Exploratory Study on Modelling Agricultural Carbon Emissions in Ireland

In 2020 Ireland missed its EU climate emissions target and without additional measures will not be on the right trajectory towards decarbonisation in the longer 2030 and 2050 challenges. Agriculture remains the single most significant contributor to overall emissions in Ireland. In the absence of effective mitigating strategies, agricultural emissions have continued to rise. The purpose of the review is to explore current research conducted in Ireland regarding environmental modelling within agriculture to identify research gap areas for further research. 10 models were selected and reviewed regarding modelling carbon emissions from agriculture in Ireland, the GAINS (Air pollution Interactions and Synergies) model used for air pollutants, the JRC-EU-TIMES, (Joint Research Council-European Union-The Integrated MARKAL-EFOM System) and the Irish TIMES model used for energy, the integrated modelling project Ireland (GAINS & TIMES), the environmental, economic model ENV-Linkages and ENV-Growth along with the IE3 and AGRI-I models. The review found that data on greenhouse gas emissions for 2019 reveals that emissions can be efficiently lowered if the right initiatives are taken. More precise emission factors and adaptable inventories are urgently needed to improve national CO2 reporting and minimise the agricultural sector’s emissions profile in Ireland. The Climate Action Delivery Act is a centrally driven monitoring and reporting system for climate action delivery that will help in determining optimal decarbonisation from agriculture in Ireland. Multi-modelling approaches will give a better understanding of the technology pathways that will be required to meet decarbonisation ambitions.

Environmental modelling of building stocks – An integrated review of life cycle-based assessment models to support EU policy making

Various environmental challenges, particularly the rising severity of the impacts of climate change, require a systematic shift in and decarbonization of the global economy. Due to their high environmental impacts, buildings and construction have a special role in decarbonization. Environmental modelling of building stock dynamics can help policy makers and inform decision making. This study presents a systematic review of both the latest scientific literature on environmental modelling of building stocks and related EU policy initiatives. Our findings illuminate the strengths and limitations of existing approaches as well as the potential of such modelling and the required directions for future development to provide effective policy support. Based on the assessment of 104 scientific papers, our study shortlisted and analysed 22 environmental building stock modelling approaches. While promising, these show various limitations on their effectiveness in supporting decarbonization efforts while avoiding burden shifting. Future building stock models should offer extended system boundaries and comprehensive life cycle assessment, improved hotspot analysis and impact monitoring across spatiotemporal scales. A long-term perspective on the entire building stock covering climate and other environmental impacts is needed, as outlined in the latest standards. By linking existing studies to related EU policy objectives, we identify various studies that investigate scenarios and strategies relevant to EU policy makers and highlight research gaps. Future research should enable comprehensive environmental assessment of building stocks across scales and emphasize the monitoring of multiple environmental impacts of building stock development to ensure compliance with environmental targets and minimization of trade-offs.

Automated Mapping of Transportation Embankments in Fine-Resolution LiDAR DEMs

Fine-resolution LiDAR DEMs can represent surface features such as road and railway embankments with high fidelity. However, transportation embankments are problematic for several environmental modelling applications, and particularly hydrological modelling. Currently, there are no automated techniques for the identification and removal of embankments from LiDAR DEMs. This paper presents a novel algorithm for identifying embankments in LiDAR DEMs. The algorithm utilizes repositioned transportation network cells as seed points in a region-growing operation. The embankment region grows based on derived morphometric parameters, including road surface width, embankment width, embankment height, and absolute slope. The technique was tested on eight LiDAR DEMs representing subsections of four watersheds in southwestern Ontario, Canada, ranging in size from 16 million cells to 134 million cells. The algorithm achieved a recall greater than or equal to 90% for seven of the eight DEMs, while achieving a Pearson’s phi correlation coefficient greater than 80% for five of the eight DEMs. Therefore, the method has moderate to high accuracy for identifying embankments. The processing times associated with applying the technique to the eight study site DEMs ranged from 1.4 s to 20.3 s, which demonstrates the practicality of using the embankment mapping tool in applications with data set sizes commonly encountered in practice.