Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos
Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos
Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
1
Gaughan, Andrea E.
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Oda, Tomohiro
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Sorichetta, Alessandro
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Stevens, Forrest R.
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Bondarenko, Maksym
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Bun, Rostyslav
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Krauser, Laura
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Yetman, Greg
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Nghiem, Son V.
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11 September 2019
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Oda, Tomohiro
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Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Stevens, Forrest R.
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Bondarenko, Maksym
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Bun, Rostyslav
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Krauser, Laura
80327e74-1ac1-4882-9b12-ca126e7f18ab
Yetman, Greg
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Nghiem, Son V.
adefb467-c15c-4092-863a-e7833765a6e9
Gaughan, Andrea E., Oda, Tomohiro, Sorichetta, Alessandro, Stevens, Forrest R., Bondarenko, Maksym, Bun, Rostyslav, Krauser, Laura, Yetman, Greg and Nghiem, Son V.
(2019)
Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos.
IoP Publishing, .
(doi:10.1088/2515-7620/ab3d91).
Abstract
Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
Text
Evaluating nighttime lights
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Submitted date: 28 June 2019
Accepted/In Press date: 21 August 2019
Published date: 11 September 2019
Identifiers
Local EPrints ID: 434120
URI: http://eprints.soton.ac.uk/id/eprint/434120
PURE UUID: 1884bd0a-32e7-4698-a043-ffd7fbe63f31
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Date deposited: 13 Sep 2019 16:30
Last modified: 17 Mar 2024 03:12
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Contributors
Author:
Andrea E. Gaughan
Author:
Tomohiro Oda
Author:
Forrest R. Stevens
Author:
Rostyslav Bun
Author:
Laura Krauser
Author:
Greg Yetman
Author:
Son V. Nghiem
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