Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Zhao, Jincai; Chen, Yulong; Ji, Guangxing; Wang, Zheng

doi:10.1016/j.jclepro.2018.01.131

Cited by 94 publications

(30 citation statements)

References 44 publications

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“…To address the above deficiencies, Chongqing, which is only municipality in the western region of China and has experienced rapid urban expansion and carbon emission growth [32], was selected as the study area. County socioeconomic development in Chongqing presents obvious spatial differences, and regional differences can be regarded as a microcosm of China [33].…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

Shi

et al. 2020

IJERPH

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Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

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Section: Introductionmentioning

confidence: 99%

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

Shi

et al. 2020

IJERPH

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“…The energy consumption and related carbon emissions from households play an important role all over the world [8][9][10][11]. Reportedly, more than 80% energy consumption and corresponding carbon emissions were allocated to the household sector for the United States [12], 75% for India [13], 74% for the UK [14] and 52% for the Republic of Korea [15].…”

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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“…The calculation of CO 2 emissions is described by Zhao et al [ 56 ], who utilized nighttime light datasets to estimate the spatial and temporal variations in urban residential CO 2 emissions. Nighttime light datasets have been used as proxies to model socioeconomic activity [ 57 , 58 ]. Due to limitations in statistical data, balanced panel data for 620 county-level cities in 30 provinces (excluding Tibet, Hong Kong, and Macao) for 2000–2015 were used in this study.…”

Section: Methodsmentioning

confidence: 99%

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Zhao

Liu

2021

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Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

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Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Cited by 94 publications

References 44 publications

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

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

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

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