Authors
Nan Zhang, Yong-Ji Luan, Si-Yi Wang, Jing Tang
Published in
Huan jing ke xue= Huanjing kexue. Volume 47. Issue 9. Pages 5936-5949. Sep 08, 2026.
Abstract
Agriculture is a significant sector of carbon emissions in China. Clarifying its emission characteristics and driving mechanisms is of great significance for promoting the green and low-carbon transformation of agriculture. Based on the panel data of 31 provincial regions across the country from 2011 to 2022, this study uses the IPCC emission coefficient method to calculate the total amount and intensity of agricultural carbon emissions, revealing their spatio-temporal evolution characteristics. Research findings indicate that the overall carbon emissions from agriculture across the country have shown a fluctuating upward trend. The total emissions from the eastern and central regions accounted for nearly 60%, while the overall carbon emission intensity decreased by 55.16%, suggesting a continuous improvement in agricultural carbon efficiency. The LMDI decomposition model was further adopted to quantify the contributions of factors such as agricultural production efficiency, economic development level, and industrial structure to the changes in carbon emissions. The random forest and SHAP methods were introduced to identify the nonlinear importance of the driving factors. The results showed that economic development level, fertilizer input, and total power of agricultural machinery were the main driving factors. The Tapio model analysis showed that since 2015, the relationship between agricultural carbon emissions and economic growth in China gradually shifted from weak decoupling to strong decoupling, but the decoupling status varied significantly among regions. The research suggests that efforts should be made to optimize the agricultural input structure and promote green technologies in a way that suits local conditions and strengthen multi-dimensional monitoring and differentiated emission reduction policies so as to accelerate the low-carbon transformation process of agriculture.
PMID:
42765216
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.
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