# **9.  Summary and Conclusions**

The Global Methane Pledge joins 122 countries in a collective effort to reduce global methane emissions at least 30 percent from 2020 levels by 2030. This paper is intended to support Pledge participants in a two-part exercise. First, it introduces a new World Bank database that reports monthly atmospheric methane (CH4) concentrations for the terrestrial globe in a spatial grid with a resolution of 25 km. Monthly means are calculated for “local anomalies” in bias-corrected XCH4 data from the European Space Agency’s Sentinel-5P (TROPOMI) satellite platform. The new database computes monthly mean values from daily time-stamped observations that have been pre-filtered for global, seasonal and latitudinal background effects. We term these “local anomalies” because they register the local emissions component of the data. Recent global research (Sha et al. 2021) has shown that the Sentinel-5P methane measures align closely with measures taken by ground stations in TCCON (the Total Carbon Column Observing Network). The database will be maintained online by the Development Data Hub of the World Bank’s Development Economics Vice Presidency.

The new database is intended to assist Pledge participants in reducing methane emissions from energy, agricultural and waste subsectors whose spatial distributions are highly non-uniform across and within countries. At present, priority areas for emissions reduction are identified with spatially-formatted “bottom-up” estimates provided by national or global emissions inventories such as the Emissions Database for Global Atmospheric Research (EDGAR). These inventories combine sectoral activity data with broadly calibrated emissions factors. Using spatial inventories like EDGAR, policy analysts can identify emissions-intensive areas, trace the emissions to specific subsectors, and perform benefit-cost assessments of alternative technologies for emissions reduction.

The second part of the paper explores the potential contribution of Sentinel-5P data to the selection of priority areas. The value added of satellite-based methane observations can only be judged after a round of comparative empirical work. In this paper, we contribute with a pilot study of CH4 emissions from irrigated rice production, which accounts for about 10% of agricultural CH4 emissions. Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methaneproducing bacteria. A variety of techniques can shorten the period of water blockage, including a single field drainage in the mid-season; alternate wetting and drying (AWD), dry seeding instead of transplanting rice into flooded fields, and “aerobic” systems that grow rice in well-drained soil. Our study aims to use the Sentinel-5P (S5P) database to learn more about local variations in methane emissions created by differences in water use and the configuration of rice fields.

We draw monthly CH4 anomaly observations from the database for areas and seasons identified by spatially-referenced data on irrigated rice production from IFPRI and seasonal production timing from RiceAtlas. We compute mean values by grid cell for the period 2020-2022 and explore their relationship with three spatially-referenced data sets that are resampled to match the 25-km global grid: (1) IFPRI estimates of irrigated rice field extents; (2) estimates of agricultural methane emissions from EDGAR, a “bottom-up” inventory that combines detailed 23