While social media has become increasingly prevalent in recent years, few studies have examined the role of social media in regulating the environmental information disclosure (EID) of high-polluting enterprises. Using a sample of 2, 211 A-share listed firms in China from 2010 to 2019, this study empirically tests the relationship between firm–investor social media interactions and the EID of high-polluting firms. The results show that social media interaction not only relieves information asymmetry in the capital market, but also triggers market and regulatory pressure for management, ultimately contributing to high-quality EID. The results are robust to a series of alternative estimation approaches and alternative measurements of core variables. Moreover, we found that the positive effect of social media interaction on EID is stronger for enterprises that receive a high level of analyst coverage and for state-owned enterprises (SOEs), but weaker for enterprises whose CEO holds a chairman position (i.e., CEO duality). In addition, further testing shows that social media interaction promotes hard EID to a larger extent than soft information, and the promotion effect is more pronounced for environment-related posts. This study deepens our understanding of how social media supplements formal regulations in the supervision of corporate EID behavior and offers important practical implications for prompting enterprises to achieve high-quality green development.

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Bolted connections are the main method of connecting the components of an aero-engine low pressure rotor. Due to the influence of the elastic interaction relationship, it is easy to cause uneven distribution of the preload force of the bolt group, although it can meet the stiffness needs; however, it may lead to the deflection of the spatial relative position of the components, which is can easily cause coaxiality overrun. In view of the contradictory problem of optimization between coaxiality and stiffness of rotor assembly, this paper proposes a semi-physical simulation optimization method for the bolt tightening process based on reinforcement learning. Firstly, by studying the elastic interaction mechanism between the bolts, the elastic interaction matrix was established using finite element simulation data. On this basis, a coaxiality prediction model for the bolt tightening process was established using a GRU (gate recurrent unit) network to realize the prediction of coaxiality in the bolt tightening process. Then, through the analysis of the bolt connection stiffness, a stiffness calculation model containing the bolt stiffness, the stiffness of the connected parts, and the contact stiffness of the joint surface in series was constructed to realize the calculation of the stiffness during the bolt tightening process. Finally, with the bolt preload force as the optimization variable, coaxiality and stiffness as the optimization target, and the tightening torque and preload force of the installed bolts in the actual assembly process as the constraints, a semi-physical simulation optimization model of the bolt tightening process was established using reinforcement learning to realize the optimization of the bolt tightening process. Moreover, through the semi-physical simulation optimization method, the bolt tightening process can be installed and adjusted at the same time.