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

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

Sustainable production is considered as an important approach to solve the dilemma of food insecurity. Green technologies have made contributions to improving food production and reducing environmental pollution. Studying the effects of green technologies on sustainable food production has great significance. The paper started with the influence mechanism of green technology application on the green total factor productivity of grain (GTFPG). With the GTFPG, green technology efficiency change of grain (GECG) and green technical progress change of grain (GTCG) measured, threshold models were constructed to explore the nonlinear impacts of various green technologies on GTFPG and the influence paths. Results indicated that the differences of GTFPG among provinces in China were decreased mainly due to the changes of GTCG, while the regional differences of GECG remained small. The impacts of green technologies had threshold effects that depended on the ecological effects of green technologies in different application stages, and were significantly different in the major and non-major grain producing areas. Meanwhile, significant differences existed in the influence paths of green technologies. In the major grain producing areas, green technologies were more likely to improve GTFPG through the GTCG path; while in the non-major grain producing areas, the GECG path and the GTCG path were both important to improve GTFPG. The differences of green technologies' threshold effects and influence paths in the major and non-major grain producing areas were caused by regional technology preference, resource endowment and technology compatibility. This study emphasizes that the development of green technologies should fully consider the resource endowment and economic development of different regions, as well as the applicability and adoption rate of green technologies.

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

Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China's AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

Study on the Spatiotemporal Evolution and Influencing Factors of Agricultural Carbon Emissions in the Counties of Zhejiang Province

The accurate measurement of agricultural carbon emissions and the analysis of the key influential factors and spatial effects are the premise of the rational formulation of agricultural emission reduction policies and the promotion of the regional coordinated governance of reductions in agricultural carbon emissions. In this paper, a spatial autocorrelation model and spatial Dubin model are used to explore the spatiotemporal characteristics, influential factors and spatial effects of agricultural carbon emissions (ACEs). The results show that (1) From 2014 to 2019, the overall carbon emissions of Zhejiang Province showed a downward trend, while the agricultural carbon emission density showed an upward trend. ACEs are mainly caused by rice planting and land management, accounting for 59.08% and 26.17% of the total agricultural carbon emissions, respectively. (2) The ACEs in Zhejiang Province have an obvious spatial autocorrelation. The spatial clustering characteristics of the ACEs are enhanced, and the "H-H" cluster is mainly concentrated in the northeast of Zhejiang, while the "L-L" cluster is concentrated in the southwest. (3) The results of the Dubin model analysis across the whole sample area show that the ACEs exhibit a significant spatial spillover effect. The disposable income per capita in the rural areas of the county significantly promotes the increase in the ACEs in the neighboring counties, and the adjustment of the industrial structure of the county has a positive effect on the agricultural carbon emission reductions in neighboring counties. (4) The grouping results show that there is heterogeneity between 26 counties in the mountainous areas and non-mountainous areas. In the 26 mountainous counties, the urbanization rate, rural population, mechanization level and industrial structure have significant negative spatial spillover effects on the carbon emissions. In the non-mountainous counties, the agricultural economic development level and disposable income per capita of the rural residents have significant spatial spillover effects on the agricultural carbon emissions. These research results can provide a theoretical basis for the promotion of the development of low-carbon agriculture in Zhejiang according to the region and category.