ArticlePloS one2026
Impact analysis of institutional framework and policy preferences with Bayesian structural equation.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Mediation analysis is able to deeply analyze the finer mechanism of the path of exposure variables leading to outcome variables and has received extensive attention in many fields in recent years. In order to further analyze China's energy cooperation with neighboring countries, this paper divides the two stages of policy formulation and implementation. It connects the logical chain of "cooperation policy preferences at the decision-making level, final cooperation policy preferences to energy trade and investment cooperation." By constructing a partial Bayesian structural equation model, we verify the mediating effect played by final cooperative policy preferences. The mediation analysis based on structural equation modeling provides the possibility to analyze the complex structure among variables, including the mediation effect. And Bayesian SEM can provide a more flexible program for the construction and estimation of complex models. In addition, this paper explores the statistical analysis strategy of mediation analysis of Bayesian structural equation modeling and its statistical performance under different data characteristics through example analysis. The empirical results show that policy preferences play a significant mediating role between institutional frameworks and energy cooperation outcomes. Specifically, the Bayesian SEM estimates a positive indirect effect of 0.305, with a standard error of 0.123, a 95% confidence interval of (0.061, 0.552), and a significance level of p = 0.006. This indicates that institutional arrangements promote energy cooperation mainly by shaping policy preferences rather than through direct effects alone. Compared with classical regression-based mediation, classical SEM, Bayesian regression, and other alternative approaches, Bayesian SEM demonstrates stronger performance in handling latent-variable uncertainty, interval estimation, and parameter stability. Across the 12 estimation methods employed in this study, the estimated indirect effects range from 0.271 to 0.312 and remain consistently significant, confirming the robustness of the mediation mechanism. This indicates that while maintaining directional consistency, it offers more robust and reliable handling of latent variables and uncertainty.
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