Applied Energy, Vol.250, 1321-1335, 2019
Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models
The United Nations Sustainable Development Goals have highlighted the challenges brought about by increasing energy consumption and climate change. Previous studies have concentrated on accounting for urban and rural household energy requirements in China at a macro-scale, which neglects the analysis of individuals and their socioeconomic driving factors at the micro-scale. To fill this gap, this study began with an accounting of energy requirements for urban and rural households based on the provincial Multi-Regional Input-Output (MRIO) tables and household survey covering over 25,000 unique samples from 25 provinces in 2012. Multilinear Regression models were employed to estimate the average effect of various demographic and socioeconomic characteristics of samples, and Tree-based models were applied to classify energy requirement groups and identify the key individual characteristics. The results suggest that the energy requirements per capita on average range from 34 to 211 GJ for urban samples and 34 to 149 GJ for rural samples across different provinces, and that the gap between individuals can be over 100 times. Indirect energy requirements representing above 90% of the total is the focus of the study. Changes in lifestyle factors include eating out, drinking and smoking, were all correlated with indirect energy requirements. Furthermore, the one-child family has had a positive effect on indirect energy requirements, while the two or more children family has had a negative effect. In addition, an individual's mental health plays a role in the level of indirect energy requirements for high-income rural residents, while geographic location plays a key role for urban residents.
Keywords:Multiregional input-output model;Household energy requirements;Machine learning;Household survey