the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Aug 2026
HBPG-CO2: a global gridded monthly anthropogenic CO2 emission dataset at 0.05° resolution for 1970–2025
Abstract. Carbon dioxide emissions, as geospatial information, are necessary to analyze seasonal and regional variability, as well as the changing distribution of anthropogenic activity. However, the availability of continuous data with high spatial and temporal resolution is limited, both sectorial and global. Therefore, in this study, we present the Hierarchical Bayesian Physics-Guided Carbon Dioxide (HBPG-CO2) emission dataset, a global dataset of emissions from multiple sources for energy, industry, buildings, land transport, agriculture, waste, and fuel production. This dataset is based on multi-information harmonization and downscaling using geospatial information from multiple sources (nighttime lighting, roads, land use, population, settlements, meteorology, and point source data support temporal reconstruction and spatial refinement), various emissions inventories such as annual and monthly data from the Emissions Database for Global Atmospheric Research (EDGAR), as well as the Open source Data Inventory for Anthropogenic Carbon dioxide (ODIAC), Community Emissions Data System (CEDS), and daily estimates by country and sector. To integrate all the data, a hierarchical Bayesian component is applied to reconstruct monthly variability outside the directly observed period, while a physics-based component restricts the specific spatial allocation for each sector and preserves emissions data, while also allowing for area expansion based on decision trees. Furthermore, the integration of fossil fuel-related covariates that are underrepresented or absent in the original EDGAR fields helps reconstruct this dataset spatially and temporally. The resulting dataset contains 672 monthly layers per sector and global layers corresponding to the date range from 1970-01 to 2025-12. Comparisons with CEDS, ODIAC, EDGAR, and other studies show strong temporal and spatial consistency, while also preserving additional small-scale structure. Therefore, HBPG-CO2 provides a long-term, high-resolution, sector-specific emissions record for atmospheric inversion, Earth system modeling, carbon cycle analysis, and regional emissions assessment. The complete HBPG-CO₂ dataset is openly accessible through Zenodo at https://doi.org/10.5281/zenodo.21507026 (Antezana Lopez, 2026).
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RC1: 'Comment on essd-2026-590', Anonymous Referee #1, 03 Sep 2026
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AC1: 'Reply on RC1', Franz Pablo Antezana Lopez, 01 Oct 2026
1. Temporal reconstruction for 1970–1999.
We agree that relying mainly on post 2000 seasonality could limit the historical reconstruction. We therefore revised the temporal model to separate long term annual evolution from within year seasonality. Annual trends are constrained using multiple compatible inventories, while monthly variability is reconstructed only from datasets with genuine subannual information. In particular, MEIC and the daily CO₂ dataset of Li et al., covering 1970–2024 and aggregated to monthly resolution, provide additional information on seasonal variability where their geographic and sectoral coverage is compatible. An empirical hierarchical Bayesian formulation is then used to reconstruct monthly composition, while the pre 2000 period is explicitly identified as reconstructed rather than observed. We also added pseudo hindcast experiments to evaluate how well the seasonal reconstruction transfers across time.2. Independent validation.
We agree that comparisons with datasets contributing to construction should not be interpreted as independent validation. The revised manuscript now clearly separates internal consistency checks from external evaluation. Vulcan V4 is used as an independent high resolution benchmark over the United States and CAMS REG over Europe, with supplementary spatial correlations of approximately \(r=0.80\) and \(r=0.91\), respectively. The previous CEDS waste slope of 11.37 is now treated as evidence of differences in sector definitions, source coverage, and accounting rather than as a measure of model performance, and the corresponding sector crosswalk is discussed explicitly.3. “Physics guided” terminology.
We agree and have removed this term. The framework is now called MCR CO₂, Multisource Constraint based Reconstruction of CO₂ Emissions. The spatial component is described as constraint based and process informed, using sector specific spatial evidence, non negativity, bounded redistribution, eligibility constraints, and exact mass conservation. This terminology more accurately reflects the statistical and accounting nature of the reconstruction. moreover, we have done some modificaations to the proccedure to enhance results and reduce error.4. Improvement over simple interpolation.
The revised evaluation now compares MCR CO₂ with mass conserving equal allocation and interpolation baselines using the same 0.10° parent cell mass. Spatial correlation, RMSE or MAE, and relative improvement are used to quantify the added value of the multisource 0.05° reconstruction. In addition, masked support recovery experiments test whether previously unresolved emission locations can be recovered without excessive false activation. Importantly, the recurrent LSTM component is also used to identify plausible source environments in cells that are zero in EDGAR or other baseline emission fields, using sector specific covariate patterns learned from locations where emissions are already represented. Accepted locations receive emissions only through bounded redistribution, so the procedure increases spatial information without creating additional emission mass.Overall, the main contribution of MCR CO₂ is the integration of a long 1970–2025 emission record with multiple independent sources of temporal and spatial information within a single reconstruction framework. Long term trends, monthly seasonality, high resolution spatial allocation, and unresolved source support are treated separately and then reconciled under the same mass conserving accounting system. This allows the dataset not only to extend monthly anthropogenic CO₂ emissions further back in time, but also to recover spatial structures and plausible emission areas that are incompletely represented in existing global inventories. The revised manuscript includes these methodological changes, additional validation, updated figures, and supplementary comparisons. The complete revised PDF will be available within the next few days.
Citation: https://doi.org/10.5194/essd-2026-590-AC1 -
AC2: 'Reply on RC1', Franz Pablo Antezana Lopez, 26 Oct 2026
Referee 1 R1.1 Historical seasonality, 1970 to 1999
Comment summary. The reconstruction of historical monthly timing, particularly for China and the former Soviet region, needs stronger support.
Response. We have clarified the distinction between EDGAR national monthly series, available from 1970, and the 0.10° monthly sector grids used from 2000. Historical reconstruction is tested against equal-month and fitting-period-mean baselines and by withholding individual sources. Before 2000, removing Li et al. increases median Buildings monthly-share RMSE by 0.425 percentage points across 23 matched cases. Removing MEIC increases Industry error by 0.136 points across 213 cases. Gains over a transferred seasonal mean remain small and sector dependent, with historical-window median differences up to about 0.02 points. The country and historical-window diagnostics make this variation explicit. We therefore describe the early monthly record as an evaluated reconstruction supported by available monthly information.
Manuscript changes. Sections 2.1, 2.3 and 4.3; Fig. S3; Tables 2 and 3.
R1.2 Independent regional evidence and Waste interpretation
Comment summary. Agreement with source inventories does not constitute independent validation, and the Waste comparison requires explanation.
Response. We now distinguish consistency with contributing inventories from spatial evaluation using distinct regional products. Against CAMS-REG in Europe, MCR-CO₂ increases JSS from 46.2% for EDGAR to 48.0% and lowers normalised RMSE from 10.976 to 8.957. Against Vulcan V4 in the United States, JSS rises from 37.6% to 47.8% and normalised RMSE falls from 10.345 to 9.942. European hotspot centroid displacement increases from 159 to 224 km, which remains reported. MCR-CO₂ and EDGAR use the same four-sector bundle, while the regional references retain their available fossil totals. We consequently interpret these results as spatial pattern concordance with explicit accounting boundaries. ODIAC comparisons assess consistency because ODIAC also contributes to allocation. The Waste discrepancy is addressed through a process-scope audit. The discussion-paper slope of 11.37 arose from comparing unmatched categories. CEDS 5C versus EDGAR Waste gives a median ratio of 23.46 and r = 0.309, whereas scope-matched EDGAR WASTE_INC reproduces EDGAR Waste (r = 1.000, slope = 1.000). The current CEDS Waste association (log-scale r = 0.16, slope = 0.13) and Fuel slope (0.36) remain visible and are interpreted as category differences.
Manuscript changes. Sections 2.1, 2.7, 4.2 and 4.4; Figs. 9, 14b, S2 and S4; Table S1.
R1.3 Interpretation of physical constraints
Comment summary. Mass conservation and nonnegativity do not, by themselves, represent atmospheric physics.
Response. We agree and have renamed the framework Multisource Constraint-based Reconstruction of CO₂ emissions (MCR-CO₂). The constraints are accounting rules comprising mass conservation, non-negativity, process eligibility and bounded transfers. They do not represent atmospheric transport or combustion physics. Sections 2.5–2.6 and Fig. 1 distinguish the parent-cell conservation used during disaggregation from country–sector–month conservation during support redistribution.
Manuscript changes. Title, Abstract and Introduction; Sections 2.3, 2.5 and 2.6; Fig. 1.
R1.4 Added value relative to simple spatial allocation
Comment summary. The study should quantify what is gained beyond a simple interpolation or redistribution of coarse emission maps.
Response. We distinguish regional product evaluation from controlled allocation sensitivity. CAMS-REG and Vulcan comparisons show higher JSS and lower normalised RMSE for MCR-CO₂ than for EDGAR in the tested domains. These comparisons include budget, placement and reference-sector coverage differences. With every 0.10° parent mass held fixed, predictor-removal experiments and uniform-allocation alternatives isolate how geographical information affects the fine grid. In the June 2024 North American window, the uniform alternative reallocates 64.6% of Power mass and 50.4% of Industry mass, measured by TVD. These are allocation sensitivities rather than observed accuracy improvements. Operational LSTM support transfers affect Agriculture and Transport only, with monthly pools bounded at 4.0% and 2.0% of global sector mass. Those sectors are excluded from the four-sector MCR-CO₂ and EDGAR bundle used in the regional comparison. The revised interpretation therefore attributes the regional pattern differences to the calibrated budgets and allocation of that bundle, while avoiding a global facility-accuracy claim.
Manuscript changes. Sections 2.5–2.8, 4.4 and 4.5; Figs. 12, S2 and S6i.
Citation: https://doi.org/10.5194/essd-2026-590-AC2 -
AC4: 'Reply on RC1', Franz Pablo Antezana Lopez, 26 Oct 2026
Please find attached the PDF with our detailed responses to all your comments and the corresponding revisions to the manuscript. We have carefully addressed each observation and made substantial improvements throughout the paper, including several important methodological and structural changes. I also revised the title following your valuable suggestion, as I believe it now better reflects the scope and characteristics of our dataset. The PDF documents all the corrections and revisions for your review and comparison. Thank you very much for your thoughtful feedback and valuable contributions, which have greatly helped us strengthen the scientific quality and overall presentation of our work. Name of data and title of paper wass changed using some advices from your comments... there s new DOI to download the data
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AC5: 'Reply on RC1', Franz Pablo Antezana Lopez, 26 Oct 2026
New version of the manuscript is also available here: https://drive.google.com/file/d/1lKSV9dZWhvzUuM6nsrUW8wzCdkpWkTfy/view?usp=sharing
Citation: https://doi.org/10.5194/essd-2026-590-AC5
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AC1: 'Reply on RC1', Franz Pablo Antezana Lopez, 01 Oct 2026
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RC2: 'Comment on essd-2026-590', Anonymous Referee #2, 02 Oct 2026
The authors present HBPG-CO2, a global high-resolution monthly inventory of anthropogenic CO2 emissions for 1970–2025, developed through temporal reconstruction and spatial downscaling of existing inventories. However, its advantages over EDGAR and comparable products are not sufficiently demonstrated, and the accounting boundaries and assumptions underlying the temporal reconstruction and spatial allocation require clarification. Another major limitation is the insufficient independent validation and lack of quantitative uncertainty analysis, which limit confidence in the claimed improvements in accuracy. My main comments are as follows.
- The added value of HBPG-CO2 relative to its input inventories and other global emission products needs to be established more clearly. Annual totals and recent monthly profiles are largely derived from EDGAR, while the land-use, road, nighttime-light, and point-source information should be assessed against that already incorporated into existing inventories. Several comparable high-resolution CO2 inventories have already been published and incorporated extensive point-source information, such as MEIC-global-CO2 (1970-2021, Xu et al., 2024, DOI: 10.1007/s11430-023-1230-3), ODIAC2025 (1km, 2000-2024, DOI: 10.5194/essd-10-87-2018) and GID-based GRACED (Dou et al., 2022, DOI: 10.1016/j.xinn.2021.100182). The authors should clearly demonstrate the advantages of HBPG-CO2 over both EDGAR, on which it is largely based, and other comparable high-resolution CO2 emission inventories. Please distinguish improvements attributable to additional source information from those arising from the redistribution of existing estimates, and quantify their effects on seasonality, source locations, and subnational emission patterns. The introduction and results should emphasize these specific advances and reduce repetitive descriptions of global emission trends and input datasets.
- The accounting boundaries of anthropogenic CO2 and the mapping to the seven output sectors need clear clarification. The author should provide a crosswalk from EDGAR, CEDS, and the daily reference product to HBPG-CO2 using source categories, explicitly identifying excluded categories and those split across output sectors. For example, EDGAR’s monthly transport aggregate includes road, rail, shipping, and aviation, whereas HBPG-CO2 is variously described as representing road or ground transport, with aviation and shipping added separately when calculating total emissions. The allocation of industrial combustion and process emissions also needs to be specified. Besides, the author should also clarify whether CO2 emissions from open biofuel combustion and land-use-related processes are included in each output sector. For agriculture and waste, specify the CO2-emitting processes covered and distinguish their accounting boundaries from those of CH4 and N2O emissions and land carbon fluxes. These definitions are also essential for assessing the suitability of cropland or pasture as spatial allocation proxies in Figure 7, since the appropriate proxies depend on whether agricultural CO2 arises from liming, urea application, fuel combustion, residue burning, or other processes.
- EDGAR_2025_GHG already provides monthly country and sector totals for 1970–2024; limited coverage of monthly gridded products does not imply an absence of historical monthly information. Why were these temporal constraints not used, and how is extrapolating seasonal patterns learned from 2000 onward to 1970–1999 justified? The authors should specify the reference year for the 12-year extrapolation limit and clarify whether a fixed seasonal profile is applied beyond that limit.
- For the 2025 extension, the author describes how monthly shares are blended but does not explain how the annual emission totals required by Equation (6) are derived. Please specify the data and methods used to extrapolate annual emissions by country and sector and allocate these totals to source grid cells. Any assumptions that retain 2024 emission totals or spatial patterns should be stated explicitly. Regular updates are important for maintaining the long-term utility of high-resolution emission inventories. The authors should also indicate whether continued updates are planned for HBPG-CO2 and explain how new data will be incorporated into subsequent releases.
- Cropland, NDVI, precipitation, and albedo characterize agricultural conditions but do not directly determine emissions from liming, urea application, or fuel combustion; population also need not reflect the locations of waste treatment or incineration facilities. The authors should explain the process basis and weighting of the sectoral proxies and assess sensitivity to alternative combinations. For power and industry, how do the 0.1° GID data listed in Table 1 locate facilities within parent cells, and what advantage does the method offer over using facility coordinates directly? Historical allocation should also account for land-use changes and facility commissioning and retirement, rather than implicitly imposing recent spatial patterns on earlier years.
- The role of XCO2 in downscaling is ambiguous. Table 1 describes the CAMS EGG4 reanalysis as an atmospheric consistency covariate, whereas Section 2.2 states that satellite XCO2 supports spatial allocation. The author needs to identify the product actually used and explain how it enters the model. Column-averaged CO2 is influenced by atmospheric transport, background concentrations, and biospheric exchange, so it cannot be directly equated with local anthropogenic emission intensity. If XCO2 is used to determine allocation weights, the authors should explain how these influences are accounted for and assess its contribution through a sensitivity analysis comparing results with and without this covariate. If its role is only diagnostic, this should be stated consistently throughout the manuscript.
- The manuscript lacks a quantitative uncertainty assessment, which is essential for evaluating the reliability of an emission inventory and its suitability for different applications. Although Figure 4 refers to standard deviations across multiple runs, the ensemble design, number of realizations, and resulting uncertainty estimates are not reported. Uncertainties in the input annual inventory totals, monthly reconstruction, and spatial allocation should be quantified, with an explanation of how input errors and their correlations propagate through normalization and transfers between grid cells.
- The relationship between conservation at the original grid resolution and the activation of new emitting cells needs clarification. Holding each original 0.10° cell total fixed prevents within-cell disaggregation from correcting allocation errors between parent cells. Conversely, transferring emissions to an originally zero-valued cell changes the original cell totals. The authors should clarify the conservation constraints applied before and after redistribution. For example, if the grid cells involved lie in different countries, are emission transfers across national boundaries permitted?
- The allocation rules do not fully address zero emissions or missing information. Equation (1) is undefined when annual emissions are zero, while Equation (9) cannot be normalized when all sub cell baseline shares are zero. The authors should explain how these cases and missing covariates are handled, clearly distinguishing true zero emissions from missing data and absent spatial support. They should also clarify whether the numerical stabilization constant introduces small artificial positive emissions, which could be misinterpreted as newly identified emission sources.
- The claimed improvement in spatial accuracy needs to be supported by direct quantitative validation. Agreement in national totals, visual correspondence with allocation proxies, and residuals relative to EDGAR do not demonstrate more accurate source locations or grid-cell emissions. Comparisons with EDGAR, ODIAC, and uniform or sectoral-proxy disaggregation baselines should assess spatial correlation, hotspot displacement, and emission concentration across spatial scales. Besides, accuracy should be evaluated against facility records, regional inventories, or suitable observational constraints which were not used in model fitting or parameter selection.
- CEDS appears to be used for both reconstruction/calibration (Table 1 and Section 3.3) and external comparison (Section 4.2). Its role in each step and the independence of the evaluation need clarification. Also, figure 9 also reports symmetric mean absolute percentage errors of 104.2% for agriculture and 140.6% for waste, with a regression slope of 11.37 for waste. Simply attributing these discrepancies to differences in sector definitions is insufficient.
- Figure 7 should include emission color scales and units, with identical ranges for HBPG-CO2 and EDGAR and clear distinctions between zero, missing, and newly activated cells. Figure 11a labels emissions as annual means but uses monthly units.
Citation: https://doi.org/10.5194/essd-2026-590-RC2 -
AC3: 'Reply on RC2', Franz Pablo Antezana Lopez, 26 Oct 2026
Response to referees
MCR-CO₂ global monthly anthropogenic CO₂ emissions at 0.05° for 1970–2025
Manuscript essd-2026-590
We thank both referees for their constructive assessments. The revised manuscript clarifies the contribution of MCR-CO₂, separates inventory consistency from regional spatial evaluation, and makes sector boundaries and conservation domains explicit. Historical source-withholding tests, fixed-mass spatial perturbations and 400 source configurations quantify sensitivity to the available information. We have also added a separate 1,000-member Monte Carlo propagation of EDGAR-referenced annual-budget uncertainty, with sector intervals, correlation tests and an explanation of propagation to monthly grid cells. The updated dataset DOI appears in the Abstract, availability statement and references. Our point-by-point responses follow. Please take care that the DOI was updated with a new enhanced version of the data and also the new version of the manuscript is udated.
Referee 2 R2.1 Contribution in relation to existing inventories
Comment summary. The novelty should be made clear relative to EDGAR, MEIC global CO₂, ODIAC and GRACED.
Response. The Abstract, Introduction and Conclusions now identify the contribution as a continuous 672-month, 0.05°, seven-sector record with traceable annual budgets, monthly timing and geographical allocation. We position MCR-CO₂ relative to EDGAR, CEDS, Li et al., MEIC-global-CO₂, ODIAC and GRACED. The framework harmonises and reconstructs their compatible information rather than introducing new activity data or emission factors. Monthly tests, regional benchmarks and fixed-mass perturbations distinguish the contributions of temporal information and spatial redistribution. The separate Monte Carlo experiment adds an explicit annual-budget uncertainty component with stated assumptions.
Manuscript changes. Abstract, Introduction and Conclusions; Sections 2.7–2.9 and 4.3–4.5; Figs. 12, 14, S2, S3 and S6; Tables 4 and 5.
R2.2 Sector definitions and avoidance of double counting
Comment summary. The sector correspondence for transport, aviation, industry, agriculture and waste needs to be unambiguous.
Response. Table S1 now identifies EDGAR source category codes, corresponding output sectors and exclusions. Terrestrial Transport excludes the separately supplied aviation and shipping layers, which enter the complete total once. Industrial combustion, industrial processes and fuel transformation are assigned by emitting process. Agriculture and Waste include the specified anthropogenic CO₂ categories and exclude CH₄, N₂O and land carbon exchange. The crosswalk identifies agricultural fuel use, liming, urea and waste incineration as processes requiring specific correspondence. CEDS, Li et al. and MEIC comparisons are restricted to compatible process definitions; unmatched categories are excluded rather than treated as zero. These rules prevent a source label alone from determining sector equivalence.
Manuscript changes. Section 2.1; Tables 1 and S1; Sections 3.1–3.2 and 4.2.
R2.3 Use of EDGAR monthly records before 2000
Comment summary. EDGAR has historical national monthly information, so the treatment of months before 2000 must be explained accurately.
Response. The manuscript now states that EDGAR national monthly series for 1970–2024 supply historical temporal information. The 2000–2024 interval refers specifically to the monthly 0.10° gridded sector maps. The revised monthly-composition model combines compatible historical records and estimates missing combinations under regularisation, followed by annual normalisation. Section 4.3 tests reconstruction against equal-month and fitting-period-mean baselines and by source withholding. The 99.39% JSS across the 2000 boundary is reported as continuity, with seasonal skill assessed separately by retrospective tests. This revised description replaces the earlier implication that historical monthly evidence was unavailable.
Manuscript changes. Sections 2.1, 2.3 and 4.3; Fig. 2 and Fig. S3; Tables 2 and 3.
R2.4 Construction and updating of 2025
Comment summary. The final year needs a transparent derivation and an update mechanism.
Response. Section 2.2 identifies 2025 as a model-based extension. The latest supported country–sector calibration factor, usually from 2024, is applied to the reconstructed 2025 fields. It does not copy the twelve monthly emissions from 2024. Terrestrial emissions change from 39.075 Gt CO₂ in 2024 to 39.385 Gt in 2025. The update mechanism replaces inherited factors and seasonal information when new compatible inventory years become available, while retaining the grid, units and sector definitions. The revised dataset record is https://doi.org/10.5281/zenodo.23337181.
Manuscript changes. Sections 2.2–2.3 and 3.1–3.2; Abstract; Code and data availability.
R2.5 Sector relevance and resolution of spatial predictors
Comment summary. The geographical proxies are indirect and coarse facility maps do not identify fine grid emitter locations.
Response. Section 2.4 explains the predictors by emitting process and identifies their effective resolution and temporal limitations. Agriculture and Waste proxies indicate plausible support rather than measured CO₂ emission rates. A 0.10° facility-related grid identifies activity within a parent cell but cannot identify its 0.05° child without finer evidence. Figure 12 quantifies the dependence on predictor families at fixed parent mass. Recent geographical predictors applied to earlier years are explicitly described as reconstruction, with positive inferred cells interpreted as modelled locations rather than dated facility observations.
Manuscript changes. Sections 2.4–2.6 and 4.4; Figs. 4, 1, 7 and 12.
R2.6 Use of atmospheric XCO₂ information
Comment summary. Atmospheric column abundance should not be treated as a direct estimate of local emissions.
Response. CAMS EGG4 XCO₂ is used only for diagnostic atmospheric context. It is excluded from emission targets, annual calibration and allocation weights because column CO₂ also reflects transport, background concentrations and biospheric exchange. Table 1 and the methods now use this definition consistently.
Manuscript changes. Table 1; Sections 2.4 and 2.7.
R2.7 Annual, monthly and spatial uncertainty assessment
Comment summary. A quantitative assessment should examine uncertainty and propagation across the annual, temporal and geographical reconstruction stages.
Response. We have added a separate 1,000-member Monte Carlo propagation of annual-budget uncertainty in addition to the 400 source configurations. The new experiment uses the CO₂-specific global IPCC-category bounds from Solazzo et al. (2021, Table 6) as a transferred reference model. Lognormal multipliers are fitted to those bounds and held constant over all years for each country–sector pair. The main case retains fully shared country errors within each sector (ρ = 1), with ρ = 0 and 0.5 tested separately. Table 5 reports the sector mapping and central 95% intervals; Fig. S6j shows their distributions, and Table S3 reports correlation and Industry-mapping sensitivity. The main intervals read from the whiskers in Fig. S6j are approximately −7% to +7% for Power and Buildings, −7% to +8% for Transport and Fuel, −23% to +21% for Industry, −47% to +21% for Agriculture, and −39% to +24% for Waste.
Equations (19–22) show the propagation through normalised monthly and spatial fractions. Conditional on those fractions, an annual multiplier scales every associated monthly cell and both sides of a within-country mass transfer. Relative annual-budget uncertainty is therefore inherited by each positive cell, and conservation holds in every realisation.
The annual table represents country-assigned support, equivalent to 91.1% of the reported terrestrial raster mass in 2025; the experiment excludes unassigned mass, aviation and shipping. These intervals describe the annual-budget component, conditional on fixed seasonal profiles, spatial weights and support decisions. The 400 configurations continue to quantify source and monthly-profile sensitivity, while the fixed-parent test quantifies spatial allocation sensitivity. All seven recent annual source-choice ranges are narrower than the transferred budget intervals. We present this as a scale comparison with stated supports, rather than treating source spread as total propagated error. The revised text thus provides a reproducible propagation analysis and clearly distinguishes the uncertainty components measured by each assessment.
Manuscript changes. Sections 2.7–2.9 and 4.5; Eqs. (19–22); Fig. S6j; Tables 4, 5 and S3; Supplementary methods for annual-budget propagation.
R2.8 Conservation during fine allocation and support adjustment
Comment summary. The method should reconcile fixed emissions within coarse parent cells with the addition of new eligible emitting support.
Response. The revised methods distinguish two conservation domains. During disaggregation, the four 0.05° children sum to their prescribed 0.10° parent. A later bounded support adjustment may transfer mass between parents within the same country, sector and month. It never transfers emissions across national borders. Parent-cell mass is therefore fixed during downscaling, while national monthly mass is fixed during support redistribution. The annual-budget propagation scales these quantities consistently in each Monte Carlo realisation.
Manuscript changes. Sections 2.5–2.6 and 2.9; Eqs. (8–22); Fig. 1.
R2.9 Zero values, missing data and numerical stabilisation
Comment summary. The equations must distinguish real zero emissions from missing information and explain what happens when a normalisation denominator vanishes.
Response. The equations and accompanying text distinguish positive totals, true zeros, missing inputs and excluded cells. True-zero annual budgets remain zero. An eligible parent with no positive allocation score uses a mass-conserving fallback without activating NoData or excluded cells. Numerical stabilisers alone do not authorise new emitting support. If no eligible recipient has a positive score, the support transfer is zero. Exported NoData (−2147483648) remains distinct from a valid emission value of zero.
Manuscript changes. Sections 2.3, 2.5–2.6 and 3.1; Eqs. (4–18).
R2.10 Spatial comparisons with external references
Comment summary. Spatial skill requires complementary baselines and geographically independent information, not only agreement with EDGAR.
Response. Figure S2 now compares MCR-CO₂ and EDGAR with CAMS-REG, Vulcan V4 and ODIAC across five domains. JSS increases in all five, from 46.2% to 48.0% in Europe, 37.6% to 47.8% in the United States, 27.7% to 30.2% in Brazil, 31.2% to 47.4% in southern Africa and Madagascar, and 28.4% to 41.8% in Australia. Normalised RMSE decreases in four domains but increases in Brazil from 13.182 to 17.467. European centroid displacement and Australian hotspot overlap also retain their less favourable results. MCR-CO₂ and EDGAR use the same Power, Industry, Buildings and Fuel bundle; reference maps retain their available fossil totals. We therefore describe pattern concordance under explicit coverage differences, rather than fully harmonised sector-level absolute accuracy. ODIAC comparisons are consistency checks because ODIAC contributes to allocation. Figures 12 and S6i complement these product comparisons with controlled fixed-mass alternatives. We have not presented inferred support as independently verified facilities. Its evaluation consists of held-out model diagnostics and bounded, within-country redistribution, with operational transfers limited to Agriculture and Transport.
Manuscript changes. Sections 2.6–2.8, 3.3 and 4.4–4.6; Figs. 12, S2, S5 and S6i; Discussion.
R2.11 Use of CEDS and large sector differences
Comment summary. CEDS contributes to model construction and its comparisons, especially for Waste and Agriculture, should not be described as independent error statistics.
Response. CEDS is now identified consistently as an input to annual reconstruction and as a source-comparison product, rather than an independent validation dataset. The major fossil sectors show close historical correspondence, while Agriculture and Waste require process-specific interpretation. The Waste and Fuel associations remain visible in Fig. 9. Section 4.2 explains the discussion-paper Waste SMAPE of 140.6% and slope of 11.37 through the scope audit. The unmatched CEDS 5C and EDGAR Waste categories yield a median ratio of 23.46 and r = 0.309. Scope-matched EDGAR WASTE_INC reproduces EDGAR Waste with r = 1.000 and slope = 1.000. Agricultural differences are interpreted using the distinction between covered process CO₂ and broader agricultural fuel-use categories (Table S1; Fig. 14b). These comparisons document accounting differences without converting source disagreement into independent reconstruction error.
Manuscript changes. Table 1; Sections 2.1 and 4.2–4.5; Figs. 9, 14b, S4 and S6; Tables 4 and S1.
R2.12 Figure scales, displayed quantities and units
Comment summary. The maps and time series require clear, internally consistent colour scales and distinguishable annual and monthly units.
Response. Figure 7 now includes annual CO₂ density colour bars in t CO₂ km⁻² yr⁻¹. Within each regional example, MCR-CO₂ and EDGAR use the same range for both 1980 and 2024; ranges differ among examples. The caption states this comparison rule. Output definitions distinguish valid zero emissions from NoData, and inferred emitting locations are described as modelled support. Figure 11 is identified as annual-normalised monthly shares rather than annual emission magnitude. Figures 5 and S2 use annual densities, while Fig. S6 identifies annual percentages, monthly-share percentage points, spatial TVD and the separate Monte Carlo budget intervals in their respective units.
Manuscript changes. Sections 2.3, 3.1 and 4.3–4.5; Figs. 5, 7, 11, S2 and S6.
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AC6: 'Reply on RC2', Franz Pablo Antezana Lopez, 26 Oct 2026
New version of the manuscript is also available here: https://drive.google.com/file/d/1lKSV9dZWhvzUuM6nsrUW8wzCdkpWkTfy/view?usp=sharing
Citation: https://doi.org/10.5194/essd-2026-590-AC6
Data sets
HBPG-CO2: a global gridded monthly anthropogenic CO₂ emission dataset at 0.05° resolution for 1970_01–2025_12 Franz Pablo Antezana Lopez https://zenodo.org/records/21507026
Model code and software
Code and data sample of HBPG-CO2: a global gridded monthly anthropogenic CO₂ emission dataset at 0.05° resolution for 1970_01–2025_12 Franz Pablo Antezana Lopez https://zenodo.org/records/21523272
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This manuscript presents HBPG-CO2, a global monthly CO2 emission dataset at 0.05° for 1970–2025 across seven sectors, constructed by reconstructing monthly shares from EDGAR using a hierarchical Bayesian formulation and downscaling the 0.10° EDGAR grid to 0.05° with covariate-weighted allocation under mass conservation. My main concern is that the dataset is a statistical reprocessing of established inventories with unconstrained uncertainties. It does not introduce new activity data, emission factors, or independently produced regional inventories.
Specific comments