Multi-agent Decision-Making Systems for Innovative Investment Models in Net-Negative Emission Technologies

Today, simply reducing carbon emissions is not enough to combat climate change; the existing carbon dioxide stock in the atmosphere must also be effectively eliminated. However, the factors that determine the success of investments in this area and the most appropriate financial models to support these investments have not been systematically analyzed in the literature. This deficiency causes decision-makers to experience uncertainty in resource allocation and reduces the effectiveness of strategic planning. To address this gap, this study develops a model that integrates parameter-driven synthetic evaluation to expand expert opinions, consensus-based expert selection with Manhattan distance-based centrality to objectively determine expert weights and cognitive maps to analyze criteria relationships. Moreover, a dynamic multi-facet fuzzy sets approach is developed to dynamically model uncertainties, introducing a new fuzzy set structure to the literature. Thanks to these methodological innovations, the model outperforms existing models in terms of both technical accuracy and analytical depth. According to the research findings, “monitoring quality” and “policy incentives” are the most critical criteria for investments in net-negative emissions technologies. Among the investment alternatives, “insurance funds for early-stage net-negative emissions technologies” and “bonds tied to verified carbon removal performance” are identified as the most effective innovative investment models. In conclusion, this research fills a significant gap in the literature, both theoretically and practically, and provides an innovative methodological contribution to decision support systems for net-negative emissions technologies within the framework of sustainable finance.