Probabilistic analysis of tripling global renewables based on Bayesian-inferred growth dynamics model

Achieving the tripling of global renewable energy capacity by 2030 is critical for meeting the 1.5 °C target. To track progress and identify gaps in the global effort, a robust, probabilistic forecasting tool is essential. However, most existing models are scenario-based and lack data-driven forecasting. This study addresses this gap by introducing a probabilistic S-curve growth model, combining the Richards curve with Bayesian inference to predict renewable energy capacity by 2030 and evaluate policy scenario effectiveness. The model analyzes 244 countries and regions, focusing on solar PV and wind power, and assesses the likelihood of meeting the tripling target. Results show that global renewable capacity is expected to reach 7295 GW by 2030, far below the 11,000 GW target. About 21.7 % of countries are on track to triple solar PV capacity, while only 4.7 % will achieve this for wind. Countries like Germany and Spain show regrowth patterns with new growth phases. Additionally, policy scenario analysis reveals that achieving the tripling target requires comprehensive policy interventions, with the ambitious scenario increasing success probability from 0.0 % to 24.3 %. This model offers real-time tracking and policy evaluation of renewable energy progress and can be adapted to other technologies, supporting climate action efforts.