Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia

Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban–rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban–rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban–rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban–rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban–rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.

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Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

Sustainability ◽

10.3390/su13031339 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1339

Author(s):

Ziyuan Chai ◽

Zibibula Simayi ◽

Zhihan Yang ◽

Shengtian Yang

Keyword(s):

Energy Consumption ◽

Carbon Emissions ◽

Carbon Emission ◽

Driving Factors ◽

Series Data ◽

Lake Basin ◽

Stirpat Model ◽

Consumption Structure ◽

Ebinur Lake ◽

Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

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Is global carbon inequality getting better or worse? A decomposition analysis of carbon inequality in intraincome and interincome groups

Management of Environmental Quality An International Journal ◽

10.1108/meq-11-2020-0257 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Rongrong Li ◽

Qiang Wang ◽

Yi Liu ◽

Rui Jiang

Keyword(s):

Carbon Emissions ◽

Carbon Emission ◽

Income Group ◽

Driving Factors ◽

Theil Index ◽

High Income Group ◽

Middle Income ◽

Content Type ◽

Middle Income Group ◽

Global Carbon

PurposeThis study is aimed at better understanding the evolution of inequality in carbon emission in intraincome and interincome groups in the world, and then to uncover the driving factors that affect inequality in carbon emission.Design/methodology/approachThe approach is developed by combining the Theil index and the decomposition technique. Specifically, the Theil index is used to measure the inequality in carbon emissions from the perspective of global and each income group level. The extended logarithmic mean Divisia index was developed to explore the driving factors.FindingsThis study finds that the inequality in carbon emissions of intraincome group is getting better, whereas the inequality in carbon emission of interincome group is getting worse. And the difference in global carbon emissions between income groups is the main source of global carbon emission inequality, which is greater than that within each income group. In addition, the high-income group has transferred their carbon emissions to upper-middle income group by importing high-carbon-intensive products to meet the domestic demand, while lower-middle-income group do not fully participate in the international trade.Practical implicationsTo alleviate the global carbon inequality, more attention should be paid to the inequality in carbon emission of interincome group, especially the trade between high-income group and upper-middle income group. From the perspective of driving factors, the impact of import and export trade dependence on the per capita carbon emissions of different income groups can almost offset each other, so the trade surplus effect should be the focus of each group.Originality/valueIn order to consider the impact of international trade, this study conducts a comprehensive analysis of global carbon emissions inequality from the perspective of income levels and introduces the import and export dependence effect and the trade surplus effect into the analysis framework of global carbon emission inequality drivers, which has not been any research carried out so far. The results of this paper not only provide policy recommendations for mitigating global carbon emissions but also provide a new research perspective for subsequent inequality research.

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Dynamic Change and Analysis of Driving Factors of Carbon Emissions from Traffic and Transportation Energy Consumption in Jilin Province

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.472.851 ◽

2014 ◽

Vol 472 ◽

pp. 851-855 ◽

Cited By ~ 4

Author(s):

Biao Gao ◽

Qing Tao Xu ◽

Yu Bo Li

Keyword(s):

Energy Consumption ◽

Carbon Emissions ◽

Carbon Emission ◽

Growth Stage ◽

Dynamic Change ◽

Driving Factors ◽

Jilin Province ◽

Energy Structure ◽

Estimation Model ◽

Transportation Energy

Based on the traffic and transportation energy consumption, the carbon emissions of traffic and transportation energy consumption are obtained by using the estimation model of carbon emissions from 1999 to 2011 in Jilin Province, and the dynamic changes and the Environmental Kuznets Curve (EKC) of carbon emissions are analyzed. The result indicates that the carbon emission of traffic and transportation energy consumption increased continuously from 99.3750×104 t to 331.8255×104 t between 1999 and 2011 in Jilin Province, the change process is divided into three stages which include the stage of the stationary growth phase, accelerated growth stage and slow growth stage, the large consumption of diesel energy is the main reason of the rapid growth in carbon emissions. The EKC of carbon emission shows the inverted U shape roughly and the turning point appeared in 2011, after 2011, carbon emissions will decrease along with the economic growth. Based on the STIRPAT model, the study reveals that elasticity coefficients of driving factors such as population, per capita GDP, the unit GDP energy consumption, the investment of traffic and transportation, city rate, the number of private cars are 0.23440.2202-0.22470.16570.2864 and 0.2163, respectively. Jilin Province must implement effective measures to change the existing development mode of traffic and transportation, change the energy structure, and increase the innovation of scientific and technological, to strive for the realization of negative growth in carbon emissions of traffic and transportation energy consumption.

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

Sustainability ◽

10.3390/su11184919 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4919 ◽

Cited By ~ 3

Author(s):

Jingbo Fan ◽

Aobo Ran ◽

Xiaomeng Li

Keyword(s):

Carbon Emissions ◽

Rural Areas ◽

Carbon Emission ◽

Low Carbon ◽

Systematic Analysis ◽

Factors Affecting ◽

Man And Nature ◽

Urban Rural ◽

The Government ◽

Urban And Rural Areas

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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Global carbon emission spatial pattern in 2030 under INDCs: using a gridding approach based on population and urbanization

International Journal of Climate Change Strategies and Management ◽

10.1108/ijccsm-04-2021-0038 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Le Tao ◽

Yun Su ◽

Xiuqi Fang

Keyword(s):

Population Density ◽

Spatial Pattern ◽

Carbon Emissions ◽

Carbon Emission ◽

Western Europe ◽

Eastern China ◽

Grid Method ◽

Content Type ◽

Northern India ◽

Global Carbon

Purpose The intended nationally determined contributions (INDCs) is a major outcome of the Paris Agreement on international cooperation to reduce emissions, and is likely to be the future scenario for carbon emissions. This paper aims to obtain the fine spatial pattern of carbon emissions in 2030, identify hot spots and analyze changes of carbon emissions with a spatial grid method. Design/methodology/approach Based on the integrated quantified INDCs of each economy in 2030, the authors predict the population density pattern in 2030 by using the statistics of current population density, natural growth rates and differences in population growth resulting from urbanization within countries. Then the authors regard population density as a comprehensive socioeconomic indicator for the top-bottom allocation of the INDC data to a 0.1° × 0.1° grid. Then, the grid spatial pattern of carbon emissions in 2030 is compared with that in 2016. Findings Under the unconditional and conditional scenarios, the global carbon emission grid values in 2030 will be within [0, 59,200.911] ktCO2 and [0, 51,800.942] ktCO2, respectively; eastern China, northern India, Western Europe and North America will continue to be the major emitters; grid carbon emissions will increase in most parts of the world compared to 2016, especially in densely populated areas. Originality/value While many studies have explored the overall global carbon emissions or warming under the INDC scenario, attention to spatial details is also required to help us make better emissions attributions and policy decisions from the perspective of the grid unit rather than the administrative unit.

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Driving Factors and Future Prediction of Carbon Emissions in the ‘Belt and Road Initiative’ Countries

Energies ◽

10.3390/en14175455 ◽

2021 ◽

Vol 14 (17) ◽

pp. 5455

Author(s):

Lili Sun ◽

Huijuan Cui ◽

Quansheng Ge

Keyword(s):

Carbon Emissions ◽

Carbon Emission ◽

Energy Intensity ◽

Driving Factors ◽

Peak Time ◽

Belt And Road Initiative ◽

Belt And Road ◽

Future Prediction ◽

Global Carbon ◽

The Belt And Road

‘Belt and Road Initiative’ (B&R) countries play critical roles in mitigating global carbon emission under the Paris agreement, but their driving factors and feasibility to reduce carbon emissions remain unclear. This paper aims to identify the main driving factors (MDFs) behind carbon emissions and predict the future emissions trajectories of the B&R countries under different social-economic pathways based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results indicate that GDP per capita and energy consumption structure are the MDFs that promote carbon emission, while energy intensity improvement is the MDF that inhibits carbon emission. Population, as another MDF, has a dual impact across countries. The carbon emissions in all B&R countries are predicted to increase from SSP1 to SSP3, but emissions trajectories vary across countries. Under the SSP1 scenario, carbon emissions in over 60% of B&R countries can peak or decline, and the aggregated peak emissions will amount to 21.97 Gt in 2030. Under the SSP2 scenario, about half of the countries can peak or decline, while their peak emissions and peak time are both higher and later than SSP1, the highest emission of 25.35 Gt is observed in 2050. Conversely, over 65% of B&R countries are incapable of either peaking or declining under the SSP3 scenario, with the highest aggregated emission of 33.10 Gt in 2050. It is further suggested that decline of carbon emission occurs when the inhibiting effects of energy intensity exceed the positive impacts of other MDFs in most B&R countries.

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Carbon Emissions in the Xinjiang Production and Construction Corps and Driving Factors

Frontiers in Energy Research ◽

10.3389/fenrg.2021.627149 ◽

2021 ◽

Vol 9 ◽

Author(s):

Meng Wang ◽

Lei Feng ◽

Pengfei Zhang ◽

Gaohang Cao ◽

Hanbin Liu ◽

...

Keyword(s):

Carbon Emissions ◽

Emission Reduction ◽

Carbon Emission ◽

Emission Inventory ◽

Driving Factors ◽

Hot Water ◽

Total Carbon ◽

Energy Structure ◽

Petroleum Processing ◽

Carbon Emission Reduction

Xinjiang production and Construction Corps (XPCC) is an important provincial administration in China and vigorously promotes the construction of industrialization. However, there has been little research on its emissions. This study first established the 1998-2018 XPCC subsectoral carbon emission inventory based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission inventory method and adopted the logarithmic mean Divisia indexmethod (LMDI) model to analyze the driving factors. The results revealed that from 1998 to 2018, the total carbon emissions in the XPCC increased from 6.11 Mt CO2 in 1998 to 115.71 Mt CO2 in 2018. For the energy structure, raw coal, coke and industrial processes were the main contributors to carbon emissions. For industrial structure, the main emission sectors were the production and supply of electric power, steam and hot water, petroleum processing and coking, raw chemical materials and chemical products, and smelting and pressing of nonferrous metals. In addition, the economic effect was the leading factor promoting the growth of the corps carbon emissions, followed by technical and population effects. The energy structure effect was the only factor yielding a low emission reduction degree. This research provides policy recommendations for the XPCC to formulate effective carbon emission reduction measures, which is conducive to the construction of a low-carbon society. Moreover, it is of guiding significance for the development of carbon emission reduction actions for the enterprises under the corps and provides a reference value for other provincial regions.

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Factors Affecting China's Carbon Trading Price—A Case Study Based on Tianjin Carbon Emissions Trading Market

10.21203/rs.3.rs-521711/v1 ◽

2021 ◽

Author(s):

Yuwei Du ◽

Songsheng Chen

Keyword(s):

Carbon Emissions ◽

Industrial Development ◽

Carbon Emission ◽

Price Index ◽

Stock Exchange ◽

Emission Trading ◽

Carbon Trading ◽

Coal Price ◽

Trading Market ◽

Carbon Emission Trading

Abstract Building a carbon emission trading market is an effective way to control carbon emissions. The carbon emission trading price is the key to the carbon trading market, and it will affect the carbon emission reduction behavior of enterprises. This study use the vector autoregression (VAR) model, the cointegration analysis, and the Granger causality test to analyze the influence of industrial development index (Shanghai Stock Exchange Industrial Index (000004.SH)), coal price index (National Coal Price Index), air quality index (AQI), and economic index (Purchasing Managers Index (PMI)) on the carbon emission trading price in Tianjin. Empirical research results based on data from January 2014 to December 2019 show that the Shanghai Stock Exchange Industrial Index and AQI are positively correlated with Tianjin carbon emission trading price, and the National Coal Price Index and PMI are negatively correlated with Tianjin carbon emission trading price. Finally, some suggestions are made to promote the rapid maturity of the national carbon emission trading market of China.

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The Study on Carbon Emission Influencing Factors of Industrial Energy Consumption of Changchun City

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.164.302 ◽

2012 ◽

Vol 164 ◽

pp. 302-305

Author(s):

Zhuo Ma ◽

Xiao Gang He ◽

Xun Zhou Tong ◽

Hai Yan Duan ◽

Xian En Wang ◽

...

Keyword(s):

Energy Consumption ◽

Influencing Factors ◽

Carbon Emissions ◽

Industrial Development ◽

Carbon Emission ◽

Low Carbon ◽

Industrial Energy ◽

Emission Changes ◽

Major Factors ◽

Industrial Energy Consumption

To make great efforts for energy saving and promote low-carbon transition of industrial development pattern have been the most crucial tasks for Changchun industrial developmen. Using Logarithmic Mean Divisia Index (LMDI) mode decomposes the carbon emission influencing factors of the industrial department in Changchun, and study on the effects of factors on the carbon emissions of industrial energy consumption. The result shows that the major factors for carbon emissions of industrial energy consumption in Changchun are economic development, the population size and the industrialization rate, and the key factors for the carbon emission changes in industrial department of Changchun are the energy consumption structure and the energy intensity.

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Spatial Pattern of a Comprehensive fE Index for Provincial Carbon Emissions in China

Energies ◽

10.3390/en13102604 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2604

Author(s):

Lili Sun ◽

Huijuan Cui ◽

Quansheng Ge ◽

Caspar Daniel Adenutsi ◽

Xining Hao

Keyword(s):

Spatial Pattern ◽

Carbon Emissions ◽

Carbon Emission ◽

Hot Spot ◽

Critical Issue ◽

Bohai Bay ◽

Low Carbon ◽

Low Carbon Economy ◽

Carbon Reduction ◽

Combat Climate Change

China has committed to ambitious targets to reduce its carbon emissions in the next decades, in order to combat climate change and improve the environment. The realization of the targets depends on the fair and effective mitigation plans of all provinces. However, with varying ecological and environmental conditions and social-economic development, it is a critical issue to quantify the provinces’ efforts equally. This paper proposed a comprehensive fE index in coordinating ecology, equity and economy, by accounting for carbon emissions and sinks to characterize provincial carbon emission status in China, from 2000 to 2017, which shows a spatial pattern of “boundary high, central low”. The provinces with higher fE value (>1.5) in boundary areas can be seen as “relative equality” provinces with good ecology circulation, equity and economic efficiency. The provinces with lower fE value (<0.7) in central areas around Bohai Bay are regarded as “severe inequality” provinces, and are identified as the hot-spot provinces, which have emitted more CO2 than their equity share by occupying the carbon emission space of other provinces in recent decades. These results could provide a reference for a provincial guide for carbon reduction and sustainable development of the low-carbon economy.

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