Dataset Information

Near-real-time daily estimates of fossil fuel CO₂ emissions from major high-emission cities in China.

ABSTRACT: Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO₂ emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO₂ emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models. Annual emissions show reasonable agreement with other datasets, and uncertainty ranges are estimated for each city and sector. CMCC provides valuable daily emission estimates that enable low-latency mitigation monitoring for cities in China.

SUBMITTER: Huo D

PROVIDER: S-EPMC9648454 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Near-real-time daily estimates of fossil fuel CO2 emissions from major high-emission cities in China.

Huo Da D Liu Kai K Liu Jianwu J Huang Yingjian Y Sun Taochun T Sun Yun Y Si Caomingzhe C Liu Jinjie J Huang Xiaoting X Qiu Jian J Wang Haijin H Cui Duo D Zhu Biqing B Deng Zhu Z Ke Piyu P Shan Yuli Y Boucher Olivier O Dannet Grégoire G Liang Gaoqi G Zhao Junhua J Chen Lei L Zhang Qian Q Ciais Philippe P Zhou Wenwen W Liu Zhu Z

Scientific data 20221110 1

Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO2 emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO2 emissions from fossil fuel and cement pro ...[more]

PMID: 36357411

Similar Datasets

Project description: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.

Project description:We developed a near-real-time estimation method for temporal changes in fossil fuel CO2 (FFCO2) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO2 and CH4 observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO2 and CH4 (ΔCO2/ΔCH4) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO2 and CH4 fluxes, we found that the ΔCO2/ΔCH4 ratio was linearly related to the FFCO2/CH4 emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO2/ΔCH4 ratios into FFCO2/CH4 emission ratios in China. The change rates of the emission ratios for 2020-2022 were calculated relative to those for the preceding 9-year period (2011-2019), during which relatively stable ΔCO2/ΔCH4 ratios were observed. These changes in the emission ratios can be read as FFCO2 emission changes under the assumption of no interannual variations in CH4 emissions and biospheric CO2 fluxes for JFM. The resulting average changes in the FFCO2 emissions in January, February, and March 2020 were 17 ± 8%, - 36 ± 7%, and - 12 ± 8%, respectively, (- 10 ± 9% for JFM overall) relative to 2011-2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, - 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, - 3 ± 10%, and - 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO2 emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai.Supplementary informationThe online version contains supplementary material available at 10.1186/s40645-023-00542-6.

Project description:BackgroundSatellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO2 concentration plumes generated by emission hotspots. CO2 emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO2 emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO2 emissions. This study assesses the uncertainties in the inversion estimates of emissions ("posterior uncertainties").ResultsThe posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO2 plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO2 retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance.ConclusionsFor all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30-11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps.

Dataset Information

Near-real-time daily estimates of fossil fuel CO₂ emissions from major high-emission cities in China.

Publications

Near-real-time daily estimates of fossil fuel CO<sub>2</sub> emissions from major high-emission cities in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets