Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing, China

Wang, Zhaohua; Yang, Yuantao

doi:10.1016/j.ecolind.2015.10.015

Cited by 84 publications

(36 citation statements)

References 49 publications

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“…Lower than in these countries, this better option to decompose and compare the effects of different impact factors on carbon footprints. Extensive studies used SDA to uncover the driving forces for historic changes in carbon footprints at the national level [39][40][41][42][43], the regional level [44][45][46], and the city level [47][48][49]. However, few studies focused on the impact factors of increasing urban household carbon footprints in China.…”

Section: Introductionmentioning

confidence: 99%

Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis

et al. 2019

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A rise in China's economy and urbanization has brought about obvious growth in the carbon footprints of urban households over the past years. In this study, input-output analysis was adopted to calculate the carbon footprints of urban households in China. Furthermore, a detailed analysis of the impact factors on indirect carbon footprints was carried out by using the structural decomposition analysis at both holistic and sectoral levels. The results showed that the carbon footprints of urban households were 941.37 MtCO 2 in 2002 , 1498 .11 MtCO 2 in 2007 , and 2139 MtCO 2 in 2012. Electricity was the main contributor to the direct urban household carbon footprints, accounting for over 40%. The category of "household facilities" contributed the most to indirect carbon footprints (306.00 MtCO 2 ) in 2012, and the "transportation" had the fastest growth rate (395%) during 2002-2012. The industrial sector of "wearing apparel" had the largest increments (139.92 MtCO 2 ) in carbon footprints during the past decade. Generally, consumption level and population size presented positive effects on the increments in indirect carbon footprints, while emission intensity decreased indirect carbon footprints. However, the impact of consumption structure and intermediate demand on indirect carbon footprints varied at holistic and sectoral levels. The mitigation efforts should focus on reducing emission intensity, shifting consumption structure and changing intermediate demand.Sustainability 2019, 11, 7157 2 of 18 allocation was more than 40% for China [16]. However, as the largest developing country, China has experienced fast urbanization during the last two decades, leading to a spike in household energy requirements [17][18][19]. Moreover, along with the improvement of income level and the changes in consumers' lifestyle, the Chinese households generally spent more money on enhancing the quality of life, such as enjoying luxurious consumption (such as overseas traveling and air conditioning) and purchasing high-carbon products (such as private cars with high gasoline consumption and home appliances with high power) [20,21]. In addition, the increasingly fast urbanization process also has a significant impact on household carbon emissions. China's urbanization rate increased from 18% in 1978 to 58% in 2017. Compared to rural households, urban households' carbon emissions are much larger because of the higher income level and modern lifestyle [22][23][24][25]. It is noteworthy that China is still accelerating the development of urbanization. One hundred million rural populations will be settled in urban areas by 2020. The urbanization rate will reach 60% by 2020 and 70% by 2030. With intensified rural-to-urban migration, the impact of urban residents on the carbon emissions will be further magnified. The research on carbon emissions of China's urban households should draw widespread attention [26][27][28]. The concept of carbon footprint is widely applied to identify both direct and indirect carbon emissions from a broader accou...

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Section: Introductionmentioning

confidence: 99%

Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis

et al. 2019

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“…For a vast country, the industrial structure and economic level of provinces/states are different, which should bring difficulties in seeking an accurate spatial calculation of CO 2 emissions for a country [28]. As far as we know, the studies focusing on regional carbon emissions of large countries were limited to some developed regions or cities such as Beijing [29,30], six large Japanese cities [31], and Guangdong Province [32]. There are few studies focusing on regions where heavy industries dominate the economy.…”

Section: Introductionmentioning

confidence: 99%

Exploring Potential Pathways toward Energy-Related Carbon Emission Reduction in Heavy Industrial Regions of China: An Input–Output Approach

et al. 2020

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It is a very urgent issue to reduce energy-related carbon emissions in China. The three northeastern provinces (Heilongjiang (HLJ), Jilin (JL), and Liaoning (LN)) are typical heavy industrial regions in China, playing an important role in the national carbon emission reduction target. In this study, we analyzed the energy consumption, carbon dioxide (CO2) emissions, and CO2 emission intensity of each sector in the three regions, and we compared them with the national level and those of China’s most developed province Guangdong (GD). Then, based on an input–output (I–O) framework, linkage analysis of production and CO2 emission from sector–system and sector–sector dimensions was conducted. The results showed that the three regions accounted for about 1/10 of China’s energy consumption and 1/6 of China’s CO2 emissions in 2012. In addition, the level of energy structure, CO2 emission intensity, and sectoral structure lagged behind China’s average level, much lower than those for GD. According to the sectoral characteristics of each region and unified backward/forward linkages of production and CO2 emissions, we divided sectoral clusters into those whose development was to be encouraged and those whose development was to be restricted. The results of this paper could provide policy–makers with reference to exploring potential pathways toward energy-related carbon emission reduction in heavy industrial regions.

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“…Therefore, more and more researchers have focused their attention on the energy-related carbon emissions caused by residential consumption [6][7][8][9][10][11]. Direct carbon emissions from residential consumption, calculated from China's Statistical Yearbook, are not large, so they can easily be ignored.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the consumption level and emission intensity were the main drivers that influence the change in indirect carbon emissions. Wang and Yang [8] analyzed the influencing factors affecting urban and rural residential carbon emissions in Beijing, through both IO-SDA and LMDI. The study's results showed that per capita GDP presented the biggest growth effect on residential consumption, and indirect carbon emissions and energy intensity were the most important factors in reducing indirect carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Indirect Carbon Emission of Residential Consumption in China: A Case of Liaoning Province

et al. 2019

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Indirect carbon emissions caused by residential consumption has gradually become the key to the formulation of carbon emission reduction policies. In order to analyze the factors that influence the provincial residential indirect carbon emissions in China, comprehensive structural decomposition analysis (SDA) and logarithmic mean Divisia index (LMDI) models are established in this paper. The Liaoning province was selected due to its typical features as a province with higher urbanization rates. The model is based on input–output tables from 2002 to 2012, including those pertaining to the carbon emission coefficient (ΔF), energy intensity effect (ΔE), intermediate demand (ΔL), commodity structure (ΔS), residential consumption structure (ΔU), residential consumption ratio (ΔR), per capita GDP (ΔA) and population size (ΔP). The results show that the consumption of urban residents is the most common and significant section causing the growth of direct and indirect carbon emissions, both of which show an obvious upward trend. Nonmetal mining is the sector experiencing the greatest growth in indirect carbon emissions. The two most influential factors of indirect carbon emissions via the consumption of rural and urban residents are the intermediate demand effect (ΔL) and the per capita GDP effect (ΔA), respectively. Reducing energy intensity and optimizing commodity structures are the most effective ways to reduce indirect carbon emissions.

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Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing, China

Cited by 84 publications

References 49 publications

Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis

Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis

Exploring Potential Pathways toward Energy-Related Carbon Emission Reduction in Heavy Industrial Regions of China: An Input–Output Approach

Factors Influencing Indirect Carbon Emission of Residential Consumption in China: A Case of Liaoning Province

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