Dynamic Modeling and Strategic Prioritization of Sustainable Development Goals

This paper introduces a novel dynamic modeling framework that integrates nonlinear programming and state transition graph algorithms to analyze the temporal progress and prioritization of Sustainable Development Goals (SDGs). The framework takes into account the complex interdependencies among SDGs to minimize the overall completion time while max-imizing policy balance. The state transition graph algorithm enhances the widely used Analytic Hierarchy Process (AHP) model by simulating network dynamics and optimizing the activation or introduction of goals. Empirical findings demonstrate a notable shift in prioritization, with SDG3 (Good Health and Well-being) emerging as the foremost priority by the year 2027. Additionally, the study proposes the incorporation of biodiversity conservation as an objective in the year 2031 to further bolster the achievement of SDGs. By providing valuable insights, this research offers a robust decision-making framework that facilitates informed policy formulation for policymakers and stakeholders engaged in sustainable development initiatives. Moreover, it contributes to a deeper understanding of the progress of SDGs, enabling more effective interventions and fostering sustainable development.