2015年《巴黎氣候協定》提出將全球升溫控制在1.5度以內的目標，但全球暖化問題仍在惡化。歐盟氣候監測機構「哥白尼氣候變化服務」於2024年3月報告顯示，該月為有記錄以來最熱的3月，且連續第10個月刷新高溫紀錄，海面溫度亦達新高。全球電子產業，特別是供應鏈碳排放占比超過四分之三的半導體製造廠，2030年的市場規模預計將翻倍成長，所以在減碳行動是屬於重要角色。半導體產業作為推動全球技術進步的核心產業之一，其減碳努力不僅是自身使命，更可透過技術與方法的創新，為其他產業提供減碳解決方案，助力全球實現碳減排目標並推動跨產業可持續發展。
半導體產業的主要碳排放來源為電力消耗，但現有產品碳足跡計算方法存在許多不確定性，特別是在無塵室環境及製程機台的電力活動數據收集與分配方面，這直接影響碳足跡管理的精確性與減碳措施的有效性。本研究針對此問題，運用特性要因分析法進行問題診斷，並透過萃智理論提出創新解決方案，包括在廠務設備系統及製程機台安裝智慧監控裝置，定期收集並數位化處理相關數據，以提升數據管理效率。
本研究建議企業導入ISO 50001能源管理系統，將廠務設備與製程機台的用電數據分開管理，並結合生產管理系統數據，建構精準的電力活動數據模型。此系統可自動計算各產品的電力活動數據，迅速識別生產過程中的用電熱點並進行改進。同時，可靠的數據亦可用於制定自主減量計畫的目標，並作為年度目標達成查核的依據。透過上述措施，企業在追求經濟效益的同時，亦能履行環境與社會責任。

The 2015 Paris Agreement set a goal to limit global warming to within 1.5°C, yet the issue of global warming continues to worsen. According to the European Union's Copernicus Climate Change Service, March 2024 was reported as the hottest March on record, marking the 10th consecutive month of record-breaking high temperatures, with sea surface temperatures reaching new highs. The global electronics industry, particularly semiconductor manufacturers, faces increasing pressure to reduce their product carbon footprint, as supply chain emissions account for over three-quarters of the sector's carbon footprint. With over one-third of the semiconductor market concentrated in East Asia and its market size expected to double by 2030, reducing carbon emissions in this sector is essential.
The primary challenge lies in accurately measuring and managing electrical activity data in semiconductor manufacturing facilities. Current product carbon footprint calculation methods face significant uncertainties, particularly in collecting and allocating electricity consumption data from cleanroom environments and manufacturing equipment. To address these challenges, this study employs TRIZ theory to develop innovative solutions for more precise carbon footprint measurement and management.
The implementation of digitalization processes, including smart monitoring devices on facility systems and manufacturing equipment, enables periodic collection and processing of relevant data, thereby enhancing data management efficiency. Furthermore, this study recommends that enterprises implement the ISO 50001 Energy Management System to effectively separate and monitor electricity data between facility systems and manufacturing equipment. By integrating production management system data, organizations can establish precise electricity activity data models that automatically calculate product-specific energy consumption and quickly identify areas for improvement in the production process.
This comprehensive approach enables enterprises to establish reliable data for voluntary reduction targets and annual achievement audits, ultimately allowing them to achieve economic benefits while fulfilling their environmental and social responsibilities. As a key driver of global technological progress, the semiconductor industry's commitment to carbon reduction serves as both a critical responsibility and an opportunity to develop innovative solutions that can be adopted across various sectors, contributing to global carbon reduction goals and promoting sustainable development.

[1]歐洲聯盟氣候監測機構「哥白尼氣候變化服務」，2024年遇最熱3月全球連10月刷新高溫紀錄。取自https://climate.copernicus.eu/march-2024-10th-consecutive-record-warm-month-globally。最後瀏覽日：2024/09/01。
[2]歐洲聯盟氣候監測機構「哥白尼氣候變化服務」，2024 年 7 月全球日平均氣溫創下新紀錄。取自https://udn.com/news/story/6812/8116685。最後瀏覽日：2024/09/07。
[3]August Rick, Katrin Wu, Tianyi Luo(2023), A forecast of tech supply chain emissions and electricity consumption by 2030。取自https://www.greenpeace.org/eastasia/invisible-emissions/。最後瀏覽日：2024/09/07。
[4]王孝君. (2014). 半導體 IC 代工產品製程技術之碳足跡分析. 交通大學工學院永續環境科技學程學位論文, 2014, 1-64.
[5]駱文傑, 林照閔, 黃雍舜, & 陳兆雄. (2011). 12 吋半導體晶圓廠房之能耗與能源消耗指標研究. 冷凍空調與能源科技雜誌, (67), 31-39.
[6]Kannaian, T. K. (2018). Capacity model with sustainability scope to predict the semiconductor manufacturing's energy consumption and carbon dioxide emissions. Texas State University, San Marcos, Texas.
[7]張筠苡，半導體封測業者的永續低碳實踐。取自https://ieknet.iek.org.tw/iekrpt/rpt_more.aspx?actiontype=rpt&indu_idno=1&domain_id=2&rpt_idno=616215597。最後瀏覽日：2024/09/07。
[8]環境部氣候變遷署(2014)，溫室氣體排放量盤查作業指引113年版。取自https://ghgregistry.moenv.gov.tw/epa_ghg/GuideAndCalculation/GuideAndCalculation.aspx。最後瀏覽日：2024/09/08。
[9]環境部(2013)，溫室氣體排放量盤查登錄及查驗管理辦法。取自https://enews.moenv.gov.tw/Page/3B3C62C78849F32F/8d5700d8-629c-4b5d-bf0a-95a9055d7821。最後瀏覽日：2024/09/08。
[10]金融監督管理委員會，上市櫃公司永續發展路徑圖。取自https://www.sfb.gov.tw/ch/home.jsp?id=95&parentpath=0,2&mcustomize=news_view.jsp&dataserno=202203030001&dtable=News。最後瀏覽日：2024/09/08。
[11]經濟部能源署，溫室氣體-電力排碳係數。取自https://www.moeaea.gov.tw/ecw/populace/content/SubMenu.aspx?menu_id=114。最後瀏覽日：2024/09/08。
[12]英國標準協會(2011)，「PAS 2050:2011：產品及服務生命週期溫室氣體排放標準」。
[13]黃雪娟(2020)，ISO溫室氣體系列國際標準趨勢介紹，永續產業發展雙月刊，53，29-39。
[14]經濟部產業發展署，「積體電路封裝測試服務」產品環境宣告-產品類別規則(草案版)。取自https://www.idbcfp.org.tw/detail.aspx?nid=3281。最後瀏覽日：2024/09/08。
[15]經濟部產業發展署(2021)，PCR 20-059液晶顯示器第4.0版。
[16]環境部，產品碳足跡資訊網-碳足跡資料庫-平台資料庫。取自https://cfp-calculate.tw/cfpc/WebPage/WebSites/CoefficientDB.aspx。最後瀏覽日：2024/09/08。
[17]Loredana, E. M.(2017). The analysis of causes and effects of a phenomenon by means of the “fishbone” diagram. Ann Econ Ser, 5, 97-103.
[18]Ilevbare, I. M., Probert, D., & Phaal, R.(2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2-3), 30-37.
[19]Ekmekci, I., & Nebati, E. E.(2019). Triz methodology and applications. Procedia Computer Science, 158, 303-315.
[20]高木芳德(2018)，創意不足？用TRIZ40則發明原理幫您解決！ ：不用再羨慕日本人的創意！，五南出版股份有限公司。
[21]魏佩如.(2010). 產品碳足跡計算不確定性分析之研究. 臺北科技大學環境工程與管理研究所學位論文, 2010, 1-138.
[22]漢克林企業有限公司，了解無塵室中三個主要空調設備 MAU、DCC、FFU 。取自https://www.hencolin.com/air-filter-articles-in-focus/understanding-the-three-major-ventilation-units-in-a-clean-room-mau-dcc-ffu/。最後瀏覽日：2024/11/15。