Regional-Scale Estimation of Electric Power and Power Plant CO2 Emissions Using Defense Meteorological Satellite Program Operational Linescan System Nighttime Satellite Data

Estimation of electric power and power plant CO2 emissions using satellite remote sensing data is essential for the management of energy consumption and greenhouse gas monitoring. For estimation, the relationship between Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS)...

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Veröffentlicht in:Environmental science & technology letters 2014-05, Vol.1 (5), p.259-265
Hauptverfasser: Letu, Husi, Nakajima, Takashi Y, Nishio, Fumihiko
Format: Artikel
Sprache:eng ; jpn
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Zusammenfassung:Estimation of electric power and power plant CO2 emissions using satellite remote sensing data is essential for the management of energy consumption and greenhouse gas monitoring. For estimation, the relationship between Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) annual nighttime stable light product (NSL) for 2006 and statistical data on power generation, power consumption, and power plant CO2 emissions in 10 electric power supply regions of Japan was investigated. Unlike other power plants, thermal plants directly emit CO2 by burning fossil fuels when generating electricity. Among the nighttime lights in the NSL, only light from thermal power is related to power plant CO2 emission. The percentage of thermal power generation to total power generation (K%) is thus a key parameter for estimating nighttime light by power consumption from thermal power plants. In this study, the DMSP/OLS annual nighttime radiance-calibrated product (RCI) for 2006 and the NSL data corrected by K% were employed to estimate electric power and power plant CO2 emissions. Results indicated that the RCI data can offer more accurate estimates of electric power consumption than can the NSL data. It was also found that NSL and RCI data corrected by K% are good proxies for estimating power plant CO2 emissions.
ISSN:2328-8930
2328-8930
DOI:10.1021/ez500093s