Comparing Urban and Rural Household CO2 Emissions—Case from China’s Four Megacities: Beijing, Tianjin, Shanghai, and Chongqing

Huang, Rui; Zhang, Shaohui; Liu, Changxin

doi:10.3390/en11051257

Cited by 27 publications

(14 citation statements)

References 78 publications

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“…Meanwhile, there is a consensus that reducing the fast increase of residential CO 2 emissions is a significant pathway to achieve energy-saving and emission-reduction targets in China [6,7]. 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].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

2020

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“…For urban Beijing, technology and consumption structure shows greater influence. Moreover, secondary industry proportion showed a positive influence in current studies, however, it has a negative influence on indirect household CO 2 emission in urban Beijing [5,60]. Considering the dense population, significant urban-rural difference, and advanced consumption level, it is necessary to provide targeted carbon emission reduction policies.…”

Section: Conclusion and Recommendationsmentioning

confidence: 79%

“…With a relatively more centralized population and greater anthropogenic activity, metropolitan areas are major sources of global carbon emissions [2,3]. In particular, Beijing, the capital of China, with higher CO 2 emissions and per capita CO 2 emissions than metropolises in developed countries [4], is becoming a common focus of carbon emission research [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Factors Controlling Urban and Rural Indirect Carbon Dioxide Emissions in Household Consumption: A Case Study in Beijing

Jin

Sun

et al. 2019

Sustainability

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Residential carbon dioxide emissions can be divided into a direct component caused by consumers via direct energy usage and an indirect component caused by consumers buying and using products to meet their needs, with a higher proportion caused by the latter. Based on Beijing panel data for 1993–2012, an economic boom period in China, indirect carbon dioxide emissions were separately calculated for urban and rural households using the consumer lifestyle approach (CLA) model. Then, an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model was used to analyze the influence from two aspects, social economy, and land use, with high precision. Results indicate that indirect CO2 emissions in Beijing households display a rising trend in urban areas but a slight decrease in rural areas. Technology influences and forest land are, respectively, the most important aspects of the social economy and land use. Higher population and urbanization resulted in enhanced emissions in both urban and rural areas. The Engel coefficient presented a negative correlation with indirect CO2 emissions for both rural and urban areas. Compared with urban areas, the per capita net income of rural areas restrained consumption. The consumption structure of urban residents was more biased toward the tertiary industry than that of rural residents. Although technical progress has proceeded, it cannot offset urban residents’ indirect CO2 emissions caused by the large amount and rapid growth of consumption. Regarding land use, urban construction land net primary productivity (NPP) was high and not an important factor contributing to indirect CO2 emissions. Forest and lawn primarily served a recreational function and exhibited a positive impact. Water and cultivated land offered insufficient production and thus had a negative influence. For rural residents, lawn and cultivated land production is self-sufficient. Forests offer a carbon sequence effect, and construction land expansion increased the proportion of developed area, offering a scale effect that resulted in reduced carbon emissions. Based on the results, alternative carbon emission reduction policies have been proposed for each tested influence aspect to reduce emissions, including policies for optimizing industrialization quality, constructing a medium-density city, increasing space efficiency, encouraging sustainable consumption behavior, and increasing the efficiency of energy utilization.

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“…Beijing, Shanghai, Tianjin and Chongqing are municipalities directly under the central government with relatively small regional areas; however, they were also obvious net PM 2.5 emissions receivers, and the advantages they have taken are also obvious. From another perspective, there is the phenomenon of population outflow mainly to China's large cities and the more developed regions, especially from Northeast China and North China ( You et al, 2018 ), which causes the developed regions to have large populations ( Huang et al, 2018 ; Song et al, 2018 ). By transferring a portion of the emissions externally, air pollution damage to human health may be alleviated to some extent; in this way, the regional inequity may be somewhat offset.…”

Section: Discussion and Policy Implicationsmentioning

confidence: 99%

A new approach to evaluate regional inequity determined by PM2.5 emissions and concentrations

Chuai

Xie³

et al. 2021

Journal of Environmental Management

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PM 2.5 is one of the most severe types of air pollution that threatens human health. Its emissions have a notable spillover effect once released into the atmosphere and transported. In domestic trade, PM 2.5 emissions can be indirectly imported from external regions. Thus, regional inequity caused by PM 2.5 needs to be integrated and comprehensively estimated. Based on PM 2.5 emissions/concentrations grid maps and an input-output model, this study first examined the temporal-spatial changes in PM 2.5 emissions/concentrations across China. Additionally, a detailed relationship between PM 2.5 emissions and concentrations was examined at multiple scales, both temporal and spatial. Finally, this study developed a new approach with which to evaluate regional inequity. The results show that PM 2.5 emissions and concentrations increased between 1990 and 2012 and 1998 and 2016, respectively; the increase was more obvious for PM 2.5 emissions. Spatially, a rapid increase in PM 2.5 emissions was observed in the North China Plain and the Sichuan Basin. Between 1998 and 2012, the distribution of PM 2.5 concentrations was similar to that of emissions; however, between 2013 and 2016, 46.6% of the total area showed a decrease, mainly in the central and southern parts of China. Relationship analysis revealed that PM 2.5 emissions and concentrations are closely correlated in both time and space. There was obvious regional inequity among provinces; developed regions always imported considerably more PM 2.5 emissions from undeveloped regions than they exported. Overall, the regional inequity estimation framework shows that provinces along the coastline, especially developed provinces, have advantages under the regional inequity estimation framework, while most of the inland regions have disadvantages, especially in the west and north.

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Comparing Urban and Rural Household CO2 Emissions—Case from China’s Four Megacities: Beijing, Tianjin, Shanghai, and Chongqing

Cited by 27 publications

References 78 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

Factors Controlling Urban and Rural Indirect Carbon Dioxide Emissions in Household Consumption: A Case Study in Beijing

A new approach to evaluate regional inequity determined by PM2.5 emissions and concentrations

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