Creating a Global Grid of Distributed Fossil Fuel CO 2 Emissions from Nighttime Satellite Imagery

Tilottama Ghosh, Christopher D. Elvidge, Paul C. Sutton, Kimberly E. Baugh, Daniel Ziskin and Benjamin T. Tuttle
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Tilottama Ghosh: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Christopher D. Elvidge: Earth Observation Group, Solar and Terrestrial Physics Division, NOAA National Geophysical Data Center, 325 Broadway, Boulder, CO 80305, USA
Paul C. Sutton: Department of Geography, University of Denver, Denver, CO 80208, USA
Kimberly E. Baugh: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Daniel Ziskin: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309, USA
Benjamin T. Tuttle: Department of Geography, University of Denver, Denver, CO 80208, USA

Energies, 2010, vol. 3, issue 12, 1-19

Abstract: The potential use of satellite observed nighttime lights for estimating carbon-dioxide (CO 2 ) emissions has been demonstrated in several previous studies. However, the procedures for a moderate resolution (1 km 2 grid cells) global map of fossil fuel CO 2 emissions based on nighttime lights are still in the developmental phase. We report on the development of a method for mapping distributed fossil fuel CO 2 emissions (excluding electric power utilities) at 30 arc-seconds or approximately 1 km 2 resolution using nighttime lights data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS). A regression model, Model 1, was initially developed based on carbon emissions from five sectors of the Vulcan data produced by the Purdue University and a nighttime satellite image of the U.S. The coefficient derived through Model 1 was applied to the global nighttime image but it resulted in underestimation of CO 2 emissions for most of the world’s countries, and the states of the U.S. Thus, a second model, Model 2 was developed by allocating the distributed CO 2 emissions (excluding emissions from utilities) using a combination of DMSP-OLS nighttime image and population count data from the U.S. Department of Energy's (DOE) LandScan grid. The CO 2 emissions were distributed in proportion to the brightness of the DMSP nighttime lights in areas where lighting was detected. In areas with no DMSP detected lighting, the CO 2 emissions were distributed based on population count, with the assumption that people who live in these areas emit half as much CO 2 as people who live in the areas with DMSP detected lighting. The results indicate that the relationship between satellite observed nighttime lights and CO 2 emissions is complex, with differences between sectors and variations in lighting practices between countries. As a result it is not possible to make independent estimates of CO 2 emissions with currently available coarse resolution panchromatic satellite observed nighttime lights. However, the nighttime lights image in conjunction with the population grid can help in more accurate disaggregation of national CO 2 emissions to a moderate resolution spatial grid.

Keywords: CO 2 grid; nighttime satellite image; LandScan population grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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