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Wind-Tree-Based Green Innovation Systems for Decarbonization Through Artificial Intelligence in Tanzanian Universities

タンザニアの大学におけるAIによる風力樹木型グリーンイノベーションシステムと脱炭素化 (AI 翻訳)

Thompson Xavier Ananth, Mohit Verma, Shashi Kant, Duncan Mwakipesile

Advances in Computational Intelligence and Roboticsジャーナル2026-05-15#再生可能エネルギー
DOI: 10.4018/979-8-3373-9978-2.ch009
原典: https://doi.org/10.4018/979-8-3373-9978-2.ch009

🤖 gxceed AI 要約

日本語

本論文は、タンザニアの大学における風力樹木型再生可能エネルギーシステムとAIを活用したグリーンイノベーションが脱炭素化に与える影響をTOEフレームワークで分析。SEMの結果、AIがグリーンイノベーションのパフォーマンスを向上させ、脱炭素化を促進する媒介効果を持つことを実証。開発途上国の高等教育機関におけるAI導入の政策的示唆を提供。

English

This paper examines how AI-enhanced green innovation systems, specifically wind-tree renewable energy, drive decarbonization in Tanzanian universities. Using a TOE framework and SEM, it finds that AI significantly improves green innovation performance and mediates decarbonization outcomes. It provides practical and policy insights for sustainability in higher education in developing countries.

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

日本の大学や研究機関にとって、AIと再生可能エネルギーを組み合わせた脱炭素化の実証事例は参考になるが、風力樹木方式は日本では普及しておらず、直接的な応用は限定的。ただし、途上国での実践知として、国際協力やSDGs達成の文脈で有益。

In the global GX context

This paper offers a rare empirical study of AI-driven green innovation in a developing-country university setting, contributing to the global discourse on decarbonization in higher education. While the wind-tree system is unconventional, the mediation role of AI in enhancing renewable energy adoption is a valuable insight for global GX practitioners seeking scalable solutions in resource-constrained environments.

👥 読者別の含意

🔬研究者:Provides empirical evidence on AI as a mediator in green innovation systems, useful for scholars studying technology adoption and decarbonization in higher education.

🏢実務担当者:Offers insights for university sustainability teams on integrating AI with renewable energy to improve energy efficiency and institutional practices.

🏛政策担当者:Highlights the potential of AI-driven green innovation in developing-country universities, informing education and climate policy in similar contexts.

📄 Abstract（原文）

This paper explores how green innovation systems can be improved with the help of artificial intelligence (AI) to push towards decarbonisation, particularly when it comes to wind-tree-based systems of renewable energy. Based on the Technology-Organisation-Environment (TOE) paradigm, the study elaborates and estimates a structural equation model (SEM) in order to investigate the connections between AI, GIS, and decarbonisation results in Tanzanian universities. The results specify that AI has a strong aptitude to enhance the performance of green innovation systems and is a mediator in the enhancement of the performance of decarbonisation. AI enhances efficient working and sustainable institutional practices by maximising energy consumption and promoting renewable energy sources. This paper offers empirical results in the case of a developing-country setting and illuminates the manner in which AI-based systems can facilitate climate action at the HEI level. Those findings are applicable as practical and policy-relevant information for sustainability in higher education.

🔗 Provenance — このレコードを発見したソース

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