An Empirical Study on the Growth of Agricultural Green Total Factor Productivity in the Huanghuai River Economic Zone by Big Data Computing

Shi

et al. 2022

IJERPH

The power source, spatial-temporal differentiation and convergence of the growth rate of green total factor productivity in China’s agriculture were analyzed. The Malmquist index was used to measure the growth rate, and the spatial-temporal convergence was tested by σ convergence, absolute β convergence, conditional β convergence and dynamic spatial convergence. The study drew conclusions that the impetus for the intensive growth of green agriculture was insufficient, and the driving force for the growth of agricultural green total factor productivity (AGTFP) in the eastern, western and central region was green technology progress. In addition, AGTFP did not have an absolute σ convergence trend. Dynamic spatial absolute β and conditional β convergence indicated that regional differences were not completely related to regional endowment conditions, and regional green agricultural production was unbalanced. This study provides an important support for regional green development in China’s agriculture.

Section: Methodsmentioning

confidence: 99%

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

Shi

et al. 2022

IJERPH

“…Kuosmanen [ 17 ] combined the strengths of the DEA and SFA models to construct a stochastic semiparametric data envelope model to analyse agricultural green productivity across countries for the period 1990–2004. Zhang Yanan et al (2022) measured the green total factor productivity of the Huaihe Economic Zone from 2004 to 2017 based on the carbon cycle, and used the spatial Durbin model to analyse the effects of seven variables on green total factor productivity, including the level of economic development, environmental regulation, the level of R&D, and the degree of openness to the outside world [ 18 ]. Yuanxin Peng et al (2022) used the Malmquist index, spatial autocorrelation analysis, and convergence analysis to analyse GTFP in 263 prefecture-level and above cities in China [ 19 ].…”

Section: Indicator Construction Data Description and Measurement Mode...mentioning

confidence: 99%

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

Chen

Ni²,

et al. 2022

IJERPH

A more scientific green economy efficiency indicator is constructed based on OH (2010), and a multiperiod spatial DID model is used to examine the impact of national credit demonstration policies on urban green economy efficiency in a sample of cities above the prefecture level in the Yangtze River Delta. The study confirms the following: (1) The national credit demonstration policy makes a significant contribution to the green economic efficiency of cities, and it is conducive to strengthening awareness of the rule of law in the market to regulate market order. (2) The demand for credit regulation in coastal areas has increased under the new development pattern, and the national credit demonstration policy has effectively enhanced green economy efficiency through institutional supply. (3) Under the national credit demonstration policy, the subprovincial level and above can mobilise more resources for policy refinement and support, reducing transaction costs and improving the efficiency of the green economy. (4) The impact of the national credit demonstration policy on the efficiency of Zhejiang’s green economy is more obvious; but, under the overall framework of the Yangtze River Delta, the policy has a more prominent role in promoting green economy efficiency in other provinces. Policy insights are as follows: (1) Different cities have different degrees of impact on the efficiency of the green economy from the national credit demonstration policy, and they should implement differentiated measures based on regional heterogeneity; (2) regulating the use of administrative resources and avoiding undue administrative intervention are important prerequisites for promoting regional integration to enhance the efficiency of the green economy; and (3) strengthening interprovincial credit policy synergies can help to alleviate administrative distortions of policy implementation and enhance the efficiency of the regional green economy.

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chen

Sun³

et al. 2022

Sustainability

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.