Methane

Agriculture is a significant contributor to greenhouse gas (GHG) emissions, with enteric methane (Ent_CH4) from cattle production being a major source. In Zambia, cattle play a critical role in rural livelihoods and food security, yet the contribution of cattle production systems to national GHG emissions remains poorly quantified. This study used the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method to estimate Ent_CH4 from Zambia’s cattle population from 1994 to 2022. The Tier 2 method provides a more accurate estimate than the Tier 1 method by incorporating country-specific data on cattle population demographics, husbandry, and feeding practices. The results show significant variations in Ent_CH4 over time, driven by changes in cattle population dynamics and production practices. This study underscored the importance of transitioning from the generalized Tier 1 to the Tier 2 method to capture the unique characteristics of Zambia’s cattle production systems. The present findings provide critical insights for developing targeted mitigation strategies that will support Zambia’s ongoing efforts to address climate change while promoting sustainable livestock production.

The cost of “carbon net zero by year 2050” for the UK will be high, and this target date can only be achieved if the project is undertaken in a progressive and timely manner; otherwise, costs will escalate. The base power source behind the UK approach to “net zero” is nuclear fission electricity power stations, and the ones currently on order are running significantly late. Renewables will provide some supply together with interconnectors, but only approx. twenty percent of the planned wind turbines are in place. The electricity distribution grid must change to satisfy the UK’s planned “electricity-based” future. Energy use for transport is also a significant fraction of total UK energy consumption and we include predictions for their associated emissions. These must be reduced in a progressive and timely fashion. Intermittent support for unreliable renewables is necessary and methods employing both liquid as well as gaseous fuels are suggested. Means to use and upgrade the existing infrastructure are considered, and a few of the basic building blocks of the future are examined regarding their installation without significant interruption to the basic UK economy. ANR/AMR and SMR are included as potential renewables support as well as base load generators, and the approx. quantity of CO₂e emissions avoided is estimated. Even though methane is a powerful greenhouse gas, the main support for renewables will be UK natural gas (methane content ~95%), with Avtur/diesel as a recommended reserve. It is suggested that methane has a significant short- to medium-term future as a transition fuel.

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH₄) Despite modest observational constraints, estimates of worldwide CH₄ emissions from rice agriculture range from 18–115 Tg CH₄ yr⁻¹. CH₄ is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O₃), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O₃ levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH₄ emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH₄ emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O₃ and asthma incidence data to assess the downstream impacts of CH₄-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH₄) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH₄ emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health.