A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

Abstract

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

“…When the Decision-Making Unit (DMU) was panel data, the Malmquist index could replicate the change in productivity better [ 11 ]. In the case of undesirable outputs, the Malmquist–Luenberger (ML) index, combining the Malmquist index and the Slacks-Based Measure (SBM), was more adaptable [ 18 , 27 ]. However, the ML index did not have the condition of circularity, and there was the possibility of no solution by linear programming.…”

“…Overall, it could be discovered that the research on AGTFP had accomplished fruitful research results, providing strong support for practical research, but there were still two deficiencies in its research. First, there were more studies on the evolution of the time dimension of AGTFP, but the studies from the spatial dimension still needed to be reinforced [ 7 , 18 ]. Second, the interpretation of the convergence mechanism of AGTFP was generally examined from a static perspective, and there was a lack of systematic research on the evaluation of its dynamic angle [ 19 , 20 ].…”

Spatial Correlation Network Structure of Carbon Emission Efficiency in China’s Construction Industry and Its Formation Mechanism

“…Therefore, identifying relationships is the key to analyzing network relationships. The existing research methods for constructing spatial association matrices are mainly the vector autoregressive (VAR) [41] and gravity models [34]. Since VAR models are sensitive to time lags, they are only applicable to data spanning a long period [7].…”

Study on the Spatial Spillover Effects of Agricultural Carbon Absorption in China

“…Li et al (2019) [54] found that the inter-provincial net agricultural carbon absorption efficiency in China showed a significant positive spatial correlation and there was local clustering; the local spatial autocorrelation Moran's I index also indicated that the inter-provincial net agricultural carbon absorption efficiency showed a clustering effect in space, the above conclusions were consistent with the findings of this paper. Chen et al (2022) [55] also argued that cross-regional cooperation's was critical for green agricultural development. On the other hand, the SDM model showed that Agricultural Industrial Restructuring (AIR), economic development level (RGDP), agricultural infrastructure level (AID), and Environmental Regulation Policies (ERP) had a significant positive effect on local agricultural carbon absorption.…”