VISUALIZATION OF THE ALGORITHM FOR MUNICIPAL GREEN BOND ISSUANCE IN UKRAINE: A GRAPH-AS-A-CODE APPROACH

Abstract

In Ukraine, despite the legislative implementation of green bonds since 2020, the market remains in its infancy, represented only by isolated corporate cases. There is a critical gap between international trends in scaling sustainable investment and the domestic financial environment, which is constrained by systemic barriers. Ukrainian territorial communities, as potential public issuers, lack effective algorithms for issuing municipal green bonds. In the context of post-war reconstruction based on environmental modernization, this financial instrument acquires strategic importance for attracting capital into renewable energy, energy efficiency, and waste management. However, the high complexity of the emission procedure creates significant risks of management errors, highlighting an urgent need for the visualization of financial and legal processes. The purpose of the study is the scientific substantiation and development of a methodology for the dynamic visualization of the municipal green bond emission algorithm in Ukraine based on the «Graph-as-a-Code» concept. This involves using generative artificial intelligence to transform semantic legal descriptions into declarative Mermaid code to minimize management risks and overcome institutional barriers. The study employs a multidisciplinary approach based on systemic analysis and logical generalization to identify barriers in the sustainable finance market. Structural-functional modeling was used to decompose the emission algorithm into six stages in accordance with budget and financial legislation. The practical implementation of the model is based on semantic analysis combined with prompt engineering, ensuring the transformation of legal descriptors into valid Mermaid code. The final stage involves graphical visualization and rendering in digital environments to convert large arrays of text into interactive diagrams. The integration of the Gemini multimodal AI model allowed for the intellectual convergence of multi-source data, eliminating information gaps. The study proposes a technology for visualizing the emission algorithm through the «Graph-as-a-Code» method, implemented via five operations: from forming the semantic description to automatic rendering on GitHub. The research resulted in a dynamic visualization of the algorithm, which allows municipalities to perceive the emission process as a clear path rather than a wall of restrictions. The scientific substantiation and practical testing of the dynamic visualization methodology prove that traditional modeling methods (UML, BPMN) require adaptation to modern digital environments through declarative markup languages. The use of AI for code generation ensures high accuracy in reflecting complex financial and legal processes. The proposed approach minimizes management risks for local self-government bodies and ensures transparency in emissions.