With business today relying increasingly more on collaboration, new product development is also on a network base. The concept of innovation ecosystem is built upon knowledge creating and sharing across companies, knowledge institutions, policy regimes, business enterprises and industry boundaries. China as the largest emerging market has witnessed innovation with interaction among government, university, industry and research. Though government plays an important role in promoting innovation, not many studies have covered the detailed dynamic process and impact of policy on forming innovation ecosystem, especially in places where initial knowledge resources such as universities and research institutes are limited. Therefore, our article aims to fill in this research gap. Through document review and case study on Changzhou region of China, ¹ we map out the local government policy concerns and changes during each stage of innovation ecosystem formation, development and expansion from 2001 to 2015. The interaction mechanism among government, university, industry and research is summarised with a proposed framework to highlight the key policy making areas. Further research areas are recommended with implication and conclusion.

Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” devices like TinyML, which supports devices from cameras to wearables, offering low-power IoT computing. This study attempts to provide a conceptual update of climate and environmental policy in open synergy with proprietary and open-source TinyML technology, and to provide an industry collaborative and policy perspective on the issue, through using differential game models. The results show that patent and open source, as two types of TinyML innovation, can benefit a wide range of low-carbon industries and climate policy coordination. From the case of TinyML, we find that collaboration and sharing can lead to the implementation of green AI, reducing energy consumption and carbon emissions, and helping to fight climate change and protect the environment.

In an effort to tackle climate change, the “Dual Carbon” target raised by the Chinese government aims to reach peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. Accordingly, policy incentives have accelerated the new energy vehicle (NEV) sector. Whilst previous studies have focused on the bilateral game between governments and manufacturers, NEV development has witnessed interaction among multiple players. In this paper, we construct a quadrilateral evolutionary game model, considering the impact of government policies, manufacturers’ R&D investments, dealers’ support, and consumer choice on the evolutionary stabilization strategy (ESS) in the context of China. The results show that: (1) in the absence of government incentives, there is no motivation for manufacturers, dealers and consumers to consider the development of NEVs; (2) government incentives affect manufacturers and consumers on the evolutionary paths in the short term. In the long term, benefit- and utility-based limited rationality has a dominant role in the ESS. This study contributes to the understanding of the multilateral dynamics of NEV innovation and provides important implications to practitioners and policy makers.

In the context of the application of artificial intelligence in an intellectual property trading platform, the number of demanders and suppliers that exchange scarce resources is growing continuously. Improvement of computational power promotes matching efficiency significantly. It is necessary to greatly reduce energy consumption in order to realize the machine learning process in terminals and microprocessors in edge computing (smart phones, wearable devices, automobiles, IoT devices, etc.) and reduce the resource burden of data centers. Machine learning algorithms generated in an open community lack standardization in practice, and hence require open innovation participation to reduce computing cost, shorten algorithm running time, and improve human-machine collaborative competitiveness. The purpose of this study was to find an economic range of the granularity in a decision tree, a popular machine learning algorithm. This work addresses the research questions of what the economic tree depth interval is and what the corresponding time cost is with increasing granularity given the number of matches. This study also aimed to balance the efficiency and cost via simulation. Results show that the benefit of decreasing the tree search depth brought by the increased evaluation granularity is not linear, which means that, in a given number of candidate matches, the granularity has a definite and relatively economical range. The selection of specific evaluation granularity in this range can obtain a smaller tree depth and avoid the occurrence of low efficiency, which is the excessive increase in the time cost. Hence, the standardization of an AI algorithm is applicable to edge computing scenarios, such as an intellectual property trading platform. The economic granularity interval can not only save computing resource costs but also save AI decision-making time and avoid human decision-maker time cost.

Nowadays, the way of Internet travel in China presents diversified development. Rapid expansion in the personal mobile terminal and Internet infrastructure for the Internet travels to the ecosystem of development and evolution. This paper explores the influence of the expansion of the e-hailing ecological community on the urban taxi ecological community, and the problems of profit-seeking, rent-setting, and rent-seeking from the capital behind the e-hailing platform. With entrance into the urban travel ecosystem with ride-hailing platform enterprises and emerging business models, how does (venture) capital cause the change of the urban taxi ecology? Is it essentially the rent-setting and rent-seeking behavior of capital that obtains the market monopoly status of e-hailing? What are the difficulties for governments and the public in regulating rent-seeking in capital and emerging business models? The aim is to explore the new governing strategy of urban taxi ecology. The paper chooses the case of the DiDi e-hailing platform, and performs a study on the utility and indifference curves quantitatively, and constructs a rent-seeking liaison between platform’s entry into the market and the platform’s monopoly. In this paper, the ecological system of the taxi industry is first sorted out, and the community relationship between the traditional cruise taxi industry and the e-hailing industry, and their market supply and demand relationship are clarified. Secondly, from the main theoretical perspective of rent-seeking behavior, this paper analyzes the causes of the rent-setting rent-seeking behavior of e-hailing platform enterprises and the venture capital behind them, and the problems of public power supervision. As the red pack & subsidy market strategy changes the utility (indifference) curve of passengers, the consumption preference of passengers is more inclined to e-hailing. With the expansion of the e-hailing community, the e-hailing platform partly replaces the regulatory role of the government, and venture capital investors as rational-economic individuals, and its agency responsibilities and profit-seeking characteristics naturally form a contradictory pair. The introduction of competition is a means among market policies to break monopoly. Even so, commercial competitors are able to avoid laws or regulations by the means of commercial mergers or through the implementation of variable interest entities control. Governments should therefore seek to participate actively in the governance of such organizations.

Alongside Patent Super Aggregators represented by Intellectual Ventures in the United States, there is a trend to construct patent intermediary in the context of platform ecosystem. Accordingly, patent operation platforms (POPs) have emerged recently in China, yet few studies focus on uncovering their structures and operating mechanisms. This article aims to explore them based on two in-depth case studies with the application of a four-dimensional service innovation framework. Findings pinpoint that POP consists of ‘Patent Plus’ database, patent service platform and two-sided patent platform, as a closed loop. In this structure, ICT plays a prominent role, connected with new service concepts, service delivery system and client interface, to operate the platform. Our article also shows implications to POP related theory and practice.