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The Research On The Impact Of Digital Transformation On Environmental Performance Of Listed Companies In Heavy Polluting Industries

Posted on:2024-09-24Degree:MasterType:Thesis
Country:ChinaCandidate:Y H DongFull Text:PDF
GTID:2531306938453274Subject:Business Administration
Abstract/Summary:PDF Full Text Request
At present,our country is in the important node of comprehensive modernization construction,but also a critical period of realizing high quality economic development.As the most active micro subject in the market economy,enterprises realize digital transformation is the only way to promote industrial upgrading and transformation.China’s industrial economy occupies a major part of the market economy,industrial economy also leads the development of heavily polluting industries,has the characteristics of high input,high consumption and high emission,and is the main producer of environmental pollution and resource waste.To better improve the efficiency of the environment,reduce pollution,protect the ecological environment and promote sustainable development of the economy,to realize the digital transformation of heavy polluting industry is imminent.Firstly,this paper analyzes and summarizes domestic and foreign literatures on digital transformation,environmental performance and corporate social responsibility,defines the core concepts of heavy polluting industries,digital transformation and environmental performance,and expounds the impact mechanism of digital transformation on environmental performance based on technological innovation,institutional theory and social responsibility theory.Secondly,the content analysis method based on machine learning is used to measure the degree of digital transformation of listed companies in heavily polluting industries and select appropriate ways to measure environmental performance.A total of 356 samples of listed companies in heavily polluting industries in Hunan,Hubei,Anhui and Jiangxi provinces from 2016 to 2020 were selected as research objects.Research hypotheses were proposed,regression models were built and data of other variables were collected and empirical analysis was carried out.Using the measurement software Stata15,the dynamic panel regression model generalized moment estimation(GMM)was used to analyze the impact of digital transformation on environmental performance and the moderating role of corporate social responsibility(CSR)in the impact of digital transformation on environmental performance.Finally,the robustness of the results was tested,and the influence mechanism of digital transformation on environmental performance and the performance under heterogeneous environmental regulations were tested.According to the empirical analysis results,it is found that:(1)the digital transformation of listed companies in heavily polluting industries can significantly promote the improvement of environmental performance,and it has a positive effect on environmental performance through technological innovation mechanism.(2)As an important moderating variable of the impact of digital transformation on environmental performance,CSR enhanced the positive effect of digital transformation on environmental performance of listed companies in heavily polluting industries.(3)There are significant differences in the impact of the digital transformation of listed companies in heavily polluting industries on the environmental performance of different intensity of environmental regulations.Among them,the digital transformation of listed companies in heavily polluting industries in regions with stronger environmental regulations significantly improves the environmental performance.Based on the above research findings,relevant policy suggestions are proposed to provide directions and ideas for further promoting the digital transformation of listed companies in heavily polluting industries and realizing environmentally sustainable development.
Keywords/Search Tags:Heavily polluting industry, Digital transformation, Environmental performance, Corporate social responsibility
PDF Full Text Request
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