Dynamic evolution and trend prediction of multi-scale green innovation in China

Abstract

Numerous studies deal with spatial analysis of green innovation (GI). However, researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China. This paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during 2000–2019. We used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales, including regional, provincial, and prefectural. Additionally, we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term development. The findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale, with significant spatial differences of GI decreasing in each scale. Furthermore, the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales, exhibiting the “club convergence” effect and a tendency to transfer to higher levels of proximity. This effect is more pronounced on a larger scale, but it is increasingly challenging to transfer to higher levels. The study also indicates a steady and sustained growth of GI in China, which concentrates on higher levels over time. These results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.

Keywords

Green innovation / Spatial pattern / Trend prediction / Multi-scale / China

Declaration of Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 41971201).

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