Content area
Abstract
The deteriorating environmental problems worldwide pose a serious challenge to human society and ecosystems, especially for enterprises that rely on natural resources. This article studies the construction of a green technology model for enterprise digital economy. Firstly, green technology innovation is combined with digital economy to construct a driving model for enterprise digital transformation. Then, regional green technology innovation capabilities are defined from three dimensions: input, output, and support capacity. Finally, advanced statistical methods such as level correlation and coefficient of variation analysis are used to rigorously evaluate and refine the green technology innovation indicator set, and a comprehensive evaluation index system for enterprise digital transformation is constructed. The experimental results show that the cumulative variance contribution rate of the four common factors before 2021 was 89.95%; the cumulative variance contribution rate of the four common factors before 2022 was 88.77%; the cumulative variance contribution rate of the four common factors before 2023 was 86.60%. Every year, the Man–machine Language index of the total GTI fluctuated between 0.980 and 1.128, with an average increase of 2.8%.
Article Highlights
Integration of Green Technology and Digital Economy: The paper emphasizes the need for a comprehensive model that integrates green technology innovation (GTI) with the digital economy, highlighting its significance in addressing environmental challenges while promoting economic growth in developed and developing countries.
Framework for Regional GTI Ability: A theoretical framework is established, defining regional GTI ability based on the input, output, and support capabilities. This framework serves as a foundation for assessing and enhancing technological innovation at the regional level, tailored to specific environmental contexts.
Quantitative Analysis of GTI Indicators: This paper uses advanced statistical methods, such as rank correlation and coefficient of variation analysis, to rigorously evaluate and refine the GTI indicator set. This article estimates the correlation and volatility between various GTI indicators by constructing a feature variance matrix, and identifies key variables that have a significant impact on GTI performance. Based on this, factor analysis was conducted to reduce data dimensionality, extract main components, and a dynamic panel Tobit model combined with CLAD estimation method was adopted. Ensure the accuracy and reliability of the results. The findings reveal consistent trends in GTI performance over recent years, underscoring the effectiveness of the proposed model in fostering sustainable economic development.
Research limitations: This article also has limitations, as the imbalance of digital infrastructure can lead to some regions not fully utilizing the opportunities of digital economic development, thereby restricting its promotion. In addition, there are also issues of data privacy, security, and high investment in technology, economy, and other aspects, which have become the main obstacles to the development of many small and medium-sized enterprises.