半導體工廠之碳排放問題的改善研究－以A 公司為研究對象;Improving Carbon Emission Management in Semiconductor Manufacturing - A Case Study of Company A

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/97287

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/97287

Title:	半導體工廠之碳排放問題的改善研究－以A 公司為研究對象;Improving Carbon Emission Management in Semiconductor Manufacturing - A Case Study of Company A
Authors:	許耘嘉;Hsu, Yun-Jia
Contributors:	工業管理研究所
Keywords:	半導體產業;碳排放管理;減碳策略;統計建模;永續發展;ESG;Semiconductor industry;carbon emission management;decarbonization strategy;statistical modeling;sustainability;ESG
Date:	2025-07-16
Issue Date:	2025-10-17 11:05:28 (UTC+8)
Publisher:	國立中央大學
Abstract:	面對氣候變遷與全球淨零碳排目標之驅動，碳排放管理已成為高耗能產業之核心議題，尤其在以能源密集著稱之半導體製造產業中更顯關鍵。為回應政策與市場對企業永續責任之高度關注，本研究以台灣具代表性之A 公司半導體測試廠為研究對象，探討其碳排放現況並提出可行之改善策略。本研究首先透過文獻探討建構永續理論基礎與碳排量化方法架構，整理半導體產業特性與國際政策趨勢，奠定研究脈絡。其次，結合A 公司實際生產數據，運用多元迴歸、決策樹與變異數分析等統計方法，建構碳排放預測模型，進一步辨識高碳排熱點作業站。並參考台積電、聯電、日月光等領導廠商節能實務，提出多項改善情境與再生能源使用方案之成效。研究結果顯示，A 公司碳排放主要集中於特定測試製程與設備運作時段，能源使用結構與設備稼動率為影響碳排差異之關鍵因子。透過優化調度、提升能效與綠電導入，可望達成顯著減碳效果。本研究最終提出結合ESG（Environmental, Social, and Governance）制度管理、智慧製造導入與政策接軌之永續轉型建議，強化企業在碳中和趨勢中的競爭力與風險韌性。;In response to climate change and the global net-zero carbon emission agenda, carbon management has become a critical issue, particularly in the energy-intensive semiconductor industry. This study focuses on a representative semiconductor testing plant, Company A in Taiwan, aiming to analyze its carbon emission profile and propose feasible mitigation strategies. The research first reviews relevant literature to establish a theoretical foundation in sustainability, carbon quantification frameworks, and international regulatory trends. Using actual production data from Company A, the study applies statistical tools such as multiple regression, decision trees, and analysis of variance (ANOVA) to construct emission prediction models and identify high-emission hotspots. Best practices from leading firms like Taiwan Semiconductor Manufacturing Company Limited (TSMC), United Microelectronics Corporation (UMC), and Advanced Semiconductor Engineering (ASE) are also analyzed to simulate improvement scenarios, including renewable energy deployment and scheduling optimization. Findings indicate that carbon emissions are highly concentrated in specific testing processes and operational timeframes. Key influencing factors include energy usage patterns and equipment utilization rates. Simulations demonstrate that optimized scheduling, energy efficiency improvements, and increased green electricity use can significantly reduce emissions. The study concludes with a set of practical recommendations integrating Environmental, Social, and Governance (ESG) smart manufacturing technologies, and policy alignment to support sustainable transformation and strengthen corporate competitiveness in the low-carbon transition.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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