Study on the Spatial Structure and Drivers of Agricultural Carbon Emission Efficiency in Belt and Road Initiative Countries

Abstract

Agricultural carbon emissions are one of the major causes of global climate change. As some of the world’s largest agricultural producers and consumers, countries along the route of the Belt and Road initiative produce significant agricultural carbon emissions. An in-depth study on the efficiency of agricultural carbon emissions in countries along the route can help countries reduce environmental load while improving agricultural production, optimizing resource use, improving agricultural production efficiency, and achieving sustainable development goals, which is significant for global climate change mitigation. Based on the relational data and network perspective, this paper takes the agricultural carbon emission efficiency of 34 countries along the route of the Belt and Road Initiative from 1995 to 2020 as the research object. It integrates the social network analysis method and other methods to realize the expansion of agricultural carbon emission efficiency in the research method. The study shows that (1) agricultural carbon emission efficiency has more room for improvement and presents complex spatially linked network characteristics; (2) the spatial correlation network of agricultural carbon emission efficiency is relatively well connected, and there is a general spatial correlation and spatial spillover effect among countries; and (3) similar differences in the proportion of primary industries and differences in informatization levels help establish spatial correlations between regions and produce spatial spillover effects. It is imperative to change global economic growth, social development, and lifestyles through green development. This study is conducive to the international community’s formulation of differentiated agricultural carbon emission reduction support mechanisms for different countries to help the countries realize the transformation of agriculture and even overall economic development as soon as possible. At the same time, accelerating the pace of emission reduction and reducing the negative impact of agricultural carbon emissions are conducive to better responding to the challenges posed by global climate change.

The Significance of Agricultural Modernization Development for Agricultural Carbon Emission Efficiency in China

“…With regard to methodology, the ACEE is usually measured by Malmquist [32] and DEA [33]. Currently, scholars' studies on the ACEE include the evaluation indexes [34], influences [35], and the decoupling effect from economic growth [36].…”

Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM

“…When analyzing the spatial correlation of carbon emission, scholars used various methods, including global spatial autocorrelation Moran's I index, local spatial autocorrelation Moran's I index, and aggregation map, to conduct exploratory spatial data analysis (ESDA) in different research areas, and then concluded that regional carbon emissions exhibit significant spatial correlation [1][2][3][4][5]. Considering the intricate nonlinear network relationships inherent in regional carbon emissions, scholars used Social Network Analysis (SNA) to construct spatial correlation networks, analyzing overall network characteristics [6,7] and individual network characteristics [8,9]. In addition, scholars used the iterative correlation convergence method (CONCOR) for spatial clustering analysis [10,11].…”