 337 data (out of a total of 2968 scrubbers) were required for determining a CF4 gas factor and that a minimum of 3 and a maximum of 45 data (out of a total of 2917 scrubbers) were required for determining a CHF3 gas factor. Our findings suggest that (a) outlier treatment can be useful when the coefficient of variation lacks information from relevant data, and (b) the CV method with outlier adjustment (Case 3) can provide the closest result to the sample size resulting from the stratified sampling method with relevant characteristics considered.