Machine Learning Models for Climate Change Effects on Economic Growth
経済成長に対する気候変動影響の機械学習モデル (AI 翻訳)
Pramod S, Manoj Kumar Tiwari
🤖 gxceed AI 要約
日本語
本論文は、気候変動が観光業に与える経済的影響を機械学習モデルで予測する手法を探る。気候予測と経済データを統合し、教師あり・教師なし学習や深層学習を用いて観光需要や収益の変化を分析する。高解像度データの重要性や地域格差・モデル不確実性の課題にも言及し、適応戦略への活用可能性を示す。
English
This chapter explores machine learning models to forecast the economic impacts of climate change on the tourism sector. It integrates climate projections with economic data, using supervised, unsupervised, and deep learning methods to predict shifts in tourism demand and revenue under different climate scenarios. The study emphasizes the need for high-resolution data and addresses challenges of regional disparities and model uncertainty, highlighting ML's role in supporting adaptive strategies for policymakers and industry stakeholders.
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
日本では観光業が地域経済に重要であり、気候変動適応策の策定にML活用の知見を提供する。ただし、日本の具体的な政策や制度（例：GX推進法）との直接的な関連は薄い。
In the global GX context
This paper contributes to the global discourse on climate risk modeling by demonstrating ML applications in a sector highly sensitive to climate change. It aligns with TCFD and ISSB frameworks that emphasize scenario analysis and climate-related risk assessment, offering methodological insights for tourism-dependent economies.
👥 読者別の含意
🔬研究者:Provides a methodological framework for applying ML to climate-economy modeling in the tourism sector.
🏢実務担当者:Offers insights for tourism businesses and regional planners to use ML for climate adaptation planning.
🏛政策担当者:Highlights the need for data-driven adaptation policies in tourism-dependent regions.
📄 Abstract（原文）
Climate change presents an unprecedented challenge to global economies, with profound and far-reaching impacts on various sectors. The tourism industry, a major contributor to the global economy, is particularly vulnerable to climate-induced disruptions, including rising temperatures, shifting precipitation patterns, and extreme weather events. This chapter explores the application of machine learning (ML) models for forecasting the economic consequences of climate change on the tourism sector. By integrating climate projections with economic data, ML techniques provide powerful tools for understanding how changes in the environment will affect tourism demand, regional economic stability, and sectoral performance. The chapter discusses the use of supervised and unsupervised learning methods, as well as advanced deep learning techniques, to predict shifts in tourism patterns and revenue generation under different climate scenarios. Emphasis is placed on the importance of granular, high-resolution data and the challenges of incorporating regional disparities and model uncertainty. The chapter also highlights how ML can support adaptive strategies for policy-makers and industry stakeholders, helping to mitigate the economic risks posed by climate change. By leveraging the capabilities of machine learning, the tourism sector can better prepare for future disruptions, ensuring resilience and sustainability in the face of a rapidly changing climate.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.71443/9789349552470-12first seen 2026-05-05 19:12:51
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