Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

Agricultural productive services are important paths to realize the development of green agriculture, while the effect of agricultural productive services on the agricultural environment and its influencing mechanism are not yet clear. With the panel data of agricultural production in China from 2004 to 2019, by using multi-output stochastic frontier analysis with an output-oriented distance function, this study investigates agricultural environmental efficiency based on net carbon sinks. Then, this study explores the effect of agricultural productive services on agricultural environmental efficiency and its mechanisms by adopting ordinary least squares regression with fixed-effect panel model, causal steps approach, and spatial econometric method. The main findings are as follows: Firstly, agricultural productive services enhance agricultural productivity and agricultural environment by optimizing inputs and increasing outputs, and thus improve agricultural environmental efficiency. This result holds steadily after using instrumental variables to deal with endogeneity, changing the measurement of the dependent and independent variables, and subdividing the sample. Secondly, the pathways of agricultural productive services affecting agricultural environmental efficiency are mainly reflected in technology progress, planting structure adjustment, factor allocation optimization, and spatial spillover. Thirdly, due to the law of diminishing marginal returns, the impact of agricultural productive services on agricultural environmental efficiency is more significant when the level of agricultural productive services is relatively low. To improve agricultural environmental efficiency, we suggest implementing different productive service strategies in different regions, strengthening information integration, and improving infrastructure.

In the context of China’s large country with small farmers, agricultural socialized services are regarded as an important way for small farmers to organically connect with modern agriculture and provide ideas for improving agricultural green total factor productivity (AGTFP). Based on data from 1066 farmers from the China Land Economy Survey (CLES), this paper takes net carbon sink as the environmental output variable, adopts the Cobb–Douglas production function for stochastic frontier method estimation, and measures the AGTFP based on the stochastic frontier analysis method with an output-oriented distance function. At the same time, through the construction of intermediary effect and regulatory effect models, it empirically analyzes the impact and mechanism of agricultural socialized services on farmers’ AGTFP from the perspective of factor allocation. The study found that agricultural socialized services not only significantly promote AGTFP, but also that the effect of AGTFP improvement is more significant as the degree of participation in agricultural socialized services increases. The main results have passed a series of robustness tests. Further research found that agricultural socialized services promote the improvement of AGTFP through the intermediary role of improving the scale of farmland and the level of agricultural green technology adoption. Off-farm employment of the rural labor force has a positive regulatory effect between agricultural socialized services and AGTFP. Therefore, it is recommended to further play the role of agricultural socialized services in optimizing the allocation of production factors and to motivate agricultural socialized service organizations to provide multiple green production services for farmers through policy support or subsidies, enhance the service capacity of agricultural socialized service organizations, and provide precise services by fully considering the differences in the endowment of production factors among farmers, as well as take multiple measures and make solid and steady progress in promoting the sustainable development of agriculture.

Farmers’ green breeding behavior is significant to environmental protection and sustainable development. Based on the micro-survey data of 1248 beef cattle farmers in Inner Mongolia Autonomous Region and Jilin Province, the OLS model and regulatory effect tests are used to analyze the influence of social learning on farmers’ green breeding behavior and to verify the mechanism of environmental regulation. Key findings include the following: (1) Social learning has a significant positive impact on farmers’ green breeding behavior. (2) Social learning has a significant positive impact on farmers’ willingness to undertake green breeding, farmers’ specific behavior of green breeding, and farmers’ continuous green breeding, among which the effect on specific behavior of green breeding is the most positive. (3) Environmental regulation plays a moderating role in the relationship between social learning and farmers’ green breeding behavior, among which all sub-dimensions of environmental regulation (incentive-based environmental regulation, binding environmental regulation and guided environmental regulation) have positive reinforcing effects, especially the most significant reinforcement effect of binding environmental regulation. Based on the research conclusions, this study proposes policy suggestions such as strengthening the training of farmers, strengthening the learning and communication among farmers’ neighbors, increasing the frequency of farmers’ use of mass media, improving the environmental regulation policy system, and promoting the coordinated development of environmental, economic, and social benefits.

It has important theoretical value and practical significance to study the impact of agricultural mechanization (AM) on agriculture environment efficiency (AEE), as AM is an important way to improve the level of rural modernization and accelerate the high-quality development of agriculture, while the increase of energy consumption of AM has brought greenhouse gas emissions. Using the panel data of 30 provinces in China from 2001 to 2019, this article adopts stochastic frontier analysis method with output oriented distance function to measure AEE based on net carbon sink, and empirically analyzes the impact of AM on AEE. The empirical analysis finds that the AEE of the whole country and all provinces shows an upward trend with time, and has significant spatial positive autocorrelation characteristics. There is a Kuznets inverted "U" relationship between AM and AEE. Meanwhile, AM has spatial spillover effect and time cumulative effect on AEE, and this basic conclusion is still robust after using instrumental variables, spatial autoregressive model, sub sample regression, changing spatial weight matrix and independent. Further research shows that the effect of AM on AEE depends on the input effect and output effect caused by AM, and the mechanism is mainly reflected in agricultural technology progress, expansion of the scale of agricultural operation, optimization of resource allocation and spatial spillover. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.