Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques.

Carbon dioxide (CO2) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high-low attribute values criterion and the extreme number-duration values criterion. A case study is conducted based on the CO2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR.

Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO2 emissions has attracted more attention. China is a major contributor of anthropogenic CO2 emissions and so it is essential to accurately estimate China's CO2 emissions and analyze their changing characteristics. This study recalculates CO2 emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R2: 0.925, 10-fold CV R2: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO2 emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO2 emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO2 emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies.