Reduction of Carbon Footprint of Dynamical System Simulation

Computer simulation, a powerful tool used in various scientific and engineering fields, boasts strong mathematical and data processing capabilities. However, its environmental impact, particularly carbon emissions and contribution to global climate change, has been largely overlooked. This study aims to improve code efficiency in simulation runs to reduce CO2 emissions associated with computational simulation. We apply Horner's method to the NARMAX model, examining three classic chaotic systems: Lorenz, Rossler, and Mackey-Glass. Results reveal a substantial decrease of 27.86% in CO2 emissions, all while maintaining model accuracy. The proposed scheme provides a feasible and sustainable solution for reducing the carbon footprint associated with computational simulation.

I. Introduction

Computer simulation plays a vital role in modern science and technology and has become an important tool for solving complex problems. By using mathematical models and computational methods to simulate real-world phenomena, computer simulation provides a reliable, efficient, and cost-effective means of solving real-world problems [1]. First and foremost, it can provide new insights and understanding for scientific research. By simulating complex phenomena, researchers can better understand the underlying principles and laws behind them. For example, in fields such as physics, chemistry, and biology, computer simulations have successfully revealed many key properties and behaviours at the micro and macro levels [2]. Moreover, computer simulations provide an efficient and reliable means of solving practical problems. In many cases, it can be very difficult, expensive, or even impossible to study phenomena through experiments and observations. At this point, computer simulations emerge as a powerful alternative. In fields such as meteorology, earth sciences, engineering, and medicine, computer simulations have been widely used for practical problem solving and decision support. For example, in the study of Hurricane Katrina, improved mathematical models were used in computer simulations to successfully track the movement of the hurricane, clearly demonstrating the changes in intensity, vortex structure, etc., and ultimately accurately predicting the arrival time and location of the hurricane [3].