In the past decade, although a series of actions have been taken to facilitate the residential energy transition to address severe air pollution issues, abruptly and substantially switching energy sources across the country raised deep concerns about energy inequality.
To address this issue, we employed nationwide survey data to identify the households that underwent a variety of energy transition pathways during this rapid transition. Our findings show that, during 2013-2017, cost-based energy inequality among Chinese households declined but still existed. More noteworthy, households that experienced energy transition were mainly dominated by low-income groups (i.e., extremely poor and poor households), with a share of around 60%. This finding also helps explain why households that adopt clean stoves often concurrently use their solid-fuel stoves in a previous study.
Our findings indicated that, with a dramatic increase in energy costs for households that switched from traditional solid fuels to clean energy, the energy cost-based inequality had declined within either urban or rural areas. However, the inequality level within the rural areas was still high, with the Gini index being 0.436 in 2017. Moreover, unlike urban households which experienced a decline in energy burden from 5.4% to 4.8% during 2013-2017, the energy burden on rural households was reinforced (increased from 5.3% to 6.5%). When measuring energy inequality in energy burden, we found that, during the rapid energy transition, both urban-rural and regional inequality in energy burden were aggravated to some extent.
By employing both linear models and nonlinear models, we found that promoting household income levels and urbanization seem to be beneficial to reducing the energy burden. In addition, there were mixed relationships between the other driving factors and household energy burden, including the average age of family members, family size, and the accessibility to gas fuels. These factors generally have a linear relationship with household energy burden in previous research. However, our study indicates the presence of nonlinear relationships, which provide a new perspective to understand how demographic and regional features impact household energy burden.