Using Holt-Winters Model and Regression Forecast Models to Describe and Predict the Concentration Level of Carbon Dioxide

DOI

10.2991/978-94-6463-516-4_3How to use a DOI?

Keywords

ternary linear regression model; Holt-winters model; simple linear regression model; carbon dioxide; global warming

Abstract

In this paper, we generally focus on analyzing the concentration of carbon dioxide from the past and predict it in the future. And by using the carbon dioxide concentration data from previous years, we established Holt-winter model, linear regression model and ternary regression model to predict the future trends. Then, we find out that the concentration level of carbon dioxide has a rapid increasing tendency in the future, and in 2050 the concentration will reach 505.23 parts per million (ppm) but not 685 ppm based on the ternary regression model, the most accurate model that was used in this paper. Also, we find out that the concentration of CO2 will reach 740 ppm in 2100. In fact, the safety range of the concentration of carbon dioxide is 400 ppm to 700 ppm. This means that governments must take actions and draw up a plan in order to fight against with global warming, or the global warming will become more and more serious which will lead to more severe ecosystem crisis.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ruolin Peng
AU  - Shichen Huang
PY  - 2024
DA  - 2024/09/17
TI  - Using Holt-Winters Model and Regression Forecast Models to Describe and Predict the Concentration Level of Carbon Dioxide
BT  - Proceedings of the 2024 5th International Conference on Urban Construction and Management Engineering (ICUCME 2024)
PB  - Atlantis Press
SP  - 14
EP  - 27
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-516-4_3
DO  - 10.2991/978-94-6463-516-4_3
ID  - Peng2024
ER  -