How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison
Timely and accurately estimating the spatial distribution of CO2 emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO2 emissions, it cannot estimate the spa...
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| Published in | Journal of cleaner production Vol. 402; p. 136656 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
20.05.2023
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| Abstract | Timely and accurately estimating the spatial distribution of CO2 emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO2 emissions, it cannot estimate the spatial distribution of different types of CO2 emissions (commercial CO2 emissions, residential CO2 emissions, light industry CO2 emissions, heavy industry CO2 emissions, and agricultural CO2 emissions). Based on the local adaptive method, this study compares the potential of POI data and NPP-VIIRS data for modeling different types of carbon emissions in China to analyze the spatial structure of carbon emissions within cities. The results showed that: (1) POI data is much more powerful and reliable than NPP-VIIRS data regarding monitoring ability at the suburbs and mountainous areas. (2) From the point of view of the estimation ability of different types of carbon emissions, in the commercial CO2 emissions and residential CO2 emissions, although the correlation coefficient between the estimation results of POI data and statistical data is not significantly improved compared with that of NPP-VIIRS data, the accuracy of the estimation results is significantly improved in terms of the spatial distribution; POI data has a significantly stronger ability to estimate industrial carbon emissions than nighttime light data. (3) From the spatial distribution structure of urban carbon emission, urban carbon emission presents a “V”-shaped distribution, with two high-value areas located in the central urban area and the industrial zone in the suburbs. This study confirms that POI data is a potential and promising data source for accurately modeling different types of carbon emissions and will help support low-carbon city management and energy allocation optimization.
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| AbstractList | Timely and accurately estimating the spatial distribution of CO2 emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO2 emissions, it cannot estimate the spatial distribution of different types of CO2 emissions (commercial CO2 emissions, residential CO2 emissions, light industry CO2 emissions, heavy industry CO2 emissions, and agricultural CO2 emissions). Based on the local adaptive method, this study compares the potential of POI data and NPP-VIIRS data for modeling different types of carbon emissions in China to analyze the spatial structure of carbon emissions within cities. The results showed that: (1) POI data is much more powerful and reliable than NPP-VIIRS data regarding monitoring ability at the suburbs and mountainous areas. (2) From the point of view of the estimation ability of different types of carbon emissions, in the commercial CO2 emissions and residential CO2 emissions, although the correlation coefficient between the estimation results of POI data and statistical data is not significantly improved compared with that of NPP-VIIRS data, the accuracy of the estimation results is significantly improved in terms of the spatial distribution; POI data has a significantly stronger ability to estimate industrial carbon emissions than nighttime light data. (3) From the spatial distribution structure of urban carbon emission, urban carbon emission presents a “V”-shaped distribution, with two high-value areas located in the central urban area and the industrial zone in the suburbs. This study confirms that POI data is a potential and promising data source for accurately modeling different types of carbon emissions and will help support low-carbon city management and energy allocation optimization.
[Display omitted] Timely and accurately estimating the spatial distribution of CO₂ emissions is crucial for formulating energy conservation and emission reduction policies. Although nighttime light data has been proved to be effective in estimating the spatial distribution of CO₂ emissions, it cannot estimate the spatial distribution of different types of CO₂ emissions (commercial CO₂ emissions, residential CO₂ emissions, light industry CO₂ emissions, heavy industry CO₂ emissions, and agricultural CO₂ emissions). Based on the local adaptive method, this study compares the potential of POI data and NPP-VIIRS data for modeling different types of carbon emissions in China to analyze the spatial structure of carbon emissions within cities. The results showed that: (1) POI data is much more powerful and reliable than NPP-VIIRS data regarding monitoring ability at the suburbs and mountainous areas. (2) From the point of view of the estimation ability of different types of carbon emissions, in the commercial CO₂ emissions and residential CO₂ emissions, although the correlation coefficient between the estimation results of POI data and statistical data is not significantly improved compared with that of NPP-VIIRS data, the accuracy of the estimation results is significantly improved in terms of the spatial distribution; POI data has a significantly stronger ability to estimate industrial carbon emissions than nighttime light data. (3) From the spatial distribution structure of urban carbon emission, urban carbon emission presents a “V”-shaped distribution, with two high-value areas located in the central urban area and the industrial zone in the suburbs. This study confirms that POI data is a potential and promising data source for accurately modeling different types of carbon emissions and will help support low-carbon city management and energy allocation optimization. |
| ArticleNumber | 136656 |
| Author | Guo, Zecheng Zhang, Xueyuan Cao, Xiaoyan Xie, Yaowen Jiao, Jizong Wei, Wei Zhu, Wanyang Liu, Jiamin Xi, Guilin |
| Author_xml | – sequence: 1 givenname: Xueyuan surname: Zhang fullname: Zhang, Xueyuan organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 2 givenname: Yaowen orcidid: 0000-0002-6625-0294 surname: Xie fullname: Xie, Yaowen email: xieyw@lzu.edu.cn organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 3 givenname: Jizong surname: Jiao fullname: Jiao, Jizong email: zhangxueyuan21@lzu.edu.cn organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 4 givenname: Wanyang surname: Zhu fullname: Zhu, Wanyang organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 5 givenname: Zecheng surname: Guo fullname: Guo, Zecheng organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 6 givenname: Xiaoyan surname: Cao fullname: Cao, Xiaoyan organization: Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, China – sequence: 7 givenname: Jiamin surname: Liu fullname: Liu, Jiamin organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 8 givenname: Guilin surname: Xi fullname: Xi, Guilin organization: College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, 730000, China – sequence: 9 givenname: Wei surname: Wei fullname: Wei, Wei organization: College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070, Gansu, China |
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| Title | How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison |
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