Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry
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-measure...
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MDPI AG
2024-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/666 |
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| author | Seongmin Kang Jiyun Woo Eui-chan Jeon Joohee Lee Daekee Min |
| author_facet | Seongmin Kang Jiyun Woo Eui-chan Jeon Joohee Lee Daekee Min |
| author_sort | Seongmin Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T09:58:51Z |
| format | Article |
| id | doaj.art-80e067dba092481ab29a9dad4ee06fbb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:51Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-80e067dba092481ab29a9dad4ee06fbb2024-01-29T13:43:33ZengMDPI AGApplied Sciences2076-34172024-01-0114266610.3390/app14020666Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display IndustrySeongmin Kang0Jiyun Woo1Eui-chan Jeon2Joohee Lee3Daekee Min4The Seoul Institute, Seoul 05756, Republic of KoreaDepartment of Climate and Environment, Sejong University, Seoul 05006, Republic of KoreaDepartment of Climate and Environment, Sejong University, Seoul 05006, Republic of KoreaDepartment of Climate and Energy, Sejong University, Seoul 05006, Republic of KoreaDepartment of Statistics, Duksung Women’s University, Seoul 01369, Republic of KoreaAiming 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.https://www.mdpi.com/2076-3417/14/2/666greenhouse gassemiconductor and display industrydestruction or removal efficiency (DRE)sample size |
| spellingShingle | Seongmin Kang Jiyun Woo Eui-chan Jeon Joohee Lee Daekee Min Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry Applied Sciences greenhouse gas semiconductor and display industry destruction or removal efficiency (DRE) sample size |
| title | Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry |
| title_full | Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry |
| title_fullStr | Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry |
| title_full_unstemmed | Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry |
| title_short | Data Processing and Sample Size Determination Approaches to Developing South Korea’s Destruction and Removal Efficiencies of the Semiconductor and Display Industry |
| title_sort | data processing and sample size determination approaches to developing south korea s destruction and removal efficiencies of the semiconductor and display industry |
| topic | greenhouse gas semiconductor and display industry destruction or removal efficiency (DRE) sample size |
| url | https://www.mdpi.com/2076-3417/14/2/666 |
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