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High-tech industrial agglomeration plays a significant role in regional sustainable development. Local governments have issued many industrial policies to accelerate the development of high-tech industries in China. Evaluating high-tech industry policies from the perspective of regional industrial synergy can prevent problems in policy implementation and promote the industrial synergy in a region. For this purpose, taking China’s Beijing-Tianjin-Hebei (BTH) region as a case, we evaluate seven policies governing the high-tech industry in this region by using the approach which integrates the policy modeling consistency index (PMC-Index) model and text mining. We propose an evaluation system with consideration of regional industrial synergy, which is based on the PMC-Index model. The results show that the lowest PMC-Index value of the seven policies is 5.30, the highest is 8.17, and the average is 6.67. Among the policies, four are of excellent or perfect grade and relatively comprehensive; three are of acceptable grade and relatively insufficient. The overall designs of the high-tech industrial policies are reasonable but there is still much room for improvement. According to the average scores of the main indicators, the policies function relatively poorly in terms of policy release agency, policy timeliness, policy type and policy receptor. The optimizations for the shortcomings of each policy are also suggested. This study may not only provide some enlightenment to policymakers, but also provide a supplement for the policy evaluation field.

Endogenous growth theories have underpinned the pivotal role of education in innovation. However, our empirical study uncovers a mixed effect of higher education on firm innovation in China. Using Chinese Patent Census Data, a unique dataset, this paper is able to quantify innovation in China by incorporating a quality dimension for the first time. By merging the patent data with the Chinese Industrial Enterprise Database and province-level data on education, we find that the number of higher education institutions has a negative impact on firm-level innovation. However, the quantity of elite higher education institutions at the provincial level exerts a positive impact on firm innovation. In addition, heterogeneity analyses show that the effect of elite higher education on firm innovation is significantly positive for privately owned enterprises, but insignificant for state- and foreign-owned enterprises. Furthermore, the positive effect of elite higher education on innovation in high-tech industries is larger than in other industries.

The market competitiveness and sustainable operation of an enterprise are closely correlated with the support of high-tech core technologies in the enterprise. This study first discusses the basic knowledge of core competitiveness, introduces the components and evaluation methods of core competitiveness, and builds an evaluation index system for core competitiveness of high-tech enterprises. Then, the Analytic Hierarchy Process (AHP) is fully discussed, during which the steps, advantages, and disadvantages of the AHP evaluation method are introduced. Finally, the Fujian Province of China is taken as an example, the relevant data are collected and processed, the impact of indicators are analyzed, and a high-tech industry core technological capability analysis indicator system is built based on the AHP method. Thus, the influence of the core technological capabilities of the high-tech industry on the sustainable competitive advantage of the enterprise is obtained. This study puts forward suggestions for maintaining the competitiveness of high-tech industries, thereby improving the competitive advantage of enterprises and achieving the sustainable management of enterprises. The result finds that if the high-tech industries continue to carry out innovation and scientific research, enterprises will maintain their competitive advantages. In summary, exploring the impact of the core technological capabilities of high-tech industries on the sustainable competitive advantages of enterprises is greatly significant for improving their competitiveness and industrial status, which enables them to be invincible in a complex environment.

With ever-increasing economic globalization and rapid advancement of science and technology, developing high-tech industries have become an important way for many countries to achieve sustainable and environmentally friendly economic development. In this article, we aim to empirically test the critical factors, which can influence the spatial spillover of a country&rsquo, s high-tech industries. Using data from the high-tech industries in China during the years of 2007&ndash, 2016, we establish a space lag model and a space error model to examine the space fixed effect, the time fixed effect, and the space-time double mixed effect in spatial spillover in high-tech industries. We compare the results of these two spatial panel models with those from a general panel model and find that the spatial spillover effect within high-tech industries is rather significant. Moreover, we find that the spatial-time double mixed of the spatial lag model is the best fitting effect. Our empirical results also show that the research and development (R&amp, D) investment and international trade can positively promote spatial spillover of high-tech industries among different regions. In terms of policy insights, our results imply that the government can establish a technology transfer platform to promote the spillover in high-tech industries. This can help achieve a sustainable and balanced development of high-tech industries.

From the perspective of green growth, which seeks to coordinate and make sustainable the development of resources, the environment, and the economy, this study&rsquo, s aim was to find out whether the high-tech industry along the Belt and Road (B&amp, R) is sustainable and effective in using resources, reducing environmental pollution, and increasing performance. This study used panel data covering 16 provinces (municipalities) along the B&amp, R in China between 2009 and 2016. This study used the directional distance function (DDF) and the global Malmquist&ndash, Luenberger (GML) index model to analyze the technological innovation efficiency (TIE) of the high-tech industry (HTI) while considering the undesirable output (environmental pollution). Further, supplemented by ArcGIS geographical analysis, this study carried out a comparative analysis of the TIE and its decomposition in the HTI along the B&amp, R from geographical and time-series dimensions. Moreover, the panel Tobit regression model was used to analyze the influencing factors of TIE. The results show that the direct financial support of the government has no impact on the improvement of TIE in the HTI, the government&rsquo, s regulation of environmental pollution can significantly affect the improvement of the TIE, the intensity of R&amp, D has a significantly negative impact on the TIE, a higher level of R&amp, D personnel in the HTI can be helpful in improving TIE, and increasing the import and export trade volumes of the HTI can promote TIE.

Compared with developed countries, the paper explores whether non-R&D innovation activities in China actually are effective and provides a guidance on how we can choose a sustainable innovation mode for non-R&D, especially considering the “threshold effect” of the heterogeneity of different regional innovation levels. The dynamic threshold panel models of the non-R&D (NRD) effect on the basis of the threshold of regional innovation level is constructed to verify the complex “threshold effect” characteristics between non-R&D and innovation performance. The empirical results are discussed according to the panel data for 30 provinces in China concerning the high-tech industry. Results argue that the mechanism of non-R&D innovation activities on innovation performance have a significantly different “threshold effect.” Interestingly, when the threshold of regional innovation keeps a low level, the negative effect of non-R&D innovation activities is much larger. When the threshold level of regional innovation increases, reaching the critical mass, the negative impact of non-R&D innovation activities on innovation performance becomes smaller. However, once the regional innovation level crosses the critical mass, the negative impact of non-R&D shows a significantly increasing tendency. Specifically, neither much higher nor much lower regional innovation level is conducive to the promoting effect of non-R&D activities. The negative impact of non-R&D on performance will decrease to the minimum only in the regions within the moderate threshold level (critical mass). The dynamic nonlinear mechanism between non-R&D activities and innovation performance is empirically studied to assist high-tech enterprises for innovation sustainability based on heterogeneity of different regional innovation levels.

Based on the panel data of 11 regions in the Yangtze River Economic Belt from 1998 to 2016, we tested and analyzed the effects of high-tech industrial expansion on green development. For these regions in the Yangtze River Economic Belt, we wanted to investigate the potential linear relationship between the scale of high-tech industry and green development or the possible threshold effect. We wanted to determine if this relationship is different in various regions of the Yangtze River Economic Belt. According to the empirical test, we found that: (1) for the entire Yangtze River Economic Belt region, the influence of high-tech industrial scale on green development doubled the threshold effect, and a marginal efficiency diminishing effect existed with the further increase in scale, (2) due to the differences among the regions, the threshold effect was different in different regions, with a double threshold effect in the lower reaches, a single threshold effect in the middle reaches, and no threshold effect in the upper reaches, and (3) regarding the high-tech industrial scale, the downstream areas were too large to weaken its promoting effect on green development. In the middle reaches, the positive impact on green development was still increasing, and the high-tech industrial scale should be further expanded. However, in the upstream areas, high-tech industrial scales did not reach the threshold value and the relationship between the high-tech industrial scale and green development was linear. Therefore, local high-tech industries should be cultivated and developed.