Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2022, 5(9); doi: 10.25236/AJCIS.2022.050912.

Research on Carbon Emission Response Strategy for Zoning Area Based on Multiple Linear Regression

Author(s)

Wanyue Li, Shirui Liu

Corresponding Author:
Wanyue Li
Affiliation(s)

School of Economics and Management, Shanghai University of Sport, Shanghai, China

Abstract

In order to achieve Chinese “Carbon Peaking and Carbon Neutrality Goals” and provide strategies for global carbon emissions, this study explored the relationship between regional carbon dioxide emission concentration and various factors by establishing a multiple linear regression model. The result showed that the concentration of carbon emissions was estimated by an optimal combination of factors affecting carbon emissions, This is more effective and practical than estimating with only one influencing factor. Among them, the important factors affecting carbon emissions are factory density, vehicle exhaust emissions, vegetation coverage and population density. Finally, it makes a reasonable response strategy to the factors of high impact on carbon emissions, and hopes to promote the development of our low carbon transformation and sustainable development, which has outstanding strategic value and positive practical significance.

Keywords

Carbon Emissions, Carbon Peaking and Carbon Neutrality Goals, Multiple Linear Regression, Zoned Area, Coping Strategies

Cite This Paper

Wanyue Li, Shirui Liu. Research on Carbon Emission Response Strategy for Zoning Area Based on Multiple Linear Regression. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 9: 77-81. https://doi.org/10.25236/AJCIS.2022.050912.

References

[1] Xu Zheng, Zuo Cheng-ji, Ding Shou-hai. Carbon Peak,Carbon Neutrality Empowers High-Quality Development: Internal Logic and Realization Path [J]. Economist, 2021(11):62-71.

[2] Feng Yu, Ma Junteng, Zhao Tingting. Analysis and Research on Agricultural Carbon Emission Digestion and Its Restricting Factors along the Yellow River Area Based on Factor Analysis Method [J]. Shanxi Agricultural Economy, 2022(03):130-132.

[3] Yang Renchao, Pu Huilan. Analysis of Forest Tourism Carbon Perception Characteristics of Tourists Based on Factor Analysis and Cluster Analysis [J]. Tourism Overview, 2020(20):29-31.

[4] He Xiaogang, Zhang Yaohui. Influence Factors and Environmental Kuznets Curve Relink Effect of Chinese Industry's Carbon Dioxide Emission-Empirical Research Based on STIRPAT Model with Industrial Dynamic Panel Data [J]. China Industrial Economics, 2012(01):26-35.

[5] Thio, Ellen, Tan, MeiXuen, Li, Liang, Salman, Muhammad, Long, Xingle, Sun, Huaping, Zhu, Bangzhu. Correction to: The estimation of influencing factors for carbon emissions based on EKC hypothesis and STIRPAT model: Evidence from top 10 countries [J]. Environment, Development and Sustainability, 2022(prepublish).

[6] YE Hanhan WANG Xianhua WU Shichao, et al. Atmospheric CO2 Retrieval Method for Satellite Observations of Greenhouse Gases Monitoring Instrument on GF-5 [J]. Journal of Atmospheric and Environmental Optics, 2021,16(03):231-238.

[7] Global 30 m impervious surface dynamic Remote Sensing products (1985-2020) website : https://zenodo.org/record/5220816#.YsZk5IRBw2w

[8] Xiaobing Li, Hong Wang, Huiling Long, Dandan Wei, Yun Bao. A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture[J]. International Journal of Remote Sensing, 2012,33(11).

[9] Halder, Tanmoy, Chakraborty, Debasish, Pal, Ramen, Sarkar, Sunita, Mukhopadhyay, Somnath, Roy, Nishtha, Karforma, Sunil. A hybrid approach for water body identification from satellite images using NDWI mapping and histogram of gradients[J]. Innovations in Systems and Software Engineering, 2021(prepublish).