Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province
As the leading grain-producing region in China, Heilongjiang Province is crucial to the country’s food security. Thus, determining Heilongjiang’s agricultural carbon emissions status and trend projections provides a baseline for supporting low-carbon emission reduction in this sector. This study ana...
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| author | Zhongxia Yu Mingcong Zhang Yingce Zhan Yongxia Guo Yuxian Zhang Xiaoyan Liang Chen Wang Yuxin Fan Mingfen Shan Haiqing Guo Wei Zhou |
| author_facet | Zhongxia Yu Mingcong Zhang Yingce Zhan Yongxia Guo Yuxian Zhang Xiaoyan Liang Chen Wang Yuxin Fan Mingfen Shan Haiqing Guo Wei Zhou |
| author_sort | Zhongxia Yu |
| collection | DOAJ |
| description | As the leading grain-producing region in China, Heilongjiang Province is crucial to the country’s food security. Thus, determining Heilongjiang’s agricultural carbon emissions status and trend projections provides a baseline for supporting low-carbon emission reduction in this sector. This study analyzes carbon emissions from crop farming and farmland soil in Heilongjiang from 2003 to 2022, focusing on two carbon sources: agricultural land use and soil. BP neural network model, emission factor coefficient approach, Tapio decoupling framework, and LMDI model are used. These findings show that Heilongjiang’s planting industry carbon emissions initially increased and then decreased, with chemical fertilizers and rice being the main sources. Harbin, Qiqihar, Jiamusi, and Suihua contribute significantly to soil carbon emissions from farming. In “weak decoupling-expanding negative decoupling-strong decoupling,” economic levels drive carbon emissions, while production efficiency is the key countermeasure. Qiqihar will not peak between 2023 and 2030, while the other 12 Heilongjiang cities will. Therefore, these emission-reduction proposals are presented: Restructuring (increasing drought-resistant and cold-climate low-carbon crops), optimizing fertilization (soil testing and organic fertilizers), and improving resource utilization can help Heilongjiang Province achieve “food security, ecological preservation, and low-carbon development” in its agricultural practices. |
| format | Article |
| id | doaj-art-0f050a70626a4724a5b3dbf99f754a2a |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-0f050a70626a4724a5b3dbf99f754a2a2025-08-20T03:10:50ZengMDPI AGAgriculture2077-04722025-05-011511112610.3390/agriculture15111126Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang ProvinceZhongxia Yu0Mingcong Zhang1Yingce Zhan2Yongxia Guo3Yuxian Zhang4Xiaoyan Liang5Chen Wang6Yuxin Fan7Mingfen Shan8Haiqing Guo9Wei Zhou10College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaSchool of Marxism, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaCollege of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, ChinaLindian County Agricultural Machinery Comprehensive Service Center, Daqing 163000, ChinaDaqing Qilong Agricultural Science and Technology Limited Company, Daqing 163000, ChinaAs the leading grain-producing region in China, Heilongjiang Province is crucial to the country’s food security. Thus, determining Heilongjiang’s agricultural carbon emissions status and trend projections provides a baseline for supporting low-carbon emission reduction in this sector. This study analyzes carbon emissions from crop farming and farmland soil in Heilongjiang from 2003 to 2022, focusing on two carbon sources: agricultural land use and soil. BP neural network model, emission factor coefficient approach, Tapio decoupling framework, and LMDI model are used. These findings show that Heilongjiang’s planting industry carbon emissions initially increased and then decreased, with chemical fertilizers and rice being the main sources. Harbin, Qiqihar, Jiamusi, and Suihua contribute significantly to soil carbon emissions from farming. In “weak decoupling-expanding negative decoupling-strong decoupling,” economic levels drive carbon emissions, while production efficiency is the key countermeasure. Qiqihar will not peak between 2023 and 2030, while the other 12 Heilongjiang cities will. Therefore, these emission-reduction proposals are presented: Restructuring (increasing drought-resistant and cold-climate low-carbon crops), optimizing fertilization (soil testing and organic fertilizers), and improving resource utilization can help Heilongjiang Province achieve “food security, ecological preservation, and low-carbon development” in its agricultural practices.https://www.mdpi.com/2077-0472/15/11/1126Heilongjiang Provincecarbon emissionsTapio decoupling modelLMDI modelBP neural network modelgreenhouse gas emission |
| spellingShingle | Zhongxia Yu Mingcong Zhang Yingce Zhan Yongxia Guo Yuxian Zhang Xiaoyan Liang Chen Wang Yuxin Fan Mingfen Shan Haiqing Guo Wei Zhou Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province Agriculture Heilongjiang Province carbon emissions Tapio decoupling model LMDI model BP neural network model greenhouse gas emission |
| title | Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province |
| title_full | Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province |
| title_fullStr | Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province |
| title_full_unstemmed | Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province |
| title_short | Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province |
| title_sort | analysis of temporal and spatial evolution characteristics and peak prediction of carbon emissions in china under the dual carbon target a case study of heilongjiang province |
| topic | Heilongjiang Province carbon emissions Tapio decoupling model LMDI model BP neural network model greenhouse gas emission |
| url | https://www.mdpi.com/2077-0472/15/11/1126 |
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