Showing papers by "Monica Crippa published in 2021"

A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018

Abstract: Global greenhouse gas emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review we synthesize the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of greenhouse gas emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Both the literature and data emphasize limited progress towards reducing greenhouse gas emissions. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases

Abstract: . The Emissions Database for Global Atmospheric Research (EDGAR) estimates the human-induced emission rates on Earth. EDGAR collaborates with atmospheric modelling activities and aids policy in the design of mitigation strategies and in evaluating their effectiveness. In these applications, the uncertainty estimate is an essential component, as it quantifies the accuracy and qualifies the level of confidence in the emission. This study complements the EDGAR emissions inventory by providing an estimation of the structural uncertainty stemming from its base components (activity data, AD, statistics and emission factors, EFs) by (i) associating uncertainty to each AD and EF characterizing the emissions of the three main greenhouse gases (GHGs), namely carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O); (ii) combining them; and (iii) making assumptions regarding the cross-country uncertainty aggregation of source categories. It was deemed a natural choice to obtain the uncertainties in EFs and AD statistics from the Intergovernmental Panel on Climate Change (IPCC) guidelines issued in 2006 (with a few exceptions), as the EF and AD sources and methodological aspects used by EDGAR have been built over the years based on the IPCC recommendations, which assured consistency in time and comparability across countries. On the one hand, the homogeneity of the method is one of the key strengths of EDGAR, on the other hand, it facilitates the propagation of uncertainties when similar emission sources are aggregated. For this reason, this study aims primarily at addressing the aggregation of uncertainties' sectorial emissions across GHGs and countries. Globally, we find that the anthropogenic emissions covered by EDGAR for the combined three main GHGs for the year 2015 are accurate within an interval of −15 % to +20 % (defining the 95 % confidence of a log-normal distribution). The most uncertain emissions are those related to N 2 O from waste and agriculture, while CO 2 emissions, although responsible for 74 % of the total GHG emissions, account for approximately 11 % of global uncertainty share. The sensitivity to methodological choices is also discussed.

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

Abstract: . A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d ) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC tr ) and the explained variation of m / z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of m / z 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m−3 (7.8 %–9.0 %) and 0.7–1.2 µg m−3 (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m−3 , 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m−3 , or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m−3 , 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m−3 (26.5 %) and 2.2 µg m−3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m−3 (3.4 %–15.9 %), from 0.6 to 2.2 µg m−3 (7.7 %–33.7 %) and from 0.9 to 3.1 µg m−3 (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 % , ±27 % , ±30 % , ±11 % , ±25 % and ±12 % , respectively.

Climate change in a changing world: Socio-economic and technological transitions, regulatory frameworks and trends on global greenhouse gas emissions from EDGAR v.5.0

Abstract: Over the last three decades, socio-economic, demographic and technological transitions have been witnessed throughout the world, modifying both sectorial and geographical distributions of greenhouse gas (GHG) emissions. Understanding these trends is central to the design of current and future climate change mitigation policies, requiring up-to-date methodologically robust emission inventories such as the Emissions Database for Global Atmospheric Research (EDGAR), the European Commission’s in-house, independent global emission inventory. EDGAR is a key tool to track the evolution of GHG emissions and contributes to quantifying the global carbon budget, providing independent and systematically calculated emissions for all countries. According to the results of the EDGAR v.5.0 release, total anthropogenic global greenhouse gas emissions (excluding land use, land use change and forestry) were estimated at 49.1 Gt CO2eq in 2015, 50 % higher than in 1990, despite a monotonic decrease in GHG emissions per unit of economic output. Between 1990 and 2015, emissions from developed countries fell by 9%, while emissions from low to medium income countries increased by 130%, predominantly from 2000 onwards. The 27 Member States of the European Union and the United Kingdom led the pathway for emission reductions in industrialised economies whilst, in developing countries, the rise in emissions was driven by higher emissions in China, India, Brazil and nations in the South-East Asian region. This diversity of patterns shows how different patterns for GHG emissions are and the need for identifying regionally tailored emission reduction measures.

Global anthropogenic emissions in urban areas: patterns, trends, and challenges

Abstract: Between 1970 and 2015 urban population almost doubled worldwide with the fastest growth taking place in developing regions. To aid the understanding of how urbanisation has influenced anthropogenic CO 2 and air pollutant emissions across all world regions, we make use of the latest developments of the Emissions Database for Global Atmospheric Research. In this study, we systematically analyse over 5 decades of emissions from different types of human settlements (from urban centres to rural areas) for different sectors in all countries. Our analysis shows that by 2015, urban centres were the source of a third of global anthropogenic greenhouse gases and most of the air pollutant emissions. The high levels of both population and emissions in urban centres therefore call for focused urban mitigation efforts. Moreover, despite the overall increase in urban emissions, megacities with more than 10 million inhabitants in high-income countries have been reducing their emissions, while emissions in developing regions are still growing. We further discuss per capita emissions to compare different types of urban centres at the global level.

Simulation of organics in the atmosphere: evaluation of EMACv2.54 with the Mainz Organic Mechanism (MOM) coupled to the ORACLE (v1.0) submodel

Abstract: . An updated and expanded representation of organics in the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) has been evaluated. First, the comprehensive Mainz Organic Mechanism (MOM) in the submodel MECCA (Module Efficiently Calculating the Chemistry of the Atmosphere) was activated with explicit degradation of organic species up to five carbon atoms and a simplified mechanism for larger molecules. Second, the ORACLE submodel (version 1.0) considers now condensation on aerosols for all organics in the mechanism. Parameterizations for aerosol yields are used only for the lumped species that are not included in the explicit mechanism. The simultaneous usage of MOM and ORACLE allows an efficient estimation, not only of the chemical degradation of the simulated volatile organic compounds, but also of the contribution of organics to the growth and fate of (organic) aerosol, with a complexity of the mechanism largely increased compared to EMAC simulations with more simplified chemistry. The model evaluation presented here reveals that the OH concentration is well reproduced globally, while significant biases for observed oxygenated organics are present. We also investigate the general properties of the aerosols and their composition, showing that the more sophisticated and process-oriented secondary aerosol formation does not degrade the good agreement of previous model configurations with observations at the surface, allowing further research in the field of gas-aerosol interactions.

A comparative study of anthropogenic CH 4 emissions over China based on the ensembles of bottom-up inventories

Abstract: . Atmospheric methane (CH 4 ) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH 4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH 4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH 4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH 4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH 4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH 4 yr −1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH 4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2017

Abstract: Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al., 2020b).

Global anthropogenic CO 2 emissions and uncertainties as prior forEarth system modelling and data assimilation

Abstract: . Anthropogenic carbon dioxide (CO2) emissions and their observed growing trends raise awareness in scientific, political and public sectors of the society as the major driver of climate-change. For an increased understanding of the CO2 emission sources, patterns and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. In this study anthropogenic CO2 emission inventories are processed into gridded maps to provide an estimate of prior CO2 emissions for 7 main emissions groups: 1) power generation super-emitters and 2) energy production average-emitters, 3) manufacturing, 4) settlements, 5) aviation, 6) transport and 7) others, with estimation of their uncertainty and covariance to be included in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The emission inventories are sourced from the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines for National Greenhouse Gas Inventories and revised information from its 2019 Refinements, and the global grid-maps of Emissions Database for Global Atmospheric Research (EDGAR) inventory. The anthropogenic CO2 emissions for 2012 and 2015, (EDGAR versions 4.3.2 and 4.3.2_FT2015 respectively) are considered, updated with improved apportionment of the energy sector, energy usage for manufacturing and diffusive CO2 emissions from coal mines. These emissions aggregated into 7 ECMWF groups with their emission uncertainties are calculated per country considering its statistical infrastructure development level and sector considering the most typical fuel type and use the IPCC recommended error propagation method assuming fully uncorrelated emissions to generate covariance matrices of parsimonious dimension (7×7). While the uncertainty of most groups remains relatively small, the largest contribution to the total uncertainty is determined by the group with usually the smallest budget, consisting of oil refineries and transformation industry, fuel exploitation, coal production, agricultural soils and solvents and products use emissions. Several sensitivity studies are performed: for country type (with well-/less well-developed statistical infrastructure), for fuel type specification, and for national emission source distribution (highlights the importance of 30 accurate point source mapping). Uncertainties are compared with United Nations Framework Convention on Climate Change (UNFCCC) and the Netherlands Organisation for Applied Scientific Research (TNO) data. Upgraded anthropogenic CO2 emission maps with their yearly and monthly uncertainties are combined into the CHE_EDGAR-ECMWF_2015 dataset (Choulga et al., 2020) available from https://doi.org/10.5281/zenodo.3712339 .

Sensitivity of air quality modelling to different emission inventories: A case study over Europe

Abstract: In this paper we run the EMEP air quality model with 3 state-of-the-art emission inventories for Europe, that is to say with CAMS-REG-AP, EMEP emissions, and EDGAR. The main aim of the work is to evaluate if the EDGAR inventory (despite its global coverage and independent approach to emission evaluation) is accurate enough, to be used for air quality modelling in Europe. To do so, the EMEP model has been run for the entire year 2015 using the 3 aforementioned emission inventories, and the resulting concentrations have been compared with ‘background’ monitoring stations. Results show that the air quality model, run with the 3 emission inventories, provide similar results despite important differences in emissions. EDGAR emissions provide slightly better validation results for PM2.5, while the EMEP emissions lead to slightly better results for yearly average NO2. The main differences among the model applications arise in the Eastern part of Europe, where the deviations between the officially estimated emissions and those independently evaluated by EDGAR are more important. Results suggest that EDGAR, despite being a methodology aimed at global coverage, with independent sources for activity level, technologies and emission factors and generic gridding practices, can be effectively used for air quality modelling in Europe. The EDGAR dataset (v5.0) used in this paper is available at: https://doi.pangaea.de/10.1594/PANGAEA.921922 .

A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019

Abstract: . To track progress towards keeping global warming well below 2 ∘ C or even 1.5 ∘ C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO 2 emissions, CH 4 emissions, N 2 O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF 6 , NF 3 ) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO 2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species ( ∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 Gt CO2 eq. consisting of CO 2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO 2 , CO 2 -LULUCF 5.7 ± 4.0 GtCO 2 , CH 4 10 ± 3.1 Gt CO2 eq. , N 2 O 2.6 ± 1.6 Gt CO2 eq. , and F-gases 1.3 ± 0.40 Gt CO2 eq. Initial estimates suggest further growth of 1.3 Gt CO2 eq. in GHG emissions to reach 59 ± 6.6 Gt CO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr −1 ) compared to 2000–2009 (2.4 % yr −1 ), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761 .

Global Gridded Daily CO$_2$ Emissions

Abstract: Precise and high-resolution carbon dioxide (CO$_2$) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO$_2$ Emission Datasets (called GRACED) from fossil fuel and cement production with a global spatial-resolution of 0.1$^\circ$ by 0.1$^\circ$ and a temporal-resolution of 1-day. Gridded fossil emissions are computed for different sectors based on the daily national CO$_2$ emissions from near real time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Carbon Grid (GID), Emission Database for Global Atmospheric Research (EDGAR) and spatiotemporal patters of satellite nitrogen dioxide (NO$_2$) retrievals. Our study on the global CO$_2$ emissions responds to the growing and urgent need for high-quality, fine-grained near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors between 2019 and 2020. This help us to give insights on the relative contributions of various sectors and provides a fast and fine-grained overview of where and when fossil CO$_2$ emissions have decreased and rebounded in response to emergencies (e.g. COVID-19) and other disturbances of human activities than any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will allow policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.