Uncovering the national and regional household carbon emissions in China using temporal and spatial decomposition analysis models

Shi, Yingying; Han, Botang; Le, H. Q.; Wei, Zixiang

doi:10.1016/j.jclepro.2019.05.302

Cited by 56 publications

(22 citation statements)

References 60 publications

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“…The energy price effect had the most important impact lowering the residential CO 2 emissions in urban and rural regions over the study period (Tables A5 and A6), which was consistent with the previous studies [10,21]. Specifically, the additive decomposition effects were -549.64 × 10 4 t, -152.25 × 10 4 t, -782.11 × 10 4 t and -128.61 × 10 4 t for urban regions (Figure 5a and Table A3), the corresponding average annual contribution rates were -20.34%, -5.55%, -22.02% and -6.95%, respectively (Table 6).…”

Section: Energy Price (Ep)supporting

confidence: 91%

“…However, the IO tables are not available every year so that SDA has the limitation to the annual analysis. Inversely, IDA is easier to acquire data and more alterative to use aggregated data to analyze any years' changes [10]. Hence, IDA is widely applied to decompose the driving factors of energy-related CO 2 emissions.…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…For China, the residential sector is the second-largest energy consumer category as well as primary CO 2 emissions source [8,9]. Specifically, the residential sector is responsible for 11.7% of energy consumption and 12.6% of CO 2 emissions in 2015 [10]. On one hand, after 40 years of "reform and opening up", China steps into the new development phase.…”

Section: Introductionmentioning

confidence: 99%

“…They often focused on exploring influencing factors of the CO 2 emissions or energy consumption in the residential sector, based on national or regional perspectives, or analyzing the relationship between economic growth and CO 2 emissions from residential sector. When exploring the drivers affecting residential CO 2 emissions or energy consumption, many factors such as income [14][15][16], population [9,11,17], urbanization [18,19], energy intensity (or energy price) [10,20,21], energy structure [6,22,23], floor space (per capita) [24,25], education level [26], etc., were discussed. For example, based on structural decomposition analysis (SDA), Zhu, et al [11] studied the indirect residential CO 2 emissions during the period 1992-2005.…”

Section: Introductionmentioning

confidence: 99%

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Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

2020

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The residential sector is the second-largest consumer of energy in China. However, little attention has been paid to reducing the residential CO2 emissions of China’s less developed or undeveloped regions. Taking Jiangxi as a case study, this paper thus aims at fully analyzing the difference of the residential energy-related CO2 emissions between urban and rural regions based on the Log-Mean Divisia Index (LMDI) and Tapio decoupling model. The main results are showed as follows: (1) Since 2008, residential energy-related CO2 emissions have increased rapidly in both urban and rural Jiangxi. From 2000 to 2017, the residential energy-related CO2 emissions per capita in rural regions rapidly increased and exceeded that in urban regions after 2015. Furthermore, the residential energy structures had become multiple in both urban and rural regions, but rural regions still had room to optimize its energy structure. (2) Over the study period, consumption expenditure per capita played the dominant role in increasing the residential energy-related CO2 emissions in both urban and rural regions, followed by energy demand and energy structure. Energy price had the most important effect on decreasing the urban and rural residential energy-related CO2 emissions, followed by the carbon emission coefficient. However, urbanization increased the urban residential energy-related CO2 emissions but decreased the CO2 emissions in rural regions. Population made marginal and the most stable contribution to increase the residential energy-related CO2 emissions both in urban and rural regions. (3) Overall, the decoupling status showed the weak decoupling (0.1) and expansive negative decoupling (1.21) in urban and rural regions, respectively.

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Section: Energy Price (Ep)supporting

confidence: 91%

Section: Decomposition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

2020

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“…There are many factors affecting household carbon emissions in China. Feng et al, Wiedenhofer et al, and Shi et al found that income has a large impact on residential carbon emissions [6,17,30]. Zhang et al found a positive correlation between income and household carbon emissions [4].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences

Fan

Ran

2019

Sustainability

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As the world’s largest emitter of greenhouse gases, China has been attracting attention. In the global carbon emission structure, the proportion of household carbon emissions continues to increase, and it is necessary to focus on the issue of household emissions. Based on the perspective of the family sector and the comparison of urban–rural and interprovincial differences, this study makes a thorough and systematic analysis of the factors affecting direct household carbon emissions. The average carbon emission of urban households is higher than that of rural households. Both personal background and household energy consumption facility use have important impacts on household carbon emissions, and the degree of impact varies between urban and rural areas and between provinces. Reducing household carbon emissions and achieving a harmonious coexistence between man and nature are the common goals of the government and society. The government should explore the model of green sustainable development on the basis of ensuring the energy needs of residents. Residents should also further establish a low-carbon life concept and focus on the cultivation of low-carbon lifestyles.

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The Influence of Population Aging on Living Carbon Emissions in the Yellow River Basin: Comparisons Between Urban and Rural Areas

Song,

Zhang,

et al. 2024

Energy Science & Engineering

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Population aging presents a major challenge to China's economic and social development. While some research has addressed the relationship between population aging and energy consumption or generalized carbon emissions, its impact on living carbon emissions has been less explored. This study uses provincial panel data from the Yellow River Basin (2006–2020) and employs a threshold regression model to analyze the relationship between population aging and living carbon emissions. A Multiple Mediation Effect Model is also applied to explore the mechanisms behind this relationship, with a comparative analysis between urban and rural areas. The results indicate a U‐shaped nonlinear effect of population aging on living carbon emissions, which are initially inhibited and then promoted, with different “turning points” for rural and urban regions. As income levels rise, population aging has a marginally increasing effect on living carbon emissions. However, upgrading the consumption expenditure structure, expanding family size, and increasing clean energy consumption can help mitigate the promoting effect of aging on emissions. The mediation model identifies four pathways through which population aging influences living carbon emissions, with income level and consumption expenditure structure playing key roles. This study offers policy insights for addressing urban‐rural disparities and fostering sustainable regional development.

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Uncovering the national and regional household carbon emissions in China using temporal and spatial decomposition analysis models

Cited by 56 publications

References 60 publications

Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

A Study on the Factors Affecting China’s Direct Household Carbon Emission and Comparison of Regional Differences

The Influence of Population Aging on Living Carbon Emissions in the Yellow River Basin: Comparisons Between Urban and Rural Areas

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