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Spatial-Temporal Pattern and Driving Factors of Carbon Emission Intensity of Main Crops in Henan Province

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  • Zhi Li

    (School of Economics and Trade, Henan University of Technology, Zhengzhou 450001, China)

  • Tingting Cao

    (School of Economics and Trade, Henan University of Technology, Zhengzhou 450001, China)

  • Zhongye Sun

    (School of Economics and Trade, Henan University of Technology, Zhengzhou 450001, China)

Abstract

Agriculture is the national economy’s primary industry, and its carbon emissions (CE) are one of the most significant factors influencing the environment. As a large agrarian province, reducing the carbon emission intensity (CEI) of agricultural is of great practical significance to the sustainable development of agriculture in Henan province. In this paper, the CEI of rice, maize, and wheat from 2001 to 2020 in 18 prefecture-level cities in Henan province was calculated, and its spatial-temporal evolution patterns were analyzed. The Spatial Dubin model was used to study the impact mechanism and spatial spillover effect of the main crops’ CEI. As a result, the following was determined: (1) The CEI of main crops in 18 cities of Henan province showed an inverted “V” shape, whereas the geographical distribution showed an oblique “T” shape mainly in the north and west. (2) The CEI of main crops was significantly different under different factors. Technical efficiency, agricultural openness, urbanization level, agriculture production agglomeration, and agriculture fiscal expenditure negatively impact the main crops’ CEI. The structure of the food industry and the cost of water for agriculture and forestry positively affect the CEI of main crops. (3) The spatial spillover effects of agricultural openness, production technology efficiency, environmental protection, and fiscal expenditure spread to the surrounding areas through factor flow, technology spillover, and policy spread. The efficiency of production technology and fiscal expenditure on environmental protection have a demonstrative effect, and the degree of agricultural openness has a siphon effect. Based on the research results, we should strengthen agriculture technology extension and investment and gradually improve technical efficiency. Agriculture should be financially supported by the government. We will actively promote the optimization of the structure of the grain industry by promoting orderly urbanization, strengthening the sharing of factors among regions, and reducing the CEI of main crops.

Suggested Citation

  • Zhi Li & Tingting Cao & Zhongye Sun, 2022. "Spatial-Temporal Pattern and Driving Factors of Carbon Emission Intensity of Main Crops in Henan Province," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16569-:d:999570
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    References listed on IDEAS

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    1. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    2. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    3. Dumortier, Jerome & Elobeid, Amani, 2021. "Effects of a carbon tax in the United States on agricultural markets and carbon emissions from land-use change," Land Use Policy, Elsevier, vol. 103(C).
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