Global carbon dioxide emissions analysis based on time series visualization

Abstract

Carbon emissions and climate change is a significant challenge faced by the world today. The effects of climate change are felt globally, and many countries are making efforts to tackle this problem. The paper aims to analyze the carbon emissions data of 159 countries spanning from 1860 to 2016. By using the method of time-series visualization, the research aims to transform this data into complex networks to uncover any hidden information that might be useful in reducing emissions. By employing k-means, cluster analysis and other methods, we found that 1) While global carbon emissions continue to rise, there is a deceleration or reduction in carbon emissions in high-income countries. This deceleration or reduction may be attributed to factors such as policy measures and industrial shifts. 2) Additionally, our analysis highlights the significant increase in carbon emissions in certain economies that are not among the world’s leading nations in terms of overall economic size. This indicates the importance of these countries recognizing the relationship between carbon emissions and their economic development levels. 3) In addition, comparing the carbon emission levels of China and the United States, it can be found that even if the total amount of economic development is similar, their carbon emission patterns may be greatly different. Finally, we suggest that in the formulation of carbon emission policies, we should not only pay attention to the similar national models of economic development, but also take into account the actual development industry and stage of the country.