Abstract:    With the advent of the knowledge economy era, the progress of science and technology has become the fundamental driving force of economic growth, and the engine role of technological innovation has become increasingly prominent. In recent years, with the rapid development and wide application of scientific technology and information technology, the proportion of high-tech industry with knowledge intensiveness, information intensiveness and technology intensiveness has gradually increased. Under the new normal background, high-tech industry plays an important role in the process of steady growth, adjusting structure and implementing innovation-driven development strategy. The transformation of R&D achievements is related to the sustainable development of the industry. 
    By taking innovative and non-innovative production elements into consideration comprehensively, this paper selects input-output variables from three aspects including labor, capital, as well as technology. Afterwards it takes provincial panel data as the sample data. Then it uses StoNED model to evaluate the transformation efficiency of R&D achievements of China′s high-tech industry from 2011 to 2016, adopts the mean of logarithmic deviation and Theil index to decompose the regional difference of the efficiency, and combines ESDA and GIS technology to carry out spatial autocorrelation analysis and visualization analysis of the efficiency. 
    The results show that the overall level of the transformation efficiency of R&D achievements of China′s high-tech industry is not high, indicating that the R&D achievements emerged in the industry have not been efficiently transformed into economic returns. The efficiency shows the characteristics of regional imbalance. In addition, the efficiency has the unbalanced characteristic of "high value in the east and low value in the west", demonstrating that there is significant spatial heterogeneity in the efficiency, and there is still much room for improvement in low efficiency regions. From the perspective of China′s three regions, the total regional difference of the efficiency is determined by intra-regional difference and inter-regional difference. And the difference within regions is greater than that between regions. It shows that the total difference is mainly caused by intra-regional difference. The difference in the western regions is the most obvious, followed by the difference in the eastern regions. And the difference in the central regions is the smallest. In the period of 2012-2016, the fluctuations of the decomposition results calculated by the mean logarithmic deviation and Theil index are fundamentally similar. Although the total difference rises or drops from 2012 to 2016, it still rises on the whole. 
     The spatial correlation and heterogeneity coexist in the transformation efficiency of R&D achievements of China′s high-tech industry. The spatial agglomeration characteristics and spatial polarization phenomena exist in the high and low efficiency regions. Globally, through the Moran scatter plot, it is found that most provinces fall into the first quadrant and the third quadrant, indicating that the efficiency has a positive correlation, which is manifested as spatial agglomeration. And only a few provinces fall into the second quadrant and the forth quadrant, indicating that the efficiency has a negative correlation, which is manifested as spatial outlier, so the efficiency has a significant positive spatial correlation at the whole level. The characteristics of spatial agglomeration are as follows: the regions with high efficiency are adjacent, and the regions with low efficiency are also adjacent. From 2011 to 2016, Moran′s I index rises or drops from 2012 to 2016, but it still drops on the whole, indicating that the total difference rises on the whole. Besides, the results of global G coefficient indicate that the efficiency has significant spatial high-value agglomeration. The overall fluctuation range of global G coefficient is not obvious, indicating that the high-value agglomeration of the efficiency does not change significantly.
      Global Moran index can reveal the spatial agglomeration characteristics of the transformation efficiency of R&D achievements of China′s high-tech industry in the whole research region. Local Moran index can be used to analyze the spatial agglomeration characteristics of the efficiency in different regions. When high-value clustering regions and low-value clustering regions coexist, they tend to cancel each other, so global G coefficient can only reveal high-value clustering or low-value clustering of the efficiency in the whole research area, whereas local G coefficient can be used to analyze the concrete spatial distribution patterns. Locally, the efficiency has a distinct spatial distribution pattern. And the efficiency is polarized in space, hot-spot areas and cold-spot areas are relatively concentrated in spatial distribution. Besides, LISA agglomeration regions and spatial cold-hot spots reflect similar spatial evolution law from 2012 to 2016. LISA agglomeration exhibits spatial agglomeration and spillover effects in local regions. Spatial distribution of cold-hot spots shows spatial polarization and high-value diffusion effects in local regions. What′s more, the high-high agglomeration regions correspond to the hot spots, while the low-low agglomeration regions correspond to the cold spots. What′s more, there is no significant local spatial autocorrelation in some regions, and global spatial autocorrelation can be further verified by local spatial autocorrelation. According to the theory of new economic geography, R&D achievements are important carriers of knowledge and technology, which are prone to produce spatial spillover effects in the transformation process of R&D achievements. The spatial diffusion effects of high-value agglomeration regions are characterized by periodical changes, which affect neighboring regions and the non-equilibrium of regional industry development. However, the radiation driving capacity of hot-spot regions is not fully reflected and the unbalanced development of regional high-tech industry is not continuously improved. The above findings imply that all regions should attach great importance to the spatial agglomeration characteristics of the transformation efficiency of R&D achievements. For H-H significant agglomeration regions, Jiangsu, Zhejiang, Anhui and other regions are the hot spots. The regions should make full use of location advantages, strengthen exchanges and cooperation with neighboring regions, and then benefit the surrounding regions. For L-L significant agglomeration regions, Inner Mongolia, Xinjiang, Tibet and other regions are cold spots. The relevant departments should give some policy preferences to support regional high-tech industry development. For L-H and H-L significant agglomeration regions, the regions are mainly distributed in Jiangxi and Sichuan. The regions should improve the industrial innovation system, build technology sharing and exchange platform, and promote industrial development.

Key words: high-tech industry, R&D achievement transformation efficiency, difference decomposition, spatial pattern evolution, StoNED model, ESDA