Explore the vast range of titles available.

Methane is a powerful greenhouse gas with a shorter lifetime than carbon dioxide (CO2), making it an important target for near‐term climate action. The Global Methane Pledge (GMP) aims to cut anthropogenic methane emissions by 30% from 2020 levels by 2030. Using an Earth system model with interactive CH4 sources and sinks, we assess the Pledge's impact through 2050. Results show that current GMP commitments deliver only a 10% cut by 2030—well below the target. Only the maximum technically feasible reduction (MTFR) pathway can achieve the 30% goal. By 2050, current GMP commitments lowers methane concentrations by 3% relative to 2025, while MTFR achieves 8%. Both pathways slow warming slightly, avoiding about 0.1°C of global temperature rise, with the Arctic seeing the greatest benefits (up to 2°C less warming). Without wider participation, the GMP with current signatories will fall short of its targets and Paris Agreement goals.

The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near‐term climate change. Emission inventories generated through surface activity measurements are reported annually or bi‐annually, and evaluated periodically through a “Global Stocktake.” Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (also called smoothing error), as‐well‐as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state‐of‐the‐art inventory (EDGAR v6.0) using satellite‐based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite‐based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, −11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25–1.3 Tg CH4/yr/yr), primarily in the Indo‐Pakistan region, sub‐tropical Africa, and the Southern Brazilian; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors. Plain Language Summary The Bayesian inverse estimation algorithms we describe here, developed previously to quantify atmospheric composition from observations of Earth's radiation, is applied one step further to quantify emissions using satellite atmospheric methane data and compare them to a reported inventory. These same algorithms allow us to quantify when this comparison is informative (total uncertainty is reduced) and when it is not. Deployment of these methods will become increasingly critical to use with the ever increasing number of satellite greenhouse gas observations and their utility not just for understanding the global carbon cycle, but for informing policy about best approaches for reducing emissions to mitigate climate change. Key Points Sensitivity of satellite observations to emissions varies dramatically across the globe Verifying reported inventories with satellite concentration data requires accounting for satellite sensitivity to emissions GOSAT satellite and EDGAR inventory confirm increasing agricultural emissions since 2009 in Brazil, Africa, and Indo‐Pakistan region

Agricultural methane emissions strongly contribute to global greenhouse gas production. Under these circumstances, meeting international climate goals, including the Global Methane Pledge or the European Green Deal, requires developing targeted mitigation strategies. However, research using advanced clustering techniques in a multilevel context remains scarce and mostly limited to CO2 emissions. This lack of time-series studies addressing regional variability hinders efforts to develop effective mitigation strategies. This study addresses three main research questions: (i) What are the main trends in agricultural methane emissions in the EU-27 countries from 2013 to 2022? (ii) How can the EU countries be classified based on agricultural methane emissions per capita? (iii) What is the impact of selected agricultural and economic indicators, including the number of live bovine animals and land use, on the clustering of methane emissions? Combining hierarchical and k-means clustering with trend analysis, this research integrates data from Eurostat and the World Bank, thereby classifying the EU-27 countries into four clusters based on their agricultural practices and methane emissions profiles. The results highlight distinct emission patterns across the EU-27 regions, with farming systems characterised by high stocking rates and intensive production generating the highest per capita emissions. By contrast, extensive systems with lower animal density exhibit reduced methane intensities. These findings underscore the need to devise effective, region-specific, data-driven policies and strategies for mitigating methane emissions.

Agriculture accounts for the largest share of anthropogenic methane emissions. Rice paddy fields emit a significant amount of methane gas worldwide. Changing paddy water management practices has an enormous potential to reduce greenhouse gases. The clean development mechanism (CDM) project uses a market mechanism to reduce methane through private participation. There are various risks associated with private investment in CDM projects, although carbon credits as an economic incentive assist in mitigating some of these risks. Farmer participation plays a key role in the success of paddy water management projects in rural areas; however, despite the significant potential to reduce global methane emissions, very few projects have been implemented. When designing a Sustainable Development Mechanism (SDM) system, it is crucial to understand why the market mechanism in the existing CDM projects has failed. This study identifies and categorizes the risks and barriers to paddy water management in CDM projects and analyzes risk management options in CDM projects in India, Indonesia, and Mozambique. The results of this study showed that aside from economic risks, barriers to the application of technology in the field pose critical risks. The lack of knowledge and implementation experiences in rural areas increases barriers to practice. This in turn causes risk of difficulties in technology transfer which can be alleviated by improving awareness and introducing new knowledge through education and training in rural project implementation. Additionally, we highlight the importance of international efforts to build governance between the private and public sectors and promote technology transfers through multi-stakeholder engagement. This study provides specific information to encourage methane reduction worldwide and vitalize rice paddy water management in carbon reduction projects.