Blockchain applications in green finance for transparency and accountability in sustainable investments - Li, Ting; Lau, Wei Theng; Haji Yahya, Mohamed Hisham (Multidisciplinary Digital Publishing Institute (MDPI), 2025)
The integration of blockchain (BC), artificial intelligence (AI), and green finance (GF) to promote sustainable investments and tackle environmental issues is examined in this study. By employing sophisticated analytical methods, the study seeks to pinpoint the main forces behind GF growth, especially in the field of renewable energy. To guarantee reliable statistical analysis, financial data from Taiwanese companies listed on the stock exchange between 2000 and 2020 are examined using the Generalized Method of Moments (GMM). Furthermore, to make use of AI’s potential to pinpoint the key elements affecting GF development and investment, attention-based convolutional neural networks (CNNs) are used. The links between GF, BC, and AI are analyzed and visualized using a novel method called the Financial Filtered Graph (FFG). The results of the study demonstrate that by increasing the precision of investment forecasts and identifying critical factors that affect GF growth, AI-driven solutions can greatly improve the sustainability of green finance strategies. The suggested methodology effectively supports sustainable investment decisions, as evidenced by its remarkable 98.8% classification accuracy. According to the findings, integrating AI and BC has a lot of potential to enhance green finance’s accountability, transparency, and decision-making processes, all of which will support long-term economic and environmental sustainability.