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    Carbon footprint of coffee production in Yunnan Province and its influencing factors
    WANG Xin-hua, WANG Shuo
    HUBEI AGRICULTURAL SCIENCES    2024, 63 (2): 19-24,78.   DOI: 10.14088/j.cnki.issn0439-8114.2024.02.004
    Abstract293)      PDF (1493KB)(206)       Save
    Taking Yunnan Province as the research area and coffee products as the research object, the carbon footprint of coffee production in Yunnan Province was calculated, the path analysis method to explore the influencing factors of carbon footprint in the coffee production process in Yunnan Province was used, the possibility of mitigating the carbon footprint was clarified, and suggestions for achieving water saving and emission reduction goals in the coffee production process were put forward, so as to provide a practical reference for better promoting the low-carbon and green development of the coffee industry in Yunnan Province.
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    Spatiotemporal pattern evolution and influencing factors of carbon emissions from arable land in the Yangtze River Economic Belt
    LIU Xuan, SUN Yan, MA Jing, ZHANG Tian-wang, CHEN Fu
    HUBEI AGRICULTURAL SCIENCES    2024, 63 (2): 1-7.   DOI: 10.14088/j.cnki.issn0439-8114.2024.02.001
    Abstract277)      PDF (5745KB)(251)       Save
    To clarify the green utilization of arable land, the IPCC carbon emission coefficient method was used to calculate the carbon emissions of arable land in 129 prefecture-level cities of the Yangtze River Economic Belt from 2000 to 2020. Spatial autocorrelation analysis was used to reveal the spatiotemporal evolution of arable land carbon emissions, and the LMDI model was used to decompose the contributions of various influencing factors. The results showed that from 2000 to 2020, the carbon emissions from arable land in the Yangtze River Economic Belt showed a downward trend over time, showing four stages, such as “maintaining stability—rapid growth—slow growth—slow decline”. In terms of space, there was a trend of high in the middle and east and low in the west, with significant global spatial autocorrelation. Local high-high clustering areas were distributed in the middle and lower reaches of the Yangtze River, low-high clustering areas were distributed in the middle reaches, and low-low clustering areas were mainly distributed in the upstream area. The promoting factor for agricultural carbon emissions within the region was the level of agricultural economy, while the inhibiting factor was mainly agricultural production efficiency, followed by agricultural production structure, and finally the scale of agricultural labor force. Therefore, there was a significant spatiotemporal difference in carbon emissions from arable land in the Yangtze River Economic Belt. Each region should develop carbon reduction strategies and land use control plans according to local conditions, improve agricultural production efficiency, optimize agricultural planting structure, strengthen regional linkage, and promote the coordinated development of low-carbon agriculture.
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