Global Hydrogen Export Business Index: An AI-Enhanced Multicriteria Decision Analysis Framework for Assessing Hydrogen Export Potential

Tang, Tao, Valasai, Gordhan Das, Lakhan, Sajid Mahmood, Sulukan, Egemen, Mahar, Abdul Waheed, Alam, Muhammad, Bhangwar, Sajjad, Bhanbhro, Riaz and Landini, Stefano (2026) Global Hydrogen Export Business Index: An AI-Enhanced Multicriteria Decision Analysis Framework for Assessing Hydrogen Export Potential. Geological Journal. ISSN 0072-1050

	Microsoft Word (OpenXML) (Landini_GHEBI 2 for Geological Journal (1)) - Accepted Version Restricted to Repository staff only until 8 January 2027. Available under License Unspecified licence. Request a copy
	Microsoft Word (rba08-Landini_etal_Global_hydro) - Published Version Restricted to Repository staff only until 7 January 2027. Available under License Unspecified licence. Request a copy

Abstract

Global hydrogen trade is emerging as a central pillar of net-zero strategies, yet there is no unified analytical framework for comparing countries' readiness to export hydrogen. This study develops the Global Hydrogen Export Business Index (GHEBI), an AI-enhanced multicriteria decision analysis (MCDA) framework that evaluates hydrogen export potential across 31 countries. The framework assesses six dimensions that policymakers and investors must consider when designing hydrogen export strategies: (1) resource availability, (2) infrastructure and technology readiness, (3) economic viability, (4) policy and regulatory environment, (5) environmental and social suitability, and (6) geopolitical and geological risk. These dimensions incorporate indicators derived from international datasets and expert consultation. Five established MCDA methods (AHP, TOPSIS, PROMETHEE II, VIKOR, and SAW) are integrated to generate a composite score, while machine-learning models are used to validate ranking stability and identify the most influential determinants. Results show substantial regional disparities: Australia, the United States, and selected Gulf countries lead due to strong renewable resources, port infrastructure, and policy support, whereas Africa and South America score lower because of infrastructure gaps, policy uncertainty, and geohazard exposure. Sensitivity analysis confirms the robustness of rankings to changes in indicator weights. The GHEBI framework offers a transparent, multidimensional tool to guide hydrogen export planning, identify investment priorities, and support the development of resilient and geologically informed hydrogen export pathways.

Item Type:	Article
Additional Information:	Data Availability Statement: The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Uncontrolled Keywords:	ai-mcda integration,energy resilience,export potential,hydrogen economy,machine learning,multicriteria decision analysis,resource sustainability,energy(all),engineering(all),sdg 7 - affordable and clean energy,sdg 13 - climate action,sdg 11 - sustainable cities and communities ,/dk/atira/pure/subjectarea/asjc/2100
Faculty \ School:	Faculty of Science > School of Engineering, Mathematics and Physics
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	08 Jan 2026 15:30
Last Modified:	21 Jan 2026 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101570
DOI:	10.1002/gj.70208

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