*2.3. Innovation Policy in China*

The knowledge emanating from research is often seen as resulting in positive externalities [39], thus exhibiting some characteristics of a public good. A public good can be defined as a good that is non-rival in its usage and is non-exclusive [40]. These properties of knowledge imply that an economy may benefit extensively from investments in knowledge development, as it tends to spill over into the economy [41]. This characteristic of knowledge as a public good does imply that markets are likely to under-supply it. The theoretical motivation for public investments stems from the notion that if the benefits of new knowledge are distributed beyond those who developed it, a market economy may generate a sub-optimal amount of research and innovation [42].

While promulgated *Outline* of the National Medium-and Long-Term Science and Technology Development Plan (2006–2020), the strategy of building an innovative country, which focused on improving triple helixes relations in NSI, was officially launched in China. Since the implementation of the plan, the R&D expenditure has been steadily improved. 94.4% of R&D funds were invested in applied research projects nowadays, but policy environments are evidently far from optimal because the technology neck problem of enterprises in various industries is still quite common, and the efficiency and effectiveness of technology transformation are still far from expected. For example, by the end of 2016 only 5034 patents and 2461 authorized patents had been transferred or licensed by 38 Central Universities in Beijing and 42 research institutes of Chinese Academy of Sciences in Beijing, accounting for only 7.8% and 9.2% of the total number of authorized patents [43].

Efforts to promote synergy of TH in NIS have been the focus of public innovation policy which concerned with fine-tuning an established system, exploring best practices of policy interventions and enabling factors of commercialization or specific transfer mechanisms. Especially in recent years, China has promoted the revision of the Law on the Promotion of the Transformation of Scientific and Technological Achievements and promulgated Several Provisions on the Implementation of the Law of the People's Republic of China on the Promotion of the Transformation of Scientific and Technological Achievements, and formulated the Action Plan for the Promotion of the Transformation of Scientific and Technological Achievements, which constitutes a "trilogy" for the promotion of the transformation of scientific and technological outcomes. At the same time, the State Council has also issued Notice of the State Council on Printing and Distributing the Construction Plan of the National Technology Transfer System and Notice of the State Council on Measures to Optimize Scientific Research Management and Improve Scientific Research Performance. In general, the optimization measures of innovation policy mainly include increasing investment in industrial R&D, improving the quality of academic research, optimizing the innovation environment and strengthening the transformation of scientific and technological achievements. Therefore, this paper uses three dimensions of "academic incentive policy, industrial incentive policy and environmental incentive policy" to characterize the current innovation policy indicators.

Based on the above literature analysis, the research framework of this paper is as Figure 1.

#### *Sustainability* **2019**, *11*, 6678

**Figure 1.** The research framework and relationship between main variables.

#### **3. Simulation Model Building**

In accordance with the research objectives and framework of this work, the variables and parameters should be measured to build the simulation model for exploring the dynamic evolutional mechanism of triple helix relations in NSI. Therefore, the questionnaire that uses a Likert 7-point scale to reflect the states of academic knowledge transfer capability, industrial absorptive capability and performance of collaborative innovation is constructed. Academic knowledge transfer capability is represented by three variables of useful knowledge generation ability, knowledge interpretation ability and knowledge dissemination ability; industrial absorptive capability is included by three variables of knowledge exploratory learning ability, knowledge sharing ability and knowledge application ability; the performance of collaborative innovation, is represented by four variables of the number of new products and services, the rate of return on innovation project, market share of new products and services, and grasping of innovation opportunity. Innovation policy as the intermediary variables affecting research-practice relations and innovation collaboration is represented by academic incentive policy, industrial incentive policy and environment incentive policy. Finally, the regression analysis method will be mainly used to estimate parameters (See Table 1 for details).


