Study on agricultural carbon emission at county scale based on neural network model
Zhang Hebing
Pan Yisha
Nie Xiaojun
Wang Chongyang
Zhang Huifang
Abstract：Objectives To calculate the agricultural carbon emissions in Pingdingshan City from 2010 to 2020,Conduct research on agricultural carbon emissions at the county scale based on neural network mod-els.Methods from the perspective of county level,the influencing factors were analyzed from the perspec-tive of input and output.On this basis,the agricultural carbon emission prediction model was established.Grey correlation analysis and Robust regression analysis were used to obtain the correlation degree of each influencing factor and the direction of influence on agricultural carbon emissions.The weight of each influ-encing factor was preliminarily determined to establish a neural network prediction model,and the predic-tion results were tested and evaluated with the actual values.Results The results showed that:(1)Ping-dingshan City was affected by the distribution of agricultural production activities,the counties and cities around the central city were the main agricultural production activities,and their agricultural carbon emis-sions were higher.(2)The results of grey correlation analysis showed that agricultural input factors had a significant impact on agricultural carbon emissions in Pingdingshan,among which fertilizer had the highest correlation with carbon emissions,while output factors had different correlation degrees.(3)The Robust re-gression analysis results gave the influence direction of each influencing factor,pointing out that maize planting had a negative correlation with the production of agricultural carbon emissions,and identified oil crops,fruits and farm workers as factors with insignificant influence on agricultural carbon emissions.(4)The correlation coefficient R2 between the prediction model results and the actual values reached 0.99,which had a good degree of fit.Conclusions The study results could provide certain support for the high-quality development of regional agriculture and the formulation of agricultural carbon emission reduction policies.
Keywords：agricultural carbon emissiongrey relationneural networkRobust regression analysisfactor influencing agricultural carbon emission
Publication Date：2025-09-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 111-120 )