scholarly journals Case Study Evidence and Behavioural Analysis of Residential Energy Consumption in the UK

2017 ◽  
Vol 06 (01) ◽  
pp. 14-40
Author(s):  
Sven Hallin ◽  
Elizabeth Hooper ◽  
Thomas Weyman-Jones
Keyword(s):  
Energy Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Behavioural Analysis ◽  
The Uk

Residential energy consumption and associated carbon emission in forest rural area in China: A case study in Weichang County

2014 ◽  
Vol 11 (3) ◽  
pp. 792-804 ◽  
Author(s):  
Fei Lun ◽  
Josep G. Canadell ◽  
Zhong-qi Xu ◽  
Lu He ◽  
Zheng Yuan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Rural Area ◽  
Carbon Emission ◽  
Residential Energy ◽  
Residential Energy Consumption

The characteristics and influential factors of direct carbon emissions from residential energy consumption: a case study of Lijiang City, China

2012 ◽  
Vol 32 (24) ◽  
pp. 7716-7721
Author(s):  
王丹寅 WANG Danyin ◽  
唐明方 TANG Mingfang ◽  
任引 REN Yin ◽  
邓红兵 DENG Hongbing
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Influential Factors ◽  
Residential Energy ◽  
Residential Energy Consumption

Impact of battery storage on residential energy consumption: An Australian case study based on smart meter data

2021 ◽  
Author(s):  
Nameer Al Khafaf ◽  
Ahmad Asgharian Rezaei ◽  
Ali Moradi Amani ◽  
Mahdi Jalili ◽  
Brendan McGrath ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Smart Meter ◽  
Battery Storage ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Australian Case

scholarly journals Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3864
Author(s):  
Qiucheng Li ◽  
Jiang Hu ◽  
Bolin Yu
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Decomposition Analysis ◽  
Theil Index ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Index Method ◽  
Inequality Measures ◽  
Influencing Mechanism ◽  
Per Capita

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces’ URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China’s policymakers.

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