Understanding Green Economic Efficiency Through Social–Ecological–Technological Systems

Abstract

Sustainable urban development is increasingly shaped by the joint evolution of social development, ecological conditions, and technological capacity. Understanding how these dimensions are associated with green economic efficiency (GEE) is therefore important for advancing urban green transition. This study examines GEE from a social–ecological–technological (S–E–T) systems perspective and incorporates spatial dependence into the empirical framework. Using panel data for 281 prefecture-level cities in China from 2011 to 2022, this study measures GEE with a Super-SBM model, characterizes its spatiotemporal evolution, and estimates a two-way fixed-effects spatial Durbin model to distinguish local and cross-city associations. The results show that: (1) urban GEE improved overall, while maintaining a clear southeast-high and northwest-low spatial gradient; (2) regional disparities widened over time, and efficiency transitions exhibited strong path dependence and spatial neighborhood effects; (3) the spatial Durbin model is preferred over the SAR and SEM alternatives, indicating that both local conditions and geographically connected cities should be considered; (4) green patenting shows the most stable positive association with GEE, both locally and across connected cities; and (5) population density and ecological conditions display differentiated local and cross-city associations. These findings show that combining the S–E–T framework with spatial panel analysis provides a systematic approach for understanding urban GEE and offers evidence for coordinated green development policies across connected cities.