An AI-Driven Quantification of the Plastic Biogeochemical Wedge in CMIP6/CMIP7 Scenario Pathways

With the Agreement on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction (BBNJ Agreement) entering into force on January 17, 2026, the governance landscape for the global ocean has fundamentally shifted. However, current Integrated Assessment Models remain largely “dematerialized,” effectively modeling the carbon mitigation but failing to account for the plastic cycle—an anthropogenic flux that explicitly threatens the “carbon cycling services” the BBNJ Agreement is now legally mandated to protect. Consequently, current scenarios could underestimate the complex chemical reactions resulted by the production, accumulation, and degradation of synthetic polymers.

We propose an AI-Enhanced Material Flow Analysis (AI-MFA). Rather than building a computationally expensive process-based module from scratch, we leverage Ensemble Machine Learning (specifically Random Forest and Gradient Boosting Regressors)—methods identified as robust and accessible in the state-of-the-art sustainability AI literature. Firstly, we train the ML ensemble on historical industrial ecology datasets (OECD Global Plastics Outlook, World Bank “What a Waste 2.0”) to learn the non-linear correlations between socio-economic drivers (GDP, urbanization, industrial structure) and plastic flows. Secondly, we apply these trained models to the deterministic socio-economic drivers of the Shared Socioeconomic Pathways (SSPs) used in CMIP6 and proposed for CMIP7. This allows us to “project” the plastic reality into the future for scenarios ranging from SSPs to emission-driven pathways in ScenarioMIP-CMIP7. Thirdly, we estimate three critical fluxes: (a) the production material wedge, (b) the accumulated environmental stock, and (c) the degradation impact potential.

We anticipate establishing a “Plastic Intensity Baseline” for current CMIP7 pathways. Preliminary hypothesis testing suggests that regional rivaly scenarios (e.g., SSP3) contain a “material blind spot” equivalent to substantial unmodeled material pollution. By quantifying the “Plastic Biogeochemial Wedge”—the divergence between the baseline and the circular economy. This metric will serve as a proxy for evaluating whether specific climate pathways risk violating the BBNJ Agreement's mandate to maintain ecosystem integrity in areas beyond national jurisdiction.