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International Conference on Discovery Science

DS 2017: Discovery Science pp 223–238Cite as

Discovering Hidden Knowledge in Carbon Emissions Data: A Multilayer Network Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10558))

Abstract

In this paper, we construct the first human carbon emissions network which connects more than a thousand geographical locations based on their daily carbon emissions. We use this network to enable a data-driven analysis for a myriad of scientific knowledge discovery tasks. Specifically, we demonstrate that our carbon emissions network is strongly correlated with oil prices and socio-economic events like regional wars and financial crises. Further, we propose the first multilayer network approach that couples carbon emissions with climate (temperature) anomalies and identifies climate anomaly outlier locations across 60 years of documented carbon emissions data; these outlier locations, despite having different emission trends, experience similar temperature anomalies. Overall, we demonstrate how using network science as a key data analysis technique can reveal a treasure trove of knowledge hidden beneath the carbon emissions data.

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Notes

  1. 1.

    In this work, we restrict the climate anomalies to temperature anomalies (i.e., deviation of observed temperature data from long-term means).

  2. 2.

    Since we are plotting the \(0.1\%\) longest links (distance-wise), the links between Australia and New Zealand do not appear due to their close proximity.

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Acknowledgments

This work was supported in part by the US National Science Foundation (NSF) under CyberSEES Grant CCF-1331804. The authors also acknowledge useful discussions and constructive feedback received from Dr. Da-Cheng Juan from Google Research in the early stages of the manuscript.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Kartikeya Bhardwaj, HingOn Miu & Radu Marculescu

Authors
  1. Kartikeya Bhardwaj
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  2. HingOn Miu
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  3. Radu Marculescu
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Corresponding author

Correspondence to Kartikeya Bhardwaj .

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

  2. Hokkaido University, Sapporo, Japan

    Takuya Kida

  3. National Institute of Informatics, Tokyo, Japan

    Takeaki Uno

  4. Gakushuin University, Tokyo, Japan

    Tetsuji Kuboyama

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Bhardwaj, K., Miu, H., Marculescu, R. (2017). Discovering Hidden Knowledge in Carbon Emissions Data: A Multilayer Network Approach. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-67786-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67785-9

  • Online ISBN: 978-3-319-67786-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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