This paper focuses on the sustainable development dilemma of agricultural production in China under the pattern of intensive management, which is seriously challenged by agricultural non-point source pollution. The key to effectively break through the dilemma is to promote the co-governance of agricultural non-point source pollution control by stakeholders including local governments, new agricultural operators and traditional farmers. Accordingly, this paper discusses the interactive decision-making relationships between new agricultural operators and traditional farmers under the guidance of local governments, by constructing a trilateral evolutionary game model, as well as analyzing evolutionary cooperative stability strategies and realizing the simulation of evolution processes in different scenarios by MATLAB. The results show that new agricultural operators play a leading role in agricultural non-point source pollution control, whose strategies have effects such as technology spillover. The rewards from the superior government will support local governments in taking proactive action in the co-governance of agricultural non-point source pollution control, and then local governments can offer technical support and subsidies to new agricultural operators and traditional farmers for reducing their costs. Furthermore, this paper also finds that there are green synergy effects among the groups, where the variations of parameters and strategies by one group would affect the two others. Additionally, agricultural land operation rights transfers would cause traditional farmers to take more time to cooperate in the co-governance of agricultural non-point source pollution control. In order to promote the multi-agent co-governance of agricultural non-point source pollution control under intensive management pattern, this paper suggests that it should be necessary to reduce their costs and improve incentives, as well as to increase the common interests among groups and enhance their green synergy effects.

The influencing mechanism of agricultural non-point source pollution under intensive agricultural management is complicated. This paper adopted provincial panel data from 2008 to 2020 to estimate the level of agricultural intensive management, the agricultural chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) emissions and emission intensity of agricultural non-point source pollution in different regions of China and analyze the spatial-temporal differentiation characteristics. Moreover, the mediation effect model and spatial spillover effect model were adopted to further explore the influence mechanism of agricultural intensive management on agricultural non-point source pollution. The results show that (1) The total emissions and emission intensity of agricultural non-point source pollution both showed an increasing trend, and these areas with high levels of agricultural non-point source pollution are roughly consistent with those areas with high-level of agricultural intensive management. (2) At the overall level, there were mediating effects of natural ecology, agricultural land management, planting and rearing structure and pollution control investment between the relationship of agricultural intensive management and agricultural non-point source pollution, among which agricultural land management was the largest. Additionally, there was significant spatial heterogeneity in the influencing mechanism of agricultural intensive management on non-point source pollution. (3) There were significant spatial agglomeration characteristics in both agricultural intensive management and agricultural non-point source pollution, which showed a fluctuating trend of “rise-decline-rise-decline”. (4) Agricultural intensive management has a significant positive spatial spillover effect on COD, TN and TP emissions of agricultural non-point source pollution. However, environmental regulation could cause agricultural non-point source pollution to be transferred nearby. Scientific understanding of the spatio-temporal differentiation characteristics and influencing mechanism of agricultural non-point source pollution under the agricultural intensive management model is conducive to providing reference for policy regulation.

Environmental regulation and financial support for agriculture are regarded as important means to promote agricultural green development. Based on provincial panel data of 31 provinces in China between the years 2000 and 2020, this paper explores the interactive effect, spatial spillover effect and spatio-temporal heterogeneity of environmental regulation and financial support for agriculture on agricultural green development. The empirical conclusions are as follows: (1) The level of agricultural green development increases year by year with a spatial characteristic of high in the east and low in the west. Moreover, there is a spatial spillover effect of agricultural green development with spatiotemporal heterogeneity. (2) Environmental regulation would not only reduce the level of local agricultural green development but also inhibit the adjacent regions, which is contrary to the impact of financial support for agriculture. (3) The interactive relationship of environmental regulation and financial support for agriculture has a spatial spillover effect on agricultural green development, which is more significant in the systematization stage of agricultural green development and most significant in the east-middle region.

The diffusion of green agricultural production under intensive management pattern is an interactive process of strategy comparison and learning on complex networks among traditional farmers and new agricultural operation entities. Based on the theory of evolutionary game and complex networks, we construct evolutionary game models on the scale-free networks to simulate the evolution process of green agricultural production under the market mechanism and the government guidance mechanism, respectively. The comparison analysis results in different scenarios show that the stable state of the green agricultural production network is determined by interactions among the subjects. Detailed experimental results indicate that the double-score system under government guidance mechanism has a significant effect on the diffusion of the green agricultural production, of which the extra reward or penalty obtained from government is crucial. Besides, the diffusion of the green agricultural production under the market mechanism is mostly affected by the net profit of green agricultural production. These results are of great significance for increasing efficiency of government’s incentive and promoting the initiatives of traditional farmers and new agricultural operation entities in the green agricultural production.

The construction of green and smart cities is an important approach to enhancing the level of high-quality development and modern governance, in which infrastructure construction is the antecedent condition. From the perspective of green total factor productivity (GTFP), this paper adopts the SBM–GML (Slack-Based Model and Global Malmquist–Luenberger) index to measure the urban green and smart development level (GSDL) considering smart input–output factors. Based on the panel data of China’s 223 prefecture-level cities from 2005 to 2018, the dynamic impacts, temporal, and spatial differences of energy, transportation, and telecommunication infrastructure construction on the urban GSDL are discussed, and the threshold effects of urban scale are tested. The following conclusions are drawn: (1) On the whole, energy infrastructure inhibits the urban GSDL, while transportation and telecommunication infrastructures significantly promote it. There are distinct spatial and temporal characteristics among the impacts of these three infrastructures on the urban GSDL, in which the facilitating role of transportation and telecommunication infrastructures are further enhanced during the period of 2013–2018. Furthermore, the impacts of these three infrastructures on the urban GSDL all show “U” shape in terms of non-linearity. (2) Economic development level and industrial structure have significant positive effects on the urban GSDL, whereas human capital only has positive effect in the northeast and southwest regions, and government scale shows no positive impact yet. (3) There is a single threshold for the impact of urban scale on these three infrastructures, among which the impacts of energy and transportation infrastructures on the urban GSDL remain consistent before and after the threshold, while the impact of telecommunication infrastructure on the urban GSDL varies from having no significance to being positive when crossing the threshold. Thus, capital investment for infrastructure construction should be further allocated reasonably, the positive potential of human capital should be fully released, and the urban scale should be appropriately controlled in the future.