A multiyear, global gridded fossil fuel CO₂ emission data product: Evaluation and analysis of results

Abstract

High-resolution, global quantification of fossil fuel CO 2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high-resolution fossil fuel CO 2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long-term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long-term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter-term variations reveals the impact of the 2008-2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO 2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO 2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set.

Targeted emission reductions from global super-polluting power plant units

“…There are a few databases of global power plants available for CO 2 emissions, for example, the Carbon Monitoring for Action (CARMA) database 8 and an improved version of the Fossil Fuel Data Assimilation System (FFDAS) database 31 . CARMA has been widely used in bottom-up emission inventories to allocate power plant emissions 6 , which estimated plant-level CO 2 Global PM 2.5 emissions (kt per year) 500 1,000 1,500 2 ,000 2 ,500 0 3,000 R e p la c e w it h h ig h e c ie n c y Nature SuStaiNability emission factor of each power plant, and then calculating CO 2 emissions based on these inputs 8 .…”

“…CARMA has been widely used in bottom-up emission inventories to allocate power plant emissions 6 , which estimated plant-level CO 2 Global PM 2.5 emissions (kt per year) 500 1,000 1,500 2 ,000 2 ,500 0 3,000 R e p la c e w it h h ig h e c ie n c y Nature SuStaiNability emission factor of each power plant, and then calculating CO 2 emissions based on these inputs 8 . An update of FFDAS utilizes an updated and improved global power plant emission data product that includes improved location information and individual power plant uncertainties 31 , which uses data from both public disclosure data and the WEPP database.…”

“…A publicly available, consistent global power plant emission dataset with detailed information can provide a firm basis for such discussions, for example, by highlighting effective ways to mitigate air pollution. Previous studies have compiled global and regional power plant CO 2 emission databases 8,[29][30][31] or regional databases for air pollutant emissions 6,9,10 , and noted the potential for substantial emission reductions from addressing a disproportionately small share of power plants [32][33][34] . Here, we develop a new global database of CO 2 , SO 2 , NO x and primary PM 2.5 emissions from fossil-fuel-and biomass-burning power-generating units as of 2010, which we name the Global Power Emissions Database (GPED); use it to identify the most-polluting units by region, fuel type and pollutant; quantify the disproportionalities of generating capacity and air pollutant emissions; and in each case highlight the best opportunities for reducing those undesirable emissions.…”

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…For example, Oda and Maksyutov (2011) develop ODIAC (Open-source Data Inventory of Anthropogenic CO 2 ), a global gridded CO 2 inventory constructed using a database of CO 2 point sources and satellite images of lights at night. Rayner et al (2010) and Asefi-Najafabady et al (2014) develop a data assimilation framework known as FFDAS (Fossil Fuel Data Assimilation System). The authors use datasets like population density, carbon intensity of energy, and satellite images of lights at night, and they report national emission totals.…”

Cities, traffic, and CO 2 : A multidecadal assessment of trends, drivers, and scaling relationships

“…Used alone, population may be a valid predictor for residential and commercial sector emissions, but it performs poorly when used to model emissions from power stations or the on-road sector (3,4,13). Recent global inventories, such as the Fossil Fuel Data Assimilation System, partially correct for this deviation by modeling power plant emissions directly as point sources, although on-road emissions are still spatially allocated using population and luminosity data (15). The Emissions Database for Global Atmospheric Research (EDGAR, version 4.2) used a wide variety of sector-specific variables to allocate national CO 2 emissions onto a 0.1°global grid (14), but it used only road density to distribute emissions spatially (16).…”