Production-based carbon emission, and transportation in China: probing the role of clean energy based on simulation and machine learning

The potential of clean energy is constrained by various social, environmental, and technological challenges. The current work is likely the first to observe the influence of economic complexity on production-based carbon emissions to develop clean and sustainable energy solutions that address climate change apprehensions. The study examines the impacts of clean energy, economic complexity, public service transportation, and improving technology on production-based carbon emissions. This study employ the novel dynamic autoregressive distributive lag simulation method and a novel machine learning technique, Kernel-based regularized least squares, to ascertain the causal effect among variables. The results reveal that economic complexity is escalating environmental contamination. Similarly, public service transportation increases environmental costs, but clean energy and improving technology decrease the production-based emissions in China. These findings offer valuable guidance for China’s public transportation system to integrate and consolidate the road, railway, airline transportation, and aviation sectors.