Quantifying the Safe Operating Space for Land-System SDG Achievement via Machine Learning Meta-Modelling and Scenario Discovery

Abstract

We developed a machine learning based meta-model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a meta-model to replicate the Land-Use Trade-Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio-economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO2-1, a > 9% biodiversity levy on carbon plantings, and carefully regulated land-use policy. Machine learning based meta-modelling teamed with scenario discovery revealed the policy and scenario settings required for a sustainable future for the Australian land sector.