###### 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. semiconductors and technological advancements [2]. In response, Korean semiconductor and display industries are making various efforts to reduce GHG emissions, one of which was the launch of a public-private partnership called the Semiconductor and Display Carbon Neutrality Committee [3]. In South Korea, the estimation of GHG emissions from the semiconductor and display industry relies on Tier 2a and 2b methods proposed in the Intergovernmental Panel on Climate Change (IPCC) guidelines and the IPCC default values. Among the parameters for calculating GHG emissions, the volume fraction of gas and the residual percentage of gas in Bombe are applied to the data of the business site when available. The IPCC default values are applied if there is no data available at the business site [4; 5; 6]. In the context of our study, destruction and removal efficiency (DRE) refers to the efficiency of scrubbers in reducing greenhouse gas emissions in the semiconductor and display industry. The IPCC coefficient for each type of greenhouse gas is averaged over the characteristics of the target scrubber. The IPCC guidelines recommend using country-specific values to calculate GHG emissions as a way to improve inventory reliability. However, South Korea is currently using the IPCC default values for the sector's DRE due to the absence of country-specific estimates. In the case of South Korea, only a limited number of business sites calculate GHG emissions by applying the values estimated by themselves. To improve the reliability of the GHG inventories, it is ideal to develop national GHG emission factors based on an exhaustive survey of on-site data. However, conducting a comprehensive survey for all business sites is practically infeasible due to physical and time constraints. The determination of a reliable DRE for each type of GHG with limited data requires rigorous statistical approaches to optimizing the sample size and treating outliers effectively. This is especially the case for semiconductor and display industries, considering that there are various types of GHGs (about 11 types, including PFCs and HFCs) and a large number of scrubbers (more than 3000). Aiming as a preliminary study for the development of South Korea's national GHG emission factors, the present study explores different approaches to determining the optimal number of samples and processing them so the sampled data can be representative of the population.