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 in the red and blue data points, the system’s capacity to filter noise while still maintaining effi- cient environmental control is shown by the smooth membership functions. A clearly spec- ified fuzzy logic system for climate and environmental control is depicted in this collection of charts. The data sets’ logical grouping, adaptive crossover points, and seamless transitions demonstrate a reliable technique for simulating overlapping or uncertain situations that are frequently present in real-world settings. To establish the fuzzy rules for the climate plant, we have some certain range for climate change model. For the variable identification, we assume with a range of 0 → 1, let SLR and WE stand for sea level rise and wave energy, respectively. Fuzzy rules can be modeled as: • SLR must be high (0.8) and WE must also be high (0.7) for fuzzy carbon climate to be severe (0.75). • In the event where both SLR and WE are low (0.2 and 0.3, respectively), fuzzy carbon cli- mate is minimal (0.25). Under the existing circumstances, the fuzzy inference system combines these criteria to estimate the severity of fuzzy carbon climate. This example shows how T–S models can fore- cast how climatic factors would affect it. These models, which make use of fuzzy logic to rep- resent the intricate interactions between sea level rise, wave energy, and erosion susceptibility, provide light on the possible threats that climate change may pose to coastal communities.

From Eq (1), the global carbon cycle can be seen to operate like a nonlinear augmented system whose outputs depend on time [29,30] which in turn influences its temperature nonlinearly. This system represents the active part of the carbon cycle. The matrix 𝔹 presents all trans- fer rates between system compartments and all mass inputs into it; its vector s accounts for mass inputs to it. A portion of its activity as a component of the carbon cycle is represented by this compartmental mechanism. Therefore, in terms of ocean sediments and geological reser- voirs, the atmosphere, terrestrial biosphere, and ocean are open in [31]. The temperature of the surface functions f are dependent on the state of carbon cycle activities, solar radiation, and other radiative forcing agents ℝ [32]. Some presumptions are listed below before mov- ing on to the core controllability and stability. In Sect 5, the authors expand the method to a worldwide carbon–climate control network, using the fuzzy model presented here as the basis for building interconnected control strategies. Assumption 1. All diagonal elements are negative. Assumption 2. All other variables other than diagonal elements entries are positive. Assumption 3. Sum of all columns are positive. 5 Novel approach for control theory interconnected Global Climate–Carbon Network In this section, the authors apply current control frameworks to the global climate–carbon system (e.g., [36] on congestion control in compartmental networks). Our contribution is

to adapt these concepts to the nonlinear, stochastic, and multi–scale dynamics of the Earth’s carbon cycle, whereas previous work has mostly concentrated on artificial or isolated sys- tems. In particular, the authors combine a compartmental carbon–climate model with fuzzy logic–based membership rules, allowing for adaptive control of carbon flows and greenhouse gas emissions in the face of uncertainty. By creating a closed–loop, feedback–oriented strat- egy that recommends shorter-horizon, adaptive control actions, this fusion enables us to go beyond long–term, scenario–driven projections. Therefore, we have developed completely PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 13/ 30