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 applied for control Input for Feedback Mechanisms; System Identification and Observability, and Policy Evaluation and Adjustment. The outputs from the system, such as measurements of atmospheric CO2 concentrations and surface temperatures, are crucial for closed–loop control systems. These outputs help in adjusting control actions based on real–time data, allowing for adaptive management of the carbon–climate system. Model identification, which entails build- ing mathematical models from observed data, depends on outputs. Understanding the dynamics of the carbon–climate system and evaluating its observability–the capacity to deduce the system’s state from sparse observations are aided by this approach. Monitoring plant’s outputs offer vital data for assessing how well–functioning the policies in place are. Data sharing is applicable to this article as no data–sets were generated or analyzed during the current study. In case of ethical statement, both human and/or animal investigations were not approved for this research. 7 Conclusions It is possible to model Carbon–climate control as a closed–loop issue that continuously modi- fies the system’s activity in response to measurements and uncertainty. In this paper, we pro- vide a new mathematical description of the carbon–climate structure based on fuzzy systems in order to formalize the closed–loop control problem. A compartmentalized representation of the global carbon structure can be used to achieve particular mathematical properties such

as dynamic stability by utilizing the compartmental structure of the system of equations. With our simulation results, the authors demonstrated that fuel requirement and the ability of the plant to transport geological reservoirs for carbon, such as the deep sea, could be effectively managed to manage the global carbon cycle. By analyzing the demand and internal dynam- ics of the system, we obtained a mathematical rule for prescribing actions. This rule can be applied to adaptive carbon cycle management. Unlike scenario–based emission route pre- scriptions, this technique could be useful for policy decisions made on shorter time horizons than those imposed by long–term scenarios. 7.1 Future direction Fuzzy logic is used to model and control the intricacies and uncertainties in climate systems in order to control the global climate based on fuzzy systems with carbon emissions. The complexity and uncertainty of climate models, data quality and availability, scalability issues, interpretability and transparency, integration with other models, and adaptability and evo- lution are some of the limitations of this approach, despite its potential benefits. Fuzzy sys- tems are a useful tool for handling the uncertainties and complexity of climate modeling, but they have a lot of drawbacks. To overcome these constraints, fuzzy logic must be com- bined with other modeling techniques, data quality must be improved, and computer power must be increased. Collaboration between scientists and policymakers is necessary to cre-

ate resilient, open, and flexible systems that can efficiently guide climate action and decision– making. Furthermore, our recently proposed fuzzy approach [39] can be used to improve a few more directions. Additionally, the following T–S models can be used to further improve the efficiency of Carbon–climate control: • Hybrid Approaches: Consider merging state–of–the–art machine learning techniques with T–S models to increase model adaptability and forecast accuracy. • Localized Adaptation: Considering the need for localized T–S models that account for regional climate variations, guaranteeing precise forecasts appropriate for specific locations. PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 27/ 30