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Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060708.

Evaluation of Carbon Emissions Redcution Performance Based on TOPSIS and K-Means Clustering Algorithm


Guanqiao Wang, Heng Zhou, Bohang Lin

Corresponding Author:
Guanqiao Wang

School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China


Carbon emissions are silent but deadly poison that threaten the environment human live. To accurately assess each country's level of attitudes and commitment in reducing carbon emissions, the article establishes an evaluation model of carbon emissions reduction performance. First of all, TOPSIS based on the entropy weight method (EWM) is adopted to score 198 countries. Then, K-Means clustering algorithm is used to classify the countries into five distinct categories: Top, Good, Middle, Underachievers, and Poor. The number of countries in each category is 5, 66, 51, 36, and 40 respectively. To verify the rationality of classification, carbon emissions data from 1970 to 2021 around the world is utilized to conduct Spearman correlation analysis.


Carbon emissions reduction, TOPSIS, K-Means clustering algorithm, Correlation analysis

Cite This Paper

Guanqiao Wang, Heng Zhou, Bohang Lin. Evaluation of Carbon Emissions Redcution Performance Based on TOPSIS and K-Means Clustering Algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 51-58. https://doi.org/10.25236/AJCIS.2023.060708.


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