Study on the spatial correlation network structure of agricultural carbon emission efficiency in China

Abstract

Achieving carbon neutrality requires high efficiency in agricultural carbon emissions. This study employs a super efficiency Slack Based Measure-Data Envelopment Analysis (SBM-DEA) model to measure the Agricultural Carbon Emission Efficiency (ACEE) of 31 provinces, cities, and autonomous regions within the Chinese Mainland from 2001 to 2021. Additionally, it utilizes the modified gravity model and a social network analysis to establish the spatial correlation relationship of ACEE, and extensively investigates the characteristics and transmission mechanism of China's spatial correlation network structure regarding ACEE. The findings reveal the following: 1) The spatial correlation relationship of China's ACEE from 2001 to 2021 exhibits a complex network structure; 2) in terms of the overall network structure characteristics of the spatial correlation, the ACEE network demonstrates a high degree of correlation and displays a stable temporal evolution trend; 3) concerning the centrality network structure characteristics of the spatial correlation, most provinces in China experience a continuous decline in point centrality and near centrality, while the interdependence of the ACEE between provinces increases; and 4) regarding the clustering characteristics of the spatial correlation, variations exist in the correlation among the four plates of the ACEE. However, they mostly assume a mediating role, and in 2021, the ACEE network sectors witnessed a robust interoperability.

Keywords

• agricultural carbon emission efficiency (acee)
• spatial correlation network structure
• super-efficient sbm model
• social network analysis