A united framework modeling of spatial-temporal characteristics for county-level agricultural carbon emission with an application to Hunan in China

被引:0
|
作者
Yao, Yao [1 ]
Bi, Xu [1 ]
Li, Chunhua [1 ]
Xu, Xuanhua [2 ]
Jing, Lei [1 ]
Chen, Jiale [1 ]
机构
[1] Cent South Univ Forestry & Technol, Changsha 410004, Peoples R China
[2] Cent South Univ, Changsha 410083, Peoples R China
基金
中国国家自然科学基金;
关键词
IEDR paradigm; Agricultural carbon emission; Spatio-temporal heterogeneity; SBM-DDF model; Hunan in China; GREENHOUSE-GAS EMISSIONS; CO2; EMISSIONS; ENVIRONMENTAL EFFICIENCY; ENERGY-CONSUMPTION; ECONOMIC-GROWTH; REDUCTION; PROVINCE; CLIMATE; SECTOR; OPPORTUNITIES;
D O I
10.1016/j.jenvman.2024.121321
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Effectively tackling extreme climate change requires sound knowledge about carbon emissions and their driving forces. Currently, agricultural carbon emission assessment often deals with its inventory, efficiency, determinants, and response independently, which will leave out the complex interactions among its various components, thus there is a lack of comprehensive, scalable, comparable explanations for agricultural carbon emissions. Herein, we introduce an integrated agricultural carbon emission assessment framework (IEDR): Inventory (I) x Efficiency (E) x Determinants (D) x Response (R) , which was then applied to an illustration for the county -level agricultural carbon emissions in Hunan Province, China. Results show that: (1) Agricultural carbon emission inventory (ACEI) increased from 20.06 x 10(6) tC in 2006 to 21.99 x 10(6) tC in 2014 and decreased to 19.07 x 10(6) tC by 2020, depicting a fluctuating trend. Meanwhile, there was remarkable spatial heterogeneity, with higher ACEI in the North and South than in the East and West. (2) Agricultural carbon emission efficiency (ACEE) increased from 0.8520 in 2006 to 0.8992 in 2020, depicting a growing trend driven by technological progress. Spatially distributed in contrast to ACEI, regions with higher ACEE were located in the eastern and western areas. (3) ACEI was negatively correlated with ACEE (-0.657), indicating that increasing ACEE is a key strategy for reducing emissions. (4) The natural environment, rural development level, and policy support had critical impacts on ACEE and ACEI. In particular, the cultivated area and rural water affairs development were significant influences on ACEE and ACEI. Given the externalities of carbon emissions and its important public goods characteristics of the atmosphere, local carbon issues are also global concerns. Therefore, the case study of the IEDR model not only validates this theoretical paradigm and realizes regional responsibility for global carbon reduction but also supports and expands the theoretical and empirical corpus in the field of agricultural carbon emissions and efficiency, providing insights and references for other global regions facing similar challenges.
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页数:16
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