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 DOI Creative Commons

Zhongyang Yu,

Mingcong Zhang,

Yingce Zhan

и другие.

Agriculture, Год журнала: 2025, Номер 15(11), С. 1126 - 1126

Опубликована: Май 23, 2025

As the leading grain-producing region in China, Heilongjiang Province is crucial to country’s food security. Thus, determining Heilongjiang’s agricultural carbon emissions status and trend projections provides a baseline for supporting low-carbon emission reduction this sector. This study analyzes from crop farming farmland soil 2003 2022, focusing on two sources: land use soil. BP neural network model, factor coefficient approach, Tapio decoupling framework, LMDI model are used. These findings show that planting industry initially increased then decreased, with chemical fertilizers rice being main sources. Harbin, Qiqihar, Jiamusi, Suihua contribute significantly farming. In “weak decoupling-expanding negative decoupling-strong decoupling,” economic levels drive emissions, while production efficiency key countermeasure. Qiqihar will not peak between 2023 2030, other 12 cities will. Therefore, these emission-reduction proposals presented: Restructuring (increasing drought-resistant cold-climate crops), optimizing fertilization (soil testing organic fertilizers), improving resource utilization can help achieve “food security, ecological preservation, development” its practices.

Язык: Английский

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 DOI Creative Commons

Zhongyang Yu,

Mingcong Zhang,

Yingce Zhan

и другие.

Agriculture, Год журнала: 2025, Номер 15(11), С. 1126 - 1126

Опубликована: Май 23, 2025

As the leading grain-producing region in China, Heilongjiang Province is crucial to country’s food security. Thus, determining Heilongjiang’s agricultural carbon emissions status and trend projections provides a baseline for supporting low-carbon emission reduction this sector. This study analyzes from crop farming farmland soil 2003 2022, focusing on two sources: land use soil. BP neural network model, factor coefficient approach, Tapio decoupling framework, LMDI model are used. These findings show that planting industry initially increased then decreased, with chemical fertilizers rice being main sources. Harbin, Qiqihar, Jiamusi, Suihua contribute significantly farming. In “weak decoupling-expanding negative decoupling-strong decoupling,” economic levels drive emissions, while production efficiency key countermeasure. Qiqihar will not peak between 2023 2030, other 12 cities will. Therefore, these emission-reduction proposals presented: Restructuring (increasing drought-resistant cold-climate crops), optimizing fertilization (soil testing organic fertilizers), improving resource utilization can help achieve “food security, ecological preservation, development” its practices.

Язык: Английский

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