<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study delves into the spatial-temporal dynamics of urban digital advancement and its green economic efficacy through methods like kernel density analysis, exploratory spatial data analysis, Gini coefficient, and coefficient of variation. Utilizing entropy weighting and regression analysis, it investigates the influence of digital development on green economic efficiency. Sustainable development performance (EWS), the volume of green patent applications (Lngrd), and other pivotal control variables are incorporated into a regression framework to elucidate the mechanisms by which digital evolution impacts green economic efficiency. Empirical findings reveal a consistent annual growth rate exceeding 5% across six regions, showcasing the steady progress and substantial achievements in digitalization. However, a wide disparity exists within enterprise digitalization levels, with the maximum digital transformation index at 4.996, a minimum of 0.002, and a median of 0.696, indicating an ongoing uneven development. Digital transformation significantly enhances corporate green technological innovation, evidenced by a coefficient of 0.014. Its effect on sustainability performance is notably more substantial in enterprises with higher equity concentrations (0.004) and greater pollution levels (0.005).</p>
</abstract>

This study delves into the spatial-temporal dynamics of urban digital advancement and its green economic efficacy through methods like kernel density analysis, exploratory spatial data analysis, Gini coefficient, and coefficient of variation. Utilizing entropy weighting and regression analysis, it investigates the influence of digital development on green economic efficiency. Sustainable development performance (EWS), the volume of green patent applications (Lngrd), and other pivotal control variables are incorporated into a regression framework to elucidate the mechanisms by which digital evolution impacts green economic efficiency. Empirical findings reveal a consistent annual growth rate exceeding 5% across six regions, showcasing the steady progress and substantial achievements in digitalization. However, a wide disparity exists within enterprise digitalization levels, with the maximum digital transformation index at 4.996, a minimum of 0.002, and a median of 0.696, indicating an ongoing uneven development. Digital transformation significantly enhances corporate green technological innovation, evidenced by a coefficient of 0.014. Its effect on sustainability performance is notably more substantial in enterprises with higher equity concentrations (0.004) and greater pollution levels (0.005).

A sustainable mechanism for digitization to promote green technological innovation in enterprises

Keyi College of Zhejiang Sci-Tech University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study delves into the spatial-temporal dynamics of urban digital advancement and its green economic efficacy through methods like kernel density analysis,...

A sustainable mechanism for digitization to promote green technological innovation in enterprises

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 07. Apr. 2024

Akzeptiert: 18. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1009

Schlüsselwörter
Kernel density analysis, Spatiotemporal evolution characteristics, Entropy weight method, Regression model, Green technology

© 2024 Yamei Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A sustainable mechanism for digitization to promote green technological innovation in enterprises

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 07. Apr. 2024

Akzeptiert: 18. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1009

SchlüsselwörterKernel density analysis, Spatiotemporal evolution characteristics, Entropy weight method, Regression model, Green technology

© 2024 Yamei Zhu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Kernel density analysis, Spatiotemporal evolution characteristics, Entropy weight method, Regression model, Green technology