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Temporal and Spatial Differentiation and Driving Factors of China’s Agricultural Eco-Efficiency Considering Agricultural Carbon Sinks

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  • Shilin Li

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

  • Zhiyuan Zhu

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

  • Zhenzhong Dai

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

  • Jiajia Duan

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

  • Danmeng Wang

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

  • Yongzhong Feng

    (College of Agronomy, Northwest A&F University, Yangling 712100, China
    The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, China)

Abstract

Climate change, greenhouse gas emissions, and food security have put forward higher requirements for sustainable agricultural development. Agricultural ecological efficiency (AEE) is an important indicator to evaluate the sustainable development of agriculture. Low carbon agriculture promotes sustainable agricultural development. Agricultural carbon sinks are an important output of agricultural production, but they have not been fully reflected in the current research on agricultural ecological efficiency. In this study, agricultural carbon sinks are considered as one of the expected outputs of AEE. The data envelopment method was used to measure the AEE of 31 provincial-level administrative regions in China from 2000 to 2019, and the AEE of China was compared with and without carbon sinks. The Gaussian kernel function was used to estimate the time evolution of regional differences in AEE. A geodetector model was used to detect the drivers of spatial differentiation of AEE in China. The results showed that considering agricultural carbon sinks as one of the expected measurement outputs brings the estimated AEE closer to reality. From 2000 to 2019, China’s AEE showed an upward trend, and the efficiency value increased from 0.48 to 0.95, an increase of 97.92%. The spatial distribution pattern of AEE in China was Northeast > West > Central > East, with obvious differences among provinces. The industrialization level, urban–rural gap, agricultural economic level, agricultural disaster rate, and urbanization level were the leading driving forces for the spatial differentiation of AEE in China. The research will help to reveal the dynamic characteristics, spatial differentiation characteristics, and driving factors of China’s agricultural ecological efficiency, and provide a scientific reference for the realization of sustainable agricultural development and high-quality development.

Suggested Citation

  • Shilin Li & Zhiyuan Zhu & Zhenzhong Dai & Jiajia Duan & Danmeng Wang & Yongzhong Feng, 2022. "Temporal and Spatial Differentiation and Driving Factors of China’s Agricultural Eco-Efficiency Considering Agricultural Carbon Sinks," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1726-:d:947151
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    2. Shulong Li & Zhizhang Wang, 2023. "Time, Spatial and Component Characteristics of Agricultural Carbon Emissions of China," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    3. Xiaoyan Sun & Shuya Guang & Jingjing Cao & Fengying Zhu & Jianxu Liu & Songsak Sriboonchitta, 2023. "Effect of Agricultural Production Trusteeship on Agricultural Carbon Emission Reduction," Agriculture, MDPI, vol. 13(7), pages 1-18, July.

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