Designing a Sustainable Green Hydrogen Supply Chain Network: Multiobjective Optimization With AI‐Assisted Smart Inspection and Public Engagement Strategies

Achieving net‐zero emissions requires the integration of low‐carbon technologies and sustainable operations within emerging energy sectors such as green hydrogen. This study develops a novel multiobjective optimization framework for designing a solar‐based green hydrogen supply chain network (GHSCN), which is structured around the principles of the triple bottom line (TBL) sustainability approach. The model integrates smart production through AI‐assisted autonomation for quality control, variable production rate‐based electrolysis, and green technology investments, along with novel demand modeling that responds to public awareness and pricing strategies. By capturing the complex interactions between social participation, carbon‐pricing mechanisms, life‐cycle emissions, and infrastructure selections, the framework directly addresses major challenges in reaching climate goals. The framework includes mechanisms for carbon taxation and investment incentives, along with life‐cycle assessment (LCA) indicators that account for social impacts such as employment, safety, and economic value. To generate a balanced set of optimal solutions across environmental, economic, and social goals, the model applies an augmented ‐constraint method combined with lexicographic optimization. Results show that a 50% reduction in solar electricity costs can increase profitability by over 21% while high levels of public awareness led to greater adoption of green hydrogen even at lower investment levels. A comparative analysis demonstrates that the framework integrates behavioral insights, social evaluation, and sustainability‐driven decision‐making, exceeding current approaches. This research provides valuable strategies for policymakers, investors, and industries operating toward a sustainable energy supply chain, offering a meaningful step toward global net‐zero emission targets.