Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China

Rong, Peijun; Zhang, Yan; Qin, Yaochen; Liu, Gangjun; Liu, Rongzeng

doi:10.1016/j.jenvman.2020.110895

Cited by 55 publications

(26 citation statements)

References 28 publications

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“…The exploratory spatial data analysis method was used to verify whether the observed value of a unit has spatial correlation with the observed values of its neighboring units (Li et al, 2018). The global Moran's I index is used to measure the global spatial correlation, while the local Moran's I index in LISA (local indicators of spatial association) was used to measure the local spatial correlation (Rong et al, 2016). Their formulas (Anselin, 1995;Gallo and Ertur, 2003) are as follows:…”

Section: Exploratory Spatial Data Analysis Methodsmentioning

confidence: 99%

“…The exploratory spatial data analysis method was used to verify whether the observed value of a unit has spatial correlation with the observed values of its neighboring units ( Li et al, 2018 ). The global Moran's I index is used to measure the global spatial correlation, while the local Moran's I index in LISA (local indicators of spatial association) was used to measure the local spatial correlation ( Rong et al, 2016 ). Their formulas ( Anselin, 1995 ; Gallo and Ertur, 2003 ) are as follows:

where I is the global Moran's I index; X i and X j are the observed values of unit i and j ; W ij is the spatial weight matrix (with 1 as adjacent, and 0 as non-adjacent), S 2 represents the variance; K represents the number of observation units; I* is the local Moran's I index; W pq is the normalized form of the spatial weight matrix; and Z p , Z q are the normalized forms of the observed values in unit p and q .…”

Section: Methodsmentioning

confidence: 99%

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Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

Xie

Qin

et al. 2020

Science of The Total Environment

Self Cite

113

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Spatial and temporal differentiation of COVID-19 epidemic spread in China and its influencing factors are analyzed. • The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. • The population inflow from Wuhan and strength of economic connection are the main factors affecting the epidemic spread. • The interaction influence of detection factors on the epidemic spread exceeds that of the single factor. • When the average temperature in winter is maintained at 11-16°C, the epidemic spread rate is higher.

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Section: Exploratory Spatial Data Analysis Methodsmentioning

confidence: 99%

“…The exploratory spatial data analysis method was used to verify whether the observed value of a unit has spatial correlation with the observed values of its neighboring units ( Li et al, 2018 ). The global Moran's I index is used to measure the global spatial correlation, while the local Moran's I index in LISA (local indicators of spatial association) was used to measure the local spatial correlation ( Rong et al, 2016 ). Their formulas ( Anselin, 1995 ; Gallo and Ertur, 2003 ) are as follows:

Section: Methodsmentioning

confidence: 99%

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

Xie

Qin

et al. 2020

Science of The Total Environment

Self Cite

113

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“…Long et al also used spatial autocorrelation analysis and investigated the spatiotemporal variation of CO 2 emissions generated by household private cars [25]. Using spatial autocorrelation analysis, Rong et al investigated the spatial autocorrelation of RCEs in Kaifeng [26]. However, there are limitations in the previous studies on spatial effects in that these studies just considered an attribute rather than a relationship and believed only geographically adjacent areas to be correlated; the spatial association is always regarded as geographic adjacency.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically Weighted Regression

Sun

Jia

et al. 2022

Land

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As China’s second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO2) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China’s residential CO2 emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy.

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“…However, only a few studies were conducted to quantify the effect of urban sprawl on residential carbon emissions and carbon intensity. As an example, Rong et al [ 37 ] deduced that urban sprawl drove the increase in urban residential carbon emissions according to the spatial patterns. There was barely direct evidence showing the influence of urban sprawl on residential carbon intensity.…”

Section: Introductionmentioning

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

IJERPH

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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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Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China

Cited by 55 publications

References 28 publications

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically Weighted Regression

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

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