RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2023, Vol. 32 >> Issue (11): 2403-2417.doi: 10.11870/cjlyzyyhj202311015

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EKC-based Test for Agricultural Carbon Emissions in Yangtze River Economic Belt and Analysis of Driving Factors

TIAN Yun,CAI Yan-rong   

  1. (Zhongnan University of Economics and Law,Wuhan 430073,China)
  • Online:2023-11-20 Published:2023-11-28

Abstract: Taking the Yangtze River Economic Belt as the research object, this paper discussed the relationship between agricultural carbon emissions and per capita net income of rural residents based on the characteristics of the current situation of agricultural carbon emissions in each province from 2005 to 2020. The driving factors of agricultural carbon emissions were also analyzed, and the agricultural carbon emissions of each province from 2023 to 2030 were projected. The results showed that:(1)The agricultural carbon emissions showed significant changes in either growth or decline. During the sampling period, 5 provinces were in the trend of growth, of which Yunnan province was the largest. Six provinces were in the trend of decline, of which Shanghai was the largest. Agricultural carbon emissions were generally "high in the middle and low in the east and west".(2)The EKC test results showed that the EKC relationship existed in all provinces of the Yangtze River Economic Belt, however the overall trend was not completely consistent. Based on the difference of EKC curves, 11 provinces could be divided into three types: inverted "N" type, inverted "U" type and positive "U" type.(3)The results of STIRPAT showed that the number of people employed in the primary industry and the gross output value of agriculture, forestry, animal husbandry and fishery dominated agricultural carbon emissions, while the level of agricultural technology had a suppressive and less powerful effect.(4)The GM (1,1) projections showed that the agricultural carbon emissions showed obvious fluctuations.The effectiveness in reducing emissions from 2023 to 2030 varied from province to province. Based on the forecast results, the 11 provinces could be divided into three types: "continuously declining", "continuously growing" and "rising then falling".

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