the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Apr 2023
A global catalogue of CO2 emissions and co-emitted species from power plants at a very high spatial and temporal resolution
Santiago Enciso
Carles Tena
Oriol Jorba
Stijn Dellaert
Hugo Denier van der Gon
Carlos Pérez García-Pando
Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors and emission ratios. The resulting catalog contains annual emission information for more than 16000 individual facilities at their exact geographical location. Each facility is linked to a specific temporal (i.e., monthly, day-of-the-week and hourly) and vertical distribution profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including the Carbon Monitoring for Action (CARMA) and the Emissions Database for Global Atmospheric Research (EDGAR) databases, as well as officially reported emission data. An overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/mxjo-nram, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) EU-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).
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Marc Guevara et al.
Status: closed
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RC1: 'Comment on essd-2023-95', Anonymous Referee #1, 21 Jul 2023
This work presents a global emission dataset of CO2 and co-emitted species including NOx, SO2, CO and CH4 for power plants in 2018. The database assigned more than 16000 individual facilities with geographical locations. Temporal proxies and vertical spatial distribution factors were also provided. The data is publicly available to the community and can be used to support atmospheric modeling activities. Overall, the manuscript is written in a logical way. But lots of details regarding how the raw data is reported, how the gap filling is done, and how to harmonize various information are not clarified. On the other hand, authors should point out the assumptions clearly especially when “lending” information from one power plant to another. I suggest re-visiting the methodology section carefully with clearer statement. The figures and tables also need to be updated.
The detailed comments are listed below.
- Line 10: can you explain why using “catalogue” instead of “inventory” or just “dataset”?
- Line 13: what emission ratios? It’ll bring confusion to readers when you mention “ratios” in an abstract but not specify what it is.
- Line 14: as my understanding, the temporal and vertical distribution profiles are NOT facility-specific. It’s shared within a country. Please revise this.
- Line 51: can you add comparisons with GPED since they are bottom-up emission estimates at a facility-level.
- Section 2.1: I’m confused by the statement of “selection of facilities”. Where are the raw data reported to and what’s your criteria of selecting the data? Please specify.
- Line 80 – 90: Lots of information but not clear. Can you draw a global map with the data sources labeled? Or prepare a table?
- Line 80 – 90: Which year of these data are available?
- Line 95: what's the criterion when there are conflicts between different dataset? For example, how do you know two power plants are the same between two dataset? What if different capacities are reported for the “same” power plants from different dataset?
- Line 135 – 140: emission controls of NOx and SOx on power plants are widely applied over developed countries like the U.S. and developing countries like China. How do you take the emission control into consideration in your emission estimation?
- Line 150 – 180: please specify the assumption when gap-filling the emission values step-by-step.
- Line 192: The ratios can vary significantly by countries and fuel type due to pollution abatement strategies. It can introduce large uncertainties when assuming each country can share the same SOx/CO2 ratios for all power plant units.
- Line 235: So, the temporal profiles are not facility-specific. It’s country and fuel-dependent.
- Line 321: this dataset doesn’t include hydropower, right?
- Figure 5: please zoom in a little bit of the map (duplicate US and East Asia maps are shown). The circles are not clear. I suggest using colors to scale the annual emissions, but with various symbols (rectangles, circles, etc.) to denote the fuel type.
- Line 433: please double check this, or give references.
- Figure 10: the figure is hard to understand.
Citation: https://doi.org/10.5194/essd-2023-95-RC1 -
RC2: 'Comment on essd-2023-95', Anonymous Referee #2, 25 Jul 2023
This study constructed a global inventory of carbon and pollutant emissions from over 16,000 power plants in 2018, utilizing multiple data sources. Such work is foundational and significant. Despite the author's detailed methodological explanations and my agreement with his very good work on time allocation, I still have two methodological concerns:
- I have strong doubts about the accuracy of air pollutant emissions at the power plant level due to lack of data sources. The article attempts to fill the data gaps by using the proportional changes between pollutants and CO2. While this method may be reliable for CO and CH4, it may encounter problems when applied to SO2 and NOx due to variations in air pollution control levels. Need to explain more or switch to a better approach.
- The estimation of NOx and SO2 emissions in regions outside the United States and Europe based on the proportion of CO2 and pollutants in the eGRID database overlooks the differences in pollution control in different regions. Applying air pollution control levels from the United States to other regions may result in significant deviations.
Other minor concerns include:
- The resolution of Figure 5 is too low, making it difficult to interpret.
- It would be beneficial to include comparisons of other pollutants in Figure 6, particularly NOx and SO2.
- Can the current data sources and methods support the extension of the analysis to multiple years?
- The handling of temporal allocation methods is commendable.
Citation: https://doi.org/10.5194/essd-2023-95-RC2 -
RC3: 'Comment on essd-2023-95', Tomohiro Oda, 02 Aug 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-95/essd-2023-95-RC3-supplement.pdf
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AC1: 'Comment on essd-2023-95', Marc Guevara, 14 Oct 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-95/essd-2023-95-AC1-supplement.pdf
Status: closed
-
RC1: 'Comment on essd-2023-95', Anonymous Referee #1, 21 Jul 2023
This work presents a global emission dataset of CO2 and co-emitted species including NOx, SO2, CO and CH4 for power plants in 2018. The database assigned more than 16000 individual facilities with geographical locations. Temporal proxies and vertical spatial distribution factors were also provided. The data is publicly available to the community and can be used to support atmospheric modeling activities. Overall, the manuscript is written in a logical way. But lots of details regarding how the raw data is reported, how the gap filling is done, and how to harmonize various information are not clarified. On the other hand, authors should point out the assumptions clearly especially when “lending” information from one power plant to another. I suggest re-visiting the methodology section carefully with clearer statement. The figures and tables also need to be updated.
The detailed comments are listed below.
- Line 10: can you explain why using “catalogue” instead of “inventory” or just “dataset”?
- Line 13: what emission ratios? It’ll bring confusion to readers when you mention “ratios” in an abstract but not specify what it is.
- Line 14: as my understanding, the temporal and vertical distribution profiles are NOT facility-specific. It’s shared within a country. Please revise this.
- Line 51: can you add comparisons with GPED since they are bottom-up emission estimates at a facility-level.
- Section 2.1: I’m confused by the statement of “selection of facilities”. Where are the raw data reported to and what’s your criteria of selecting the data? Please specify.
- Line 80 – 90: Lots of information but not clear. Can you draw a global map with the data sources labeled? Or prepare a table?
- Line 80 – 90: Which year of these data are available?
- Line 95: what's the criterion when there are conflicts between different dataset? For example, how do you know two power plants are the same between two dataset? What if different capacities are reported for the “same” power plants from different dataset?
- Line 135 – 140: emission controls of NOx and SOx on power plants are widely applied over developed countries like the U.S. and developing countries like China. How do you take the emission control into consideration in your emission estimation?
- Line 150 – 180: please specify the assumption when gap-filling the emission values step-by-step.
- Line 192: The ratios can vary significantly by countries and fuel type due to pollution abatement strategies. It can introduce large uncertainties when assuming each country can share the same SOx/CO2 ratios for all power plant units.
- Line 235: So, the temporal profiles are not facility-specific. It’s country and fuel-dependent.
- Line 321: this dataset doesn’t include hydropower, right?
- Figure 5: please zoom in a little bit of the map (duplicate US and East Asia maps are shown). The circles are not clear. I suggest using colors to scale the annual emissions, but with various symbols (rectangles, circles, etc.) to denote the fuel type.
- Line 433: please double check this, or give references.
- Figure 10: the figure is hard to understand.
Citation: https://doi.org/10.5194/essd-2023-95-RC1 -
RC2: 'Comment on essd-2023-95', Anonymous Referee #2, 25 Jul 2023
This study constructed a global inventory of carbon and pollutant emissions from over 16,000 power plants in 2018, utilizing multiple data sources. Such work is foundational and significant. Despite the author's detailed methodological explanations and my agreement with his very good work on time allocation, I still have two methodological concerns:
- I have strong doubts about the accuracy of air pollutant emissions at the power plant level due to lack of data sources. The article attempts to fill the data gaps by using the proportional changes between pollutants and CO2. While this method may be reliable for CO and CH4, it may encounter problems when applied to SO2 and NOx due to variations in air pollution control levels. Need to explain more or switch to a better approach.
- The estimation of NOx and SO2 emissions in regions outside the United States and Europe based on the proportion of CO2 and pollutants in the eGRID database overlooks the differences in pollution control in different regions. Applying air pollution control levels from the United States to other regions may result in significant deviations.
Other minor concerns include:
- The resolution of Figure 5 is too low, making it difficult to interpret.
- It would be beneficial to include comparisons of other pollutants in Figure 6, particularly NOx and SO2.
- Can the current data sources and methods support the extension of the analysis to multiple years?
- The handling of temporal allocation methods is commendable.
Citation: https://doi.org/10.5194/essd-2023-95-RC2 -
RC3: 'Comment on essd-2023-95', Tomohiro Oda, 02 Aug 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-95/essd-2023-95-RC3-supplement.pdf
-
AC1: 'Comment on essd-2023-95', Marc Guevara, 14 Oct 2023
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-95/essd-2023-95-AC1-supplement.pdf
Marc Guevara et al.
Data sets
CoCO2 global emission point source database Marc Guevara, Santiago Enciso, Carles Tena, Oriol Jorba, Carlos Pérez Garcia-Pando, Stijn Dellaert, Hugo Denier van der Gon https://doi.org/10.24380/mxjo-nram
Marc Guevara et al.
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