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 pollutants, Wu et al. [8] highlighted the potential of geospatial AI in air pollution prediction. In order to maximize the integration of renewable energy sources and minimize emissions, Meng et al. [9] suggested low–carbon dispatching solutions for integrated energy systems. In their evaluation of district sharing plans for heating and cooling using ground source heat pump (GSHP) systems, Zhang et al. [10] highlighted workable ways to reduce carbon emis- sions in urban energy use. In order to show how large-scale interactions affect global climate variability, Zhu et al. [11]looked at the effects of tropical regions on Arctic climate patterns. Lastly, in [12] looked at how cloud computing may decouple urban carbon footprints and provided solutions for sustainable urbanization. When taken as a whole, these works are sig- nificant because they tackle climate change from various but complementary angles, such as urban carbon management, predictive AI, and system optimization. This lays the groundwork for creating integrated frameworks that integrate sustainability objectives, technology, and policy. 1.1 Significance of T-S fuzzy model for Climate Change The use of Takagi–Sugeno Fuzzy Models (T–S models) in climate change study is crucial to comprehending the many facets of this worldwide problem. By addressing nonlinear link- ages within climate systems, these models offer a unique perspective and transcend the limi- tations of traditional linear methodologies. By effectively managing uncertainty, they address

the inherent ambiguity in climate data [13]. It is also essential to incorporate expert infor- mation, which allows climate scientists to offer qualitative insights and increase forecast accuracy. T–S models have a revolutionary effect on a number of applications, from predict- ing short–term climate changes to assessing the potential effects on sectors like agriculture, water resources, and energy. Their ability to facilitate scenario analysis and policy evaluation empowers decision-makers to make educated choices, directing society toward effective miti- gation and adaptation strategies for climate change [14]. In a world grappling with the sever- ity of climate change, Takagi–Sugeno Fuzzy Models are an essential tool that offer insights into the complex interplay of variables and uncertainties affecting our planet’s future [15]. A comprehensive overview of global greenhouse gas (GHG) emissions by sector for 2016– when total emissions reached 49.4 billion tonnes of CO2–is shown in Fig 1’s block diagram. With 38.2% of global emissions, the energy sector is the largest contributor, as the graphic Fig 1. Emissions of greenhouse gases worldwide by sector [16]. https://doi.org/10.1371/journal.pone.0333846.g001 PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 3/ 30