A Regression Analysis on the Effects of Factors on Plastic Waste Production

Yang, Jun; Zhang, Lingchang; Wei-tao, Wang; Qiu, Zheyong

doi:10.1088/1755-1315/603/1/012013

Cited by 1 publication

(1 citation statement)

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“…This value also suggests that the two independent variables have a statistically significant impact on the dependent variable. The goodness of fit for the regression analysis is supported by the R‐value of 0.975 (Yang J. et al., 2020).…”

Section: Regression Analysis and Hypothesis Testmentioning

confidence: 94%

A discussion to promote global sustainability techniques for motivating plastic waste to minimize carbon footprints

Kalita

Singh

Baruah

et al. 2023

Environmental Quality Mgmt

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This paper emphasizes the significant challenges facing the sustainable environment, including managing and handling plastic waste and reducing carbon footprints. To tackle these challenges, it is essential to identify people's awareness levels of waste handling techniques and their pro‐environmental behaviors. The study focuses on Guwahati, one of the most important cities in Northeast India, which generates increasing plastic waste daily. The paper aims to identify the factors contributing to the reduction of carbon footprints resulting from plastic waste management activities. The data collected from 1326 respondents was analyzed using factor analysis, and the reliability of the dataset was confirmed using Cronbach's alpha (0.84 for the awareness level of waste management techniques and 0.780 for the prevalent mode of plastic waste management techniques). KMO (0.796), Bartlett's test of sphericity (p < 0.001), and determinant score (0.019) were used to assess the data adequacy and factorability of the dataset, and the results were found to be satisfactory. Principal component analysis, exploratory factor analysis, and varimax orthogonal rotation method were used to identify high‐loaded factors by reducing the number of variables. The results showed that two highly loaded components, namely awareness level of waste management techniques (AWMT) and prevalent mode of plastic waste management techniques (PWMT), explained 27.53% and 24.34% of the total variance, respectively, with eigenvalues of 3.35 and 2.88. The regression model confirmed the statistical significance of these factors (p < 0.001) and their relationship with the dependent variable, greenhouse reduction (GHGR). The study proposes that minimizing carbon footprints in the environment can be achieved by focusing on a limited number of controllable factors such as AWMT and PWMT. This study provides valuable insights to the authorities in controlling waste generation and achieving a pollution‐free environment.

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Section: Regression Analysis and Hypothesis Testmentioning

confidence: 94%