Q-Learning Algorithm and Molecular Fuzzy Modelling for Evaluating the Carbon Footprints-Based Sustainable Energy Investments

This study addresses a critical gap in the literature regarding the lack of dynamic and causality sensitive decision support frameworks for carbon footprint based sustainable energy investments. Existing studies largely rely on static expert based or conventional fuzzy decision-making models, which are limited in capturing expert heterogeneity, learning dynamics, and complex interdependencies among strategic factors. To overcome these limitations, this paper proposes an integrated framework that combines Q learning with molecular fuzzy cognitive maps and a fuzzy molecular ranking approach. Q learning is employed to balance expert evaluation matrices by dynamically adjusting the judgments of less experienced decision makers based on reinforcement learning principles, thereby improving the consistency and reliability of expert inputs. The proposed model is empirically validated through a real-world expert-based case study involving three decision makers with heterogeneous experience levels and five alternative sustainable energy investment strategies evaluated under economic, environmental, social, and technical criteria. Molecular fuzzy cognitive maps enable the modeling of nonlinear causal relationships and uncertainty through geometry-based normalization, enhancing the robustness and adaptability of the weighting process across different learning rates and structural assumptions. Compared to conventional hybrid fuzzy MCDM models, the proposed framework demonstrates higher result stability and methodological flexibility while preserving interpretability. The results confirm the practical applicability of the model and provide actionable insights for policymakers and investors, identifying early-stage renewable energy startups as the most impactful strategy for reducing carbon footprints.