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

Zhongxia Yu, Mingcong Zhang (), Yingce Zhan, Yongxia Guo, Yuxian Zhang, Xiaoyan Liang, Chen Wang, Yuxin Fan, Mingfen Shan, Haiqing Guo and Wei Zhou
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Zhongxia Yu: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Mingcong Zhang: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Yingce Zhan: School of Marxism, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Yongxia Guo: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Yuxian Zhang: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Xiaoyan Liang: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Chen Wang: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Yuxin Fan: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Mingfen Shan: College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163000, China
Haiqing Guo: Lindian County Agricultural Machinery Comprehensive Service Center, Daqing 163000, China
Wei Zhou: Daqing Qilong Agricultural Science and Technology Limited Company, Daqing 163000, China

Agriculture, 2025, vol. 15, issue 11, 1-21

Abstract: 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.

Keywords: Heilongjiang Province; carbon emissions; Tapio decoupling model; LMDI model; BP neural network model; greenhouse gas emission; sustainability; emission intensity; prediction model; regional planning; climate-smart agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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