Research on Innovation Performance in Heterogeneous Region: Evidence from Yangtze Economic Belt in China

Abstract

Innovation has become the main impetus for regional development. Effective utilization of innovation resources is crucial in promoting sustainable innovation. From the theoretical aspect, there still exists uncertainty of how to effectively evaluate innovation performance. From the empirical aspect, we still doubt whether regions of higher economic level or high innovation quantity really show positive regional innovation performance, especially in heterogeneous regions. This paper uses DEA-Malmquist index to measure regional innovation performance of the Yangtze River Economic Belt in China. Regions of similar performance levels are grouped by ward clustering, analysis regional innovation performance characteristics, and problem-solving paths of regions in different development stages. The empirical research proves that overall performance of Yangtze River Economic Belt is not high. The economic core area has realized increase of innovation volume through large amount of material input and resource consumption, instead of realizing full utilization of innovative resources; how to improve the utilization rate of existing technical resources has been neglected. Different regions with similar innovation performance show different characteristics and innovation problems, including resource mismatch, input redundancy, or insufficient output. There are also some differences in the way the region’s specific innovation performance is improved.

Research on Regional High-Tech Innovation Efficiency and Influence Factors: Evidence from Yangtze River Economic Belt in China

“…e overall high-tech efficiency of the western region is higher than that of the middle region; the innovation efficiency of the high-tech industry in Yunnan and Chongqing is higher than that in other regions. is is consistent with the conclusion that the main goal of interregional network innovation cooperation innovation of western region is to realize the new technology application and marketization [5].…”

“…erefore, on the one hand, the Yangtze River economic belt is an important region for high-tech industry development in China. On the other hand, the obvious regional heterogeneity also makes great differences in its innovation level and efficiency [5]. e Yangtze River economic belt has become a typical region of economic and geographical research, which has strong representativeness and importance.…”

Examining the impact of urban management on regional innovation efficiency in China’s manufacturing industry with a focus on modern sustainable energy: utilizing a non-radial distance function method

“…This study conducts a comparative analysis of regional innovation efficiency in China's manufacturing industry, focusing on the consequences of urban management. By considering regional heterogeneity and employing the non-radial distance function method, this research provides a comprehensive evaluation of innovation efficiency, considering both input reduction and output increase simultaneously (Gayosa and Cabanda, 2014;Zou and Zhu, 2020;Feng et al, 2022). The results of this study contribute to enhancing our understanding of the role of urban management in promoting regional innovation efficiency in China's manufacturing industry.…”

“…Additionally, the research integrates common Frontier theory with the Malmquist-DEA-Slacks-Based Measure (MSBM) model and develops the Modified Malmquist-DEA-Slacks-Based Measure-Panel (MMSBMP) model. This methodological fusion facilitates a comprehensive analysis of innovation efficiency and its correlation with urban management (Zou and Zhu, 2020;Zou et al, 2022;Hou et al, 2023). The study employs the Luenberger index methodology to assess the innovation efficiency of China's manufacturing industry within a specific time frame, enabling a differentiated evaluation of efficiency and identification of potential sources of efficiency losses.…”

“…Many researchers have increasingly used Data Envelopment Analysis (DEA) to measure manufacturing innovation efficiency, both domestically and internationally. Various studies have employed DEA models to examine regional innovation efficiency in China and have provided valuable insights and policy recommendations for enhancing efficiency and reducing disparities (Gayosa and Cabanda, 2014;Zou and Zhu, 2020;Feng et al, 2022;Zou et al, 2022;Hou et al, 2023). Nevertheless, the existing body of literature that evaluates manufacturing innovation efficiency often neglects the importance of individual input or output indicators in the measurement process and fails to account for negative values associated with these indicators.…”