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 this research. Author Muhammad Hashim Bukhari is employed by Saudi Aramco. Saudi Aramco provided funding, but had some additional role in study design, simulation analysis, decision to publish, and preparation of the manuscript. All other authors declare no competing interests. 1 Introduction One of the most important global issues of the present time is climate change, which is unquestionably a result of human activity. The term “climate change” refers to a general alter- ation of the earth’s climate, which includes changes to land and ocean temperatures as well as precipitation and snowfall amounts. The following are some pertinent climate–related factors: rays of the sun, earth, the oceans, the wind, the rain, the snow, the woods, the deserts, the savannas, and all human activity. Before industrialization, greenhouse gases were naturally balanced and their impact on climate was relatively stable. The greenhouse effect, which pre- vents the earth’s atmosphere from reflecting the sun’s heated infrared radiation, has been the primary driver of climate change since industrialization. The intricate relationships between several natural elements and human–caused impacts make it difficult to study and predict climate dynamics [1]. To deal with this complexity, researchers have developed novel mathe- matical models that can capture the uncertainties and non-linear interactions inherent in the climate system. One such model that has gained recognition for its ability to convey intricate

relationships, adjust to shifting circumstances, and provide insightful predictions in the con- text of climate change is the T–S (Takagi–Sugeno) fuzzy framework [2]. Based on fuzzy logic principles, the T–S fuzzy model offers a structured framework for representing complex sys- tems by breaking them down into the smallest, locally valid sub–models [3]. A comprehensive representation of the entire system is then produced by combining these sub–models. Given the complex interactions between multiple components in the subject of climate change, the T–S fuzzy model’s capacity to capture nonlinearities and uncertainty proved crucial. This paradigm provides a means of representing complex processes using fuzzy sets and linguistic variables, which is ideally suited to the inherently vague and imprecise nature of climate data. Earth’s climate dynamics are defined by a variety of elements, such as temperature, precipi- tation, concentrations of greenhouse gases, ocean currents, and others. These variables exhibit feedback loops, thresholds, and nonlinear interactions that are challenging to incorporate into conventional linear models [4]. By segmenting the input space into numerous parts, each of which is linked to a locally dependable linear sub-model, the T–S fuzzy model gets around this problem. This localized modeling approach can be used to depict nonlinear behaviors for certain ranges of input variables. In addition, due to natural fluctuations, lack of data, and changes in human activities, there are still unknown factors contributing to climate change.

The T–S fuzzy framework [5] is highly effective at handling uncertainties, as it incorporates fuzzy set theory to explicitly model imprecision. This competency is crucial when working with climate projections, as evaluating uncertainty is necessary for making wise decisions. This study of climate change modeling using the T–S fuzzy framework covers the fundamen- tal concepts of the T–S model, its application to climate systems, and its advantages over tra- ditional modeling approaches. By accepting the inherent complexity and uncertainty of cli- mate dynamics, the T–S fuzzy model opens up new avenues for enhancing our understanding of climate change, assisting with well–informed policy decisions, and fostering a sustainable future. In order to combat atmospheric systems, recent research emphasizes the expanding con- vergence of energy systems, environmental sustainability, and artificial intelligence. In their investigation of cellular and biochemical pathways, Li et al. [6] offered insights into reg- ulatory mechanisms that may have consequences for climate–resilient systems and bio- engineering. Multi-source information fusion strategies were developed in [7] and can improve predictive environmental models by increasing accuracy in the face of ambiguity. By directly connecting advanced analytics to the monitoring and mitigation of climate–related PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 2/ 30