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Exploring household emission patterns and driving factors in Japan using machine learning methods

Chen, P; Wu, Y; Zhong, H; Long, Y; Meng, J; (2022) Exploring household emission patterns and driving factors in Japan using machine learning methods. Applied Energy , 307 , Article 118251. 10.1016/j.apenergy.2021.118251. Green open access

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Abstract

Given by the ambitious GHG mitigation targets set by governments worldwide, household is playing an increasingly important role for reaching listed reduction goals. Consequently, a deep understanding of its emission patterns and the corresponding driving factors are of great importance for exploring the untapped potential of household. However, how to accurately capture household emission features still demand further support from both data and method development. To bridge this knowledge gap, we try to use machine learning technology, which is well linked to the micro-level household survey data, to identify key determinants that could explain the household home-energy consumption and associated emissions. Here, we investigate the household CO2 emissions based on a representative survey which covers 31,133 households in Japan. Six types of machine learning process are employed to find key factors determining to different household emission patterns. Results show that demographic structure, average age and electricity-intensive appliances (electric water heaters, electric heaters, etc.) are most significant driving factors that explain differences in household emissions. Results also further verified that differences in driving factors can be observed in identifying various household emission patterns. The results of study provide vital information for the customized decarbonization pathways for households, as well as discussing further energy-saving behaviours from data-oriented method.

Type: Article
Title: Exploring household emission patterns and driving factors in Japan using machine learning methods
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.apenergy.2021.118251
Publisher version: https://doi.org/10.1016/j.apenergy.2021.118251
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Household carbon emission, Factor analysis, Japan, Machine learning, Decarbonization
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10142954
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