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

Abstract

Background Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed. Results This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (− 62%), New York City (− 45%), Washington D.C. (− 42%) and Toronto (− 33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC’s nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates. Conclusions The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.

A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories

“…At the prefectural city level for JJJ and other cities, the estimated differences generally increased from less than 10% to more than 300% when more inventories were included (Table 2) [5,[64][65][66]. Generally, the prefecturalcity-statistical data-based inventories of CHRED and CEADs were more consistent than the others.…”

The impact of human and livestock respiration on CO2 emissions from 14 global cities

“…The 14 reported global major cities according to the research of Chen et al [24] are selected as the study area and include Bangkok, Beijing, Shanghai, Delhi, Cape Town, Sao Paulo, Tokyo, Greater Paris, Greater London, Los Angeles, Manhattan, New York City, Washington D.C., and Greater Toronto (see Table 4 and Additional le 1: Figure S1). The de nitions of the 14 cities ranges from 'district' to 'metropolitan' (see also Table 4) [25].…”

“…The CO 2 from human and livestock respiration is directly emitted within global city boundaries, which is equivalent to scope 1 [26]. To compare the CO 2 emissions from human and livestock respiration within these global cities, we retrieved the in-boundary anthropogenic FFE from Chen et al [24], who estimated the total FFE directly within the city boundaries of these 14 cities and metropolitan areas around the world.…”

“…To match the years with FFE in 14 cities from Chen et al [24], we used linear interpolation to obtain the annual human respiration after extracting the total CO 2 release from each city every 5 years. For livestock, we assume that the trend of livestock production in each city is close to that of the country (for the data, please see Table 3).…”

“…We also compares the ratio of total respiration to in-boundary FFE associated with other sectors (Figure 6), including heating and industrial fuels, industrial processes, ground transportation, and large point sources, according to the results from Chen et al [24]. Based on the average of the 14 cities, the average contribution of human and livestock respiration is comparable to that of large point sources (in-boundary) and is greater than that of industrial processes.…”