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 new mathematical control laws, but rather because we have extended and reframed existing approaches for use in climate feedback management, where socioeconomic decision horizons, nonlinearities, and incomplete observability present particular difficulties. In order to address the issue of controlling the global climate–carbon network, we will now enhance the model of our plant to incorporate control signals denoted as u(t) and noise represented as p(t). The original mapping function 𝟋c will now involve the consideration of two additional variables. 𝟋c ∶ = (s0, 𝜈, u, p) ↦ s (7) The expression 𝟋c may be extremely complex, encapsulating the climate mechanism’s non- autonomous, unpredictable, multi–scale, and stochastic properties. We now isolate in u(t) parameters that are controllable, such as emissions from fossil fuels and decreases in emis- sions, carbon storage in biological products and earth’s reservoirs, and radiation from the sun management, even though 𝜈(t) still represents outside factors that affect the system’s behav- ior, such as variations in solar activity or Earth’s rotation. Additionally, the authors distin- guish between model unreliability and process disturbance using the vector–valued measure p(t). We usually only have access to a limited collection of data and measurements y(t) to com- prehend the framework, and we frequently do not know the full state s(t) of the entire system. The findings, which come from a measurement network, provide information on a number

of factors, including gross manufacturing, atmospheric CO2 concentrations, and atmospheric surface temperature. We now provide a mapping between these observations and the state of the system. 𝟋y ∶ = (s, u, 𝜔) ↦ s (8) From the above (Eq 8), the outcome may be influenced by the inputs u(t), which in turn can be influenced by measurement noise 𝜔(t). The combination of 𝟋c and 𝟋y forms our model, which is impacted by the inputs u(t) and output (y(t)) has been observed. Conse- quently, the task that society is confronted with is to ascertain the necessary course of action for u(t) in the coming years, such as for t ∈ [2020, 2100], to mitigate the risk of hazardous warming. Remark 3. To effectively address climate change, certain key variables and parameters must be controlled. These can be grouped into two main categories: Emissions of greenhouse gases and Climate Mitigation Techniques. Here’s a detailed breakdown: • Greenhouse Gas Emissions (GHGs). • Climate Mitigation Techniques. The primary causes of the burning of fossil fuels (power plants, automobiles), industrial oper- ations, deforestation, and changes in land use have an impact on control methods through the use of renewable energy, enhanced energy efficiency, forest restoration, and tree planting. On the other side, carbon sequestration can be described by planting trees and restoring forests to absorb CO2. The effectiveness is rely on the highly in suitable regions, with long-term benefits. Secondly, Soil Carbon Sequestration have capability to enhancing soil management practices to increase

carbon storage, which significant effect on potential, especially in agricultural regions. This fac- tor can further improving the efficiency of energy use in buildings, transportation, and industry. Reduces energy demand and emissions, cost-effective with immediate benefits. Effective control PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 14/ 30