Academic Journal of Science and Technology ISSN: 2771-3032 | Vol. 5, No. 1, 2023 71 Research on the Evaluation and Prediction of the Operation Efficiency of China's Science and Technology Business Incubators Liguang Chen School of Economics and Management, Tianjin University of Technology and Education, Tianjin, 300222, China Abstract: Based on the division of the three major economic regions in the east, the middle and the west, the operational efficiency of science and technology incubators in 31 provinces (cities) from 2010 to 2019 and 2019 is analyzed by DEA-BCC model, and the development trend of science and technology incubators in the next six years and their contribution to GDP are predicted by using the grey model GM (1,1). It is concluded that while giving consideration to fairness, we should pay attention to the total allocation of scientific and technological resources between the eastern, central and western regions to improve the efficiency of scientific and technological resources allocation, promote the development of high-tech in late-developing regions, accurately allocate resources and stabilize regional economic development. Keywords: Technology incubator, Data envelopment analysis, Grey prediction, Operating efficiency. 1. Introduction In the context of globalization and the advent of the era of knowledge economy, science and technology are increasingly closely linked with the economy. Scientific and technological innovation has become an important force to promote the economic transformation and industrial upgrading of countries and regions. As a test field for China's scientific and technological innovation, science and technology business incubators (hereinafter referred to as "incubators") mainly provide services such as physical space, infrastructure, technical consultation, investment and financing for small and medium-sized high-tech enterprises that have not yet formed, reduce entrepreneurial risks, improve enterprise innovation capabilities, promote enterprise growth, drive employment by entrepreneurship, promote regional economic development level and economic structure transformation. This paper evaluates the operational efficiency of science and technology incubators from 2010 to 2019 at the level of the three major economic regions in the east, west and middle of China, and forecasts the demand for science and technology resources in China, providing reference for optimizing the operation of science and technology incubators. 2. Indicator Selection and Data Source According to the three major economic regions in the east, middle and west of China, and combined with the input and output indicators, the operation efficiency of the incubator is evaluated. This paper will select input indicators from three perspectives of incubator resources, material resources and human resources, and output indicators from three perspectives of incubator process resources, economic benefits and incubation results, as shown in Table 1. Table 1. Incubator indicator system Grade I index Grade II index Grade III index Input indicators Incubators Input number of incubators material resources Site area human resources Number of employees Output indicators incubated resources incubated enterprises incubated enterprises Total income Graduated enterprises Graduated enterprises 3. Analysis Results of DEA-BCC Model The operational efficiency of incubators in China from 2010 to 2019 is calculated by using Deap2.1 software. The analysis results are as follows.The comprehensive efficiency value is a key indicator reflecting the allocation of incubator resources. The effectiveness of the allocation of various scientific and technological resources can be measured according to the ratio of its input and output. The closer the comprehensive efficiency value is to 1, the better the allocation of various incubator resources is. The analysis results of DEA-BCC model show (see Table 2) that the average comprehensive efficiency of incubator allocation in China from 2010 to 2019 is 0.9851, which is in a good level overall, but there is still a large room for improvement. From the perspective of each year, except 2012, 2013, 2015 and 2016, DEA is effective in all other years, indicating that the input matches the expected output and the incubator's input- output resources are fully utilized. In 2012 and 2015, DEA was weak, that is, the growth rate of output was lower than that of input, and the allocation of resources was redundant; In 2013 and 2016, DEA was invalid, the return to scale was increasing, and the total amount of incubator resources was insufficient. 72 Table 2. Operation efficiency of incubators in China from 2010 to 2019 Annual comprehensive efficiency Pure technical efficiency Scale efficiency Scale return 2010 1.000 1.000 1.000 -- 2011 1.000 1.000 1.000 -- 2012 0.979 1.000 0.979 drs 2013 0.945 0.995 0.950 irs 2014 1.000 1.000 1.000 -- 2015 0.998 1.000 0.998 drs 2016 0.929 0.952 0.976 irs 2017 1.000 1.000 1.000 -- 2018 1.000 1.000 1.000 -- 2019 1.000 1.000 1.000 -- Average 0.9851 0.9947 0.9903 -- From the specific analysis of 31 provinces (cities) (see Table 3), taking 2019 as an example, Beijing, Shanghai, Fujian and Hainan in the eastern region, Heilongjiang in the central region, and Tibet and Ningxia in the western region are DEA effective, scientific and technological resources are reasonably allocated, and are in a leading position in the development of incubators in China; Tianjin, Jiangsu, Zhejiang, Shandong and Guangdong in the eastern region, Henan and Hubei in the central region, and Yunnan and Xinjiang in the western region are weak and effective in DEA, that is, the input resources under the current scale can still be fully utilized, and there is resource redundancy; The DEA of Hubei, Liaoning and Guangxi in the eastern region, Shanxi, Inner Mongolia, Jilin, Anhui and Jiangxi in the central region, and Chongqing, Sichuan, Guizhou, Shaanxi, Gansu and Qinghai in the western region is invalid, indicating that the utilization of scientific and technological resources has not reached the optimal output. In 24 provinces, the ratio of input and output is unreasonable due to the inefficiency of pure technical efficiency or scale efficiency. It can be seen that there are obvious differences in the operating efficiency of incubators among regions in China, and the allocation of scientific and technological resources is uneven. Table 3. Allocation efficiency of incubators in 31 provinces (cities) of China in 2019 Provincial comprehensive efficiency Pure technical efficiency Scale efficiency Scale return Scale return Beijing 1.000 1.000 1.000 -- Tianjin 0.833 0.951 0.875 drs Hebei 0.720 0.758 0.949 irs Shanxi 0.784 0.793 0.989 irs Neimenggu 0.673 0.704 0.956 irs Liaoning 0.940 0.965 0.974 irs Jilin 0.594 0.627 0.947 irs Heilongjiang 1.000 1.000 1.000 -- Shanghai 1.000 1.000 1.000 -- Jiangsu 0.754 1.000 0.754 drs Zhejiang 0.828 1.000 0.828 drs Anhui 0.778 0.831 0.937 irs Fujian 1.000 1.000 1.000 -- Jiangxi 0.709 0.836 0.849 irs Shandong 0.898 1.000 0.898 drs Henan 0.689 0.849 0.812 drs Hubei 0.900 1.000 0.900 drs Hunan 0.751 0.866 0.868 irs Guangdong 0.785 1.000 0.785 drs Guangxi 0.857 0.879 0.974 irs Hainan 1.000 1.000 1.000 -- Chongqing 0.951 0.954 0.996 irs Sichuan 0.825 0.905 0.912 irs Guizho 0.564 0.580 0.973 irs Yunnan 0.980 1.000 0.980 drs Xizang 1.000 1.000 1.000 -- Shanxi 0.517 0.669 0.774 irs Gansu 0.773 0.815 0.948 irs Qinghai 0.866 0.873 0.992 irs Ningxia 1.000 1.000 1.000 -- Xinjiang 0.939 0.986 0.952 drs 73 4. Grey Prediction Model The grey prediction model GM (1,1), as a typical model of grey prediction theory, has the characteristics of less samples, incomplete sample information and high prediction accuracy. At present, it is widely used in agriculture, energy and economy, and has solved a lot of practical problems. The GM (1,1) model is used to predict the development trend of the incubator resource input and output indicators from 2020 to 2025, and the fitting degree of the grey prediction model is judged according to the fitting degree test standard. Table 4. Grey prediction model and test results of incubators in China from 2020 to 2025 Index parameter value fitting equation C value P value model accuracy grade Count the number of incubators a=-0.0724 b=5246.3363 73359.2086e0.072k- 72463.2086 0.0177 1 1 Site area a=-0.0613 b=15357.8063 253579.076e0.061k- 250535.176 0.0316 1 1 Incubated enterprises a=-0.0644 b=234271.5378 3694138.79e0.064k- 3637756.79 0.045 1 1 Total income of incubated enterprises a=-0.0477 b=10335.8281 220013.525e0.048k- 216684.025 0.1569 1 1 Number of employees in incubated enterprises a=-0.0478 b=375.3833 7971.001e0.048k- 7853.2071 0.0707 1 1 Cumulative graduation enterprise a=-0.0635 b=170292.9751 2718264.13e0.064k- 2681779.13 0.0264 1 1 GDP a=-0.0881 b=432306.8424 b=5319120.91e0.0881k- 4907001.6 0.0017 1 1 According to the test, the average relative error of various economic indicators and GDP of incubators in China is 10.04%, 6.72%, 11.96%, 14.85%, 14.85%, 9.28%, 9.45% and 10.05% respectively, and the model fitting accuracy is high, which is level 1; The C values of the fitting equation are 0.0177, 0.0316, 0.045, 0.1569, 0.0707, 0.0264 and 0.0017, respectively, which are less than 0.34. The model is judged as good, and the grade is grade 1; The small error probability P is 1, greater than 0.95, and the accuracy level is 1. To sum up, the GM (1,1) model has a high fitting accuracy and can be used for the medium and long term forecast research of China's incubator development trend and GDP.The prediction results of GM (1,1) model show that the input-output indicators of China's incubator resources are increasing year by year from 2020 to 2025, and the average annual growth rates of the six indicators are 19.24%, 15, 56%, 14.42%, 9.46%, 9.61% and 15.99% respectively. It can be seen that with the increase of the number of incubators and the area of their sites, the total income of incubating enterprises and the cumulative graduation enterprises and other output indicators will also increase accordingly, indicating that the number of incubating enterprises and their employees in China's future incubators may still increase. Table 5. Predicted Values of Incubator Indicators Year Number of incubators site area incubating enterprises employees of incubating enterprises cumulative graduates enterprises 2020 6109 16083. 621 247595 328 173279 2021 6959 17934. 021 278485 358 195171 2022 7872 19901. 373 311429 390 218496 2023 8853 21993. 07 346564 424 243350 2024 9909 24216. 971 384036 459 269833 2025 11043 26581. 431 424000 496 298050 Average annual growth rate 10.37% 8.73% 9.38% 7.14% 9.46% 5. Conclusion 5.1. Significant differences in the operating efficiency of incubators between the eastern, central and western regions The analysis results based on the DEA-BCC model show that the overall operational efficiency of China's incubators showed an upward trend from 2010 to 2019, but the operational efficiency difference was significant and the resource utilization rate of some provinces (cities) was low. Among the provinces where DEA is effective, five provinces are in the central and eastern regions of China, including Beijing, Shanghai, Fujian and Hainan; The central region is Heilongjiang. About half of the 24 provinces in which DEA is invalid have large incubators and insufficient total income 74 of incubating enterprises. Most of these provinces are concentrated in the central and western regions, which indicates that there is a regional imbalance in the operation efficiency of incubators in China that cannot be ignored. Guided by national policies, actively improve the construction of high-tech industry service system, balance the total allocation of scientific and technological resources in the eastern, central and western regions, while taking into account fairness, and improve the allocation efficiency of scientific and technological resources. First, control the areas with excessive allocation of scientific and technological talents, transfer personnel to areas with less allocation, and avoid talent waste; The second is to appropriately reduce the area of the incubator, and optimize the allocation scale and proportion on the basis of the existing investment scale, combined with policy guidance and scientific and technological resources planning, taking into account the key factors affecting the low allocation efficiency. 5.2. Accurately configure incubator resources The forecast results show that the number of incubators, the area of the site, the number of incubating enterprises and the number of employees of incubating enterprises in China will increase year by year, and the total income of incubating enterprises and the cumulative graduation enterprises will increase simultaneously. Among them, the annual contribution rate of total income GDP of incubated enterprises is about 0.8%, indicating that the operation of incubators can stimulate economic growth stably. With the arrival of the era of "big data" and "5G", the demand for scientific and technological talents such as information technology, biotechnology, new material technology and new energy technology is increasing, and the incubator resource allocation is facing huge challenges. In this regard, the precise allocation of incubator resources is particularly important. First, reduce the operation area of incubators in some provinces (districts, cities), allocate the redundant employees of incubating enterprises reasonably, actively introduce high- tech talents, and improve the utilization rate of resources. 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