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ID:
098207
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| Publication |
2010.
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| Summary/Abstract |
We evaluate the joint use of satellite-observed intensity of urban nightlights and census-based population density data as constraints on the spatial structure of CO2 emissions from fossil fuels. Findings are: (1) the probability that population density exceeds a given value follows a power-law distribution over two orders of magnitude of population density, encompassing the 40% of the global population at the highest densities. (2) The corresponding probability distribution for nightlights intensity also follows a power-law, departing near instrumental saturation. (3) Assuming that the true nightlights intensity distribution follows the power-law above instrumental saturation, we obtain a correction for saturation errors in the nightlights data. The amplification of nightlights intensity required to correct for saturation errors is estimated to be a factor of 1.15-1.23 globally and much greater in regions with high nightlights intensities. (4) Correcting for saturation, we observe clear empirical relationships between nightlights intensity and areal densities of energy consumption, fossil-fuel emissions and economic activity, holding throughout the development spectrum. (5) We indicate how these relationships underpin a fossil-fuel data assimilation system (FFDAS) for estimating fossil-fuel CO2 emissions.
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| 2 |
ID:
098535
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| Publication |
2010.
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| Summary/Abstract |
We evaluate the joint use of satellite-observed intensity of urban nightlights and census-based population density data as constraints on the spatial structure of CO2 emissions from fossil fuels. Findings are: (1) the probability that population density exceeds a given value follows a power-law distribution over two orders of magnitude of population density, encompassing the 40% of the global population at the highest densities. (2) The corresponding probability distribution for nightlights intensity also follows a power-law, departing near instrumental saturation. (3) Assuming that the true nightlights intensity distribution follows the power-law above instrumental saturation, we obtain a correction for saturation errors in the nightlights data. The amplification of nightlights intensity required to correct for saturation errors is estimated to be a factor of 1.15-1.23 globally and much greater in regions with high nightlights intensities. (4) Correcting for saturation, we observe clear empirical relationships between nightlights intensity and areal densities of energy consumption, fossil-fuel emissions and economic activity, holding throughout the development spectrum. (5) We indicate how these relationships underpin a fossil-fuel data assimilation system (FFDAS) for estimating fossil-fuel CO2 emissions.
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