Exploring the Determinants of Renewable Energy Consumption: A Bayesian Monte Carlo Simulation Analysis of Technology, Economic Growth, CO2 Emissions, and Digital Financial Inclusion

Abstract

Amid escalating climate crises and the global push for energy transition, renewable energy consumption (REC) has become a key priority for sustainable development. However, the global energy system remains heavily dependent on fossil fuels, accounting for nearly 80% of total energy use and 76.7% of greenhouse gas emissions. This reliance underscores the urgent need to promote factors driving the shift toward renewable energy, including technological innovation (TI), digital financial inclusion (DFI), economic growth (GDP), and CO2 emission control. Addressing gaps in integrated approaches and methodological challenges in prior studies, this research applies a Bayesian Monte Carlo method to estimate the probabilistic impacts of these factors on REC, using panel data from 58 countries between 2004 and 2022. The results reveal that TI exerts a positive and highly significant influence on REC (coefficient = 0.1097; probability of effect = 100%), reaffirming its critical role in enhancing efficiency, lowering costs, and fostering advancements in smart grids and energy systems through the integration of AI, IoT, and Big Data. DFI also shows a positive, albeit moderate, effect on REC (coefficient = 0.1891; probability of effect = 67.63%), while GDP demonstrates a strong positive association with REC (coefficient = 0.8750; probability of effect = 83.44%). In contrast, CO2 emissions have a significant negative effect on REC (coefficient = −0.2297; probability of effect = 86.13%), providing vital insights for policymakers aiming to align energy transition efforts with sustainability objectives.