Revealing the effectiveness of hydropower innovations on emission level in Japan: evidence from machine-learning algorithm

This study explores how oil efficiency and hydropower innovation affect carbon emissions in Japan. In this direction, the novel multivariate quantile on quantile regression technique is applied to explore the period from 2000 to 2020. In addition, to prevent omitted variable bias, the environmental implications of industrialization, urbanization, and economic growth are also observed. The empirical findings indicate that oil efficiency, industrialization, and hydropower innovations are all lower carbon emissions. However, urbanization and economic growth harm the environmental quality. Lastly, a robustness check is performed using a kernel-based regularized least squares technique based on machine learning. The results of this investigation support the claim that oil efficiency and hydropower innovation can reduce emissions. Furthermore, it is found that the emission-reducing impact of both gets stronger as the emission level rises. © The Author(s) 2026.