The Significant Factors in Refuse Classification Based on Correlation and Multiple Regression Analysis

Authors

  • Liqian Zheng

DOI:

https://doi.org/10.54097/hset.v25i.3416

Keywords:

Refuse classification, linear regression, t-test.

Abstract

With the rapid growth of China's economy, great achievements have been made in various constructions. Meanwhile, huge resources and environmental damage have been paid. To reduce pollution and protect environment, the continuous implementation of mandatory waste classification policy in various cities in China, refuse classification has been a practical problem that tightly related to mankind’s live. In this article, it finds the significant factors that affect the effectiveness of the refuse classification as well as the people’s attitude toward it. Based on the questionnaire, people’s will turn positive as the classification commences and guidance takes quite an important role in the process. Refuse classification is a long-term project beneficial to the country and the people. Contemporarily, the government has been advocating waste classification, but it has not been effectively implemented in the actual operation process. On the basis of in-depth analysis of the problems existing in the implementation of garbage classification, key objects are found to form a green and environment-friendly resource conserving society. These results provide some reference for further garbage classification.

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References

Cao C. Economic Analysis of Waste Classification. Green technology, 2022, 24 (4): 237-240245.

Tencent New. I am the first to classify garbage, 2022.1.26, Tencent, Retrieved from: https://new.qq.com/ rain/a/20220126a0bc6300.

Haikou Daily: Tips on Domestic Waste Classification, 2021.11.22, Retrieved from: http://szb.hkwb.net/ szb/html/2021-11/22/content_554319.html.

Wang Zheng. Study on Countermeasures for Urban and Rural Waste Classification. Rural Economy and Technology, 2021, 32 (14): 30-32.

Wei Zhai. Analysis and research on environmental monitoring data based on multiple linear regression. Microcomputer Application, 2022, 38 (3): 186-188, 192.

Chen X. Analysis on influencing factors of air quality index based on linear regression model: taking Dazu District of Chongqing as an example. Environmental Impact Assessment, 2021, 43 (5): 79-82.

Li C, Yu F, Liu J, et al. Comprehensive evaluation of water quality based on multivariate statistical analysis. Journal of Water Resources and Water Engineering, 2006, 17 (4): 36-40.

Zhuang Z, Hu X, Ma X. Multivariate regression model and application of water environment carrying capacity and economic efficiency. Resources and Environment in Arid Areas, 2007, 2l (9): 4l-45.

Zhao M, Li K, Zhang Z. Study on the relationship between red tide characteristic organics and red tide by multiple regression method. Journal of Sun Yat sen University, 2003, 42 (1): 35-38.

Kong B. Discussion on waste classification. China Science and Technology, 2019 (19): 6-7.

Garbage classification is imperative! Sohu, August, 2019, Retrieved from: https://www.sohu.com/a/ 332476564_120055797.

James G, Witten D, Hastie T, et al. An introduction to statistical learning. New York: springer, 2013.

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Published

13-12-2022

How to Cite

Zheng, L. (2022). The Significant Factors in Refuse Classification Based on Correlation and Multiple Regression Analysis. Highlights in Science, Engineering and Technology, 25, 37-45. https://doi.org/10.54097/hset.v25i.3416