Modelling the Doughnut of social and planetary boundaries with machine learning

Most national governments pursue GDP growth as a primary objective. However, measures of human well-being correlate with GDP only up until a point, and evidence shows that GDP growth is tightly coupled to environmental degradation. Achieving a high level of human well-being within planetary boundaries may thus require new policy approaches that move beyond the pursuit of GDP, such as those advocated within the "post-growth" literature. A popular framework here is the "Doughnut" of social and planetary boundaries, which has inspired the development of ecological macroeconomic models that incorporate sustainable thresholds for both social and environmental indicators.Machine learning (ML) can enhance these models in many ways. Here, we focus on two core aspects: searching desired model behavior and optimizing transitions towards it. We apply standard ML techniques to a simple consumer-resource model, exploring how different consumption and efficiency policies impact sustainability. Using a random forest classifier, we identify policy conditions that align with the Doughnut framework, providing an interpretable pathway to sustainability. Additionally, reinforcement learning (RL) can optimize trajectories in the model parameter space to reach model behavior corresponding to a sustainable regime.While ML methods present challenges, such as the number of data points and hyperparameter optimization for classification, they also offer several useful tools, including for data sampling optimization and model explainability. Overall, our proposal shows how ML can support the development of ecological macroeconomic models that address the complexity of achieving good social outcomes for all people within planetary boundaries.