Prediction of Digital Economy Development Levels in Urban Cities Based on the GCSA-GM(1,N) Model

被引:0
作者
Wu, Chengxuan [1 ]
Tian, Cheng [1 ]
Wang, Fang [1 ,2 ]
Cheng, Wenxin [1 ]
机构
[1] Xidian Univ, Sch Econ & Management, Xian 710126, Peoples R China
[2] Shaanxi Soft Sci Inst Informatizat & Digital Econ, Xian 710126, Peoples R China
基金
中国国家自然科学基金;
关键词
Digital economy; Geometric causality strength analysis; GM(1; N);
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Based on the digital economy index (DEI) and Technological Innovation, Industrial Structure, GDP and Openness to the Development Index data of 15 sub-provincial cities from 2017 to 2021, we construct a framework to predict the development potential of the urban digital economy and analyse the spatial evolution trend under the 'small data' scenario using geometric causal strength analysis GM(1,N)and the gravity center model. The empirical analysis reveals that,15 sub-provincial cities, at least one of the influencing factors has acausal relationship with the urban DEI that is greater than 0.5. The average forecast error of the GM(1,N) model based oncausalitystrengthin15 sub-provincial cities is less than1%in 2022. This reflects that four influencing factors can be used as an effective indicator to measure the level of digital economic development. The forecast results also indicate that the digital economy center of China's sub-provincial cities will evolve from north to south and from east to west in 2022-2025. Finally, this study presents suggestions from three aspects: Strengthening technological innovation, promoting industrial digital transformation and upgrading, and strengthening cross-regional cooperation and exchanges
引用
收藏
页数:119
相关论文
共 31 条
  • [11] Liu SF, 2010, UNDERST COMPLEX SYST, P1
  • [12] Advance in grey system theory and applications in science and engineering
    Liu, Sifeng
    Tao, Yong
    Xie, Naiming
    Tao, Liangyan
    Hu, Mingli
    [J]. GREY SYSTEMS-THEORY AND APPLICATION, 2022, 12 (04) : 804 - 823
  • [13] How does digital economy affect green total factor productivity? Evidence from China
    Lyu, Yanwei
    Wang, Wenqiang
    Wu, You
    Zhang, Jinning
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 857
  • [14] Osmanbegovic E, 2019, INT SCI C ECON INTEG, P189
  • [15] Wang F, 2023, J GREY SYST-UK, V35, P49
  • [16] Assessing the digital economy and its carbon-mitigation effects: The case of China
    Wang, Jianda
    Dong, Kangyin
    Dong, Xiucheng
    Taghizadeh-Hesary, Farhad
    [J]. ENERGY ECONOMICS, 2022, 113
  • [17] Digital economy, entrepreneurship and energy efficiency
    Wang, Lianghu
    Shao, Jun
    [J]. ENERGY, 2023, 269
  • [18] Digital economy and urban low-carbon sustainable development: the role of innovation factor mobility in China
    Wang, Lulu
    Chen, Leyi
    Li, Yushuang
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (32) : 48539 - 48557
  • [19] Is Digital Adoption the way forward to Curb Energy Poverty?
    Wang, Ping
    Han, Wei
    Rizvi, Syed Kumail Abbas
    Naqvi, Bushra
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2022, 180
  • [20] Digital economy, industrial structure upgrading, and residents' consumption: Empirical evidence from prefecture-level cities in China
    Wang, Yizi
    Li, Lanyi
    [J]. INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2024, 92 : 1045 - 1058