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<p>The structure of energy consumption and reducing the carbon footprint has become an important issue in the field of carbon and energy conservation. This paper adopts spatial econometric Analysis to construct a framework for analyzing the influencing factors of carbon emissions based on the STIRPAT model. It applies the K-means algorithm to effectively cluster and classify the energy consumption of different regions. Then, the article analyzed these clustering results in depth using the Kaya constant equation to calculate the carbon emissions of each area. The results of the carbon footprint analysis reveal that the Gini coefficient of carbon emissions in the eastern region peaked at 0.352 in 2014, while decreasing to a low of 0.284 in 2019. the western and central areas have the highest Gini coefficients of carbon emissions at 0.271 and 0.248, respectively. furthermore, from 2015 to 2022, the ecological pressure on the carbon footprint of the whole industry has always remained at 3.033 above, reaching a historical high of 3.433 in 2022.The application of this paper not only helps to solve the problems in the existing carbon footprint evaluation methods, but also provides a scientific basis for more effective management and reduction of carbon emission.</p>
</abstract>

The structure of energy consumption and reducing the carbon footprint has become an important issue in the field of carbon and energy conservation. This paper adopts spatial econometric Analysis to construct a framework for analyzing the influencing factors of carbon emissions based on the STIRPAT model. It applies the K-means algorithm to effectively cluster and classify the energy consumption of different regions. Then, the article analyzed these clustering results in depth using the Kaya constant equation to calculate the carbon emissions of each area. The results of the carbon footprint analysis reveal that the Gini coefficient of carbon emissions in the eastern region peaked at 0.352 in 2014, while decreasing to a low of 0.284 in 2019. the western and central areas have the highest Gini coefficients of carbon emissions at 0.271 and 0.248, respectively. furthermore, from 2015 to 2022, the ecological pressure on the carbon footprint of the whole industry has always remained at 3.033 above, reaching a historical high of 3.433 in 2022.The application of this paper not only helps to solve the problems in the existing carbon footprint evaluation methods, but also provides a scientific basis for more effective management and reduction of carbon emission.

Regional spatial econometric Analysis of carbon footprint of energy consumption based on clustering algorithm

Applied Mathematics and Nonlinear Sciences

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

The structure of energy consumption and reducing the carbon footprint has become an important issue in the field of carbon and energy conservation. This paper...

Regional spatial econometric Analysis of carbon footprint of energy consumption based on clustering algorithm

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 10 sty 2024

Przyjęty: 20 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0461

Słowa kluczowe
AT model, K-means clustering algorithm, Regional spatial measurement analysis

© 2024 Shuo Yang et al., published by Sciendo

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

Regional spatial econometric Analysis of carbon footprint of energy consumption based on clustering algorithm

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 10 sty 2024

Przyjęty: 20 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0461

Słowa kluczoweAT model, K-means clustering algorithm, Regional spatial measurement analysis

© 2024 Shuo Yang et al., published by Sciendo

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

Słowa kluczowe
AT model, K-means clustering algorithm, Regional spatial measurement analysis