关键词: 创新生态系统, 创新能力, 驱动机制, 组态效应, 模糊集定性比较分析

Abstract:

As the space carrier of innovation activities and the gathering place of innovation element resources, cities are indispensable responsible subjects and action units for promoting low-carbon development and addressing climate change. Innovation is a critical driver of urban economic growth and social development, and low-carbon urban innovation is an important approach to achieving green and low-carbon development. It is particularly urgent to study the innovation ability and driving mechanism of low-carbon cities to improve their innovation ability. Therefore, based on the innovation ecosystem theory, this research proposes a configuration analysis framework to study the innovation ability of low-carbon cities and takes 68 low-carbon cities as case samples. Using the method of fuzzy set qualitative comparative analysis (fsQCA), this research explores the complex causal mechanism of the low-carbon city innovation ecosystem driving innovation ability improvement. The question that this research wants to solve is: Is there a necessary condition for a low-carbon city to generate high innovation ability? Which paths can enable low-carbon cities to show high innovation abilities? Which approach will inhibit the improvement of innovation ability?

First, based on the innovation ecosystem theory, this research constructs a configuration analysis framework to study the innovation ability of low-carbon cities. Secondly, the necessary condition test is carried out using the method of QCA. The results show that the technological innovation subject, the knowledge innovation subject, human resources, financial resources, innovation infrastructure, and innovative social environment elements are not the necessary conditions for high innovation ability or non-high innovation ability. Thirdly, using the fsQCA method to analyze the configuration, study the driving mechanism of low-carbon city innovation ability, and name the discovered configuration. This research analyzes the driving paths of the city's high innovation and non-high innovation ability. The results show that there are five driving paths for the high innovation ability of low-carbon cities. Respectively, enterprise-intelligence-facility-driven, agent-intelligence-facility-balanced-driven, capital-facility-balanced-driven, resource-environment-balanced-driven and resource-facility-driven. Five paths lead to low-carbon cities with non-high innovation ability, among which non-high knowledge innovation subjects and non-high innovation resources are important reasons for non-high innovation ability. Moreover, the antecedents of the high and non-high innovation ability of cities show asymmetric characteristics. Each city should choose a suitable path according to its characteristics to improve the city's innovation ability. The innovation ability of a city is affected by the synergy of many factors. The improvement of technological innovation subject, innovation resources and innovation infrastructure are the key to promote the improvement of urban innovation ability. Actively promoting the balance of innovation subjects, innovation resources, and environment to promote the overall innovation ability of low-carbon cities. The urban innovation ecological configuration proposed in this research still has shortcomings and needs to be improved in the follow-up research. Limited by data availability, there are certain flaws in the variable design. It can improve the measurement of innovation ability further. This research focuses on horizontal analysis, only analyzes the static relationship between urban innovation ecosystem and innovation ability, and does not consider the dynamic evolution of each variable over time. The QCA method of multi-period and multi-linear growth can be considered to analyze and compare the configuration solutions of different periods, and further, explore the dynamic evolution of the driving mechanism of urban entrepreneurial ability.

Key words: innovation ecosystem, innovation ability, driving mechanism, configuration effect, fuzzy sets qualitative comparative analysis