Spatial-Temporal Variation, Influencing Factors, and Trend Prediction of Agricultural Carbon Emissions in Henan Province

Abstract

[Objective] The spatial-temporal evolution characteristics and driving mechanism of agricultural carbon emissions in Henan Province were determined to predict the change trend of agricultural carbon emissions during the next ten years in order to formulate an agricultural carbon sequestration and emission reduction scheme, and to promote the transformation to low-carbon and green agricultural production. [Methods] We used a carbon emission equity evaluation model, GeoDetector, and the GM (1,1) model to accomplish the study objectives. [Results] ① Agricultural net carbon emissions in Henan Province declined over time during the study period, with a distribution pattern of "high in the southwest and low in the northeast" . Carbon emissions from animal husbandry accounted for a large proportion of total emissions, and carbon emissions were mainly from cattle, sheep, and pigs. Wheat, corn and vegetables contributed more to carbon absorption than other sinks. ② The ecological carrying coefficient of agricultural carbon emissions was higher in the north and south, and lower in the west. The coefficient of economic contribution was high in the southeast and low in the southwest. ③ The agricultural employee population, per capita disposable income of rural residents, agricultural machinery gross power, and fiscal expenditures on education were the key factors affecting spatial differences in agricultural carbon emissions, and interactions among these factors were strong. ④ Agricultural net carbon emissions in Henan Province will continue to decrease from 2021 to 2030. It is estimated that Henan Province will achieve the goal of agricultural carbon neutrality by 2029. [Conclusion] In the future, Henan Province should strengthen science popularization, actively promote low-carbon agricultural production technology, and increase the efficiency of comprehensive utilization of agricultural resources. Additionally, all localities should give increased attention to system integration, strengthening regional cooperation, and achieving regional integration of agricultural carbon emission reduction.

Keywords

• agricultural carbon emissions
• spatial-temporal differentiation
• fairness evaluation model
• geodetector
• grey prediction
• henan province