A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses

Electric power material warehouses are critical to optimizing power grid supply chains and reducing carbon emissions, aiding the power sector’s decarbonization and climate goals. Nevertheless, to our knowledge, there are no comprehensive assessments of the life cycle carbon emissions associated with...

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Main Authors: Yongjun Luo, Xinyi Tang, Lei Geng, Xiang Yao, Feihong Li, Xudong Li, Qingrui Wang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/17/24/6352
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author Yongjun Luo
Xinyi Tang
Lei Geng
Xiang Yao
Feihong Li
Xudong Li
Qingrui Wang
author_facet Yongjun Luo
Xinyi Tang
Lei Geng
Xiang Yao
Feihong Li
Xudong Li
Qingrui Wang
author_sort Yongjun Luo
collection DOAJ
description Electric power material warehouses are critical to optimizing power grid supply chains and reducing carbon emissions, aiding the power sector’s decarbonization and climate goals. Nevertheless, to our knowledge, there are no comprehensive assessments of the life cycle carbon emissions associated with storage warehouses, so the emission reduction potential of the ever-increasing number of automated technologies is still unknown. This study presents an extensive life cycle carbon footprint assessment model tailored for electric power material warehouses, and it encompasses both traditional and automated frameworks. Utilizing a process-based life cycle assessment (LCA) methodology, carbon emissions across five distinct stages are examined: storage buildings and facilities, loading and unloading, transportation, packaging, and information management systems. For this purpose, warehouses in Jiangsu Province, China, are employed as a case study. The results show that automating warehouses can achieve a reduction in total carbon emissions of 42.85% compared with traditional warehouses, with total life cycle emissions of 39,531.26 tCO<sub>2</sub>, and the transportation stage is identified as the predominant contributor. This research not only offers actionable recommendations for strategies, including renewable energy integration, intelligent control systems, and standardized packaging protocols, but also establishes a framework for future investigations of refining carbon accounting methodologies—particularly in underexplored domains such as packaging.
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spelling doaj-art-6bd46f6ca6df4e5c82dc58f8717815ff2025-08-20T02:53:29ZengMDPI AGEnergies1996-10732024-12-011724635210.3390/en17246352A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material WarehousesYongjun Luo0Xinyi Tang1Lei Geng2Xiang Yao3Feihong Li4Xudong Li5Qingrui Wang6Jiangsu Anfang Power Technology Co., Ltd., Taizhou 225300, ChinaSchool of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210018, ChinaYangzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Yangzhou 225002, ChinaChina-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaElectric power material warehouses are critical to optimizing power grid supply chains and reducing carbon emissions, aiding the power sector’s decarbonization and climate goals. Nevertheless, to our knowledge, there are no comprehensive assessments of the life cycle carbon emissions associated with storage warehouses, so the emission reduction potential of the ever-increasing number of automated technologies is still unknown. This study presents an extensive life cycle carbon footprint assessment model tailored for electric power material warehouses, and it encompasses both traditional and automated frameworks. Utilizing a process-based life cycle assessment (LCA) methodology, carbon emissions across five distinct stages are examined: storage buildings and facilities, loading and unloading, transportation, packaging, and information management systems. For this purpose, warehouses in Jiangsu Province, China, are employed as a case study. The results show that automating warehouses can achieve a reduction in total carbon emissions of 42.85% compared with traditional warehouses, with total life cycle emissions of 39,531.26 tCO<sub>2</sub>, and the transportation stage is identified as the predominant contributor. This research not only offers actionable recommendations for strategies, including renewable energy integration, intelligent control systems, and standardized packaging protocols, but also establishes a framework for future investigations of refining carbon accounting methodologies—particularly in underexplored domains such as packaging.https://www.mdpi.com/1996-1073/17/24/6352carbon footprintlife cycle assessmentelectric power material warehouseautomated warehouse
spellingShingle Yongjun Luo
Xinyi Tang
Lei Geng
Xiang Yao
Feihong Li
Xudong Li
Qingrui Wang
A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
Energies
carbon footprint
life cycle assessment
electric power material warehouse
automated warehouse
title A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
title_full A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
title_fullStr A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
title_full_unstemmed A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
title_short A Comprehensive Life Cycle Carbon Footprint Assessment Model for Electric Power Material Warehouses
title_sort comprehensive life cycle carbon footprint assessment model for electric power material warehouses
topic carbon footprint
life cycle assessment
electric power material warehouse
automated warehouse
url https://www.mdpi.com/1996-1073/17/24/6352
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