持続可能なスマートモビリティエコシステムのための低炭素・省エネUAV運用
Low-carbon and energy-aware UAV operations for sustainable smart mobility ecosystems (原題)
B.K. Tripathy, Riya, Hemant Kumar Saini
🤖 gxceed AI 要約
日本語
本書では、UAV（ドローン）のライフサイクル全体での炭素排出を削減する省エネルギー手法を探求する。AIベースの最適化（機械学習による省エネ経路、強化学習による航法、GANによる飛行エネルギー予測）を紹介し、ブロックチェーンによる炭素会計の透明化や量子最適化の可能性も論じる。持続可能なUAVエコシステム構築に向けたベストプラクティスと今後の研究課題を提示する。
English
This chapter explores energy-saving solutions to reduce the carbon footprint of UAVs across their life cycle. It covers AI-based optimization (ML for energy-aware routing, RL for navigation, GANs for flight energy prediction), blockchain for transparent carbon accounting, and quantum optimization for fleet scheduling. It concludes with best practices and future research directions for sustainable UAV ecosystems.
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
日本では物流・インフラ点検でのドローン活用が進む中、本稿の省エネ・低炭素化手法はSSBJ開示やScope3対応に資する。特にAI最適化と炭素会計の組み合わせは、日本企業のサステナビリティ報告に応用可能。
In the global GX context
Globally, this aligns with ISSB and CSRD disclosure requirements by addressing life-cycle emissions and AI-driven optimization. It offers insights for sustainable mobility and smart city initiatives, relevant to TCFD-aligned climate risk assessments.
👥 読者別の含意
🔬研究者:AI最適化と炭素会計を統合したUAV持続可能性の研究フレームワークを提供。
🏢実務担当者:ドローン運用の省エネと炭素排出削減の具体的な手法を導入する際の参考。
🏛政策担当者:UAVの環境規制や持続可能なモビリティ政策の策定に示唆。
📄 Abstract（原文）
Unmanned aerial vehicles (UAVs) will be part of the future mobility framework and smart ecosystem infrastructure. However, the growing application of UAVs results in massive energy consumption and environmental issues, particularly associated with short battery life, power-intensive activities, and carbon emissions throughout the UAV life cycle. This chapter presents an in-depth exploration of energy-saving solutions that reduce the carbon footprint of UAVs without affecting the reliability of operation and the alignment with the international sustainability models and sustainable development goals priorities. It finds the factors that burn energy of UAVs, balances the environmental effects of battery technologies, and illuminates the origin of emissions in the life cycles of manufacturing, charging, and disposal. In addition to these findings, this chapter explores the advanced AI-based optimization techniques. The models that are among them include machine learning models that are used in energy-aware routing, reinforcement learning that is used in efficient navigation, and also generative adversarial network (GAN)-based simulation that is used in predicting the flight energy. It is described that carbon accounting and energy tracing can be more transparent with the help of blockchain, and quantum optimization is a next-generation tool for ultra-efficient fleet scheduling. Also, material innovations like lightweight materials, hybrid solar propulsion, and the power systems of the next generation are taken into consideration. The chapter closes with the presentation of operational best practices, success stories, hurdles, and upcoming research themes for constructing a scalable, low-carbon, and sustainable UAV ecosystem of the future.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1201/9781003731580-9first seen 2026-08-19 04:39:56
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