Research on eco-efficiency of provinces along the Belt and Road in China based on DEA methods and AI prediction algorithms

Under the strategic goals of promoting ecological civilization and advancing regional coordinated development, China's Belt and Road provinces face dual pressures from economic growth and environmental protection. Therefore, a scientific assessment of eco-efficiency is crucial for achieving regional sustainable development. This study aims to systematically examine eco-efficiency and its spatiotemporal evolution across Belt and Road provinces, while also projecting future development trends. To achieve this, the study employs the super-efficiency Epsilon-Based Measure model to measure static efficiency and integrates the Global Malmquist-Luenberger index to analyze dynamic changes. Utilizing Kernel Density Estimation and Moran index to reveal spatial characteristics. A new information-preferred damped cumulative gray model with artificial intelligence is introduced to provide high-precision predictions. The study finds: (1) The overall eco-efficiency level remains low with a declining trend, exhibiting significant regional variations. (2) The technological progress index is a central driver of eco-efficiency. (3) Spatial heterogeneity in eco-efficiency is particularly pronounced, exhibiting spatial clustering characteristics. (4) The forecast indicates that overall eco-efficiency will show an upward trend in the future. Based on the above conclusions, this study puts forward corresponding policy suggestions. These recommendations are of great significance for promoting the sustainable improvement of eco-efficiency in the provinces along the Belt and Road in China.