The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform

The purpose of this study is to explore the impact of dynamic adaptation of corporate innovation culture and market demand on corporate sustainable development and the differences in corporate types and regions. The research sample covers 150 listed companies and 100 non-listed companies in eight industries and three economic regions: the eastern coastal area, the rise of central China, and the development of the western region from 2015 to 2020. The theoretical framework is constructed using the system dynamics model, and the empirical methods of multivariate regression analysis such as ordinary least squares, fixed effect model, and instrumental variable method are used for research. The main findings include that there is a significant positive synergistic relationship between corporate innovation culture and market demand, and there are differences in development models among enterprises of different types and regions. These results have important policy implications and can provide reference for the National Development and Reform Commission, the Ministry of Industry and Information Technology and other relevant departments to formulate policies such as industrial guidance and innovation incentives, help enterprises achieve sustainable development, and enhance the competitiveness of the national industry.

Authors

Institutions

Publication Details

Journal
Energy Informatics
Published
2025-05-08
DOI
https://doi.org/10.1186/s42162-025-00521-9
Citations
2
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
6.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform

Sen Wang
2 citations
Energy Informatics
Energy, Environment, Economic Growth
6.26
article

The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform

Sen Wang
article en
2 citations

Abstract

The purpose of this study is to explore the impact of dynamic adaptation of corporate innovation culture and market demand on corporate sustainable development and the differences in corporate types and regions. The research sample covers 150 listed companies and 100 non-listed companies in eight industries and three economic regions: the eastern coastal area, the rise of central China, and the development of the western region from 2015 to 2020. The theoretical framework is constructed using the system dynamics model, and the empirical methods of multivariate regression analysis such as ordinary least squares, fixed effect model, and instrumental variable method are used for research. The main findings include that there is a significant positive synergistic relationship between corporate innovation culture and market demand, and there are differences in development models among enterprises of different types and regions. These results have important policy implications and can provide reference for the National Development and Reform Commission, the Ministry of Industry and Information Technology and other relevant departments to formulate policies such as industrial guidance and innovation incentives, help enterprises achieve sustainable development, and enhance the competitiveness of the national industry.

Energy InformaticsVol. 8(1)
Henan University of Urban Construction (CN), Henan Polytechnic University (CN)
Decent work and economic growth
Openalex Percentile: Top 4%
Energy, Environment, Economic Growth
6.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.