摘要
贵州开始重点发展大数据相关产业以来,全省GDP从2015年的1.05万亿元增加至1.78万亿元,而GDP增速近10年位居全国前列,近几年位居全国前3。利用贵州省各部门投入产出表,通过对大数据相关产业划分,利用威弗托马斯指数构建贵州省产业关联网络,社会网络分析方法对贵州省大数据相关产业的网络指标进行分析,分析贵州大数据相关产业对于贵州经济发展的影响,以及大数据相关产业在贵州经济中的地位。结果表明:大数据相关产业在贵州属于高端型产业,有较强经济拉动力,带动其他产业发展的同时也促进大数据相关产业的自身发展,对贵州经济产生促进作用,大数据相关产业的软件和信息服务方面在贵州省产业中处于较为中心的位置,且已经普及到各产业之中。
Since Guizhou began to focus on the development of big data-related industries,its GDP has increased from 1.05 trillion yuan in 2015 to 1.78 trillion yuan,and its GDP growth rate has been among the top three in China in recent years.This paper uses the input-output table of various departments in Guizhou Province,divides the big data-related industries,constructs the industry association network of Guizhou province by using the Weaver Thomas Index,and analyzes the network indicators of the big data-related industries in Guizhou province by using the social network analysis method.The influence of big data-related industries on Guizhou's economic development and the position of big data-related industries in Guizhou's economy are analyzed.The research results show that the big data-related industry is a high-end industry in Guizhou,with strong economic driving force.It not only drives the development of other industries,but also promotes the self-development of the big data-related industries and plays a promoting role in the economy of Guizhou.The software and information services of the big data-related industries occupy a relatively central position in the industry of Guizhou,and have been popularized in various industries.
作者
熊超
王品
肖建雄
陈涛
Xiong Chao;Wang Pin;Xiao Jianxiong;Chen Tao(School of Big Data Statistics,Guizhou University of Finance and Economics,Guiyang,Guizhou 550025,China;Department of Information Engineering,Information Science and Technology College of Guiyang,Guiyang,Guizhou 550025,China)
出处
《绿色科技》
2023年第7期267-271,共5页
Journal of Green Science and Technology
基金
贵州财经大学2021年度在校学生科研项目(编号:2021ZXSY01)。
关键词
投入产出表
威弗托马斯指数
贵州省产业关联网络
网络结构分析
input-output table
Weaver Thomas index
Guizhou industry association network
network structure analysis