Spatiotemporal characteristics of carbon emissions in energy-enriched areas and the evolution of regional types
There is great potential for carbon emission reduction in energy-enriched areas. It is important to master the spatiotemporal characteristics and driving factors of carbon emissions to achieve the goal of carbon emission reduction. Previous studies on carbon emissions mainly focused on the numerical...
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| Published in | Energy reports Vol. 7; pp. 7224 - 7237 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.11.2021
Elsevier |
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| Abstract | There is great potential for carbon emission reduction in energy-enriched areas. It is important to master the spatiotemporal characteristics and driving factors of carbon emissions to achieve the goal of carbon emission reduction. Previous studies on carbon emissions mainly focused on the numerical changes in regional carbon emissions. There have been few studies on spatiotemporal characteristics, making it difficult to formulate carbon emission reduction strategies according to local conditions. This study is based on the carbon emission calculation method proposed by the Intergovernmental Panel on Climate Change (IPCC), taking the method of exploratory spatial data analysis (ESDA), standard deviation ellipse (SDE) analysis and the geographically weighted regression model (GWR) to analyse the spatiotemporal evolution characteristics and determinants and dividing regional types of carbon emissions. The results show that aggregate carbon emissions and other carbon emission indicators presented an upward trend from 2000 to 2016, and the growth momentum of carbon emissions was difficult to curb in the short term. The carbon emissions of the study area are relatively concentrated in spatial; the direction of carbon emissions presented a trend of “northeast–southwest”, and the main axis and centre of carbon emissions tend to move northward over time. There are six regional types of carbon emissions in the study area. The low total amount–low intensity–low pressure type (L-L-L) became the dominant regional type of carbon emissions. The results of the GWR model showed that the degree of influence of explanatory variables on carbon emissions in descending order is urbanization rate > industrial structure > population > population density > per capita GDP. |
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| AbstractList | There is great potential for carbon emission reduction in energy-enriched areas. It is important to master the spatiotemporal characteristics and driving factors of carbon emissions to achieve the goal of carbon emission reduction. Previous studies on carbon emissions mainly focused on the numerical changes in regional carbon emissions. There have been few studies on spatiotemporal characteristics, making it difficult to formulate carbon emission reduction strategies according to local conditions. This study is based on the carbon emission calculation method proposed by the Intergovernmental Panel on Climate Change (IPCC), taking the method of exploratory spatial data analysis (ESDA), standard deviation ellipse (SDE) analysis and the geographically weighted regression model (GWR) to analyse the spatiotemporal evolution characteristics and determinants and dividing regional types of carbon emissions. The results show that aggregate carbon emissions and other carbon emission indicators presented an upward trend from 2000 to 2016, and the growth momentum of carbon emissions was difficult to curb in the short term. The carbon emissions of the study area are relatively concentrated in spatial; the direction of carbon emissions presented a trend of “northeast–southwest”, and the main axis and centre of carbon emissions tend to move northward over time. There are six regional types of carbon emissions in the study area. The low total amount–low intensity–low pressure type (L-L-L) became the dominant regional type of carbon emissions. The results of the GWR model showed that the degree of influence of explanatory variables on carbon emissions in descending order is urbanization rate > industrial structure > population > population density > per capita GDP. |
| Author | Yu, Jialuo Wang, Jijun Han, Xiaojia Xia, Yu |
| Author_xml | – sequence: 1 givenname: Xiaojia surname: Han fullname: Han, Xiaojia email: hanxiaojia18@mails.ucas.ac.cn organization: Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, Shaanxi, China – sequence: 2 givenname: Jialuo surname: Yu fullname: Yu, Jialuo email: yujialuo18@mails.ucas.ac.cn organization: Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, Shaanxi, China – sequence: 3 givenname: Yu surname: Xia fullname: Xia, Yu email: ucas104@163.com organization: Institute of Soil and Water Conservation Northwest A&F University, Yangling 712100, Shaanxi, China – sequence: 4 givenname: Jijun surname: Wang fullname: Wang, Jijun email: jjwang@ms.iswc.ac.cn organization: Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, Shaanxi, China |
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| Keywords | Driving factors Regional types Spatiotemporal evolution Carbon emissions Energy-enriched area |
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