Can the Adjustment of China’s Grain Purchase and Storage Policy Improve Its Green Productivity?

Abstract

While the sustainability of grain production has been extensively studied, there have been few studies focusing on the impact of grain policy adjustment on its sustainable production, and the quantitative relationship between these two aspects and the internal mechanism is not completely clear. The main objective of this paper was to explore the impact of grain purchase and storage policy (GPSP) adjustment on its green productivity by expounding the evolution logic and influence mechanism of GPSP. Therefore, taking maize production as an example, this paper constructs the analysis framework of the evolution logic and influence mechanism, and the super-epsilon-based measure model (Super-EBM) is adopted to measure maize green productivity (MGP) in main producing areas from 1997 to 2019, then two groups of difference-in-differences (DID) models are constructed to study the influence of the temporary purchase and storage policy (TPSP) and the producer subsidy policy (PSP) on MGP. The main conclusions include: the implementation of TPSP reduces MGP in Heilongjiang, Jilin, Liaoning and Inner Mongolia (experimental group), whereas the implementation of PSP improves MGP in these provinces is due to the difference in policy effects under the different regulatory objectives and measures; under the demonstration effect of two policies, the increase in effective irrigation and agricultural financial expenditure are important factors to improve MGP, but the backwardness of agricultural mechanization has been hindering the improvement of MGP; after the reform from TPSP to PSP, the continuous increase in production capacity hinders the improvement of MGP under the support effect, the impacts of farmers’ income and agricultural production price on MGP both shift from negative to positive under the wealth effect, and the influence of production agglomeration on MGP shifts from negative to positive under the siphon effect. The excessive implementation of GPSP has seriously affected the sustainability of grain production, thus, this study has certain practical significance and guiding value. The paper emphasizes that the effective way to achieve sustainable food production is to combine the adjustment of GPSP with improving the subsidy mechanism, enhancing the agricultural mechanization and maintaining the appropriate scale of operation.

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

“…Currently, productivity is widely measured using the non-parametric approach. In a non-parametric method such as envelopment analysis (DEA), the efficiency of a decision-making unit is described by the relationship between inputs and outputs on a linear piecewise frontier constructed by the DEA model [ 23 , 68 , 69 , 70 ]. In addition, some scholars have combined DEA models with machine learning to optimize the productivity measures [ 71 , 72 ].…”

Threshold effects of green technology application on sustainable grain production: Evidence from China

“…where Ẽ represents the value of green productivity of grain; x ij is the input variable matrix, with specific indicators including planting area, fertilizer, pesticide, agricultural film, diesel oil, seed, electricity for irrigation, labor and machinery (Liu and Feng, 2019;He et al, 2021;Li and Lin, 2022); y rj represents the desirable output, which is expressed in grain production; v tj represents the undesirable output, including carbon emissions and non-point source pollutions (the measurement of carbon emissions follows the methods of Liu et al, 2013 andChen et al, 2021; and the measurement of non-point source pollutions follows the methods of Chen et al, 2006 andZou et al, 2020)…”

“…Considering that the GTFPG is affected by various factors, in order to remove the interference of other factors on green technology, this paper uses the research approaches of Xu et al (2020); He et al (2021); Yang et al (2022b), and Li and Lin (2022) selects control variables from production condition, production decision, agglomeration capacity, financial support, economic development and natural disaster. Production condition increases the marginal desirable output or reduce the undesirable output by matching with the productivity level (Jiang et al, 2020;Li and Lin, 2022), and agricultural mechanization level and irrigation level are selected as proxy variables; production decision affects productivity by changing the proportion of production elements and production scales (Jiang et al, 2020;Liu et al, 2020;Li et al, 2020), and planting structure and rural income level are selected as proxy variables; agglomeration capacity improves resource utilization efficiency through knowledge spillover and energy structure optimization (Li and Lin, 2022;Yang et al, 2022b), and grain production agglomeration is selected as the proxy variable; financial expenditure affects productivity by improving production input, management level and service quality (Chen et al, 2021;He et al, 2021), and agricultural fiscal level and agricultural investment level are selected as proxy variables; economic development improves green productivity by influencing the adoption of green technologies and environmental awareness (Xu et al, 2020;Liu et al, 2022), and urbanization level and trade dependence level are selected as proxy variables; natural disasters have directly led to the decline of grain output and the increase of energy and chemical products input (Chen et al, 2021;He et al, 2021;Liu et al, 2022), and disaster incidence level, temperature fluctuation level and precipitation fluctuation level are selected as proxy variables. The specific calculation method of each control variable is shown in Table S2.…”

“…Its pursuit of maximized output has brought about resource waste, land overdraft, non-point source pollution, greenhouse gas emissions, etc. ( Shen et al., 2018 ; Li and Lin, 2022 ), especially in Asia, where extensive agricultural production mode is expected to increase greenhouse gas emissions by 37% in 2050 ( Frank et al., 2019 ). Sustainable agriculture highlights the use of advanced technologies and management to ensure the quality of agricultural products and ecological security, and improve comprehensive economic benefits ( Liu et al., 2020 ; Guo et al., 2022a ).…”

Strengthen or weaken? Research on the influence of internet use on agricultural green production efficiency

“…The third is the selection of models on the impact of subject behavior on production efficiency. The endogenous switching regression model (ESR) and propensity score matching model (PSM) are mainly applied to explore the relationship between the internet and technical efficiency, as well as the relationship between risk perception and farmers' fertilizer use (Tang et al, 2018;Zheng et al, 2021;Zhu et al, 2021), using DID model to explore the impact of policies on grain green production efficiency (Li and Lin, 2022), models such as PSM-DID was used to explore the impact of urban energy policies on green production efficiency (Zhuo et al, 2022), or use PSM model to classify respondents for correlation analysis (Li et al, 2020;Mario et al, 2014). For example, Zhu et al (2021) applied the endogenous switching regression model to explore the Internet's impact on Apple's production technical efficiency.…”