2017
DOI: 10.1038/s41560-017-0003-1
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Measurement of inequality using household energy consumption data in rural China

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Cited by 161 publications
(79 citation statements)
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References 34 publications
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“…Rao and Ummel (2017) show that other drivers than income, such as identity, can influence ownership of household appliances and that the influence of income differs by appliance and by region. Wu, Zheng, and Wei (2017) argue that inequality in energy is systematically different from income inequality and that it can provide additional insights on consumption inequality. Differences in electrification rates across countries have been related to the urbanization rate, education level, and the availability of renewable energy sources, with weaker links to per capita GDP and funding availability (Magnani and Vaona 2016).…”
Section: Measuring Inequality In Technologies and Infrastructure Servmentioning
confidence: 99%
See 1 more Smart Citation
“…Rao and Ummel (2017) show that other drivers than income, such as identity, can influence ownership of household appliances and that the influence of income differs by appliance and by region. Wu, Zheng, and Wei (2017) argue that inequality in energy is systematically different from income inequality and that it can provide additional insights on consumption inequality. Differences in electrification rates across countries have been related to the urbanization rate, education level, and the availability of renewable energy sources, with weaker links to per capita GDP and funding availability (Magnani and Vaona 2016).…”
Section: Measuring Inequality In Technologies and Infrastructure Servmentioning
confidence: 99%
“…The literature shows many examples of the Gini coefficients and Lorenz curves being used for assessing the inequality of domains other than income, such as: house size (Kohler et al 2017), happiness (Bennett and Nikolaev 2017) solid waste arising from material resource use (Druckman and Jackson 2008), historical (Groot 2010) and future (Zimm and Nakicenovic 2019) carbon emissions, material indicators (e.g. domestic extraction, domestic material consumption and material footprint) and indicators measuring the intensity with which human society uses terrestrial ecosystems (Teixidó-Figueras et al 2016), education (Sauer and Zagler 2014;Vinod, Yan, and Xibo 2001), health (Williams and Cookson 2000), spatial inequity in transportation (Jang et al 2017), internet bandwidth (Hilbert 2016), mobile phones, radios and bikes (Bento 2016), energy (Wu, Zheng, and Wei 2017) and electricity (Jacobson, Milman, and Kammen 2005).…”
Section: Gini Coefficient and Lorenz Curvementioning
confidence: 99%
“…Given the time and budget constraints, we decided to give up sampling all provinces in China. Instead, we invited scholars with backgrounds or experience in energy economics, agriculture economics, statistics, and field survey to discuss proper sampling selection strategy, and eventually selected 12 representative provinces that vary substantially in terms of energy types, spatial location, climatic conditions and socioeconomic indicators (Wu et al, 2017). These provinces include Hebei, Heilongjiang, Jiangsu, Zhejiang, Fujian, Hubei, Hunan, Guangdong, Sichuan, Yunnan, Shaanxi, and Gansu.…”
Section: Survey Design and Processmentioning
confidence: 99%
“…Excepting designing the questionnaire and training the interviewers, the Department of Energy Economics is also responsible for controlling data quality. For more details on the energy survey design, sampling and implementation, data quality control, and sample representativeness, the reader is referred to the publications by Wu et al (2017), Yu and Guo (2016), and Zheng et al (2014). As mentioned, the survey process should survey 3900 rural households.…”
Section: Survey Design and Processmentioning
confidence: 99%
“…Therefore, the type and amount of household energy consumption have different performance within and across regions and countries [24]; also, understanding the spatial pattern and the underlying drivers of variations of the household energy consumption thus helps to identify challenges and opportunities and provide advice for future policy measures [25]. In China, due to the vast territory and the differences among regional social, environmental, and economic conditions, households have quite different energy use performance [23], such as a result in inequality [26]. Therefore, under such a circumstance, it is critical to conduct a systemic review to illustrate the overall situation as well as the detailed mechanisms of the household energy consumption in China.…”
Section: Introductionmentioning
confidence: 99%