Power grid technical renovation projects are implemented through project-based supply chains involving equipment procurement, logistics coordination and on-site construction under market, delivery and carbon constraints. Their final cost is jointly affected by engineering quantities, supplier behavior, lead-time uncertainty, material price volatility and sustainability requirements. Existing studies usually emphasize technical parameters and direct expenditure, whereas supplier reliability, green procurement, carbon intensity and procurement contingency effects are only indirectly incorporated. This study develops a dynamic Bayesian model for carbon-adjusted cost forecasting and investment priority support in power grid technical renovation projects. Based on 800 anonymized project-level records, a random forest is first used to identify informative engineering, supply chain and sustainability variables. These variables are then organized in a Bayesian network that links observed evidence, intermediate cost nodes and the carbon-adjusted cost target. A dynamic evidence-weighting mechanism updates posterior cost beliefs as supplier, logistics, market and carbon information become available during implementation. Compared with static Bayesian inference, XGBoost, an improved BPNN and GRA-based benchmarks, the proposed model yields lower MAE and RMSE. Ablation and scenario analyses further show that supply chain and sustainability variables improve both predictive performance and decision interpretability. The results provide a quantitative basis for budget control, green procurement adjustment, contingency allocation and sustainable asset renewal prioritization in energy enterprises.
Song et al. (2026) studied this question.