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Research on Smart Forest with Less Carbon Using Multiple Linear Regression Models and TOPSIS Entropy

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DOI: 10.23977/erej.2022.060210 | Downloads: 4 | Views: 66


Wenjing Zhao 1, Hanzhi Zhang 1


1 School of business, Xi 'an International Studies University, Xi 'an 710000, China

Corresponding Author

Wenjing Zhao


In the era of rapid development of the Internet, climate change is an increasingly serious threat to carbon dioxide emissions and fixed amounts and human health, and its governance problems need to be solved urgently. Based on forest data from 193 countries by the World Bank, we first developed carbon sequestration models that determine the amount of carbon dioxide sequestration in forests. Then with K-Means++ and system clustering models, we combined the comprehensive scores of the three major indicators of nature, economy and society under the TOPSIS entropy rights law, SPSS software and the decision-making evaluation model of integrated forest management to evaluate the use value of forests. Finally, combining the positive impact of harvesting on forest management and the macro and micro impacts under the comprehensive forest management plan, we present the research results of selective logging to promote carbon sequestration at reasonable intervals, which makes the study of forests and carbon sequestration more meaningful.


Multiple linear regression models, TOPSIS Entropy, K-Means++ clustering model, Carbon sequestration model, Decision-making evaluation model of integrated forest management


Wenjing Zhao, Hanzhi Zhang, Research on Smart Forest with Less Carbon Using Multiple Linear Regression Models and TOPSIS Entropy. Environment, Resource and Ecology Journal (2022) Vol. 6: 83-89. DOI:


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