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.

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.

When using a traditional econometric model to study the promotion effect of the cultural industry on social development in cross-border context, only the mathematical formula is available for the quantitative analysis of social and economic problems, but disadvantages such as complex processing process, low efficiency, and low accuracy are still encountered. This article designs a research model to study the promoting effect of cultural industry on social development in the context of cross-border integration. Based on the economic measurement results of cultural tourism industry, the coupling principle is used to quantitatively analyze the coupling and coordination degree of the development of cultural tourism industry under cross-border integration. Using dynamic panel theory, this article studies the role of cultural tourism industry integration in social and economic development. The experimental results show that the model can effectively analyze the promoting effect of cultural industry on social development in the context of cross-border integration, in which the error rate is only 0.31 and the maximum growth rate is as high as 98%, indicating small error and high efficiency of the proposed model.

The support of external organizational forces is essential for the promotion of agricultural ecological efficiency to improve agricultural green development and boost China’s prosperity in agriculture. To identify the different impact of different organizational support on improving farmers’ agricultural ecological efficiency and investigate the mechanism by which organizational support affects agricultural ecological efficiency, this study explores the internal logic of farmers’ promotion of agricultural ecological efficiency and empirically examines the impact of organizational support and knowledge distance on agricultural ecological efficiency using 1011 household-level survey data from Henan province in China. The study shows the following: (1) Enhancing organizational support can significantly promote agricultural ecological efficiency, and the effect of organizational support in descending order is agricultural material distributors, peasant cooperatives, village committees, agricultural technology service centers, and agricultural associations. (2) Knowledge distance partially mediates the influence of organizational support on agricultural ecological efficiency. The mediating mechanism of spatial distance is the most significant, followed by content distance and cognitive distance. (3) Market-oriented organizations play a stronger role in incremental support, and government organizations play a stronger role in radical support. This study adds considerable value to the empirical literature and provides precise guidance for improving agricultural ecological efficiency.

Agricultural mechanization is an important factor to improve the green total factor productivity of 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 output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.