JADE: Developing AI-based models to create optimized adaptive investment policies that balance environmental impacts and economic growth

This paper explores the development of an AI-based model designed to create adaptive investment policies that balance environmental impacts and economic growth. The hypothesis is that for any given interconnected system of industrial producers and populations, and a relative weighting of economic growth and environmental impacts, there exists an optimal solution for economic transfers that will maximize the overall utility. The model simulates three case studies: smartphone manufacturing, gasoline production, and wind turbine deployment, considering their environmental impacts on affected populations, especially vulnerable communities disproportionately harmed by pollution. In addition to addressing specific local impacts, the model accounts for global environmental issues, such as greenhouse gas emissions. Machine learning algorithms are used to optimize a payment structure known as JADE (Joint Adaptive Dividends for the Environment) that requires polluters to invest in supporting the communities they affect. The results indicate that targeted investments through this adaptive framework can enhance long-term economic growth while promoting environmental sustainability. Specifically, the findings reveal that the AI-optimized JADE payments significantly improve aggregate utility across all three scenarios, demonstrating 37-64% improvement over the status quo. This work highlights the potential of AI-driven policy models to address critical challenges resolving competing environmental and financial interests.