Sustainability impacts of green hydrogen production technology: An agent-based model simulation.
グリーン水素製造技術の持続可能性影響：エージェントベースモデルシミュレーション (AI 翻訳)
L. Cricelli, Sara Ianniello, Pierpaolo Pragliola, Serena Strazzullo
🤖 gxceed AI 要約
日本語
グリーン水素技術の導入を促進する要因をエージェントベースモデル（ABM）とAHPを組み合わせて分析。イタリア・カンパニア地域を対象に2025～2050年のシミュレーションを実施。環境意識、社会的可視性、経済的実現性が主要な導入要因であり、有利な政策・コスト条件下では2050年までに導入率87％、年間10億ユーロ超のエネルギー節約、100万トン超のCO2削減、最大16,800人の雇用創出が見込まれる。
English
This study combines Agent-Based Modeling and Analytic Hierarchy Process to simulate green hydrogen technology adoption in Campania, Italy (2025-2050). Key drivers include environmental concerns, social visibility, and economic feasibility. Under favorable policy and cost conditions, adoption can reach 87% by 2050, yielding over €1 billion annual energy savings, >1 million tons CO2 avoided, and up to 16,800 new jobs.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文はイタリア・カンパニア地域を対象としているが、ABM-AHP手法は日本の地域水素導入シミュレーションにも応用可能。日本の水素基本戦略や地域エネルギー計画への示唆を含む。
In the global GX context
This paper contributes to understanding hydrogen adoption dynamics using a novel ABM-AHP method. It highlights the importance of social visibility and environmental concerns, relevant for global hydrogen deployment strategies under the EU Hydrogen Strategy and similar initiatives.
👥 読者別の含意
🔬研究者:Offers a validated ABM-AHP methodology for simulating hydrogen technology adoption, useful for researchers in energy transition modeling.
🏢実務担当者:Provides scenario analysis for green hydrogen adoption, can inform corporate investment decisions and policy advocacy.
🏛政策担当者:Highlights policy and cost conditions needed to achieve high adoption rates, useful for designing hydrogen support schemes.
📄 Abstract（原文）
Green hydrogen is increasingly considered a promising option for decarbonizing energy-intensive sectors, but its adoption remains uncertain because it depends on heterogeneous actor behaviour, market conditions, and policy support. To address this issue, this study combines Agent-Based Modelling (ABM) and the Analytic Hierarchy Process (AHP). The aim is to simulate the adoption of a green hydrogen production technology, investigate the main drivers of adoption, and assess its environmental, economic, and social implications in the Campania region, Italy, over the 2025-2050 period. The ABM-AHP approach allows for the integration of expert knowledge into agent decision rules, enhancing the realism of adoption dynamics across diverse actor types and validating the model at the macro-level. The ABM includes 359 potential adopters and 367 external agents, enabling the simulation of interactions among industries, commercial areas, institutions, power grid operators, suppliers, and electric vehicle owners. Adoption decisions are modelled through a utility function grounded in utility maximization and innovation diffusion theory. Adoption occurs when utility exceeds a predefined threshold. The model is then used to test alternative scenarios based on hydrogen demand, technology cost reduction, and degradation rate. Main results highlight that environmental concerns, social visibility, and economic feasibility are key drivers of technology adoption. This study shows that green hydrogen can achieve adoption rates of up to 87% by 2050 under favourable policy and cost conditions. At this level, the system delivers annual benefits exceeding €1 billion in energy savings, more than one million tons of CO2 emissions avoided, and the creation of up to 16,800 new jobs. While based on scenario-driven simulations, the findings offer useful insights for strategic energy planning and stakeholder engagement within regional sustainability transitions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1016/j.jenvman.2026.129910first seen 2026-06-10 05:22:35
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