Solar versus Electrolysis Methods for Green Hydrogen Production: A Meta-Analysis of Efficiency, Yield, and Cost Performance
Abstract
The global transition to sustainable energy systems has accelerated interest in green hydrogen production, with solar and electrolysis methods emerging as leading technological pathways. However, systematic comparative evidence across multiple performance dimensions remains limited, creating challenges for evidence-based technology selection decisions.
This study aims to compare the effectiveness of solar and electrolysis methods for green hydrogen generation through systematic review and meta-analysis, examining efficiency, hydrogen yield, and levelized cost of hydrogen (LCOH) across recent scientific literature.
A comprehensive literature search was conducted across multiple databases (Web of Science, Scopus, PubMed, IEEE Xplore, ScienceDirect) for studies published from 2015-2025. Data were extracted and standardized using established conversion factors for efficiency (%), hydrogen yield (kg/day), and cost (USD/kg). Statistical analyses used Mann-Whitney U tests due to assumption violations, with effect sizes calculated using Cohen's conventions.
Forty-seven studies met the inclusion criteria, providing 8-20 studies per outcome measure. Electrolysis methods demonstrated significantly higher efficiency (55.8% vs 19.4%, p = 0.001, large effect r = 0.62) and more consistent, lower costs ($5.09 vs $8.03/kg, p < 0.10, medium effect r = 0.335). No statistically significant difference was found in hydrogen yield despite solar methods showing 14.72 times higher geometric mean, indicating that deployment scale influences yield more than technology choice.
Electrolysis methods currently offer superior consistency and commercial readiness, while solar approaches show potential for breakthrough performance under optimal conditions. The findings suggest that diversified technology portfolios may optimize adoption of green hydrogen across countries, with technology selection depending on specific application requirements, risk tolerance, and local conditions rather than technological superiority.
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Introduction
The global green hydrogen market has experienced unprecedented growth, with production capacity reaching 0.3 million tons annually in 2022 and projections indicating expansion to 38 million tons by 2030 (International Energy Agency, 2023). Leading developed economies have established ambitious targets: Germany aims for 5 GW electrolyzer capacity by 2030, Japan targets 3 million tons of hydrogen imports annually, and Australia plans 1.7 GW of renewable hydrogen projects, while developing nations including India (5 million tons production target), Brazil (18 GW planned capacity), and South Africa (500 MW electrolyzer installations) are rapidly scaling their hydrogen capabilities (Hydrogen Council, 2024).
Conclusion
This 47-study meta-analysis presents thorough evidence resolving the comparative effectiveness of solar and electrolysis approaches to green hydrogen production in terms of efficiency, yield, and cost.
Research Question 1 revealed statistically significant efficiency differences, with electrolysis methods demonstrating superior performance (55.8% vs 19.4% mean efficiency, large effect size r = 0.62). However, measurement context differences between system-level solar and component-level electrolysis efficiency may partially explain this substantial gap.
Research Question 2 showed no statistically significant yield differences despite solar methods achieving 14.72 times higher geometric mean yield (5.42 vs 0.37 kg/day). The extreme variability spanning 7-8 orders of magnitude indicated that study scale and implementation context impact yield more than technology choice.
Research Question 3 revealed marginally significant cost differences (p < 0.10, medium effect r = 0.335), with electrolysis providing more consistent and lower costs ($5.09 ± $0.84/kg) compared to solar methods ($8.03 ± $5.16/kg). Electrolysis demonstrated 57.8% cost advantage with 6.1 times lower variability, indicating superior affordability and investment predictability.