The implementation of low-carbon behavior by citizens is of the utmost importance in constructing China’s ecological civilization and achieving its dual-carbon objectives. As a result, exploring the formation and recurrence mechanisms of carbon-neutral citizenship behavior may have a positive impact on realizing China’s carbon reduction targets. This study explores a comprehensive analysis method of multi-subject interactive evolution of carbon-neutral citizenship behavior. It expands the connotation of behavioral intervention from individual single execution (citizens actively adhere to carbon-neutral behavior) to multi-driven implementation (citizens inspire other residents to comply with carbon-neutral behavior based on their own adherence). Furthermore, this study constructs a collaborative and interactive “follow–drive” mechanism for carbon-neutral citizenship behavior. Through Python software 3.8 simulation, this study examines the formation and stabilization process of carbon-neutral citizenship behavior under different influencing factors. The research findings are as follows: (1) If the government neglects its duties more severely, it is more inclined to adopt incentive policies, thereby increasing the likelihood that both kinds of the citizens will choose to follow carbon-neutral behavior. This suggests that the proactive introduction of relevant policies and regulations by the government has a positive influence on citizens’ carbon-neutral behavior. (2) With a higher perceived level of psychological–physical bimetric health among citizens, both kinds of the citizens are more inclined to follow and drive carbon-neutral behavior, while the chances of the government selecting incentive policies decrease, and it takes longer to attain final stability (i.e., selecting incentive policies). (3) In situations where there is a greater loss of group norms in the external environment of the citizen group, both kinds of the citizens are more likely to opt for and drive carbon-neutral behavior. This, in turn, reduces the likelihood of the government selecting incentive policies. Finally, based on the research findings, relevant policy recommendations are given.