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The Spatiotemporal Differentiation Characteristics and Driving Forces of Carbon Emissions from Major Livestock Farming in the Shaanxi–Gansu–Ningxia Region

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  • Hao Wu

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China)

  • Tongtong Shan

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China)

  • Hassan Saif Khan

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China)

  • Lin Dong

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China)

  • Hua Li

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China)

Abstract

Given the background of ecological fragility in western China, the northward migration of the livestock industry, and the “carbon peak” in China, it is practically significant to discuss the evolution of carbon dioxide equivalent emission intensity (CEI) in major livestock (pigs, cattle and sheep) rearing in the Shaanxi–Gansu–Ningxia (SGN) region. This discussion aims to protect the ecology of western China, achieve sustainable and healthy development of the livestock industry, and realize the national goal of “double carbon”. In this study, we utilized statistical data from 2010 to 2021 for pigs, cattle, and sheep at the municipal level in the SGN region. We applied the methodology provided by the IPCC to comprehensively measure the carbon dioxide equivalent emissions (CEs), explore spatial and temporal trends, and analyze the driving forces behind spatial variations in the intensity with the assistance of GeoDetector. The following conclusions were drawn: Firstly, the total CEs generally exhibit fluctuating and increasing patterns. Moreover, the total CEs in different cities (states) within the region show obvious variations, with a tendency to shift toward the north. Secondly, the CEI demonstrates a clear downward trend. However, the CEI in different cities (states) exhibits increasing spatial heterogeneity. Furthermore, the western part of the region is evolving toward high-value areas, while the eastern part is evolving toward low-value areas. Lastly, the results of the GeoDetector indicate that the core driving factors are the pig, cattle, and sheep rearing structure; the urban population proportion; and the per capita gross national product. In summary, the total amount of CEs demonstrates a fluctuating increase, while the intensity shows a clear downward trend. Therefore, it is recommended to reduce CEs from livestock rearing in this region by optimizing the rearing structure of pigs, cattle, and sheep, promoting low-carbon consumption, and moderately importing livestock products.

Suggested Citation

  • Hao Wu & Tongtong Shan & Hassan Saif Khan & Lin Dong & Hua Li, 2024. "The Spatiotemporal Differentiation Characteristics and Driving Forces of Carbon Emissions from Major Livestock Farming in the Shaanxi–Gansu–Ningxia Region," Agriculture, MDPI, vol. 14(10), pages 1-16, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1748-:d:1491947
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    References listed on IDEAS

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    1. Xiaocang Xu & Xiuquan Huang & Jun Huang & Xin Gao & Linhong Chen, 2019. "Spatial-Temporal Characteristics of Agriculture Green Total Factor Productivity in China, 1998–2016: Based on More Sophisticated Calculations of Carbon Emissions," IJERPH, MDPI, vol. 16(20), pages 1-16, October.
    2. Zhang, Na & Jing, Yong-Cai & Liu, Cheng-Yu & Li, Yao & Shen, Jing, 2016. "A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats," Ecological Modelling, Elsevier, vol. 329(C), pages 5-17.
    3. Klarin Tomislav, 2018. "The Concept of Sustainable Development: From its Beginning to the Contemporary Issues," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 21(1), pages 67-94, May.
    4. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    5. Dequan Hao & Rui Wang & Chaojie Gao & Xinyan Song & Wenxin Liu & Guangyin Hu, 2022. "Spatial-Temporal Characteristics and Influence Factors of Carbon Emission from Livestock Industry in China," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
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