What factors affect household energy consumption in mega-cities? A case study of Guangzhou, China

Jiang, Lu; Shi, Xiaonan; Wu, Shuang; Ding, Bowenpeng; Chen, Yamei

doi:10.1016/j.jclepro.2022.132388

Cited by 18 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In terms of occupation, most of the respondents were office workers, general workers, and freelancers, accounting for 62.34%, and the average education level of the respondents was tertiary level. The chart below shows the structure of the questionnaire design ( Figure 2 ) [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

Chen

Jiang

2022

IJERPH

Self Cite

View full text Add to dashboard Cite

China’s household energy consumption has obvious regional differences, and rising income levels and urbanization have changed the ability of households to make energy consumption choices. In this paper, we analyze the energy consumption characteristics of urban village residents based on microlevel household survey data from urban villages in Guangzhou, China. Then, the results of modeling the material flows of per capita carbon emissions show the most dominant type of energy consumption. OLS is applied to analyze the influencing factors of carbon emissions. We find that the per capita household carbon emissions in urban villages are 722.7 kg/household.year, and the average household carbon emissions are 2820.57 kg/household.year. We also find that household characteristics, household size, household appliance numbers, and carbon emissions have a significant positive correlation, while income has no significant effect on carbon emissions. What is more, the size and age of the house have a positive impact on carbon emissions. Otherwise, the new finding is the demonstration that income is not significantly correlated with household carbon emissions, which is consistent with the characteristics of urban villages described earlier. On the basis of this study, we propose more specific recommendations regarding household energy carbon emissions in urban villages.

show abstract

Section: Methodsmentioning

confidence: 99%

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

Chen

Jiang

2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally, the best panel spatial econometric model selection can be determined using the Lagrange Multiplier test or evaluated through post-regression information criteria [40].…”

Section: Spatial Econometric Modelmentioning

confidence: 99%

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Song,

Tian,

et al. 2024

Land

View full text Add to dashboard Cite

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

show abstract

“…Carbon emissions directly generated by the household sector are from the direct combustion of fossil fuels for household activities (e.g., cooking, clothing, and housing) [14], which could be influenced by household income [15]. Growing household income resulted in the enormous increase in energy consumption of the household sector over the last two decades, directly leading to a huge rise in carbon emissions [16]. In addition, indirect emissions of the household are embedded in intermediate transactions [14], indicating that the production sectors generate carbon emissions driven by household consumption.…”

Section: Introductionmentioning

confidence: 99%

Modelling Structural Effect and Linkage on Carbon Emissions in China: An Environmentally Extended Semi-Closed Ghosh Input–Output Model

Nie

Gao

2022

Energies

View full text Add to dashboard Cite

The carbon emissions of sectors and households enabled by primary inputs have practical significance in reality. Considering the mutual effect between the industrial sector and the household, this paper firstly constructed an environmentally extended semi-closed Ghosh input–output model with an endogenized household sector to analyze the relationship between carbon emissions and the Chinese economy from the supply-side perspective. The structural decomposition analysis and the hypothetical extraction method were remodified to identify the supply-side driving effects of the changes in carbon emissions and investigate the net carbon linkage. The results show that the electricity, gas, and water supply sector was the key sector with the highest carbon emission intensity enabled by primary inputs. The household sector had an above 93% indirect effect of the enabled intensity, with its enabled intensity dropping significantly by more than 55% from 2007 to 2017. The operating surplus and mixed income caused 3214.67 Gt (34.17%) of the enabled emissions in 2017. The supply-side economic activity, measured by the value added per capita, was the main factor of the carbon emission growth, mainly attributed to the development of the manufacturing sector and the electricity, gas, and water supply sector. The emission intensity and allocation structure both brought a decrease in carbon emissions. The electricity, gas, and water supply sector and the manufacturing sector were the major sources of the supply-induced cross-sectoral input emissions, while the commercial and service sector and the household sector were the top source of supply-induced cross-sectoral output emissions. This paper sheds light on the policies of the carbon emission abatement and the adjustment of the allocation structure from the perspective of supply.

show abstract

What factors affect household energy consumption in mega-cities? A case study of Guangzhou, China

Cited by 18 publications

References 41 publications

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Modelling Structural Effect and Linkage on Carbon Emissions in China: An Environmentally Extended Semi-Closed Ghosh Input–Output Model

Contact Info

Product

Resources

About