Font Size: a A A

Research On The Influencing Factors Of The Development Of China’s High-tech Manufacturing Industry

Posted on:2024-08-29Degree:MasterType:Thesis
Country:ChinaCandidate:J ZhengFull Text:PDF
GTID:2557307106970519Subject:Statistics
Abstract/Summary:PDF Full Text Request
High-tech manufacturing,which covers many key areas such as scientific knowledge reserve,technological development breakthroughs and energy efficiency improvement,plays an important role in promoting national industrial restructuring and enhancing the international competitiveness of high-tech industries.By understanding the current development situation of China’s high-tech manufacturing industry,this paper analyzes the possible problems and main influencing factors in the development,and provides suggestions for promoting the rapid development of China’s high-tech manufacturing industry.Firstly,this paper compares the development of high-tech manufacturing industry by region,and finds that all regions are developing steadily.The output value of high-tech manufacturing industry in eastern China accounts for the largest proportion,while the growth rate of high-tech manufacturing industry in central and western China is relatively high.The development of high-tech manufacturing industry was studied by industry,and it was found that electronic and communication equipment manufacturing,computer and office equipment manufacturing accounted for a relatively high proportion,and the growth rate continued to rise after 2019.Secondly,combined with the existing research on the influencing factors of the development of high-tech manufacturing industry,the regression model is constructed,and the significant variables affecting the development of high-tech manufacturing industry are: R&D investment,energy efficiency,digital economy index,industrial agglomeration,full-time equivalent of R&D personnel,number of effective invention patents,and external investment.Four variables of R&D investment,energy efficiency,digital economy index and industrial agglomeration were selected for indepth analysis.The impulse response analysis shows that the initial R&D investment has a significant positive adjustment effect on the high-tech manufacturing industry,and the influence gradually weakens with the increasing of the input.The threshold model is used to show that the influence of digitalization on high-tech manufacturing industry is positive,and the positive effect shows a trend of first rising and then declining.Energy efficiency has a certain impact on the development of high-tech manufacturing industry.All provinces are constantly improving energy efficiency,and the efficiency of technological progress is fastest in Guangdong Province.The agglomeration degree of high-tech manufacturing industry in China is significantly different among different regions.The agglomeration degree of high-tech manufacturing industry in eastern China is relatively high(Beijing and Guangdong are the highest),while the agglomeration level of high-tech manufacturing industry in western China is relatively low(Xinjiang is the lowest),and the agglomeration degree of high-tech manufacturing industry in Beijing,Tianjin and Shanghai is significantly decreased.The agglomeration of real estate industry in Shanxi,Henan,Anhui and Chongqing increased significantly.Finally,the Bayesian network model of high-tech manufacturing industry is constructed,and it is found that the variables that directly affect the development of high-tech manufacturing industry are R&D investment and industrial agglomeration,and the conditional probabilities that these two variables are at different levels of low,medium and high respectively are obtained.SVR model and ARIMA model are used to predict the development of high-tech manufacturing industry respectively.It is found that the prediction effect of SVR model based on influence factors is better than that of ARIMA model based on time series.
Keywords/Search Tags:High-tech manufacturing, R&D investment, Energy efficiency, Digital Economy Index, Industrial agglomeration, Bayesian networks
PDF Full Text Request
Related items