AI-Powered Framework for Circular Procurement Transparency in Municipal Systems ^†

Research Gap:

Although international discourse increasingly centers on circular economy principles, public-purchasing frameworks, especially in municipal settings, still rely on ad hoc methods and lack systematic, data-driven instruments for integrating sustainability and transparency into supplier evaluation. While ISO 20400 [1] offers a foundation of guiding concepts, fully digital, context-sensitive applications for working-level users are scarce, particularly in fast-growing urban economies.

Aim:

This paper presents an artificial intelligence-based framework intended to strengthen circular procurement by improving transparency, sustainability performance monitoring, and decision support services within municipal purchasing systems.

Introduction:

Circular public procurement is central to advancing sustainable city life. Still, purchasing teams rarely have data-rich tools that let them check suppliers’ sustainability, spot long-term risks, and match buying choices to circular economy aims. Injecting artificial intelligence (AI) into these tasks can turn vague green ideals into clear, repeatable steps.

Methods:

The study adopts a design science research approach to conceptualize and structure the proposed framework. Key sources include ISO 20400 guidelines, circular economy literature, and AI-use cases in procurement. The framework comprises three modules: (1) a supplier sustainability scoring engine, (2) ESG document intelligence via natural language processing (NLP), and (3) predictive analytics for circularity forecasting.

Results:

The proposed framework comprises modular, AI-supported components that carry out supplier evaluation, sustainability reporting, and risk shielding in real time. Each module targets a distinct procurement challenge, ranging from the analysis of disparate ESG documents to the prediction of lifecycle circularity impacts. The system integrates smoothly with current public procurement processes and aligns with prevailing regulatory standards.

Conclusions:

The framework offers a replicable model for digitalizing circular procurement in municipal systems. It contributes to bridging the gap between sustainability policy and procurement execution through AI-driven tools. The framework is currently informing the conceptual development of a prototype platform, aimed at supporting transparency, sustainability, and circularity in municipal procurement systems.

This research contributes directly to SDG 12 (Responsible Consumption and Production) and SDG 11 (Sustainable Cities and Communities), and supports SDG 16 (Peace, Justice and Strong Institutions) by promoting transparent, accountable procurement systems in public governance.