1. Introduction
The concept of “green development” was first introduced by the United Nations Development Programme in 2002 and is widely regarded by society as the ideal path to achieve the organic integration of economy and environment [
1]. In line with the concept of green development in agriculture, the Chinese government introduced and implemented regulations in May 2008 regarding the disclosure of information from government departments that publish pollution data and the Ministry of Environmental Protection’s information disclosure measures, with a focus on promoting green and sustainable development in agriculture. The research questions are as follows: What is the level of total factor productivity in China’s agricultural environment under strict environmental regulation? With the gradual breakdown of administrative barriers, does China’s green total factor productivity in agriculture show a trend towards agglomeration at the spatial level? What is the correlation between green total factor productivity in agriculture in eastern, central, and western China, as well as in southern and northern regions, due to differences in geographical location and degree of economic development?
In a national context where green development has become the main goal of Chinese agriculture, an accurate grasp of the actual effects of environmental regulations on the efficiency of economic development in agricultural development is of great theoretical and practical value for the next stage of agricultural green development policy formulation in China and other developing countries similar to it. Therefore, in the research design of this paper, firstly, the theoretical and empirical studies are summarised and a more scientific approach is adopted to measure the green total factor productivity of agriculture in each province of China. Secondly, we innovate the use of social network analysis to analyse the spatial distribution pattern of green total factor productivity in agriculture at the provincial level in China. Thirdly, we focus on the regional interaction of green total factor productivity in agriculture in these key regions, taking into account the development disparities between the eastern, central, and western parts of China, as well as the southern and northern regions.
From the research literature, the exclusion of resource factors does not fully reflect the characteristics of agricultural development. Therefore, some scholars have included resource and environmental factors in the measurement models of agricultural productivity and used different methods to obtain green total factor productivity in agriculture [
2,
3,
4]. For example, Tone (2003) [
5] developed a standard DEA-SBM efficiency model that includes non-desired output, and after incorporating slack variables into the function, it could better correct the error problems of the general DEA model in radial and angular aspects. In their work on measuring total factor productivity (TFP) of Swedish pulp mills, Chung et al. (1997) [
6] included, for the first time, pollution emissions as a non-desired output and developed the directional distance function (SBM). Based on this study, Kuosmanen (2013) [
7] combined the strengths of DEA and SFA models to construct a stochastic semi-parametric data envelope model (StoNED) to analyse country-specific agricultural green productivity for the period 1990–2004, using OECD countries and selecting data on agricultural CO
2 emissions, nitrogen stocks, and phosphorus stocks. Xiaocang Xu et al. (2020) attempted to incorporate environmental pollution into the framework of agricultural productivity analysis by using soil N
2O emissions as an important variable with which to measure agricultural green total factor productivity (AGTFP) [
8]. Dongdong Liu et al. (2020) used a super SBM model to calculate China’s carbon emission-based agricultural total factor productivity based on provincial agricultural panel data in China, and used kernel density estimation to examine its dynamic evolution [
9]. Chen Yufeng et al. (2021) took carbon emissions and agricultural surface source pollution (ANSP) as non-expected outputs, used a three-stage data envelopment analysis (DEA) method combined with an SBM model to remove the effects of environmental factors and random errors, explored the true AGTFP of 30 Chinese provinces from 2000 to 2017, and further explored the spatial distribution and dynamics of AGTFP before and after adjustment to seek the reasons behind it [
10]. Chen Yanling et al. (2022) used the recent 15-year provincial panel SBM-ML index method to measure agricultural productivity from the perspective of environmental constraints with agricultural surface source pollution as a non-desired output, and a dynamic panel regression model was used to empirically analyse the factors affecting agricultural productivity [
11]. Huang Xiuquan et al. (2022) constructed two different data envelopment analysis models, combining the green Luenberger productivity indicator (GLPI), a two-year weight-corrected Russell model, and a two-year bounded adjustment model to measure AGTFP in China and decompose AGTFP growth at both the production and factor levels to examine its drivers [
12]. Based on panel data from 2001 to 2019 for 30 Chinese companies, Zhu Yingyu et al. (2022) measured the green total factor productivity of China’s plantation industry based on the net carbon sink using stochastic frontier analysis with an output-oriented distance function, and empirically investigated the impact of agricultural mechanisation on the green total factor productivity [
13]. Yuanxin Peng et al. (2022) used the Malmquist index, spatial autocorrelation analysis, and convergence analysis to analyse the GTFP of 263 prefecture-level and above cities in China [
14]. Zhang Yanan et al. (2022) measured green total factor productivity in the Huaihe Economic Zone based on the carbon cycle in the period 2004–2017, and used a spatial Durbin model to analyse the effects of seven variables on green total factor productivity, including the level of economic development, environmental regulation, R&D level, and openness to the outside world [
15]. Yining Zhang et al. (2022) measured green total factor productivity in the Chinese manufacturing industry using the Malmquist–Luenberger (ML) model based on provincial panel data from 2008 to 2017, and further constructed an empirical model to analyse the impact mechanism of green total factor productivity [
16]. Fang Lan et al. (2022) used the SBM-GML index model to measure agricultural green total factor productivity based on provincial panel data in China from 2002 to 2015, and systematically examined the impact of crop insurance on agricultural green total factor productivity and its mechanism of action [
17].
A number of scholars have conducted in-depth studies on the analysis of the spatial interaction of green total factor productivity in agriculture. Fredriksson and Millimet (2002) [
18] were the first to study the spatial spillover effects of government policies that could lead to inter-regional resource flow mechanisms and scalar competition mechanisms [
19]. Due to the spatial scale of agricultural green total factor productivity interactions, governments tend to weaken the intensity of local environmental regulations for the purpose of attracting competing high-quality agricultural production factors, thus triggering the phenomenon of bottom-up racing for environmental quality between regions [
20]. In contrast, some studies have argued that quality agricultural resources have a higher demand for the environment and that there is also a race to the top in inter-regional agricultural development [
21]. As a result, agricultural green total factor productivity is networked at a spatial scale and in turn influences the green economic efficiency of regions due to cooperative or competitive strategies between local governments [
22]. Zhangqi Zhong et al. (2019) used data envelopment analysis to construct a spatial panel data model with embedded climate change factors to measure agricultural total factor productivity in China, and then explored the possible impact of climate change on agricultural total factor productivity in provincial regions of China [
23]. Wang Haoran et al. (2020) used stochastic frontier analysis, the Malmquist index, and the spatial Durbin model to examine the spatial effects of green technology innovation on green total factor productivity from a regional perspective [
24]. Zhang Xueyao et al. (2021) used ICT and panel spatial measurement (PSM) models to measure the overall characteristics, temporal changes, and regional differences in agricultural development in 30 Chinese provinces from 2000 to 2019 from the perspective of resources and environment, and constructed a panel data measurement model using generalised least squares to analyse the main factors affecting performance development [
25]. Xingming Li et al. (2022) used a data envelopment analysis (DEA) model and the Malmquist–Luenberger (ML) index to measure China’s tourism GTFP from 2007 to 2018 and analyse spatial and temporal differences [
26]. Huaping Zhang et al. (2022) established an assessment index system (AIS) for GTFP, and used the EBM model to calculate the GTFPs of 30 Chinese provinces from 2000 to 2019. They analysed the spatial correlation between the GTFPs of each province and discussed the convergence between them using spatial panel data [
27]. Shiying Hou et al. (2022) used a spatial econometric model to investigate the spatial effects of market integration on regional green total factor productivity and the transmission mechanism by calculating the Malmquist–Luenberger index based on panel data of 30 Chinese provinces from 2008 to 2017 [
28]. Qiang Li et al. (2022) used a slack metric-based data envelopment analysis (DEA-SBM) super-efficiency model to measure agricultural environmental total factor productivity (ETFP) in 30 provinces and regions in China. Based on the measurement results, the impact of the urban–rural income gap on agricultural ETFP was empirically tested using a spatial autoregressive (SAR) model and estimation methods [
29].
However, an analysis of the existing literature reveals two points. Firstly, from the perspective of research objects, most of the existing studies have been conducted on green total factor productivity and agricultural total factor productivity, while few studies have been conducted on agricultural green total factor productivity. Secondly, from the perspective of research methods, most of the existing studies have used time series models, panel models, and spatial econometric models, while few studies have used social network models. In comparison with other methods, the use of social network models can both portray the spatial structure of green total factor productivity in agriculture in a given period and better analyse the evolutionary trends of green total factor productivity in agriculture in different time dimensions, with a variety of advantages applicable to the study. Therefore, there is a need to focus on green total factor productivity in agriculture and to combine research methodological innovations to examine a country’s development from a spatial dynamic evolutionary perspective. Indeed, given the spatial spread and interaction of policies and factors, the assessment of agricultural green total factor productivity of a given region cannot be undertaken in isolation, but must take into account both its own efficiency and the role of the efficiency of other regions in the local context, i.e., the region must be analysed in the context of a network formed by links with other regions. Social network analysis (SNA) is a powerful tool for studying social phenomena and structures, based on a “relational” perspective, which can better reflect the relative position and network characteristics of a province in a nationwide network of agricultural green total factor productivity [
30]. Therefore, in this paper, we propose to use social network analysis to measure the overall profile of agricultural green total factor productivity in China and the position of each province in the network. On this basis, the VAR impulse response function (IRF) is applied to further examine how agricultural green total factor productivity in a particular region is affected by other regions, considering the economic linkages between the three major regions of China: north and south, central, and east and west (eastern provinces include Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, and Guangdong; central provinces include Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; western provinces include Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang). The VAR impulse response function (IRF) is used to further examine how the green total factor productivity of agriculture in a given region is affected by other regions. The research in this paper is based on the following hypotheses: in the development of a market economy, due to the profit-seeking motive of enterprises, the allocation of resources and factors, etc., between regions is inevitable, so the magnitude of agricultural green total factor productivity in a region will not only be influenced by the development of the local economy, but also, in the long run, will be increasingly influenced by other regions, especially geographically adjacent regions. The research methods are based on the more scientific slack-based model (SBM) global Malmquist–Luenberger (GML) index to measure the green total factor productivity of agriculture in China’s provinces, and the innovative use of social network analysis (SNA) to examine the spatial linkages and network structure of green total factor productivity in China’s agriculture. Furthermore, a vector autoregressive model (VAR) impulse response function (IRF) was used to analyse the regional interaction of green total factor productivity in China’s agriculture. The aim of this paper is to establish a social network-based analytical framework through a systematic analysis of the spatial structure, dynamic evolution, and regional interaction of agricultural green TFP in China, in which it is possible to study the open economic linkages of a region at a spatial scale while taking into account developments over time, and in doing so, stimulate the thinking of policy makers.
4. Regional Interaction Analysis of Agricultural Green Total Factor Productivity in China
With the deepening and expansion of the market economy, the green development of regional agriculture is increasingly influenced by exogenous factors, superimposed on path-dependent effects, and the spatial correlation is constantly reinforced. However, it should be noted that it takes a long time for different regions to interact with each other and form a more stable spatial distribution pattern, i.e., spatial correlation must be analysed in a longer time dimension to be meaningful; therefore, spatial correlation analysis is not contradictory to the assumption that all DMUs should be independent in DEA analysis. In order to validate the spatial correlation of green total factor productivity in Chinese agriculture, we measured the global Moran I index (Moran’s I), whose mathematical expression is as follows.
In the above equation,
and
denote the observed values of the examined indicators in regions
i and
j, respectively;
n is the number of spatial cells; and
is the spatial weight matrix, which is used to measure the interrelationship between neighbouring regions. In this paper, we define the value of a regional neighbourhood as 1, otherwise the value is 0. Moran’s I takes values in the range [−1, 1], tends to −1 for negative spatial correlation, tends to 1 for positive spatial correlation, and equals 0 for no spatial correlation. We used OpenGeoDa software to create Moran scatter plots of China’s agricultural green total factor productivity in 2008 and 2019 (
Figure 6), and the plots show that Moran’s I values in 2008 and 2019 are 0.2404 and 0.2148, respectively, which verifies the existence of the spatial correlation of China’s agricultural green total factor productivity.
On the basis of spatial correlation analysis, considering regional heterogeneity, impulse response function (IRF) analysis was conducted by building a VAR model to further explore the regional interaction of green total factor productivity in agriculture in east, central, and west China, and in south and north China, resulting in the impulse response function composite plot reported in
Figure 7, where the horizontal axis indicates the number of lag periods for the effect of shocks; the vertical axis indicates agricultural green total factor productivity; the solid line indicates the impulse response function, which represents the response of a particular region to a green agricultural efficiency shock from other regions; and the dashed line indicates the positive and negative two-times standard deviation bands.
For the analysis of image features, in terms of regional interactivity in the east, central, and west, and in the south and north, we found that the impulse response curves of east to central, west to central, and west to east showed roughly opposite trends, while the impulse response curves of west to central interaction showed roughly the same trend. This indicates that the interaction between the centre and the west and the east has generally improved agricultural green total factor productivity in the east, but has had an insignificant positive effect on agricultural green total factor productivity in the region, i.e., a significant siphon effect and a non-significant trickle-down effect. In contrast, the interaction between the central and western regions, with the direction of the impulse responses converging, indicates that the central region can also positively influence the efficiency of the agricultural green economy in the western region while improving its own agricultural green total factor productivity, showing a certain win-win pattern of agricultural development in the two regions. In terms of regional interaction between the south and the north, the intensity of the impulse response of the south to the north is much higher than that of the north to the south. On the one hand, this indicates that the south has a greater driving effect on the green total factor productivity of agriculture in the north, and on the other hand, it indicates that there is still a lack of innovative division of labour in agricultural development between the south and the north of China, and that the degree of industrial integration needs to be strengthened. Therefore, in order to further improve the overall level of agricultural green total factor productivity in China and optimise the cross-regional layout of agriculture, the next stage should focus on strengthening the rational allocation of factors and the division of labour between the middle and the west, the south, and the north.
5. Conclusions
In this study, we measured agricultural green total factor productivity in each province of China based on the SBM-GML method and examined the spatial network structure of agricultural green total factor productivity in China and the regional interaction between the east, middle, and west using social network analysis (SNA) and impulse response function (IRF) in the VAR model, respectively. We found that agricultural green total factor productivity from 2008 to 2019 was generally higher in the southern provinces than in the northern provinces, and that the distribution of agricultural green development quality was more dispersed in the north, with development differences between different provinces being more pronounced than in the south. The spatial network structure of agricultural green economic efficiency across Chinese provinces is taking shape, experiencing an evolutionary path from polycentric to monocentric to polycentric conditions, with the same subgroup of provinces often having similarities at the geographical or economic level, indicating that China’s agricultural green total factor productivity has basically formed a benign interaction pattern of competing upwards in terms of improving agricultural green economic efficiency. China’s resilience in green agricultural development has gradually increased with the advantage of a mega market and the potential of domestic demand, and the spatial network is characterised by a certain degree of self-stability. With the in-depth promotion of the national strategies of rural revitalisation and building a new development pattern, the density of total factor productivity networks and the density of cohesive subgroups in China’s agricultural environment are both showing an increase, indicating that the spatial interaction of agricultural green development is strengthening, which has important practical significance for cross-regional cooperation in agriculture and the extension and integration of industrial chains; for example, the cross-regional agricultural cooperation in central and western China in the form of counterpart cooperation has achieved satisfactory results. The interaction between the eastern, central, and western regions has generally improved the total factor productivity of the agricultural environment in the east, but not in the central and western regions, meaning that the siphon effect is significant but the trickle-down effect is not obvious. Therefore, for policy makers, in order to improve the green total factor productivity of Chinese agriculture, targeted policies related to cross-regional cooperation in terms of factor mobility and industrial division of labour can be considered, taking into account the actual agricultural development in the mid-west and the south–north. For academic researchers, the possible inspiration of this paper is that for different research topics, appropriate research methods must be selected and applied in order to provide a more profound analytical portrayal of the real world. The social network analysis method adopted in the paper can obtain appropriate answers to the question of the spatio-temporal development of regional economies in an open economy, but it cannot do anything about the analysis of closed economies due to geographical and institutional factors. At a time when globalised markets are encountering increasing challenges and food security and environmental protection are prominent issues, the use of social network analysis tools to study the position and changing trends of countries in international trade in agriculture, in order to maintain global trade security and promote sustainable agricultural development, is a promising, challenging, and meaningful exercise.