How to accurately assess the spatial distribution of energy CO2 emissions? Based on POI and NPP-VIIRS comparison

• Published in: Journal of cleaner production Vol. 402; p. 136656
• Main Authors: Zhang, Xueyuan, Xie, Yaowen, Jiao, Jizong, Zhu, Wanyang, Guo, Zecheng, Cao, Xiaoyan, Liu, Jiamin, Xi, Guilin, Wei, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20.05.2023
• Subjects: carbon; carbon dioxide; China; CO2 emission; energy; energy conservation; industry; mountains; NPP-VIIRS; POI; Spatial distribution; urban areas; China; NPP-VIIRS; Spatial distribution; POI; CO2 emission; China
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2023.136656

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]