Study on the Evolution, Driving Factors, and Regional Comparison of Innovation Patterns in the Yangtze River Delta

Abstract

The differences in innovation, and the resulting inefficient allocation of innovation resources, are key factors affecting the high-quality development of urban agglomerations. In the context of China’s upgrading of the integrated development of the Yangtze River Delta (YRD) to a national strategy, the study of innovation patterns and driving factors in this highly developed urban agglomeration provide references and experiences for high-quality development and innovation improvements in other urban agglomerations. Using prefecture-level patent data from 2000 to 2018, this study analyses the evolution characteristics of the innovation patterns in the YRD, from the perspective of innovation level and innovation growth, based on the coefficient of variation, locational Gini coefficient, and the relative development rate index. Then, using the knowledge production function, this study quantitatively explores the driving factors for innovation from multiple perspectives. The main findings are as follows. The differences in urban innovation levels decrease with improvements in the innovation level of urban agglomerations. In terms of the evolution of the spatial pattern of innovation levels, the “core–periphery” and “south–north” differences are highly stable; however, the innovation levels of some peripherical cities improve. The growth of urban innovation levels show significant regional differences, with fast-growing cities clustered in the core area, and high-value areas characterized by proximity diffusion. Based on the innovation level in different periods, cities are divided into low–low, low–high, high–low, and high–high types. There are spatio–temporal differences in the driving factors for innovation. On the one hand, different periods show an intensification of factor inputs and external linkage effects, as well as the differentiation of urban development state effects. On the other hand, there are differences among different types of cities, with low–low cities mainly driven by factor inputs, urban development state, and internal opening-up; low–high and high–high cities are greatly influenced by factor inputs and urban development state. By expanding on existing studies, the present research provides a refined reference for the formulation of scientific policies aimed at promoting innovation development in China.