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

Abstract

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.

The green productivity of broiler production in China: Considering the resource utilization of manure

“…This study briefly introduces the absolute β convergence model and the conditional β convergence model adopted in previous research to identify GTFP convergence of broiler chickens in China [ 46 , 47 ]. …”

Spatial Association Network and Driving Factors of Agricultural Eco-Efficiency in the Hanjiang River Basin, China

“…Others have used Moran's I and the Local Indicators of Spatial Association (LISA) index to analyze the spatial and temporal patterns [13]. Some studies have analyzed the convergence of spatial differences in agricultural eco-efficiency and concluded that the efficiency values in the study area do not exhibit absolute α-convergence and absolute β-convergence characteristics [37], but that geographically adjacent regions influence the surrounding areas through efficiency spillovers. Besides analyzing agricultural eco-efficiency time series evolution, spatial distribution differences, and trends, social network analysis methods are often used to analyze the structural and interactive nature of different spatial units [38,39].…”

Green-Biased Technical Change and Its Influencing Factors of Agriculture Industry: Empirical Evidence at the Provincial Level in China

“…The difficulty in estimating provincial agricultural capital stock is one of the most important reasons. In most studies, intermediate inputs such as fertilizer, machinery, pesticides, agricultural film, water, and energy are substituted for agricultural capital factors [ 2 , 6 , 17 , 48 , 49 , 50 , 51 ]. However, these intermediate inputs are not ideal substitutes for agricultural capital factors [ 3 , 52 ].…”

“…The following are the main reasons for inadequate consideration: first, the majority of the literature employs parametric methods that require strict assumptions on production functions, making it difficult to account for agricultural pollution emissions effectively. Second, to achieve the goal of high-quality development, both non-point source pollution and carbon emissions in agriculture cannot be ignored [ 51 , 53 ]. However, most current studies only consider one of them.…”