Precise and representative quantification of greenhouse gas (GHG) emissions is essential for evaluating the effectiveness of mitigation strategies aimed at achieving net-zero targets, and for providing rigour and integrity to the voluntary carbon credit market. This project focuses on the accurate and representative monitoring of of three powerful GHGs - methane, nitrous oxide, and carbon dioxide - in farmland, water, and forest environments. These environments are major producers/absorbers of these gases. However, their realistic monitoring is greatly undermined by the substantial spatial and temporal variability of emissions, which conventional methods, despite their precision, fail to capture comprehensively. Current monitoring techniques use expensive techniques for acquiring precise measurements only representative of a small footprint (e.g. closed chambers, optical sensors). Eddy covariance towers have a much larger sensing footprint (roughly 200m diameter), and provide continuous measurements in time. However, these are pieces of costly capital infrastructure, requiring complex operation and maintenance, and their representativeness is quite limited by their height and static position. We propose the deployment of a robotic flux tower system, comprising low-cost sensor arrays tethered to a ground robot by an aerodynamic balloon. The adaptable sensing altitude of the platform (via adjustable tether length) will allow spatially representative measures over complex natural environments. Being tethered to a robot, the flux tower can be easily transported to sites where GHG measurements are needed. Additionally, a balloon can stay in place for long durations, giving sufficient time resolution to fully capture the gas dynamics of the study location. The system will allow emissions data collection in a way that is scalable and transferable to any site where GHG emissions are a concern (e.g. agriculture, water treatment, landfill, oil and gas). Autonomous ground vehicles and tethered balloons are both well-established technologies, and the robotic component of the study can be implemented quickly by experienced engineers. The most significant challenge is instead the provision of accurate sensors that are sufficiently lightweight that they can be integrated with a small robot. This is challenging but feasible using the latest off-the-shelf miniature sensors, when enhanced with additional conditioning electronics and data postprocessing. By combining cutting edge sensing / robotics innovation with modern techniques in data science, the project's datasets and collection techniques will become a platform for further development of robotic environmental monitoring which can support sustainable development and will ultimately help to ensure emissions targets can be reached in the UK.