Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model

Zhu, Qin; Peng, Xinjun; Kai-ya, WU

doi:10.1016/j.enpol.2012.05.068

Cited by 152 publications

(57 citation statements)

References 17 publications

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“…Unlike in the USA [68][69][70], lifecycle analyses of indirect goods and services are not available in China. Therefore, indirect carbon emissions from household consumption were calculated using input-output analysis, following Qu et al [1,2], Liu et al [3], Vringer and Blok [4], Wang et al [71] and Zhu et al [72]. Required data were collated from the Chinese Input-Output [75].…”

Section: Estimation Of Indirect Household Carbon Emission (Hces)mentioning

confidence: 99%

A Comparison of Household Carbon Emission Patterns of Urban and Rural China over the 17 Year Period (1995–2011)

Maraseni

Liu

et al. 2015

Energies

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Abstract:The household sector consumes a large amount of goods and services and is therefore a major source of global carbon emissions. This study aims to analyze per person household carbon emission (HCEs) patterns of urban and rural China over the period from 1995 to 2011. Annual macroeconomic data for the study were obtained from authentic Chinese government sources. Direct HCE estimates for each fossil fuel were obtained using the IPCC's reference approach, and indirect HCEs were calculated by input-output analysis. In 1995, per person HCEs from direct sources for urban and rural China were 0.50 tCO2 and 0.22 tCO2, respectively; by 2011, these values had increased to 0.60 tCO2 and 0.61 tCO2, an increase of 20% and 177.27%, respectively. Similarly, in 1995, per person HCEs from indirect sources for urban and rural China were 0.43 tCO2 and 0.16 tCO2, respectively; by 2011, these values had increased to 1.77 tCO2 and 0.53 tCO2, respectively, an increase of 306% and 235%. The reasons for these differences and the sets of policies required to rectify increasing emissions are discussed. If current trends and practices continue, with a RMB1000 increase in per capita income from 2011 levels, per person HCEs in urban and rural China will increase by 0.119 tCO2 and 0.197 tCO2, respectively. OPEN ACCESSEnergies 2015, 8 10538This result indicates that the sector of society which is most vulnerable will contribute most to China's increasing HCEs. Therefore, while developing energy consumption and emissions reduction policies and programs, principles of fairness and equity need to be followed.

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Section: Estimation Of Indirect Household Carbon Emission (Hces)mentioning

confidence: 99%

A Comparison of Household Carbon Emission Patterns of Urban and Rural China over the 17 Year Period (1995–2011)

Maraseni

Liu

et al. 2015

Energies

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“…Using the IO-SDA (input-output structural decomposition analysis), Zhang examined the supply-side structure effect on the production-related carbon emissions in China from 1992 to 2005 [16], and Liu et al evaluated the energy embodied in the international trade of China during the same period [17]; results show that increasing exports of energy-intensive goods enlarged energy embodied in trade, mainly due to the rapid growth of manufacturing sectors. Zhu et al adopted IO-SDA method to investigate the indirect carbon emissions from residential consumption in China from 1992 to 2005 [18], results show that the rising residential consumption level accelerated the growth of residential indirect carbon emissions. By combining structural decomposition and input-output analysis framework, Guan et al found that efficiency gains in production sectors could not cope with the increasing emissions in China from 2002 to 2005 [19]; they then forecasted that household consumption, capital investment and exports growth would largely increase the carbon emissions up to 2030, while efficiency gains would partially offset the projected increases [20].…”

Section: Introductionmentioning

confidence: 99%

Structural Decomposition Analysis of Carbon Emissions and Policy Recommendations for Energy Sustainability in Xinjiang

Wang

2015

Sustainability

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“…The constant 44/12 represents the ratio of the molecular weights of CO 2 to C. The detailed coefficients of each fuel type are listed in Table 1. As for indirect CO 2 emissions that are related to the consumption of household goods and services, they can be estimated through input-output analysis, which is a quantitative economic technique representing the interdependencies between different sectors (Zhu et al 2012). and it has been widely used by other researchers (Qu et al 2013a, b;Zhu et al 2012).…”

Section: Estimation Of Direct and Indirect Per Capita Hcesmentioning

confidence: 99%

“…As for indirect CO 2 emissions that are related to the consumption of household goods and services, they can be estimated through input-output analysis, which is a quantitative economic technique representing the interdependencies between different sectors (Zhu et al 2012). and it has been widely used by other researchers (Qu et al 2013a, b;Zhu et al 2012). In fact, the estimation of indirect CO 2 emissions is still of great challenge due to the difficulty in data availability of indirect goods and services in China.…”

Section: Estimation Of Direct and Indirect Per Capita Hcesmentioning

confidence: 99%

A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China

Qin

Liu

et al. 2015

Environ Sci Pollut Res

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Given the large expenditures by households on goods and services that contribute a large proportion of global CO 2 emissions, increasing attention has been paid to household CO 2 emissions (HCEs). However, compared with industrial CO 2 emissions, efforts devoted to mitigating HCEs are relatively small. A good understanding of the effects of some driving factors (i.e., urbanization rate, per capita GDP, per capita income/disposable income, Engel coefficient, new energy ratio, carbon intensity, and household size) is urgently needed prior to considering policies for reducing HCEs. Given this, in the study, the direct and indirect per capita HCEs were quantified in rural and urban areas of China over the period 2000-2012. Correlation analysis and gray correlation analysis were initially used to identify the prime drivers of per capita HCEs. Our results showed that per capita income/disposable income, per capita GDP, urbanization rate, and household size were the most significantly correlated with per capita HCEs in rural areas. Moreover, the conjoint effects of the potential driving factors on per capita HCEs were determined by performing principal component regression analysis for all cases. Based on the combined analysis strategies, alternative polices were also examined for controlling and mitigating HCEs growth in China.

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Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model

Cited by 152 publications

References 17 publications

A Comparison of Household Carbon Emission Patterns of Urban and Rural China over the 17 Year Period (1995–2011)

A Comparison of Household Carbon Emission Patterns of Urban and Rural China over the 17 Year Period (1995–2011)

Structural Decomposition Analysis of Carbon Emissions and Policy Recommendations for Energy Sustainability in Xinjiang

A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China

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