Regional Disparities in Emissions of Rural Household Energy Consumption: A Case Study of Northwest China

Abstract

Abstract:The purpose of this paper is to present the emissions status of multiple rural areas from the perspective of a field survey and make up for the defects of the traditional emission cognition of single type of area. The basic data in the lower reaches of the Weihe River of Northwest China were collected through household questionnaire surveys, and emissions from rural household energy consumption were calculated in the paper. In addition, the grey relational analysis method was used to identify influential factors of emission disparities. The results show that the total emissions of the plain, loess tableland, and Qinling piedmont areas are 1863.20, 1850.43, and 2556.68 kg, respectively. Regional disparities in emissions of rural household energy consumption vary greatly. CO 2 emissions are highest in the Qinling piedmont area, followed by the loess tableland area. For other emissions, there is no fixed order of the three areas, which suggests that disparities in emissions are connected with the dominant type of energy consumption. Diversification of energy use might not necessarily produce higher emissions, but the traditional biomass energy pattern does generate more emissions. The regional supply capacity of household energy is the original influence factor of disparities in emissions, and factors that influence these disparities are directly related to differences among farmers, followed by the age structure, educational background, income level, occupation, and so on.

Stacked Use and Transition Trends of Rural Household Energy in Mainland China

“…23−25 Though living conditions in rural China have improved quickly, millions of rural residences in western China and mountainous regions are still too poor to use clean energy. 26 Generally, those who need to heat their home in winter often use multiple energies, as demonstrated by a significantly positive correlation between the average number of energy types used and the HDD (Figure 1a). The HDD is proportional to heating energy consumption.…”

“…Despite the fact that many households can now afford new and cleaner fuels, elder residents of these homes often prefer free biomass fuels to supply specific indoor energy services. Transitions are thus complicated for a number of reasons, including, for instance, household educational level, habits, awareness of indoor pollution, adverse health impacts, willingness to change, etc. − Though living conditions in rural China have improved quickly, millions of rural residences in western China and mountainous regions are still too poor to use clean energy …”

A Review of Rural Household Energy Poverty: Identification, Causes and Governance

“…Miah et al (2010) [38] started from the different usage to study the difference in household energy consumption in different regions of Bangladesh and pointed out that energy-poor rural areas choose more initial energy sources to reduce the cost of energy consumption and avoid the possibility of households facing energy poverty. Wu et al (2013) [46] showed that differences in the type of regional geographic environment can greatly affect the choice of energy for domestic use in rural households and then affect the availability of energy for domestic use in rural households and the governance of energy poverty.…”

Households’ Electricity Consumption in Hungarian Urban Areas

“…The literature on specific topics is discussed in the relevant section. At present, almost 40% of the urban population comes from surrounding settlements as immigrants [4][5][6][7][8][9][10][11][12][13][14][15][16], which is one of the main reasons for the rapid acceleration in the urbanization process [17][18][19][20][21][22]; however, the removal of the surrounding urban areas, previously agricultural lands, from agricultural cultivation to allow urban expansion and declaring them construction areas, enabling the construction of new residential areas, can also be considered as an indirect urbanization process. Overall, therefore, the economic potential found in urban space is a strong driver of settlement in the city, capable of automating the metropolitan system [23][24][25][26][27].…”