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 makes clear. Energy consumption in buildings (14.2%), and transportation (7.2%), and are the main sources of energy in this sector, with residential and commercial buildings account- ing for the majority of contributions. Livestock, deforestation, and soil management are the main drivers of the 12.4% contribution from the Agriculture, Forestry & Land Use sector. Waste (1.2%) and Industry (5.2%), on the other hand, are lesser but nonetheless important sources. The graphic also shows possible ways to improve resource efficiency through dig- ital transformation, which makes it possible for improved automation, optimization, and monitoring. GHG emissions worldwide by sector (derived from [Ref [16]). The baseline data supporting the fuzzy control method used in this investigation is shown in this picture. The urgent need for adaptive control strategies–which are further explored in Sects 3–6, which is demonstrated by the significant contributions from the energy, building, and transporta- tion industries. In order to improve sustainability and lower emissions, the green–highlighted section describes significant research applications such as smart buildings, intelligent ware- houses, and wireless industrial automation. Industries may fix inefficiencies, reduce their environmental effect, and aid in the fight against climate change by incorporating these cutting–edge solutions. For more comprehensive review, readers can refer the references [17, 18].

Climate mechanism and global warming are complicated environmental issues brought on by both natural and human–caused causes. The interrelated causes, feedback systems, and ensuing effects on the environment and human society are depicted in this block dia- gram. The main contributors to climate change include deforestation, agriculture, and the use of fossil fuels, all of which increase the emission of greenhouse gases including nitrous oxide, carbon dioxide, and methane. These gases exacerbate the greenhouse effect and fuel global warming by trapping heat in the Earth’s atmosphere. The Fig 2 emphasizes the func- tion of feedback loops, wherein thawing permafrost, increasing water vapor, and decreased snow cover all contribute to the acceleration of climate change. Environmental effects of these changes include habitat loss, ecosystem collapse, biodiversity loss, increased weather, glacial retreat, and sea level rise. Human problems including agricultural failure, flooded towns, pop- ulation displacement, and health hazards are further exacerbated by these environmental repercussions. The root causes and effects of global warming and climate change. The nonlin- ear feedback loops (such as water vapor amplification and permafrost thawing) that are sub- sequently modeled using fuzzy rules (Table 1) and assessed in simulation tests (Figs 12–20) are highlighted in this schematic. It is clear from comprehending the chain reactions shown in this graphic that climate change is a complex problem that needs immediate attention to

lessen its profound effects on ecosystems and human culture. Applying T–S fuzzy models in climate change has practical significance. These models offer an easy and efficient approach to incorporate uncertainty in input and output variables, such as projected greenhouse gas (GHG) emissions and temperature increases. T–S fuzzy mod- els help capture the relationships between GHG concentrations and temperature variations by using linguistic rules derived from emission scenarios and climate models [19,20]. Fuzzy models can also account for climate sensitivity uncertainty, giving a more thorough evalua- tion of temperature rises [21]. Decision–makers can test alternative strategies and analyze their results at different levels by using fuzzy models to measure regional sustainability develop- ment under climate change [22]. In general, T–S fuzzy models provide useful tools for making decisions and comprehending the effects of climate change. In [23], two strategies for controlling complex dynamical systems are outlined: open–loop control, also known as pre-computed control (OCLC), and closed-loop control. As shown in Figs 3 and 4, feedback (closed–loop) control adjusts actions based on observations of the sys- tem state, allowing it to correct deviations and account for random perturbations caused by PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 4/ 30