Digital Support for the Prioritization of Green Investment Projects in Ukraine’s Post-War Reconstruction

Ukraine’s post-war reconstruction requires substantial investment resources, making the transparent and well-grounded prioritization of investment projects particularly important. The purpose of this study is to develop a methodological approach to prioritizing green investment projects and to establish a functional model for the digital support of the corresponding assessment procedure.The study draws on a systematic analysis of approaches to public investment prioritization, sustainability assessment, and digitalization of public investment management. A three-stage procedure is proposed, distinguishing strategic alignment, compliance with green investment requirements, and comparative project ranking. Five groups of prioritization criteria are identified: economic performance, environmental benefits, resilience and social significance, financial capacity, and project feasibility. The proposed assessment procedure involves indicator normalization, weighted aggregation, and sensitivity analysis to examine the robustness of project rankings.A functional model of digital support is developed to link the stages of project assessment with the required information resources, digital processing operations, and evaluation outputs. The model comprises information, analytical, and control and documentation components. Its implementation may rely on existing categories of digital tools for standardized project data collection, automated calculations, verification, and traceability of assessment results.The proposed approach provides a methodological basis for transparent, comparable, and reproducible assessment of green investment alternatives within Ukraine’s public investment management system. Project rankings are regarded as analytical inputs to investment decisions rather than automatic funding allocations. Further research should focus on empirical testing of the proposed procedure and assessing the robustness of rankings under alternative weighting scenarios.