Sustainability is a key factor in petroleum industry development, determining whether an enterprise has the ability to maintain high-quality safety management in the long term. In order to reduce occupational injuries and accidents, and to improve safety in the petroleum industry, this study proposes a hybrid approach based on the fuzzy analytical hierarchy process (FAHP), human factors, and the fuzzy comprehensive evaluation (FCE) method in order to assess safety performance in a petroleum enterprise. This paper is comprised of four stages. In the first stage, a model is constructed for assessing the safety of the petroleum industry based on a literature review. In the second stage, we use the FAHP to determine the weights of five factors and 19 sub-factors. In the third stage, employees are the subjects of a questionnaire on the safety performance of the petroleum enterprise. According to the analysis of the assessment results, we focus on improving employees’ safety behaviors and mental health. A second round of questionnaires is distributed to the employees, and a second set of assessment results obtained. Finally, the results of the two evaluations are compared, and the effectiveness of the combination of FAHP, human factors, and FCE is verified.

Emotion plays an important role in decision making. In an emergency, panic can spread among crowds through person-to-person communications and can cause harmful effects on society. The aim of this paper is to propose a new theoretical model in the context of epidemiology to describe the spread of panic under an emergency. First, according to divisions in personality in the context of psychology, groups are divided into a level-headed group and an impatient group. Second, individuals in the two groups have unique personalities. Thus, the level-headed group only infects within the group, while the impatient group considers emotional infection within the group and cross infection between the groups. Then, a nonlinear infection rate is used to describe the probability of infection after an infected person contacts a susceptible person, which is more in line with the real situation. After that, the level-headed group–impatient group nonlinear SIRS panic spreading model is developed. Stable analysis of the model is obtained using the Lyapunov function method to study the stability of the panic-free equilibrium and panic-permanence equilibrium. Finally, simulations are carried out to dynamically describe the spread process of group emotional contagion.

Cluster behavior is prevalent in nature. Many individuals change their behavior to adapt to a dynamically changing environment by following simple rules of behavior and interacting with information from neighboring individuals. In this study, the traditional public goods game model is improved by combining the advantages of game theory and interactive learning. A strategy adaptive evolution method based on a public goods game is proposed. The emergence of cooperative behavior in weighted networks under the co-evolution of game strategies and node weights is explored in conjunction with multi-agent interactive learning. The results show that in a public goods game with strategic adaptation, a person’s influence becomes greater if their level of adaptation exceeds the desired level, and less otherwise. This weight adjustment is defined by the intensity parameter δ. A moderate δ value can effectively facilitate the occurrence of cooperative evolution. The level of cooperation depends mainly on the weight distribution of participants, which leads to the formation of cooperative clusters controlled by high-weighted cooperators. Even with the great temptation to defect, these cooperators can prevail over defectors. The adjustment of node weights increases the heterogeneity of individuals. This research provides a viable pathway to solve social dilemmas and will further promote the application of multi-agent intelligent decision making.

To study the rescue behavior of respondents to major sudden traffic accidents, this study considered an accident rescue system composed of responses from the government and social organizations. Based on differential game theory, three game models of independent rescue, cooperative rescue, and government-subsidized rescue were constructed to assess the responses from the government and social organizations. The equilibrium strategy and an optimal rescue trajectory for the government and social organizations were obtained. To improve the effectiveness of the rescue efforts of the government and social organizations, the maximum entropy optimization model was applied to quantify the risk of road congestion and potential secondary disaster risks, and the impact of key parameters such as road congestion risk and rescue timeliness on the effectiveness of the rescue efforts and its socioeconomic benefits were analyzed. When certain conditions were met, the level of effective assistance and total socioeconomic benefits displayed an increasing trend from independent assistance by the government and social organizations to government-subsidized assistance, and then to cooperative assistance by both parties. The decay rate of assistance effectiveness had an inverse impact on the effective rescue response. Additionally, using the maximum entropy model to quantify the degree of congestion risk not only enabled the influencing factors at the accident site to be considered, but also helped to improve the socioeconomic benefits obtained by governments and social organizations in their rescue response to major traffic accidents. Finally, through a sensitivity analysis of the key parameters, i.e., the direct economic losses recovered by the government and social organizations during their rescue efforts, the value generated by the potential economic activities of the government and social organizations, the impact coefficient of the government and social organization's rescue efforts on potential economic activities, and the impact coefficient of government and social organization investment in rescue, it was found that they all positively affected the effectiveness of the rescue.

Digital transformation represents a strategic imperative for enterprises pursuing high-performance growth. This study selects A-share listed enterprises from 2014 to 2023 as the research sample and empirically examines the impact of digital transformation on enterprise risk-taking, as well as its internal transmission mechanisms, using a fixed effects model. The findings indicate that digital transformation has a significant positive effect on promoting enterprise risk-taking, particularly in state-owned enterprises and those with lower media attention. The director network and economic policy uncertainty positively moderate this relationship. Results from the mechanism analysis show that digital transformation enhances enterprise risk-taking through independent mediating channels that alleviate enterprises’ financing constraints and increase innovation investment, as well as through the chain mediation channel of “alleviating financing constraints → increasing innovation investment”. This research clarifies the specific mechanism underlying the impact of digital transformation on enterprise risk-taking and provides new evidence for understanding how digitalization enhances enterprise risk-taking by easing financing constraints and stimulating innovation. It holds important significance for helping enterprises improve their risk-taking capacity and promote sustainable development.