The synergistic development of green innovation (GI), ecological efficiency (EE), and the digital economy (DE) is pivotal for achieving China's high-quality economic development and ecological civilization goals. This study establishes a comprehensive evaluation index system for these three subsystems. Utilizing panel data from 30 Chinese provinces (excluding Chinese Hong Kong, Chinese Macao, and Chinese Taiwan) from 2012 to 2022, we measure their development levels using the entropy weight method and super-efficiency SBM model. A tri-system coupling coordination degree model (CCDM) is constructed to quantify their interactive relationships. Furthermore, spatial autocorrelation analysis (Moran's I) is employed to uncover their spatial aggregation characteristics. The findings reveal that: (1) The overall development levels of GI, EE, and DE have shown an upward but volatile trend, with significant regional imbalances. (2) The national coupling coordination degree has steadily increased from 0.385 (mild imbalance) in 2012 to 0.465 (on the verge of imbalance) in 2022, yet it remains at a low level with considerable room for improvement. (3) Spatially, the coordination level exhibits a distinct "ladder-like" distribution, descending from the eastern to the western regions. Global spatial autocorrelation is significantly positive, indicating a strong spatial agglomeration effect. Local spatial analysis identifies stable High-High (H-H) agglomerations in the eastern coastal provinces (e.g., Shanghai, Jiangsu, Zhejiang, Shandong) and Low-Low (L-L) agglomerations in the northwestern regions (e.g., Gansu, Qinghai, Ningxia, Xinjiang) since 2019. Based on these spatiotemporal evolution patterns, this study proposes targeted policy recommendations for categorized regional development to foster synergistic advancement and narrow regional disparities.

Fangyan Ma, Haiping Nong. Engineering Coupling Coordination and Spatial-Temporal Evolution of Green Innovation, Ecological Efficiency, and Digital Economy in China. Academic Journal of Business & Management (2025), Vol. 7, Issue 12: 109-117. https://doi.org/10.25236/AJBM.2025.071214.

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