## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.1 Renewable Hydrogen Production
[57] developed a GIS-based geospatial clustering model for Pakistan that combines six critical geospatial factors: land availability, water and road access, water stress, slope, and proximity to protected regions. By applying these exclusion criteria and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms within GIS environments, researchers can detect large-scale facilities equipped with PV and electrolyzer systems. These clustering methods support broader spatial planning and scenario modeling, but their accuracy depends heavily on the availability of harmonized, high-quality, and up-to-date datasets from multiple domains. An important trend that emerges from the literature concerns the alignment between geospatially optimal regions and the technological specificity of the production method. For instance, PEM and Alkaline Electrolysis (AEL) technologies are frequently assessed in inland or semi-arid zones with high solar potential, such as parts of northern Africa, Australia, Chile, and southwestern United States, where water access is possible through nearby aquifers or desalination infrastructure. In contrast, studies on Solid Oxide Electrolysis Cells (SOEC) are predominantly located in coastal or industrial port areas, such as Saudi Arabia, Northern Europe, and East Asia, where high-temperature waste heat or industrial synergies may improve system efficiency. Likewise, emerging approaches such as photocatalytic hydrogen production tend to focus on hyper-arid zones with extreme solar radiation (e.g., Namibia or the Arabian Peninsula) although this line of research remains largely experimental. In addition to these technological-geographical linkages, the geographic distribution of the studies reveals certain patterns. A significant proportion of research efforts are concentrated in countries with ambitious hydrogen strategies and strong geospatial data infrastructures, notably Germany, Spain, China, Australia, and the United States. These countries lead not only to technological development but also to methodological advances for spatial modeling and decision support. Conversely, regions with high renewable potential but less-developed geospatial capabilities, such as parts of Latin America or sub-Saharan Africa, are underrepresented in the literature, suggesting both a gap and an opportunity for future international collaboration. These insights are summarized in Table 5, which categorizes the main applications of geospatial techniques in renewable hydrogen production, the predominant methods employed, their specific uses, and the primary challenges identified across the literature. \begin{table}
\begin{tabular}{c c c c c} \hline
**Application** & **Ref.** & \
\begin{tabular}{c} **Main Geospatial** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Primary Uses of the** \\ **Geospatial Techniques** \\ \end{tabular} & **Challenges** \\ \hline Site selection & [53; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77] & GIS + MCDM & Identification of suitable \\  & & & locations based on renewables, land use, water, \\  & & & grid access \\ \hline \end{tabular}
\end{table} 
Table 5: Summary geospatial techniques in the production stage of the renewable hydrogen value chain. Despite these advances, several gaps remain in the literature. The integration of GIS with techno-economic models capable of simulating levelized costs of hydrogen (LCOH) under spatial constraints is still limited. Nevertheless, [PERSON] et al. (2019) demonstrated an effort by modeling the techno-economic potential of offshore wind-powered hydrogen production in Brazil using integrated GIS. The spatial optimization of emerging technologies such as photocatalytic hydrogen production or offshore wind-powered is only beginning to be explored. Additionally, the hydrological dimension, especially in arid or water-stressed regions, remains insufficiently addressed, even though water availability is a key limiting factor for electrolysis-based hydrogen production.