A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus
Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actio...
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MDPI AG
2025-05-01
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| author | Xiangze Wang Jingqi Deng Tingting Hu Dungang Gu Rui Liu Guanghui Li Nan Zhang Jiaqi Lu |
| author_facet | Xiangze Wang Jingqi Deng Tingting Hu Dungang Gu Rui Liu Guanghui Li Nan Zhang Jiaqi Lu |
| author_sort | Xiangze Wang |
| collection | DOAJ |
| description | Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO<sub>2</sub>-eq. Based on gross emissions (28,306.43 t CO<sub>2</sub>-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO<sub>2</sub>-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. |
| format | Article |
| id | doaj-art-0406bdaeadaa41668d687c78ba7cd797 |
| institution | DOAJ |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-0406bdaeadaa41668d687c78ba7cd7972025-08-20T03:12:07ZengMDPI AGSystems2079-89542025-05-0113539510.3390/systems13050395A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University CampusXiangze Wang0Jingqi Deng1Tingting Hu2Dungang Gu3Rui Liu4Guanghui Li5Nan Zhang6Jiaqi Lu7Innovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaCentre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester M13 9PL, UKInnovation Center for Environment and Resources, Shanghai University of Engineering Science, No. 333 Longteng Road, Songjiang District, Shanghai 201620, ChinaDecarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO<sub>2</sub>-eq. Based on gross emissions (28,306.43 t CO<sub>2</sub>-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO<sub>2</sub>-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector.https://www.mdpi.com/2079-8954/13/5/395life-cycle assessmentoptimization modelcarbon reduction frameworkdynamic strategy |
| spellingShingle | Xiangze Wang Jingqi Deng Tingting Hu Dungang Gu Rui Liu Guanghui Li Nan Zhang Jiaqi Lu A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus Systems life-cycle assessment optimization model carbon reduction framework dynamic strategy |
| title | A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus |
| title_full | A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus |
| title_fullStr | A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus |
| title_full_unstemmed | A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus |
| title_short | A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus |
| title_sort | life cycle carbon reduction optimization framework for production activity systems a case study on a university campus |
| topic | life-cycle assessment optimization model carbon reduction framework dynamic strategy |
| url | https://www.mdpi.com/2079-8954/13/5/395 |
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