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 Remark 1. The climate model presented here is the particular application area in which the generic T–S fuzzy methodology of Sect 2 is used; it is not separate from the fuzzy identification framework. By approximating the temperature feedback functions f(⋅) and nonlinear transfer rates (matrix 𝔹) in Eq (1), the fuzzy sub–models integrate data–driven fuzzy identification into the climate control environment. 4 New fuzzy model for Global Climate Model We model the global carbon–climate plant as a nonlinear augmented system (1), building on the fuzzy identification concepts discussed in Sect 2. Fuzzy rules convert qualitative climate knowledge (such as ”high CO2 with high temperature → accelerated warming”) into quantita- tive model structure, and T–S fuzzy sub–models derived from available data capture the com- partmental dynamics (carbon exchange among atmosphere, biosphere, and ocean). Our focus here is still on carbon–climate dynamics, but the framework is broad enough to be applied to other environmental problems. The above model (1) illustrates how Fuzzy Models can be used to predict changes in climate environment due to rising sea levels. The main steps have been listed below: Scenario: Fuzzy carbon climate prediction under sea level rise in the following way: Step 1 (Variable Identification): Sea level rise (SLR) and wave energy (WE) are two climatic factors to consider. Define the terms ”low,” ”medium,” and ”high,” as well as their membership criteria, for each.

Step 2 (Fuzzy Rules): Specify the linguistic effects of SLR and WE on fuzzy carbon climate by developing fuzzy rules. Consider this: • If both SLR and WE are high, then there will be severe fuzzy carbon climate. • If both SLR and WE are low, then there will be little fuzzy carbon climate. Step 3 (Fuzzy Inference System): In order to forecast the severity of fuzzy carbon climate based on the current sea level rise and wave energy conditions, apply the fuzzy rules. The model predicts severe fuzzy carbon climate (0.65), for instance, if SLR is medium (0.6) and WE is high (0.7). Step 4 (Defuzzification): From the fuzzy output, generate a precise value that, under the given circumstances, indicates the extent of fuzzy carbon climate. This can be achieved by taking a weighted average of the fuzzy rule’s output values. 4.1 From compartmental dynamics to a T–S fuzzy model Let 𝜉(t) = ⎛ ⎜ ⎝ 𝜉A(t) 𝜉T(t) 𝜉S(t) ⎞ ⎟ ⎠ T , denote the carbon stock in atmosphere, terrestrial biosphere, and surface ocean (PgC). The Eqs (12)–(9) give: ̇ 𝜉(t) = 𝔹(𝜉, t) 𝜉(t) + m(t), m(t) = [𝜆F→A(t), 0, 𝜆D→S]⊤, (2) with entries of 𝔹(𝜉, t) defined by the fluxes 𝜆X→Y (Eq (12)) with parameters 𝛾1, 𝛾2. We define the scheduling vector: z(t) = [z1(t), z2(t), z3(t)] = [ 𝜉A(t) 700 , 𝜉T(t) 3000 , 𝜉S(t) 1000 ] , (3) where augment z(t) with exogenous radiative forcing. PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 9/ 30