Modelling the Doughnut of social and planetary boundaries with frugal machine learning

TL;DR

This paper demonstrates how frugal machine learning techniques like Random Forest and Q-learning can identify policy parameters that ensure social and environmental sustainability within the Doughnut framework, using a simple macroeconomic model.

Contribution

It introduces the application of frugal ML methods to find sustainable policies in the Doughnut model, providing a proof-of-concept for their effectiveness.

Findings

ML methods can identify policy parameters consistent with sustainability.

Reinforcement learning can optimize trajectories towards sustainable policies.

Frugal ML approaches are effective in simple ecological macroeconomic models.

Abstract

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.