Supply chain carbon finance indexing with generative AI and advanced data analytics techniques

Abstract

Supply chain carbon finance plays a key role in providing financial incentives, supporting green projects, and promoting decarbonization of supply chains. Progress monitoring of such efforts requires robust indices that track carbon-intensive supply chain operations and assess carbon finance readiness. Current index generation methods use qualitative and optimization models such as AHP, DEA, etc., which introduce biases and overlook model variability and complexity, thereby affecting index reliability. Utilizing advanced Generative Adversarial Networks (GANs) and Principal Component Analysis (PCA), the main goal is to robust supply chain carbon finance index to assess the readiness of countries for carbon financing. Hierarchical cluster analysis is used to group countries, providing better insights for strategic decision-making. The results indicate that China, USA, UK, and India collectively accounting for over 50% of carbon finance needs. This study assesses each country's carbon finance readiness and identifies clusters of countries with similar readiness levels for targeted policy recommendations and tailored interventions. We propose a novel Carbon Finance Contribution Mechanism to decarbonise supply chains and equitably distribute the financial responsibility of emissions reductions between producing and consuming countries. The paper also highlights research opportunities in generative AI and machine learning for creating similar indices in supply chain domains.