Exploring Dynamic Pathways of Green Technology Innovation Efficiency in Provincial Regions of China

The efficiency of green technology innovation functions as a fundamental catalyst for realizing the synergy between innovation-driven development and ecological transformation, which is essential for achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). This study assesses green technology innovation efficiency across 30 Chinese provinces from 2013 to 2024 using the Data Envelopment Analysis (DEA) model. It constructs an analytical framework that integrates institutional settings, industrial structure, and market environment, and applies dynamic Qualitative Comparative Analysis (QCA) to examine the configurational effects of these factors on green technology innovation efficiency. Additionally, the Kruskal-Wallis rank sum test is employed to investigate spatial disparities in the configurational coverage at the provincial level. To address the temporal heterogeneity arising from events such as the COVID-19 pandemic, this study employs a dynamic QCA approach that captures both between-group and within-group variations across different time periods. The results indicate that no single factor constitutes a necessary condition for high green technology innovation efficiency; instead, three distinct pathways are identified as equally effective. Moreover, significant regional differences are observed in the configurational distribution of green technology innovation efficiency across the eastern, central, and western regions of China. These findings provide practical implications for policymakers seeking to advance China's sustainable development agenda through differentiated regional innovation strategies. This paper presents a multidimensional analytical framework and utilizes dynamic QCA to offer a comprehensive examination of complex causal relationships.