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  • Jul. 16, 2025

Arid Land Geography ›› 2025, Vol. 48 ›› Issue (5): 879-892.doi: 10.12118/j.issn.1000-6060.2024.329

• Carbon Emissions • Previous Articles     Next Articles

Spatial-temporal differentiation characteristics and influencing factors of agricultural carbon emissions in Gansu Province

MA Haiqing1(), CHEN Qiangqiang1,2()   

  1. 1. College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2. Gansu Ecological Construction and Environmental Protection Research Center, Lanzhou 730070, Gansu, China
  • Received:2024-05-28 Revised:2024-07-29 Online:2025-05-25 Published:2025-05-13
  • Contact: CHEN Qiangqiang E-mail:18419286658@163.com;jjglxy666@126.com

Abstract:

Identifying the spatiotemporal characteristics and influencing factors of agricultural carbon emissions is essential for reducing uncertainty in carbon emission reduction target accounting and implementing accurate policies to achieve those targets. Based on agricultural carbon emission estimates in Gansu Province, China, from 2014 to 2022, this study employed the Moran’s I index to analyze the spatiotemporal differentiation characteristics of carbon emissions. The dynamic evolution trends were analyzed using the kernel density estimation method, and a geographically weighted regression model was constructed to identify the factors influencing agricultural carbon emissions. The results showed the following. (1) From 2014 to 2022, the agricultural carbon emissions in Gansu Province tended to decline. However, emissions from animal husbandry, identified as the primary source, exhibited an upward trend. The order of carbon emissions by region from highest to lowest was as follows: Hexi oasis agricultural area>Longdong-Longzhong Loess Plateau dry farming area>alpine pastoral area> Longnan Mountain rain-fed agricultural area. (2) The spatial agglomeration of total agricultural carbon emissions was weak and concentrated within the Hexi Oasis agricultural area. Notably, Jiuquan City and Zhangye City exhibited high-low agglomeration, while Jinchang City exhibited low-high agglomeration. Other regions exhibited no significant agglomeration patterns. (3) The carbon emission intensity across all four agricultural regions declined over the study period, and regional differences in the emission intensity gradually narrowed. (4) Per capita gross agricultural product, agricultural industrial structure, and total population demonstrated a significant role in reducing agricultural carbon emission. The disposable income of rural residents, amount of agricultural fertilizer used, and total power of agricultural machinery caused an increase in the carbon emissions. Finally, it was recommended to improve livestock and poultry varieties, implement moderate-scale breeding practices, and ensure accurate operation and management across the entire industrial chain to increase emission reduction efforts in animal husbandry. Furthermore, minimizing the use of agricultural materials such as fertilizers and plastic films, along with promoting the application of green technologies, is essential to achieving agricultural carbon reduction goals.

Key words: agricultural carbon emission, agricultural carbon emission coefficient, kernel density estimation, geographical weighted regression, Gansu Province