## 4 Discussion
### Use of Geospatial Techniques in HVC  
Location plays a key role in all HVC stages, and geospatial techniques, such as GIS, provide a differential value in the optimal selection of the most suitable locations. In this case, GIS offers an incomparable framework with a multitude of tools and functionalitiesthat allow the integration of a large amount of geolocalized information from different sources and in different formats. In addition, emerging energy technologies cannot be evaluated solely from an economic perspective, but must also consider environmental, social, and regulatory aspects. The innovative combination of GIS with other enabling technologies such as MCDMs or AI can help in the process of automating and reducing the subjectivity of decision-making processes. It should be kept in mind that traditional decision-making processes require expert personnel with extensive experience and knowledge in the field to validate and define the multi-criteria problem adequately. Renewable hydrogen, being an emerging technology, does not have a multitude of experts capable of dealing with complex decision-making processes. In addition, implementation of GIS-MCDM, GIS-IA, or GIS-MCDM-IA frameworks, not only allows automating a large part of the decision-making process, but also allows processing more information in less time, increasing the number of criteria that define the problem and improving the quality and accuracy of the final decision. Despite the dominance of GIS, emerging hybrid workflows are steadily gaining ground. Multi-criteria decision-making, remote sensing, clustering algorithms, techno-economic models, IoT-enabled monitoring, machine-learning spatial analytics and digital twins prove their value in enhancing precision and speeding up planning. Despite the growing potential of AI and hybrid geospatial frameworks, several critical challenges must still be addressed. One major limitation is the lack of high-quality, publicly available datasets needed to train AI algorithms and validate results. In addition, available datasets, whether provided by public institutions or acquired through commercial sources, often lack standardization, which hampers the transferability and applicability of these frameworks across different geographical contexts. Lastly, although not exclusive to geospatial applications, all AI-based developments must undergo rigorous validation to ensure public trust and social acceptance of the outcomes. In this regard, the training of new professionals, discussed in the cross-cutting challenges section, is essential to ensure the robustness and reliability of future geospatial-AI applications. Geophysical techniques have shown to be highly applicable in the prospecting and analysis of subway hydrogen storage. The characterization of subway caverns with hydrogen storage potential is key to be able to analyze the viability of the facility, considering the capacity of the cavity and its stratigraphic characteristics, which determine the equipment needed to ensure the supply of hydrogen in adequate quantity and quality. Also in the storage stage, the combination of GIS and IoT makes it possible to monitor and determine in real time cavity alterations, such as hydrogen leaks, and to issue alarm signals or act automatically to improve the safety of the facilities. Nevertheless, progress is constrained by fragmented, low-resolution datasets and a lack of standardized protocols for integrating spatial data across sectors, leading to heavy computational loads. A particularly under-studied area is the thermal and materials impact of hydrogen cycling in underground storage boreholes, which highlights the need for improved sensing approaches. Practical opportunities lie in extending geophysical surveys, such as seismic or electromagnetic methods, to characterize subsurface cavern capacity and stratigraphy, and in leveraging multispectral and infrared satellite imagery to detect pipeline leaks and thermal anomalies, with alerts seamlessly integrated into GIS platforms.