Public pro-environmental behavior plays a positive role in solving environmental pollution problems. In a real socioeconomic system, because public pro-environmental behavior has characteristics of externality and interactivity, a number of factors, such as external information and the behavior of others, could affect the pro-environmental behavior of individuals who optimize their own strategies by interacting with the outside world; thus, public pro-environmental behavior and social interaction are very closely related. In order to study the impact of social interaction on public pro-environmental behavior and its mechanisms, the authors of this paper conducted an empirical study based on an Ordinary Least Square (OLS) regression model and data from the Chinese General Social Survey (CGSS). The empirical results show that: (1) social interaction has a promoting effect on public environmental protection behavior, and social interaction has a more significant impact on private environmental protection behavior; (2) the public will not only adjust their own environmental protection behavior by directly observing the behavior of others, they will also obtain environmental protection knowledge through social interactions which thus have a positive impact on their behavior. It is of great practical significance to study the impact of social interactions on public pro-environmental behavior.

In real life, garbage has caused great pollution to the environment. A garbage classification system is an effective way to manage this issue, and is an innovation in Shanghai, China. Innovation diffusion is the topic of this paper. This study uses a mathematical statistics method to formulate individual bounded rationality, and uses the specific graph structure of a scale-free network to characterize group structure. Then, a model of group behavior is constructed and the simulation experiment is run on the Python platform. The results show that: (1) In the case of general cognitive ability and high value innovation, most individuals in the group will accept the innovation in the process of innovation dissemination in a garbage classification system after several rounds of the game; (2) it is more helpful to improve the cognitive ability of individuals and the true value of innovation for the diffusion of innovation; and (3) the larger a group, the greater the scope of innovation diffusion and the more time is needed. It is helpful to expand the scope and reduce the time of innovation diffusion by increasing connections among individuals. The innovation of this study is the characterization of individual bounded rationality, which has a certain theoretical value. Meanwhile, the research results of this paper have important practical significance for the promotion of garbage classification, which can be used to popularize the concept of garbage classification.

China has been actively taking actions to control carbon emissions and promoting development of a carbon market. However, there are many disadvantages in a carbon market, owing to various designs and policies still under trial and implementation. Adopting the multi-agents technique, we constructed a framework about national carbon market to estimate the effect of a different design of policy made on the GDP (Gross Domestic Product) and environment. In particular, national and regional abatement policies were analyzed in our study. The results showed the carbon-trading mechanism can effectively reduce carbon emissions and make a negative impact on GDP. National abatement can neither be too high nor too low for reducing carbon emissions and maintaining economic stability. For different regions, the central region was impacted the most by a carbon trading mechanism, and the east region was the opposite. Moreover, the “sweeping approach” policy should be replaced by a regional “discriminating policy” because the abatement requirement to the western region was low and to the eastern region was relatively high, which is more beneficial to China’s regional development.