Global carbon dioxide emissions analysis based on time series visualization - Liu, Xuanyu; Zhao, Meilin; Miao, Qinghong (Frontiers in Physics, 2023)
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. To further analyze the data, the k-means method is used to cluster the 159 countries into categories based on their carbon emissions. This will help identify the countries that have similar emissions patterns, enabling the development of tailored solutions for each category. We found that i) 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. ii) 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. iii) Furthermore, a comparison of carbon emissions levels between China and the United States reveals disparities in the data. These disparities indirectly reflect variations in economic development patterns between the two countries.