Impact of AI robotics, income, clean energy subtypes, and energy transition on environmental progress: An incremental analysis for China by KRLS model

As artificial intelligence (AI) has been widely applied in economies, understanding its environmental implications is highly critical. Consistent with this phenomenon, this research uncovers the impact of AI robotics (AIR), income, clean energy subtypes, and energy transition (ETI), along with traditional factors (namely, trade openness, urbanization, and financial development), on environmental progress. In this vein, the study focuses on China, the leading carbon dioxide (CO ₂ ) emitter, uses CO ₂ emissions (sustainable development index) in the main (robustness analysis) as the dependent variable, uses AI robotics as a novel proxy for AI, and performs novel Kernel-regularized least squares (KRLS) model on the most recent data from 2000 to 2024. The results suggest that (i) AIR, income, ETI, trade openness, and urbanization are not helpful to decrease CO ₂ emissions, while financial development has an insignificant increasing impact; (ii) among clean energy subtypes, only nuclear energy has a reducing impact on CO ₂ emissions at lower percentiles; (iii) in terms of interaction of AIR with clean energy subtypes, only hydro (other renewable) energy is statistically significant (insignificant) decreasing impact on CO ₂ emissions at lower percentiles; (iv) KRLS model obtains successful estimation results, reaching ∼99.73%; and (v) the robustness checks using an alternative dependent variable confirm the empirical results. Therefore, the research empirically attests to the inefficient use of AIR, an unsuccessful ETI approach, and ineffective use of clean energy subtypes in China. At the same time, the marginal impacts of the factors considered on CO ₂ emissions differ across percentiles. Based on these findings, we outline relevant policy implications for China.