Evaluation and Refinement of a Novel Data-Driven Inverse Integrated Assessment Model focusing on Primary Energy

Abstract

Climate Integrated Assessment Models (IAMs) typically focus on energy and land use to project emissions, and can be used by simple climate models to project temperature outcomes. However, the decision-makers might also be interested in the reverse perspective: given a desired temperature rise, what is the corresponding energy structure? A climate scenario database includes various models, assumptions, discrete values, and incomplete data. To answer the question, machine learning (ML) techniques, Specifically Random Forest (RF), were employed to create an inverse emulator and predict primary energy sources (fossil, oil, renewable) for the mid-20st century (2050), taking into account temperature projections for the end of the century (2100) with reference to the IPCC AR6 dataset. Two methods were employed to select the emulator's input variables: a systematic and a manual selection approach. The uncertainties in the study, including input, parameter, and implementation uncertainties, were addressed using the Monte Carlo method. Finally, two cases were analyzed in detail to explore the potential of the emulator.