Modelling the Doughnut of social and planetary boundaries with frugal machine learning
This work addresses sustainability policy-making by showing how ML can help identify optimal trajectories, though it is incremental as it focuses on a simple model as a first step.
The authors tackled the problem of applying machine learning to the Doughnut framework for sustainability by using frugal ML methods like Random Forest and Q-learning to find policy parameters that achieve environmental and social goals, demonstrating a proof-of-concept with a simple macroeconomic model.
The 'Doughnut' of social and planetary boundaries has emerged as a popular framework for assessing environmental and social sustainability. Here, we provide a proof-of-concept analysis that shows how machine learning (ML) methods can be applied to a simple macroeconomic model of the Doughnut. First, we show how ML methods can be used to find policy parameters that are consistent with 'living within the Doughnut'. Second, we show how a reinforcement learning agent can identify the optimal trajectory towards desired policies in the parameter space. The approaches we test, which include a Random Forest Classifier and $Q$-learning, are frugal ML methods that are able to find policy parameter combinations that achieve both environmental and social sustainability. The next step is the application of these methods to a more complex ecological macroeconomic model.