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

<p>The digital economy has become an important driving force for green energy consumption, and its impact has covered rural areas. This paper establishes a comprehensive evaluation model of green energy consumption based on group principal component analysis, and at the same time establishes evaluation indicators of the green energy consumption level of rural residents to explore the impact of the development of the digital economy on green energy consumption. Based on the comprehensive evaluation model and evaluation indexes, the green energy consumption level of rural residents in the “2+26” cities and the three major economic zones is analyzed. The rural areas with higher green energy consumption levels by 2022 include Tangshan, Jining, and Kaifeng, with 0.794, 0.788, and 0.852, respectively. The willingness of rural residents in the three major economic zones to consume green energy is in line with the trend of the degree of development of the digital economy, in which the willingness to consume green energy is highest among rural residents under the age of 20 in the eastern region, with an average of 4.81. At the same time, the development of the digital economy has a direct effect on the level of consumption of green energy. The regression coefficient of the direct effect on the level of green energy consumption is 0.289. Overall, the development of the digital economy has a greater promotion effect on the level and willingness of rural residents to consume green energy.</p>
</abstract>

The digital economy has become an important driving force for green energy consumption, and its impact has covered rural areas. This paper establishes a comprehensive evaluation model of green energy consumption based on group principal component analysis, and at the same time establishes evaluation indicators of the green energy consumption level of rural residents to explore the impact of the development of the digital economy on green energy consumption. Based on the comprehensive evaluation model and evaluation indexes, the green energy consumption level of rural residents in the “2+26” cities and the three major economic zones is analyzed. The rural areas with higher green energy consumption levels by 2022 include Tangshan, Jining, and Kaifeng, with 0.794, 0.788, and 0.852, respectively. The willingness of rural residents in the three major economic zones to consume green energy is in line with the trend of the degree of development of the digital economy, in which the willingness to consume green energy is highest among rural residents under the age of 20 in the eastern region, with an average of 4.81. At the same time, the development of the digital economy has a direct effect on the level of consumption of green energy. The regression coefficient of the direct effect on the level of green energy consumption is 0.289. Overall, the development of the digital economy has a greater promotion effect on the level and willingness of rural residents to consume green energy.

Exploring the Impact of Digital Economic Development on Green Energy Consumption of Rural Residents Using Principal Component Analysis Methods

Applied Mathematics and Nonlinear Sciences

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

The digital economy has become an important driving force for green energy consumption, and its impact has covered rural areas. This paper establishes a...

Exploring the Impact of Digital Economic Development on Green Energy Consumption of Rural Residents Using Principal Component Analysis Methods

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 25 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0187

Parole chiave
Principal component analysis, Comprehensive evaluation, Evaluation index, Green energy consumption

© 2024 Jing An, published by Sciendo

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

Exploring the Impact of Digital Economic Development on Green Energy Consumption of Rural Residents Using Principal Component Analysis Methods

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 18 dic 2023

Accettato: 25 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0187

Parole chiavePrincipal component analysis, Comprehensive evaluation, Evaluation index, Green energy consumption

© 2024 Jing An, published by Sciendo

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

Parole chiave
Principal component analysis, Comprehensive evaluation, Evaluation index, Green energy consumption