the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 May 2025
Reporting of Gridded (0.1°×0.1°) Methane Emission Data for India to Redefine Global Climate Studies
Abstract. Methane (CH4) is a predominant climate-forcing agent and has become a focal point of global climate discussions, owing to its significant contribution to atmospheric warming. The ambiguity surrounding the relative contributions of various natural and anthropogenic sources, coupled with associated uncertainties, poses significant challenges to assessing methane emissions in developing nations like India. To address these challenges and better understand the methane-emitting sources, this study presents a comprehensive high-resolution gridded (0.1°×0.1°) inventory of CH4 emission by including 25 distinct anthropogenic and natural sources in India for 2023 by adopting the IPCC bottom-up approach. The estimated CH4 over India is 37.79 Tg/yr, which will redefine the contribution of various sources. The agriculture sector contributed ~50 % followed by wetlands (8.6 %), fossil fuel and waste management. This study reports the first-ever comprehensive emissions from natural sources like wetlands and termites. The Indo-Gangetic Plain (IGP) and coastal states show elevated emissions with Uttar Pradesh contributing the highest (10.8 %) followed by Gujarat (9.4%), and Maharashtra (8.6 %). However, surprisingly cities exhibit lower CH4 as compared to other semi-urban/rural regions. This developed dataset can be a valuable input to optimize the climate study by filling the data gap, enabling policymakers to formulate various mitigation measures. The emission dataset can be accessed through the Zenodo repository https://doi.org/10.5281/zenodo.14089138 (Sahu S.K., 2024).
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CC1: 'Comment on essd-2025-65', Ravi Yadav, 12 Jun 2025
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CC2: 'Reply on CC1', Saroj Kumar Sahu, 19 Jun 2025
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Response: Thank you for your comment. We acknowledge that our estimated total methane emissions are higher than some prior inventories. The difference is attributed to the inclusion of additional or underrepresented sources like the emission from Wetlands, including subsectors like inland water bodies, marine aquaculture and mangrove, and Termite being reported nationwide for the first time, followed by an updated and region-specific Tier II and Tier III approach. The reported work includes 25 distinct sources missing in the previous estimations. Moreover, country-specific technological emission factors have improved the current estimation. Furthermore, the current reporting of methane emissions is for the recent base year of 2023, making the activities more profound than before. These improvements likely contribute to the higher national methane estimates. The detailed discussion on emission comparison has been reported in section 3.3 Intercomparison with previous studies.
Secondly, the lower Indo-Gangetic Plain emerges as a hotspot due to multiple methane-intensive activities, such as high livestock density and a ruminant population in states like Bihar and West Bengal, which significantly elevate enteric fermentation emissions, Extensive rice cultivation under flooded conditions, tributaries of major rivers flows downwards making the emission from wetlands higher than the upper IGP regions, presence of Sunder ban Mangrove delta and fossil fuel mining aggravate the methane emission in the lower IGP region. Additionally, higher population density leads to higher emissions from cooking and waste generation collectively, making the lower IGP region a methane hotspot.
Finally, the total methane emission dataset is publicly available through the Zenodo repository at: https://doi.org/10.5281/zenodo.14089138. Additional data or related resources can be shared upon reasonable request and are open for scientific collaborations.Citation: https://doi.org/10.5194/essd-2025-65-CC2
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CC2: 'Reply on CC1', Saroj Kumar Sahu, 19 Jun 2025
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CC3: 'Comment on essd-2025-65', Namrata Sahu, 11 Sep 2025
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This is a very insightful study. I would like to know if industrial methane emissions have been included in the inventory. Also, I am curious why the uncertainty is much higher for some sectors compared to others, and how this difference may affect the overall spatial pattern of methane emissions across the country.
Citation: https://doi.org/10.5194/essd-2025-65-CC3 -
AC1: 'Reply on CC3', Saroj Kumar Sahu, 29 Sep 2025
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Thank you for your comment. We have taken major methane-emitting industries like Oil and Gas Industries, Coal mining, Thermal Power plant, Brick Kiln and waste management like Solid waste Landfill and burning, along with the industrial wastewater treatment sector in our inventory. Though we acknowledge that chemical processing and various manufacturing industries emit methane, we haven’t taken these industries into account due to the lack of activity data and corresponding technological emission factors. We will definitely try to robust our comprehensive emission inventory by including these sectors in our future revisions. Secondly, the higher uncertainty in some sectors, like the wetlands and termite sector, is due to limitations of detailed activity data and the availability of updated regional emission factors. Unlike the natural sources, the waste management sector has the least uncertainty because of the availability of city and plant-specific waste generation activity data. While these uncertainties may influence localized estimates, the overall spatial pattern remains robust, with major hotspots consistently aligned with high agricultural and waste management activities. The spatial heterogeneity is limited due to the gridded emission distribution, as the grid receiving the large polluting point sources exhibits higher emissions than the surrounding regions. Similarly, the grid containing a higher livestock population and agricultural area is better identified than the surrounding regions in the emission distribution, which embodies the spatial robustness of this inventory.
Citation: https://doi.org/10.5194/essd-2025-65-AC1
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AC1: 'Reply on CC3', Saroj Kumar Sahu, 29 Sep 2025
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RC1: 'Comment on essd-2025-65', Anonymous Referee #1, 09 Oct 2025
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The manuscript, "Reporting of Gridded (0.1°×0.1°) Methane Emission Data for India to Redefine Global Climate Studies" by Mishra et al., addresses the important and timely topic of quantifying and spatially representing methane (CH₄) emissions across India. Methane plays a significant role as a climate forcing gas, and there are large uncertainties in regional CH₄ budgets, especially in developing countries. Thus, this study is highly relevant for improving emission inventories and supporting the Global Methane Pledge and the Global Greenhouse Gas Watch (G3W) frameworks. The authors attempted to present a comprehensive, 0.1°×0.1° gridded inventory for 2023 that incorporates both anthropogenic and natural sources, as outlined in IPCC guidelines. However, despite the significance of the topic, the manuscript, in its current form, requires substantial revision to improve methodological transparency, contextual integration, and scientific robustness.
Major comments:
The main concern is the limited transparency of the methodology used to derive the 0.1°×0.1° gridded emissions. Although equations (1–15) outline a general framework, the description lacks sufficient detail to understand how the spatial allocation of emissions was implemented. It is unclear which proxy datasets were used to distribute emissions geographically, how high-resolution input data were obtained for different source sectors, and if region-specific adjustments or scaling procedures were applied. To ensure reproducibility and allow for a meaningful evaluation of the results, the manuscript must provide a thorough description of the data sources, spatial disaggregation methods, emission factors, and activity data used for each category (e.g., livestock, rice paddies, fossil fuels, waste management).
The methodology for uncertainty quantification is unclear and needs substantial improvement. Figure 5, which shows sector-wise uncertainty estimates, is difficult to interpret. The reported total uncertainty of ±59% is unusually large compared to other inventories and requires justification. The authors should clearly describe their approach to uncertainty estimation and specify how they defined, combined, and propagated uncertainties in emission factors and activity data across sectors. They should also provide details on assumed distributions and aggregation procedures to ensure reproducibility. Figure 5 should be revised and accompanied by a concise discussion that identifies the major contributors to the total uncertainty and explains why it is so high compared to other studies.
This study attempted to present a valuable dataset that could help fill a significant gap in methane emission information for India. The inclusion of 25 source categories, as well as the effort to quantify emissions from natural sources such as wetlands and termites, demonstrates the study's comprehensive scope and is commendable. However, the methodology is described only superficially, making it difficult to assess the reproducibility and robustness of the results. The comparison with previous studies is limited and omits several key references, which weakens the scientific context and credibility of the analysis. Furthermore, Figure 4, intended to support the intercomparison, is confusing because it includes unrealistically low total estimates derived from studies considering only a limited subset of emission sectors. A clearer, more balanced comparison that includes comprehensive inventories would significantly strengthen the manuscript.
Section 3.3 largely overlaps with the introduction and does not offer new insights. The intercomparison should focus on a quantitative evaluation against prior bottom-up and top-down estimates. Several key studies are missing from the literature review and CH₄ budget comparison, including (Miller et al. 2019; Ganesan et al. 2017; Chandra et al. 2021), as well as the updated Global Methane Budget (Saunois et al. 2024).
In conclusion, while this paper tries to address a critical gap in regional methane emission data, the methodological details, intercomparison rigor, and literature integration must be substantially improved. With clearer explanations of the gridding approach and inclusion of the missing references, as well as stronger data analysis, the manuscript could become a valuable contribution to CH₄ emission research and policymaking. At this stage, the recommendation is a major revision.
Minor comments:
- Fig. 1b: The labels on the y-axis are not clearly defined.
- Fig. 2: The axis and color bar labels should be enlarged.
- Lines 277-278: " The methane footprint of Indians is found to be 27 kg and the per square km area is attributed to 11.6 tonnes." This sentence is confusing and should be revised.
- Fig. 4: Why are some estimations so low? Apparently, some of the works considered here include only separate categories and do not provide good total estimations.
References:
Chandra, N., P. K. Patra, J. S. H. Bisht, and A. Ito. 2021. “Emissions from the Oil and Gas Sectors, Coal Mining and Ruminant Farming Drive Methane Growth over the Past Three Decades.” Journal of The. https://www.jstage.jst.go.jp/article/jmsj/99/2/99_2021-015/_article/-char/ja/.
Ganesan, Anita L., Matt Rigby, Mark F. Lunt, Robert J. Parker, Hartmut Boesch, N. Goulding, Taku Umezawa, et al. 2017. “Atmospheric Observations Show Accurate Reporting and Little Growth in India’s Methane Emissions.” Nature Communications 8 (1): 836.
Miller, Scot M., Anna M. Michalak, Robert G. Detmers, Otto P. Hasekamp, Lori M. P. Bruhwiler, and Stefan Schwietzke. 2019. “China’s Coal Mine Methane Regulations Have Not Curbed Growing Emissions.” Nature Communications 10 (1): 1–8.
Saunois, Marielle, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph G. Canadell, et al. 2024. “Global Methane Budget 2000–2020.” https://doi.org/10.5194/essd-2024-115.
Citation: https://doi.org/10.5194/essd-2025-65-RC1
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Methane Emission Data Saroj Kumar Sahu https://doi.org/10.5281/zenodo.14089138
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This manuscript presents a significant advancement by addressing the data gap in gridded methane emission datasets for large countries such as India through an indigenously developed product. However, it raises two critical questions: What factors contribute to the present total methane emissions being higher than previously reported in other studies? And what specific conditions or activities designate the lower Indo-Gangetic Plain (IGP) region as a methane hotspot? Finally, I would like to inquire about the availability of this dataset for broader modeling studies.