New measurement and spatio-temporal heterogeneity of regional green innovation efficiency in China

The traditional economic development mode can no longer meet the increasingly stringent demands for innovation and environmental sustainability. Consequently, green innovation has emerged as a pivotal development trend. Accurate measurement of green innovation efficiency is crucial in this context. This study employs the SBM-DDF-GML model to evaluate the green innovation efficiency of 30 Chinese provinces from 2000 to 2020, incorporating enhanced indicators for both expected and unexpected outputs. Additionally, the K-means algorithm, a machine learning technique, was utilized to cluster the comprehensive development factors of these provinces, enabling an analysis of their spatiotemporal heterogeneity. The findings indicate that the improved model enhances the precision of regional green innovation efficiency rankings, providing a more accurate reflection of actual regional changes. Furthermore, compared to traditional regional classification methods, the K-means clustering based on comprehensive regional development factors exhibited greater inter-group differences, aligning more closely with the heterogeneity analysis of regional green innovation efficiency. The spatiotemporal heterogeneity analysis of the new groupings revealed that the evolution of green innovation efficiency is predominantly influenced by advancements in green innovation technology.