Exploring household emission patterns and driving factors in Japan using machine learning methods
نویسندگان
چکیده
Given by the ambitious GHG mitigation targets set governments worldwide, household is playing an increasingly important role for reaching listed reduction goals. Consequently, a deep understanding of its emission patterns and corresponding driving factors are great importance exploring untapped potential household. However, how to accurately capture features still demand further support from both data method development. To bridge this knowledge gap, we try use machine learning technology, which well linked micro-level survey data, identify key determinants that could explain home-energy consumption associated emissions. Here, investigate CO2 emissions based on representative covers 31,133 households in Japan. Six types process employed find determining different patterns. Results show demographic structure, average age electricity-intensive appliances (electric water heaters, electric etc.) most significant differences also verified can be observed identifying various The results study provide vital information customized decarbonization pathways households, as discussing energy-saving behaviours data-oriented method.
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ژورنال
عنوان ژورنال: Applied Energy
سال: 2022
ISSN: ['0306-2619', '1872-9118']
DOI: https://doi.org/10.1016/j.apenergy.2021.118251