Factors Influencing the Spatial Difference in Household Energy Consumption in China

Abstract

Abstract:What factors determine the spatial heterogeneity of household energy consumption (HEC) in China? Can the impacts of these factors be quantified? What are the trends and characteristics of the spatial differences? To date, these issues are still unclear. Based on the STIRPAT model and panel dataset for 30 provinces in China over the period 1997-2013, this paper investigated influences of the income per capita, urbanization level and annual average temperature on HEC, and revealed the spatial effects of these influencing factors. The results show that the income level is the main influencing factor, followed by the annual average temperature. There exists a diminishing marginal contribution with increasing income. The influence of urbanization level varies according to income level. In addition, from the eastern region to western region of China, variances largely depend upon economic level at the provincial level. From the northern region to southern region, change is mainly caused by temperature. The urbanization level has more significant impact on the structure and efficiency of household energy consumption than on its quantity. These results could provide reference for policy making and energy planning.

Household Energy Consumption Behaviour for Different Demographic Regions in Thailand from 2000 to 2010

“…Lin et al [19] used 2011 REC panel data for 28 provinces of China and stated that population growth, residential energy use per capita and GDP per capita (indicator of the level of economic development or household income) are the main contributors of the growth of China's REC. Ding et al [8] used panel data for 30 provinces and found that economic factors, including the income level, have significant positive influences on increasing household energy consumption in China. Zhao et al [9] revealed that elevated income and urbanisation in China have led to a large share of energy expenditures in total living expenditures.…”

“…Lenzen et al [17] indicated that socioeconomic-demographic variables (such as age, household size, urbanity, education and others) influenced changes in residential energy requirements but at different levels of impact within each country. Ding et al [8] indicated that urbanisation does not affect the quantity of change in energy consumption in China but affects the structure and efficiency of energy consumption behaviour; moreover, urbanisation dominates the switch from traditional biomass consumption to commercial energy use as a result of the desire for convenience and comfort [9,23]. Scholars found that the age of a family member correlated with energy consumption levels in Hangzhou, China [24] and Japan [25].…”

“…Subsequently, herein, the changes in total energy consumption by the household group are decomposed into effects caused by changes in the seven key components on the right-hand side of Equation (8). The first, second and third terms represent the effects caused by the changes in energy efficiency, production structure and household consumption structure, respectively.…”

“…The change in total REC from 2000 to 2010 can be disaggregated into seven factors, as described in Equation (8). The result of the driving factor analysis of Thailand's change in REC is shown in Figure 3.…”

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

“…As for the influence factors of rural emission characteristics, empirical analysis or some specific explanatory variables and an econometric model are often presented according to different purposes and needs. Growth in income and changes in lifestyle are considered to be the two key factors affecting rural household energy consumption and CO 2 emissions [10,11]. The energy ecological footprint and indirect carbon emissions in rural household consumption are positively influenced by the Engel coefficient, energy intensity, and tertiary industry proportion, while negatively influenced by urbanization level and per capita income in which the improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model was used to empirically study their influences [12].…”