A Narrative on Learning and Monte Carlo Simulation to Confirm the Likelihood of a Cost-Effective Transition to Decarbonized Ethanol Resources

This document explores the application of Bayesian inference and Monte Carlo simulations to evaluate the cost-effectiveness and likelihood of decarbonizing ethanol. It highlights the effects of learning and the economic benefits of reducing greenhouse gas emissions through ethanol use. The analysis uses experience curves to estimate the scale of possible cost reductions in ethanol production over time, while Monte Carlo simulations provide a range of potential outcomes for future costs. The findings suggest decarbonizing ethanol can lead to lower costs and significant environmental and economic benefits.