The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.

The Science and Technology Innovation Center holds a pivotal position in the national science and technology innovation system, and a scientific evaluation of the “Sci-tech Innovation Center” will guide its construction direction. This study found the advantages and disadvantages of the four cities through comparison; Hence improvement suggestions were proposed for the weaknesses of the four cities. There are two main paths for the government to drive technology innovation: STI (Science and Technology Innovation) mode and DUI (Doing, Using, Interacting) mode. With the aid of the evaluation index system of the Sci-tech Innovation Center, this article uses fuzzy sets, rough sets and fuzzy dynamic clustering methods to comprehensively evaluate the effects of driving technology innovation in the four cities of Beijing, Shanghai, Shenzhen and Guangzhou. The results found that Shenzhen has a significant effect in DUI, and Beijing has a significant effect in STI. The choice of path is related to the abundance of innovation resources.