Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model

Abstract The need for green innovation in the high-tech industry has become a critical path to sustainable economic development. However, evaluating green innovation efficiency (GIE) and its spatial characteristics within China’s high-tech industry remains underexplored. This study uses the three-stage undesirable SBM model to assess GIE in China’s high-tech industry from 2006 to 2022. Various spatial analysis methods, including the Theil index, Moran index, Standard Deviation Ellipse, Spatial Markov Chain, and β-convergence model, are applied to examine spatial differences, clustering patterns, and convergence trends of GIE across eight economic regions in China. The model adjusts input indicators to incorporate technological and environmental factors, providing a deeper understanding of the relationship between GIE and regional dynamics. The quantitative results show an increase in GIE from 0.350 to 0.566, with technological and environmental factors playing a significant role. The study highlights increasing spatial disparities in GIE, with the Northern Coastal Region achieving the highest levels. Spatial clustering analysis reveals distinct patterns: the Southern Coastal Region shows High-High clustering, while the Northeast Region exhibits Low-Low clustering. GIE demonstrates club convergence, β-convergence, and spatial spillover effects. These findings underscore the effectiveness of green innovation practices and offer insights into spatial dynamics, providing guidance for targeted interventions and promoting inclusive growth across regions.

Abstract licence: CC BY-NC-ND

40 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.