Design of global climate control based on fuzzy systems with concept of carbon emissions

PLOS One Design of global climate control based on fuzzy systems 𝔹(ℂ, t) is a state– and time–dependent compartmental matrix: ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ –𝜉–1 A (𝜆A→T + 𝜆A→S) 𝜉–1 T 𝜆T→A 𝜉–1 S 𝜆S→A 𝜉–1 A 𝜆A→T –𝜉–1 T 𝜆T→A 0 𝜉–1 A 𝜆A→S 0 –𝜉–1 S (𝜆S→A + 𝜆S→D) ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (11) and m(t) ∶= (𝜆F→A(t), 0, 𝜆D→S)T , and then the plant fits into (1) without considering temper- ature. From the global climate carbon emissions theory, the authors presents the new fuzzy rules in Table 1. To describe the nature A compartmental nonlinear time–dependent system can be stated as follows: {̇ 𝜉(t) = 𝔹(ℂ, t)𝜉(t) + m(t), t > t0, 𝜉 (t0) = 𝜉0 (12) From the equilibrium point of the uncontrolled system (u(t) = 1), we ran it at the point 𝜉0 = (700, 3000, 1000)T from 1765 forward until 1900, at which point the control function was turned on, u(t) = 𝜓(𝜅(t)). The global carbon cycle was assumed to be controlled by humanity after 1900, as all fossil fuel emissions were allowed until this date. After selecting the fuzzy rules from the Sect 4, the effectiveness of our proposed algorithms can be found in Figs 12, 13, and 14 respectively. In Fig 12, the response of atmospheric (𝜉A) with and without fuzzy rules can be analyzed. While in Figs 13 and 14 presented the response of terrestrial biosphere (𝜉T) and surface ocean (𝜉S) with and without fuzzy rules. Fuzzy logic and fuzzy rules play a significant role in modeling and understanding complex systems like global climate. Here are the key aspects of how fuzzy rules contribute to global climate stud-

ies, e.g., Handling Uncertainty and Ambiguity, Modeling Complex Relationships, Integrating Diverse Data Sources, Improving Climate Prediction Models, Adaptive Systems, and Risk Assess- ment and Decision Support. However, there are some practical examples that can be effective by using our new proposed algorithm in Climate Change Impact Studies, Weather Forecast- ing, and Environmental Monitoring. In summary, fuzzy rules are invaluable in global climate studies for their ability to handle complexity, integrate diverse data, and improve predictive models, ultimately aiding in more effective climate management and decision–making. 6.1 Prediction analysis of external/internal air temperature Two figures are showing the predicted vs. measured air temperature data and the associated prediction error are included in Figs 15 and 16. The fact that the two lines closely follow one Table 1. Fuzzy rules for global climate system. Rule No. IF Condition 1 IF Condition 2 THEN Result 1 Temperature is High CO2 is High Chance of High Temperature 2 Temperature is Low CO2 is Low Chance of High Snowfall 3 Temperature is High Terrestrial biosphere (𝜉T) is High Increase the risk of Species Extinction 4 Temperature is Low Terrestrial biosphere (𝜉T) is Low Decrease the risk of Species Extinction 5 Sea surface is High Terrestrial biosphere (𝜉T) is Low Increase in Biodiversity 6 Sea surface is Low Terrestrial biosphere (𝜉T) is High Decrease in Biodiversity 7 Wind speed is High Surface ocean (𝜉S) is Large Structural damage in Buildings & Trees

8 Wind speed is Low Surface ocean (𝜉S) is Low No effect 9 Wind speed is High Climate risk High Increase Energy sources 10 Wind speed is Low Climate risk Low Decrease Power output https://doi.org/10.1371/journal.pone.0333846.t001 PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 18/ 30