Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights.