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 of these variables and parameters involves a combination of reducing GHG emissions, enhanc- ing carbon sequestration, and exploring innovative climate mitigation techniques. Each strategy’s effectiveness can vary based on geographical, technological, and economic factors. A compre- hensive and integrated approach, supported by strong policies and international cooperation, is essential to achieve significant climate mitigation outcomes. 5.1 Designing control systems involves the process of creating control laws Measurements are mapped to control actions through a control law 𝕂. Control laws are typ- ically designed by tuning parameters or design structures using properties or system behav- ior. The majority of previous studies of closed-loop control have focused on controlling cli- mate or socio-ecological systems [33,34]. In all of these studies, linear or linearized models are employed and/or linear feedback control methods are utilized, with proportional–integral controllers and their modifications being the most commonly used. Many applications have been successfully implemented with PI controllers. The significant limitation of this method is the requirement that the system remain near the point at which it has been linearized, i.e., the point where the nonlinear system was linearized. Climate change, however, is by its very nature nonlinear. It is possible to exploit nonlinear interactions for control by linearizing

around a specific operating point, eliminating critical nonlinear interactions, which are criti- cal for prediction. It is also important to note that disturbances and uncertainties are likely to cause significant changes to the system and render the controller invalid as a result. A model– based control approach appears to be promising and can have a greater impact given the exis- tence of realistic climate models. Using a simple model as part of the model–based control strategy, [35] provide a good example of this concept. In addition to minimization or maxi- mization of the control objective, The control work as established by the control rule 𝕂 itself may likewise be penalized by u(t). When using a compartmentalized nonlinear closed–loop controller, [36] showed how to prevent mass congestion in compartmental networks automat- ically. To maximize the network’s throughput, reservoirs that temporarily store carbon can be designed and implemented to maximize the carbon management strategy. The controller can figure out how much mass can enter the network and calculate mass allowances. We think this control mechanism can be helpful as a model worldwide system for reducing climate change and the management of carbon. 6 Simulation test scenarios By contrasting the framework proposed by [36], which attempts to prevent congestion in compartmental networks, the authors demonstrate the effectiveness of closed–loop control in this example. The idea behind global CO2 cycling is that has been transferred across several

compartments before being released into the deep ocean. Our goal is to prevent the atmo- spheric compartment from overflowing when we release carbon from fossil fuels. The sur- face of the ocean and grassland biosphere exchange carbon with the atmosphere, but they are only able to capture and digest a certain quantity of it. Fig 11 shows the behavior of net fluxes between the deep seafloor and the surface, allowing us to develop a controller that will indicate the amount of carbon from fossil fuels that may enter the atmosphere. In this case, temperature feedback is not taken into account for convenience. This paper presents a modified version of [37,38] simple nonlinear global carbon cycle model. This can be seen in Fig 11 as the dashed rectangle within the green box, which repre- sents the following three carbon components: PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 15/ 30