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

Zhang, Xueyuan; Xie, Yaowen; Jiao, Jizong; Zhu, Wanyang; Guo, Zhang; Cao, Xiaoyan; Liu, Shiyuan; Xi, Guilin; Wei, Wei

doi:10.1016/j.jclepro.2023.136656

Cited by 21 publications

(1 citation statement)

References 32 publications

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“…Letu [32] also attempted to estimate CO 2 emissions from power plants in Japan in 2006 using DMSP-OLS, demonstrating the high estimation accuracy of the system's radiance-calibrated image product. Additionally, there are studies that combine NPP-VIIRS and POI data to estimate land-use-specific CO 2 emissions for the 2010s [33,34]. Yang et al [35] estimated the spatial distribution of CO 2 emissions using NPP-VIIRS data for the Pearl River Delta Region, China, in 2017.…”

Section: Urban Structures and Ghg Emission Estimates Using Nighttime ...mentioning

confidence: 99%

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

Kii,

Matsumoto,

Sugita

2024

Remote Sensing

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As of 2018, approximately 55% of the world’s population resides in cities, and it is projected that this proportion will reach 68% by 2050. Population growth in urban areas leads to various impacts on society and the environment. In this study, we have developed a method for generating future scenarios of nighttime lights. What makes this method unique is its ability to (1) generate future gridded nighttime light intensity scenarios for cities, (2) generate future scenarios that preserve the distribution pattern of nighttime light intensity, and (3) generate scenarios that reflect urban policies. By applying this developed method, we have estimated nighttime light data for 555 cities worldwide and predicted future urban expansion and changes in carbon emissions for each SSP scenario. Consequently, both urban areas and carbon emissions are estimated to increase for the entire set of target cities, with patterns varying among cities and scenarios. This study contributes to the advancement of urban scenario research, including the estimation of future urban area expansion and carbon emissions.

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Section: Urban Structures and Ghg Emission Estimates Using Nighttime ...mentioning

confidence: 99%

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

Kii,

Matsumoto,

Sugita

2024

Remote Sensing

View full text Add to dashboard Cite

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HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China

Song,

Zhan,

et al. 2023

Computers and Electronics in Agriculture

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Optimal agricultural structure allocation based on carbon source/sink accounting

Cui,

Wang,

et al. 2024

Ecological Indicators

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

Cited by 21 publications

References 32 publications

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China

Optimal agricultural structure allocation based on carbon source/sink accounting

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