The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020
Abstract Although the issue of embodied pollutants in China’s supply chains has garnered increasing attention, the dynamic changes occurring within them are unclear. Several existing studies analyze one-year or short-term data in supply chain. China’s overall CH4 emissions have risen from 41.1 Tg in...
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Nature Portfolio
2024-05-01
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| Online Access: | https://doi.org/10.1038/s41598-024-62979-z |
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| author | Jiaxi Wu Mengxin Chen Xialing Sun Zheng Meng |
| author_facet | Jiaxi Wu Mengxin Chen Xialing Sun Zheng Meng |
| author_sort | Jiaxi Wu |
| collection | DOAJ |
| description | Abstract Although the issue of embodied pollutants in China’s supply chains has garnered increasing attention, the dynamic changes occurring within them are unclear. Several existing studies analyze one-year or short-term data in supply chain. China’s overall CH4 emissions have risen from 41.1 Tg in 2000 to 60 Tg in 2020, so conducting long-term analyses can yield a deeper understanding of the dynamic changes across the entire supply chain from production to consumption. This study uses the environmentally extended input–output analysis (EEIOA) and structural path analysis (SPA) methods to investigate the dynamic variation of China’s embodied CH4 emissions in 20 industry sectors from 2000 to 2020, aiming to determine the key supply chain and key sectors. The results reveal that from the final demand perspective, consumption, investment and export drove 52.1%, 32%, and 15.9% of embodied CH4 emissions in 2020. The sector with the highest embodied CH4 emissions has changed from “Agriculture” in 2000 to “Construction” in 2010 to “Other service and activities” in 2020. The top listed supply chain path of embodied CH4 emissions has also evolved (starting from production to consumption) from “Agriculture → Rural consumption” in 2000 to “Agriculture → Food and tobacco → Urban consumption” in 2010 to “Agriculture → Urban consumption” in 2020. Notably, the high-ranked path, “Agriculture → Food and tobacco → Rural consumption”, shows that the embodied CH4 emission flowing between agriculture and the food industry cannot be ignored. The supply chain path “Coal Mining → Nonmetal Mineral Products → Construction → Capital Formation” has risen from 17th in 2000 to 3rd in 2020. Thus, it is necessary to control CH4 emissions from sectors upstream, which are predominantly influenced by the construction industry, and a coordinated effort between sectors is also required to effectively reduce emissions. By 2020, the CH4 emissions driven by urban consumption were 3.1 times that of rural consumption. This study provides a comprehensive analysis of China's supply chain over the past two decades. In particular, it suggests policy interventions by controlling critical supply chain paths and key sectors associated with embodied CH4 emission, thereby facilitating the coordinated reduction of anthropogenic CH4 emissions. |
| format | Article |
| id | doaj-art-3845d50aa91a429db45312a8b84605ec |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3845d50aa91a429db45312a8b84605ec2025-01-12T12:25:21ZengNature PortfolioScientific Reports2045-23222024-05-0114111410.1038/s41598-024-62979-zThe temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020Jiaxi Wu0Mengxin Chen1Xialing Sun2Zheng Meng3School of Management, China University of Mining & TechnologyChina Energy Engineering Group Anhui Electric Power Design Institute Co., LtdSchool of Public Health, Shandong Second Medical UniversitySchool of Management, China University of Mining & TechnologyAbstract Although the issue of embodied pollutants in China’s supply chains has garnered increasing attention, the dynamic changes occurring within them are unclear. Several existing studies analyze one-year or short-term data in supply chain. China’s overall CH4 emissions have risen from 41.1 Tg in 2000 to 60 Tg in 2020, so conducting long-term analyses can yield a deeper understanding of the dynamic changes across the entire supply chain from production to consumption. This study uses the environmentally extended input–output analysis (EEIOA) and structural path analysis (SPA) methods to investigate the dynamic variation of China’s embodied CH4 emissions in 20 industry sectors from 2000 to 2020, aiming to determine the key supply chain and key sectors. The results reveal that from the final demand perspective, consumption, investment and export drove 52.1%, 32%, and 15.9% of embodied CH4 emissions in 2020. The sector with the highest embodied CH4 emissions has changed from “Agriculture” in 2000 to “Construction” in 2010 to “Other service and activities” in 2020. The top listed supply chain path of embodied CH4 emissions has also evolved (starting from production to consumption) from “Agriculture → Rural consumption” in 2000 to “Agriculture → Food and tobacco → Urban consumption” in 2010 to “Agriculture → Urban consumption” in 2020. Notably, the high-ranked path, “Agriculture → Food and tobacco → Rural consumption”, shows that the embodied CH4 emission flowing between agriculture and the food industry cannot be ignored. The supply chain path “Coal Mining → Nonmetal Mineral Products → Construction → Capital Formation” has risen from 17th in 2000 to 3rd in 2020. Thus, it is necessary to control CH4 emissions from sectors upstream, which are predominantly influenced by the construction industry, and a coordinated effort between sectors is also required to effectively reduce emissions. By 2020, the CH4 emissions driven by urban consumption were 3.1 times that of rural consumption. This study provides a comprehensive analysis of China's supply chain over the past two decades. In particular, it suggests policy interventions by controlling critical supply chain paths and key sectors associated with embodied CH4 emission, thereby facilitating the coordinated reduction of anthropogenic CH4 emissions.https://doi.org/10.1038/s41598-024-62979-zMethane (CH4) emissionsInput–output analysisStructure path analysisSupply chainsChina |
| spellingShingle | Jiaxi Wu Mengxin Chen Xialing Sun Zheng Meng The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 Scientific Reports Methane (CH4) emissions Input–output analysis Structure path analysis Supply chains China |
| title | The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 |
| title_full | The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 |
| title_fullStr | The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 |
| title_full_unstemmed | The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 |
| title_short | The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020 |
| title_sort | temporal variation of ch4 emissions embodied in chinese supply chains 2000 2020 |
| topic | Methane (CH4) emissions Input–output analysis Structure path analysis Supply chains China |
| url | https://doi.org/10.1038/s41598-024-62979-z |
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