Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets

Zhao, Jincai; Ji, Guangxing; Yue, Yanlin; Lai, Zongsheng; Chen, Yulong; Yang, Dongyang; Xu, Yang; Wang, Zheng

doi:10.1016/j.apenergy.2018.09.180

Cited by 145 publications

(88 citation statements)

References 57 publications

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“…Numerous studies have demonstrated that the range and intensity of NTL are closely correlated to gross regional products (GRP) [6,[15][16][17], size and density of population [17][18][19], urbanization [2,20,21], electricity consumption [1,6,[22][23][24], light pollution [25][26][27], carbon dioxide (CO 2 ) emissions [28,29], and humanitarian disasters, etc. [30,31].…”

Section: Introductionmentioning

confidence: 99%

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Lin

Shi

2020

IJGI

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The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.

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Section: Introductionmentioning

confidence: 99%

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Lin

Shi

2020

IJGI

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“…Nighttime light (NTL) data is an effective proxy for socioeconomic activity and has been widely used in many aspects, including socioeconomic statistical index estimation, urbanization, energy consumption, and ecological environments due to its availability and veracity [3][4][5][6][7][8][9][10][11][12]. Currently, two kinds of NTL data are widely applied to estimate GDP.…”

Section: Introductionmentioning

confidence: 99%

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

et al. 2020

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Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

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“…A high correlation exists between NTL data and human activities, and NTL data have the advantages of temporal-spatial continuity, independence, and objectivity. Yearly or monthly composite NTL data are increasingly used as an index to assess various socioeconomic indicators such as gross domestic product (GDP) [14][15][16], population [17,18], electricity and energy consumption [19][20][21], carbon emissions [22][23][24], and urbanization [25,26]. They are also used to extract urban built-up areas [27,28], analyze the evolution of urban agglomerations [29][30][31], and assess major disaster events [32,33].…”

Section: Introductionmentioning

confidence: 99%

Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China

et al. 2019

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Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale.

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Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets

Cited by 145 publications

References 57 publications

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China

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