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Drivers of urban and rural residential energy consumption in China from the perspectives of climate and economic effects

Abstract In this study, we investigate the driving forces behind the changes in residential energy consumption (REC) in China’s urban and rural areas over the 2001–2012 period. Based on the logarithmic mean Divisia index method, the REC changes are decomposed into seven driving forces, which are climate change, energy price, energy expenditure mix, energy cost share (in total expenditure), expenditure share (in income), per capita income and population effects. According to the results, climate effect due to increasing days with abnormal temperature, energy cost share effect characterized by more expenditure to be paid for energy use, income effect describing constant income growth in the residential sector definitely increase REC in both urban and rural areas. In contrast, energy prices and energy expenditure mix effects negatively contribute to the REC increase, respectively because of the increase in energy prices and the transition from the low-priced energy to high-priced energy. Expenditure share and population effects play opposite roles in urban and rural areas, and the reasons and implications are analysed in depth.
- Chinese Academy of Sciences China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
- Maastricht University Netherlands
- Chinese Academy of Sciences United States
- Beihua University China (People's Republic of)
Residential energy consumption (REC), China, Urban versus rural areas, Climate effect, Index decomposition analysis
Residential energy consumption (REC), China, Urban versus rural areas, Climate effect, Index decomposition analysis
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).70 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
