Agricultural cooperatives are the main vehicle for farmers to engage in green agriculture. With the digital transformation in rural areas, it is crucial to explore how cooperative members can effectively access online information and integrate it into green production decision-making processes. Based on the survey data of 530 members of rice planting cooperatives in Heilongjiang Province in China, this paper selected eight green production behaviors commonly used by rice farmers as explained variables, and constructed an ordered probit model. Using the social capital theory, the impact and mechanism of internet use on cooperative members’ green production behavior were examined. The results showed the following: (1) Internet use facilitates the cooperative members’ green production behavior. This conclusion remains valid even after addressing the endogeneity test and robustness test. (2) The heterogeneity analysis revealed that the internet is particularly effective in enhancing the green production behaviors of farmers who are less educated, middle-aged, and those with strong connections to cooperatives. (3) A further mechanism test indicates that internet use not only significantly influences farmers’ trust in cooperatives but also aids them in comprehending the cooperative’s production specifications, thereby further advancing the improvement in green production behaviors. (4) Members’ satisfaction with cooperative sales can serve as a substitute for the internet in influencing their green production behavior.

Green agricultural development opens up a feasible way for China to construct sustainable communities and promote rural revitalization strategies. However, a clear gap on the subject sets apart various regions in China, resulting in off-balance development. This paper, based on the panel data from 31 provinces (municipalities and autonomous regions) in China from 2013 to 2020, established an evaluation index system for China’s green agricultural development levels, measured the green agricultural development in different regions, and applied the log(t) model, the Ordered Probit model, and the Ordered Logit model to identify convergence trends and causes of China’s green agricultural development level club convergence, respectively. Our research found the following: (1) The green agricultural development levels in various regions in China are on the rise, and regional gaps are still significant. The output of green agricultural products is the focus of various regions in promoting green agricultural development. (2) The green agricultural development levels in China’s 31 provinces (municipalities and autonomous regions) have converged to four clubs. China is gradually forming a convergence pattern of green agricultural development levels, with the main agricultural production areas as the core. (3) The regional gap at the level of environmental regulation for green agricultural development first narrows, then expands, and then narrows. The intensity of environmental regulation in most areas is in the first stage. This indicates that China should continue to maintain the current intensity and promote green agricultural development levels to converge towards high-level clubs. In addition, the GDP, grain production area, fiscal support for agriculture, and rural human capital can narrow the gaps in green agricultural development levels between regions.