ZHANG H B, PAN Y S, NIE X J,et al.Study on agricultural carbon emission at county scale based on neural network model[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):111-120.

DOI:10.16186/j.cnki.1673-9787.2023060001

Received: 2023/06/01

Revised: 2023/08/22

Published:2025/07/23

Study on agricultural carbon emission at county scale based on neural network model

Zhang Hebing1, Pan Yisha1, Nie Xiaojun1, Wang Chongyang2, Zhang Huifang1

1.School of Surveying and Land Information Engineering， Henan Polytechnic University， Jiaozuo  454000， Henan， China；2.School of Resources and Safety Engineering， Chongqing University， Chongqing  400044， China

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 models. Methods from the perspective of county level， the influencing factors were analyzed from the perspective 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 influencing factor was preliminarily determined to establish a neural network prediction model， and the prediction results were tested and evaluated with the actual values.  Results The results showed that： （1） Pingdingshan 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 emissions 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 regression 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.

Key words:agricultural carbon emission;grey relation;neural network;Robust regression analysis;factor influencing agricultural carbon emission