The relationship between collaborative innovation and innovation efficiency has attracted the attention of researchers in recent years. However, few studies have integrated intra‐regional and inter‐regional collaborative innovations (IRCI) into a unified framework to analyze the overall impact of regional innovation efficiency. To fill this gap, this paper uses an improved Data Envelopment Analysis Model to measure the innovation efficiency of Chinese cities from 2003 to 2016 based on the regional innovation capability. Using the Coupling Coordination Degree Model to measure the degree of intra‐regional collaborative innovation, we constructed a Capability Structure Model to measure the degree of IRCI, then used the Spatial Durbin Model to empirically analyze the influence of intra‐regional and IRCI on regional innovation efficiency. The results show that: (a) both intra‐regional and IRCI promote regional innovation efficiency, but the internal factors are the primary influences on regional innovation efficiency; (b) intra‐regional collaborative innovation not only promotes local regional innovation efficiency but also promotes innovation efficiency in other regions effectively, although its driving effect on the local region is higher than on other regions; (c) there is a time lag in promoting regional innovation efficiency through the cooperation and interaction among innovation in knowledge, technological, industrial, and service, and environment capabilities. The regional innovation capacities can result in good collaborative innovation effects only after a certain period of cooperation.

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BackgroundMicroalgae frequently grow in natural environment and long-term laboratory cultures in association with bacteria. Bacteria benefit the oxygen and extracellular substances generated by microalgae, and reimburse microalgae with carbon dioxide, vitamins and so on. Such synergistic relationship has aided in establishing an efficient microalga-bacterium co-culturing mode. Obviously, the mutually beneficial relationship can be strengthened with the increase of the densities of microalgae and bacteria. However, nearly all of the early co-cultures were performed under photoautotrophic conditions, thus both microalgae and bacteria were at relatively low densities. In this study, the feasibility of bacteria-microalgae co-cultured under mixotrophic conditions was studied.ResultsFirstly, bacteria mingled with xenic microalgae were isolated and identified based on their 16S rRNA gene sequence (16S rDNA hereafter). Then, the two most frequently found strains of Muricauda sp. were co-cultured with axenic microalga (Tetraselmis chuii, Cylindrotheca fusiformis and Nannochloropsis gaditana) in extra organic carbon containing medium. At the end of a co-culture period of 33 days, we found that the final cell density of T. chuii and C. fusiformis of various treatments was remarkably higher than that of controls (21.37–31.18 and 65.42–83.47 %, respectively); on the contrary, the growth of N. gaditana was markedly inhibited. During the co-culture of bacteria with C. fusiformis, the cell density of two strains of bacteria firstly decreased, then increased and maintained at a relatively steady level. However, the cell density of bacteria performed a sustaining downward trend when they were co-cultured with T. chuii and N. gaditana.ConclusionsOur findings proved that microalgae-bacteria co-cultures under mixotrophic conditions are quite effective strategy for microalgal cultivation.

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Abstract:The economic development of China's coastal areas is being constrained by resources and the environment, with sustainable development being the key to solving these problems. The data envelopment analysis (DEA) model is widely used to assess sustainable development. However, indicators used in the DEA model are not selected in a scientific and comprehensive manner, which may lead to unrepresentative results. Here, we use the driver-pressure-state-welfare-response (DPSWR) framework to select more scientific and comprehensive indicators for a more accurate analysis of efficiency in China's coastal area. The results show that the efficiencies of most provinces and cities in China's coastal area have a stable trend. In the time dimension, efficiency was rising before 2008, after which it decreased. In the spatial dimension, China's coastal provinces and cities are divided into three categories: high efficiency, low efficiency, and greater changes in efficiency. By combining DPSWR and DEA, we produce reliable values for measuring efficiency, with the benefit of avoiding the incomplete selection of DEA indicators.

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The city is both a carrier and a subject of innovation. Based on the triple helix theory of industry–university research and the theory of spatial correlation, this study constructs a collaborative innovation framework both within the cities and between cities, and uses a network data envelopment analysis (DEA) model and spatial econometric model to measure and analyze the collaborative innovation efficiency in 75 innovative cities in China. The results show that collaborative innovation efficiency within cities is on the rise, and the efficiency of “research to production” is significantly higher than that of “learning to research.” Industrial structure and foreign factors have inhibited the efficiency improvements, and infrastructure and living standards have different promoting effects on different stages of efficiency. Between cities, capital flows have obvious spillover effects, which promote the efficiency of innovation networks, while the long‐term characteristics of institutional learning have a near‐term negative impact.