Optimizing carbon emission reduction strategies through AI-enabled modeling: paving the way to a sustainable future

Global warming caused by rising carbon emissions is a pressing issue that demands immediate action. In this paper, we delve into the topic of carbon emission reduction solutions, examining modeling techniques that can effectively address this global crisis. By exploring innovative approaches such as artificial intelligence (AI) and intelligent systems, we aim to propose robust and efficient strategies for curbing carbon emissions and mitigating the impact of climate change. This discussion serves as a platform for reevaluating existing solutions and introducing novel ideas to combat global warming. In this paper, we regard the global temperature as a system with input and output. By set up its dynamic model and formula, finding the relation between the temperature and the influencing elements. Numerical methods are given to solve the dynamical equation. Using our model and algorithms, we can easily calculate the reason of increasing global warming. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature. The statistical data of global carbon dioxide emission, estimates the total amount, accumulative amount, per capita amount of carbon emission, and predicts the future carbon emission. Carbon emission is an aftermath of global industrialization. Human being are responsible for reducing carbon emission. The plan for climate changes shall abide by a principle of "common but with difference in responsibility". Besides increasing energy efficiency, other renewable energies, low-carbon technologies, a global specific policy shall be established with a clear and measurable goal to stabilize global atmospheric carbon.