TY - JOUR
T1 - Global Hydrogen Export Business Index: An AI-Enhanced Multicriteria Decision Analysis Framework for Assessing Hydrogen Export Potential
AU - Tang, Tao
AU - Valasai, Gordhan Das
AU - Lakhan, Sajid Mahmood
AU - Sulukan, Egemen
AU - Mahar, Abdul Waheed
AU - Alam, Muhammad
AU - Bhangwar, Sajjad
AU - Bhanbhro, Riaz
AU - Landini, Stefano
N1 - 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.
PY - 2026/1/7
Y1 - 2026/1/7
N2 - 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.
AB - 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.
KW - AI-MCDA integration
KW - energy resilience
KW - export potential
KW - hydrogen economy
KW - machine learning
KW - multicriteria decision analysis
KW - resource sustainability
U2 - 10.1002/gj.70208
DO - 10.1002/gj.70208
M3 - Article
SN - 0072-1050
JO - Geological Journal
JF - Geological Journal
ER -