Differential equation model of carbon dioxide emission using functional linear regression

ABSTRACT

Carbon dioxide is one of the major contributors to Global Warming. In the present study, we develop a differential equation to model the carbon dioxide emission data in the atmosphere using functional linear regression approach. In the proposed method, a differential operator is defined as data smoother and we use the penalized least square fitting criteria to smooth the data. The profile error sum of squares is optimized to estimate the differential operators using functional regression. The solution of the developed differential equation estimates and predicts the rate of change of carbon dioxide in the atmosphere at a particular time. We apply the proposed model to fit the emission of carbon dioxide data in the continental United States. Numerical simulations of a number of test cases depict a satisfactory agreement with real data.

KEYWORDS:

• Global warming
• functional regression
• emission
• differential equation
• trends
• simulation

Acknowledgments

The authors thank Dr. Gokarna Aryal and Dr. Nawa Raj Pokhrel for fruitful discussions.

Disclosure statement

No potential conflict of interest was reported by the authors.