AI-Driven Quality Assessment and Investment Risk Identification for Carbon Credit Projects in Developing Countries

The rapid expansion of carbon credit markets in developing countries presents significant opportunities alongside substantial investment risks. Traditional assessment methodologies struggle with the complexity and heterogeneity of project data, creating barriers to effective capital allocation. This research develops an artificial intelligence-driven framework for comprehensive quality assessment and risk identification in carbon credit projects across developing nations. The proposed methodology integrates multi-dimensional feature engineering with advanced deep learning algorithms to automate project evaluation processes. Through analysis of 2,847 carbon projects across Southeast Asia, Latin America, and Sub-Saharan Africa, the framework demonstrates superior performance compared to conventional assessment approaches. The AI-driven system achieves 94.3% accuracy in quality classification and reduces assessment time by 78%. Implementation results indicate significant potential for improving investment decision-making while supporting sustainable development objectives in emerging markets.