2026 Economic Assessment of Natural Hydrogen Exploration and Production Projects for Investment and Policy Decision-Making
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작성자 관리자 작성일 26-07-15 09:51본문
- Journal
- International Journal of Energy Research
- Vol
- 2026
- Page
- 4243861
- Year
- 2026
Abstract
The discovery of natural hydrogen—often called gold or white hydrogen—has attracted attention as a potential game-changer in the hydrogen economy. This study develops a hypothetical economic assessment model for natural hydrogen exploration and production (E&P) projects, built on proxy cost data from oil and natural gas projects due to the absence of commercial-scale cases. The model assumes all produced hydrogen is converted into electricity for sale via existing grids, incorporates renewable energy certificates (RECs) to reflect environmental value, and applies historical average electricity and REC prices. Using a comparative analogy approach, the analysis evaluates project viability under various scenarios, including changes in power generation efficiency, electricity and REC prices, costs, and field size. Results indicate that REC subsidies are essential for viability at median field size, though projects remain feasible with reduced subsidies. Revenue-related factors—particularly generation efficiency and electricity prices—have greater impact than cost factors. Economies of scale substantially improve feasibility, enabling large fields to operate profitably without subsidies. Compared with other hydrogen production methods, natural hydrogen offers the lowest estimated cost and relatively low greenhouse gas (GHG) emissions. These findings suggest that policy support, such as differentiated REC incentives based on well size, can encourage investment, while technological improvements in generation efficiency should be prioritized over upstream cost reductions. However, the model is based on simplifying assumptions and may not capture cost variations from unique geophysical characteristics of natural hydrogen reservoirs or regional market constraints. As empirical data from real projects become available, the model’s assumptions should be refined to improve accuracy and applicability.