Analysis of Factors Influencing Carbon Neutrality Payment Intention: Taking Zhaotong City as an Example

DOI: https://doi.org/10.62517/jse.202411218

Author(s)

Liangyao Sa1, Qiyong Yang2, *, Xiao Wu1, Wei Hu1

Affiliation(s)

1School of Mathematics and Statistics, Zhaotong University, Zhaotong, Yunnan, China 2School of Agriculture and Life Sciences, Zhaotong University, Zhaotong, Yunnan, China *Corresponding Author.

Abstract

In order to understand the influencing factors of Zhaotong residents' willingness to pay for carbon neutrality, and strengthen the idea and behavior of carbon neutrality consumption, this paper uses the method of sampling survey to analyze Zhaotong residents. A total of 522 valid sample data were received in this survey. Through statistical analysis and the establishment of a stepwise regression model, the study found that Zhaotong residents were influenced by environmental protection awareness or people around them, and they were more willing to participate in carbon neutral action. Boys had a higher willingness to pay than girls, which was related to the cognitive and personality characteristics. Age, education, occupation and income have a positive relationship with carbon neutral willingness to pay to a certain extent, that is, the higher the age, education, occupation and income, the stronger the carbon neutral willingness to pay.

Keywords

Carbon Neutrality; Willingness to Pay; Stepwise Regression; Influence Factor

References

[1] Populus Alba, Bai Yu. Literature review of carbon neutral information in China. Library and information guide, 2022, 7 (11): 69-73. [2] Liu Dian. Research on Influencing Factors of individual 'carbon neutral' willingness to pay. Wuhan University, 2019. [3] Wang Dongyan. Indirect use value evaluation and compensation mechanism of tourism resources in Dai Garden community based on CVM method. Yunnan Normal University, 2016. [4] Huo Zhenxing, Wang Minxin, Shi Lina, Tian Xiangyang. Analysis and Prospect of the development of electric vehicles and charging facilities. Rural power management, 2023 (01): 28-30. [5] Li Junhao. Stock price prediction model based on improved multiple linear regression. Science and technology economic market, 2019. [6] He Xiaoqun, Liu Wenqing. Applied regression analysis. Fifth edition. China Renmin University Press, 2019. [7] Jia Junping. Statistics -- Based on spss. Third edition. China Renmin University Press, 2019. [8] Hou Fangyuan. Research on lean cost management of thermal power enterprises under the background of carbon neutrality. Sun Yat sen University, 2021. [9] Zhao Haijiao. Research on regional differences and influencing factors of carbon dioxide emissions in China. Northeast University of Finance and economics, 2016. [10] Wang Duidui. Statistical analysis of influencing factors of carbon dioxide emissions. Yanshan University, 2014.