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Research On The Financial Effect Of Enterprise Green Technology Innovation

Posted on:2020-09-14Degree:MasterType:Thesis
Country:ChinaCandidate:R F XiongFull Text:PDF
GTID:2439330575990822Subject:Technical Economics and Management
Abstract/Summary:PDF Full Text Request
Since the reform and opening up,with the rapid development of China's economy,it has also brought many worrying problems,such as excessive energy consumption and environmental pollution.China's manufacturing industry has made tremendous contributions to the growth of the real economy,but one of the main causes of the two major problems of excessive energy consumption and environmental pollution is China's manufacturing industry.A non-negligible part of the automobile manufacturing industry in China's manufacturing industry plays an indispensable role in people's daily life and work production,and its technological innovation activities are relatively active in manufacturing.The key to improving the competitiveness of China's manufacturing industry and realizing its structural transformation and upgrading is to implement green technology innovation strategies and implement green technology innovation measures from the bottom to the top.How to promote this move,it is very important to study the financial effects of green technology innovation in the automotive industry.The research in this paper is based on two theoretical points,one is the theory of technological innovation,and the other is the theory of sustainable development.This paper analyzes the current status of green technology innovation activities of China's automobile manufacturing enterprises,and compares the current research situation of green technology innovation in the literature.Then use the green patent quantity to measure the green technology innovation level of automobile manufacturing enterprises.The financial performance indicators use the return on assets(ROA),and construct the mathematical model to conduct empirical analysis using empirical analysis software.The research methods in this paper are four methods: literature research method,qualitative analysis method,quantitative analysis method and investigation research method.The required research data is collected by third-party organizations,such as the National Key Industry Patent Information Platform,the National Bureau of Statistics of China,and the website of Juchao Information Network.The empirical analysis software for this study was selected using Stata15.0 software.After collecting the required research data,it is processed to form panel data,mainly for the data of 34 listed companies in China's automobile manufacturing industry from 2006 to 2017.The software is then used to perform panel regression analysis on relevant research data.The results are as follows:(1)At present,China's automobile manufacturing companies have made relatively few efforts in green patents,and the number is relatively small.This shows that the level of green technology innovation is still relatively low,and the level of green technology innovation is uneven;(2)the amount of green patents There is a positive correlation between the number of invention patent applications,the number of application for patents,the number of applications for appearance patents and the rate of return on total assets;the number of green patents is positively related to the number of invention patent applications,the number of utility patent applications,and the number of applications for appearance patents;3)The amount of green patents and the number of invention patent applications have a significant positive impact on the return on total assets;the number of patent applications applied and the number of patent applications for appearance have no significant impact on the return on total assets,indicating that Chinese automobile manufacturers should increase their innovation level.The innovation of higher technology and the unity of economic and ecological benefits.
Keywords/Search Tags:Automotive Manufacturing, Green Technology Innovation, Financial Performance
PDF Full Text Request
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