Carbon emission path clusters: emission profiling to identify anomalies in carbon emission paths

Siqi Ma

doi:10.1117/12.2620419

9 December 2021 Carbon emission path clusters: emission profiling to identify anomalies in carbon emission paths

Siqi Ma

Proceedings Volume 12030, Third International Conference on Optoelectronic Science and Materials (ICOSM 2021); 120301Q (2021) https://doi.org/10.1117/12.2620419
Event: Third International Conference on Optoelectronic Science and Materials (ICOSM 2021), 2021, Hefei, China

Abstract

This study aims at identifying different patterns of carbon emission for countries and regions through the k means clustering method and its' time series variation. By forming emission trend clusters, we identify different modes of emission, which can be educational for policy makers and for further research on carbon pricing. The data we use to form the clusters and discussion the indication of the result clusters are retrieved from Our World In Data, the Global Carbon Project, the United Nation, and the World Bank. As a result, we arrive at a classification result for 225 economies, which allows us to compare the carbon emission path and identify anomalies. We cross-compare the clustering results with the United Nation's classification on economic development status. We regard the cluster with the most developed economies as our cluster of developed economies based on emission situation. Based on the similarities of our clusters and the United Nation's categorization, we identify regions whose emission situations do not agree with their economic development status as defined by the United Nation.

Citation Download Citation

Siqi Ma "Carbon emission path clusters: emission profiling to identify anomalies in carbon emission paths", Proc. SPIE 12030, Third International Conference on Optoelectronic Science and Materials (ICOSM 2021), 120301Q (9 December 2021); https://doi.org/10.1117/12.2620419

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KEYWORDS

Carbon

Profiling

Carbon dioxide

Distance measurement

Climate change

Data modeling

Image classification

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