Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO₂emission in Vietnam, Cambodia and Laos

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 CO 2 emissions, this study compares two common proxies used to disaggregate CO 2 emission estimates. We use a known gridded CO 2 model based on satelliteobserved nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO 2 , 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 CO 2 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.

Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities

“…The ODIAC nighttime light-based emission disaggregation is known to work less well in developing countries compared to developed countries, as the nightlight intensity sometime correlates poorly with human activity in developing countries [50,51]. In general, lots of low emissions in non-urban areas are missed in the light-based emission disaggregation for developing countries, and they tend to be assigned to cities instead [34].…”

Estimating CO<sub>2</sub> emissions for 108 000 European cities

“…The JRC EDGAR v6.0 (Joint Research Centre Emissions Database for Global Atmospheric Research; Crippa et al, 2020) and ODIAC (Open-Data Inventory for Anthropogenic Carbon dioxide; Oda and Maksyutov, 2011;Oda et al, 2018) inventories are wellestablished examples of global emission data products, but others have been developed (Andres et al, 1996(Andres et al, , 2016bAsefi-Najafabady et al, 2014;Nassar et al, 2013;Rayner et al, 2010;Wang et al, 2013), including some at the national/regional scale (Bun et al, 2019;Zheng et al, 2021a;Jones et al, 2021;Kurokawa et al, 2013;Meng et al, 2014). A number of these models use nighttime lights data as one input signal (or gridded population datasets, which in turn may be based on nighttime lights), though at least one study has found this is only moderately predictive (Gaughan et al, 2019).…”

Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam

“…Extensive information on urban land use, including urban construction and green coverage, helps planners prepare and arrange the structure of the urban transportation, water and green system, and determine the scale and directions of future urban development in urban master planning processes [3]. Moreover, crucial to the successful implementation of the Paris Agreement within the United Nations Framework Convention on Climate Change (UNFCCC) are accurate mapping and monitoring of greenhouse gases such as CO 2 [4]. Currently, fossil fuel CO 2 (FFCO2) emission is Remote Sens.…”

“…Founded on time-series satellite SAR data records consistently tracked at each pixel location, the method successfully detects and maps persistent (rather than temporary) building structures, which truly represent sustained human settlements in order to circumvent the shortfalls of the proxy indicator derived from NTL data [4], as illustrated in the case of Phan Thiết city versus the dragon fruit plantations in Bình Thuận (Figure 8). Such spatial data products of physical building structures are crucial for urban mapping applications, in particular for accurate estimations of FFCO2 emission required for the successful implementation of the UNFCCC Paris Agreement.…”