Owing to the effects of rural tourism and urbanization, the frequent participation of external market activities in traditional villages has increased the sensitivity and fragility of villages. This study constructs a Bayesian network model for systemic risk adaptation in traditional village industries by selecting relevant driving factors based on the nexus causality of "risk perturbation-adaptation process-adaptation outcome," using 148 Traditional Villages in She Xian, Huangshan as the experimental sample. Through simulations of adaptation under low-, medium-, and high-risk scenarios, the relationship and degree of influence between the drivers in the network model were quantified, and the adaptation mechanisms of the risk of the traditional village industry system were predicted and analyzed. Finally, ideal development methods for the current traditional villages in She Xian were proposed using the selected primary drivers. The primary findings of the study are as follows: 1) Most of the traditional villages in She Xian are in the middle to high level of industrial system risk, and show a certain advantageous concentration in space. 2) Under the low risk scenario, the ecological and cultural resources of traditional villages are effectively protected and utilized. The allocation of all parts of the industrial system is coordinated, and the ability to adapt and self-organize plays a greater role in adaptive capacity. Key traditional village subjects should be linked internally and externally in future endeavors, and joint efforts should be to made build regional tourism brands. 3) Under the medium risk scenario, traditional villages have slow economic development but stable social organization base, wherein the adaptive capacity of learning self-organization and buffering capacity have a greater impact. Traditional villages with medium risk should concentrate on activating the internal dynamics of villages and improving public services and infrastructure. 4) Under the high risk scenario, traditional villages face difficulties in industrial development and that the risk-coping mechanisms and reproduction logic of traditional villages lack the requisite social foundation. It is therefore vital to make conservation and restoration a top priority and strengthen the intervention of external factors for high-risk villages. The incorporation of Bayesian network theory into the risk assessment of traditional villages can improve current understanding of the diversity and complexity of traditional village risk adaptation process subjects and the dynamics of uncertainty influencing factors, thereby providing new ideas and theoretical references for Industrial transformation and long-term risk management of traditional villages in similar regions.
Full Text Finder