Designing a Data-Driven Sustainable Finance Model: A Pathway for Small and Medium Enterprises to Transition to Clean Energy

Abstract

The transition to clean energy is a critical global priority, yet small and medium enterprises (SMEs) face significant financial barriers that hinder their adoption of sustainable energy solutions. This study explores the development of a data-driven sustainable finance model as a strategic pathway for SMEs to access affordable capital, mitigate investment risks, and enhance financial inclusion in the clean energy sector. Grounded in stakeholder theory and sustainable development theory, the study examines how financial technology (FinTech), big data analytics, artificial intelligence (AI), blockchain, and alternative investment structures can optimize capital allocation and improve credit accessibility for SMEs. The findings reveal that while traditional financing mechanisms often exclude SMEs due to perceived risks and limited credit histories, data-driven credit scoring, blended finance, and sustainability-linked instruments can bridge financing gaps and promote investment in clean energy. However, technological limitations, regulatory inconsistencies, and financial constraints persist. The study proposes policy recommendations, including the harmonization of green finance regulations, public-private partnerships, and international cooperation, to create an enabling environment for SME participation in sustainable finance. Future research should explore the role of decentralized finance and AI-driven investment strategies in scaling clean energy transitions. This study contributes to the ongoing discourse on sustainable finance, emphasizing the importance of financial innovation in achieving global climate goals.