The Evolution and Influencing Factors of Total Factor Productivity of Grain Production Environment: Evidence from Poyang Lake Basin, China

Abstract

The total factor productivity (hereafter TFP) of grain production is important to achieve balanced development, while environmental factors are an important part of TEP. In order to explore the characteristics and patterns of the temporal and spatial evolution of the environmental total factor productivity (hereafter ETFP), the Malmquist-Luerberger index, and the spatial autoregressive panel (SAR panel) model were adopted to analyze the evolutionary rules and the influencing factors of ETFP. In this study, we took Poyang Lake, one of China’s main grain production areas, as a study area, and carried out empirical research based on grain production statistical data. The results show that: (1) ETFP shows a growth trend with the increase of grain production from 2001 to 2017, and a great potential for improvement exists. Moreover, from the perspective of time sequence evolution and decomposition of ETFP, which belongs to the dual-track driver of environmental technical efficiency and environmental technological progress, relevant technologies play an important role in promoting the improvement of TEFP; (2) Given that the objective conditions of gain production remain unchanged, the fact that the urbanization rate and average annual rainfall have a negative effect on ETFP, the explanatory variables such as the business scale per worker, the proportion of grain growing population, industrial agglomeration, the proportion of grain sown area and the average annual temperature all play a positive role. Among the variables, the business scale per worker and the proportion of grain growing population significantly affect ETFP at the 1% level. The average annual rainfall, industrial agglomeration and the proportion of grain sown area significantly affect the ETFP at the 5% level. The average annual temperature significantly affects the ETFP at the 10% level.

Analysis of Green Total Factor Productivity of Grain and Its Dynamic Distribution: Evidence from Poyang Lake Basin, China

“…The measurement methods of total factor productivity of grain include the parametric and nonparametric methods [34]. In general, the parameter method needs to set a specific form of the production function, while the non-parameter method does not need to set the specific form of the production function, which can avoid the error of the calculation results because of the selection of the wrong production function.…”

“…The Poyang Lake basin is an important grain production area in China that includes 100 counties (cities, districts), such as Nanchang County and Chaisang District. This paper considers the 19 municipal districts (including Lianxi, Qingshan Lake, Linchuan, Yushui, Anyuan, Qingyuan, Zhanggong, Xinzhou, Wanli, Changjiang, Xunyang, Yuehu, West Lake, Donghu, Jizhou, Zhujiang, Qingyunpu, Xiangdong, Yuanzhou) and Gongqingcheng city with a high level of urbanization and very low proportion of grain production, or even no grain production, and has limited effects on the research results [34]. Therefore, the study selects 80 counties (cities, districts) in Poyang Lake Basin as the research objects and the time series is from 2001 to 2017.…”

“…Chemical fertilizer and pesticide are the main sources of agricultural non-point source pollutants during rice production. Considering the data availability and the measurability of non-point source pollution, this paper only calculates the amount of non-point source pollution of chemical fertilizer as the unexpected output [34]. Relevant studies have confirmed that excessive application of chemical fertilizer not only produces negative externalities to the environment but also gradually becomes the main source of agricultural non-point source pollution [37][38][39], and the method for calculation is based on Wu's (2012) method of agricultural non-point source pollution in the Poyang Lake Area [40].…”

Towards a Sustainable Grain Production Network: An Empirical Study from Northeast China

“…In Equation (10), BC i represents the betweenness centrality of node i, g jk is the number of shortcuts between node j and k, and g jk (i) is the number of shortcuts that must pass through city i between node j and k.…”

“…The production center of gravity has shifted northward, crossing the Yellow River, and grain production has been transferred to the marginal areas with low land productivity and high natural risk-Northeast and Northwest China have become the main contributors to increase production [7]. Although China's grain output continues to increase, the grain production system is more fragile and unstable than before, with uneven and low overall grain efficiency, facing the dual challenges of lack of motivation and limited room for growth [8][9][10][11].…”