## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.1 Renewable Hydrogen Production  
Hydrogen production is widely recognized as the most technically complex and economically influential stage of the renewable HVC. This stage not only determines the feasibility of large-scale deployment but also significantly influences the competitiveness of renewable hydrogen against fossil-based alternatives. In this context, geospatial analysis emerges as a fundamental tool for guiding strategic decisions, particularly concerning the location, configuration, and optimization of hydrogen production infrastructure. The systematic literature review reveals that 48% of the studies identified focus specifically on the application of geospatial techniques to the production stage. A large proportion of these studies highlight the use of GIS, frequently in combination with MCDM methods such as the Analytic Hierarchy Process (AHP) or Best-Worst Method (BWM). These integrated approaches have demonstrated significant value in identifying optimal sites for hydrogen production facilities, considering spatial variables such as solar irradiance, wind potential, land slope, land use, water availability, and proximity to grid and transport infrastructure. For instance, [PERSON], S. et al. [53] applied GIS-AHP to the region of Morocco to map the most suitable zones for Proton Exchange Membrane (PEM) electrolysis powered by Photovoltaic (PV) and Concentrated Solar Power (CSP), combining different criteria such as elevation models, or solar radiation datasets among others. Based on the suitability ranking maps, two representative sites, one from PV scenario and one from CSP scenario, were chosen for a detailed techno-economic analysis. The results obtained demonstrate Morocco's competitiveness as they obtain a price per kilogram of hydrogen at these locations of $9.816 (PV scenario) and $5.86 (CSP scenario). However, these approaches are often hindered by the lack of standardized and high-resolution spatial data across countries, as well as the computational complexity involved in large-scale spatial modeling. Beyond traditional GIS applications, advanced spatial modeling that integrates remote sensing and geospatial data analytics has expanded the potential for precision in evaluating renewable energy inputs. Through satellite imagery, terrain data, and climatic databases, these techniques enable the assessment of solar and wind availability, terrain suitability, and even environmental constraints relevant to hydrogen production. For example, [PERSON], [PERSON] et al. [54] applied a GIS-based approach to evaluate solar and wind potential for renewable hydrogen and ammonia production in Iran. It selects four locations in which it compares three different scenarios with different solar and wind energy potential in which it performs a techno-economic analysis in detail. In more recent developments, the integration of IoT sensor technologies with GIS platforms has opened promising pathways for real-time monitoring and control in hydrogen production systems. These systems can dynamically track key variables such as electrolyzer performance, gas quality, and operational parameters, allowing for automation and predictive maintenance. [PERSON], [PERSON] et al. [55] used IoT sensors to monitor electrolyzer arrays and integrated spatial data for dynamic system optimization. [PERSON], [PERSON] et al. [56]further demonstrated integrated GIS, sensors, and satellite analysis that can be very useful to precise wind resources and thereby improve the techno-economic models of hydrogen plants. Nonetheless, the implementation of such systems is still constrained by the cost and maintenance demands of sensor networks, particularly in remote or offshore locations, and the necessity for robust data-cleaning algorithms to manage inaccuracies and sensor drift. Complementing these monitoring capabilities, clustering techniques and big data analytics are gaining traction for identifying micro-regions with high hydrogen production potential. For instance, [PERSON], [PERSON] et al.