Author(s)

Zongyang Zhang ¹, Xinyao Lin ², Yuqi Chen ³

Affiliation(s)

¹ College of Finance and Economics, Shanghai Lida University, Shanghai, China
² College of Finance and Economics, Shanghai Lida University, Wenzhou, China
³ College of Finance and Economics, Shanghai Lida University, Taizhou, China

Corresponding Author

Zongyang Zhang

ABSTRACT

Carbon emission reduction is a challenge and an opportunity for all countries in the world. In this paper, the Delphi method is applied to screen the indicators affecting carbon emission, and the first and second level weighting indicators are derived according to Spearman, heat map, and entropy weighting method, and the building life cycle carbon emission indicator system is established. Then, taking Jiangsu Province as an example, the TOPSIS method was used to comprehensively evaluate the carbon emission of buildings in prefecture-level cities in Jiangsu Province, and concluded that the carbon emission of Suzhou City residence is the most reasonable and ideal compared with other regions ten, and verified the reliability of the model. Finally, this paper is based on the construction of LSTM, linear regression, LGBM, decision tree, etc. on the carbon emissions of Jiangsu Province with the guessing model, after comparison, LSTM model performance is more excellent, so we chose the LSTM model for the measurement, and concluded that in 2023, the carbon emissions of Jiangsu Province are expected to be 127,902,900 tons.

KEYWORDS

TOPSIS, LSTM model, carbon emissions, Spearman

CITE THIS PAPER

Zongyang Zhang, Xinyao Lin, Yuqi Chen, Comprehensive Evaluation of Carbon Emissions from Residential Buildings Based on the TOPSIS Method. Environment, Resource and Ecology Journal (2023) Vol. 7: 81-88. DOI: http://dx.doi.org/10.23977/erej.2023.070510.

REFERENCES

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[2] Xu Weichao. A review of correlation coefficient studies [J]. Journal of Guangdong University of Technology, 2012, 29(3):12-17. 
[3] Huiling L I, Xue F, Zihui Z, et al. The Comprehensive Evaluation of Green Residential Buildings Based on FAHP[J].Journal of Shenyang Jianzhu University(Social Science), 2016.
[4] Shih H S, Shyur H J, Lee E S. An extension of TOPSIS for group decision making [J]. Mathematical & Computer Modelling, 2007, 45(7):801-813. 
[5] Gu Zifeng, Zhang Daoyong, Bao Zhidong, et al. Prediction of permeability using gradient boosted decision tree (GBDT)—as an example of a dense sandstone reservoir in the western long 4+5 section of Ji Plateau Oilfield[J]. Advances in Geophysics, 2021, 36(2):10.

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