コミュニティ観光における説明可能AIによる炭素予測と炭素配慮型ツーリズムのための機械学習

An Explainable AI-Powered Carbon Prediction and Machine Learning for Carbon-Aware Tourism in the Community (原題)

Sumitra Nuanmeesri

Sustainability📚 査読済 / ジャーナル2026-10-30#AI×ESG対象セクター: transport

🤖 gxceed AI 要約

日本語

地域観光の旅行単位炭素排出を予測するため、事前旅行代理変数34項目のみを用いた機械学習フレームワークを構築。650名の実証回答から生成した13,000件の合成トリップで5手法を比較し、CatBoostがMAE 1.28kgCO2e、R²0.948で最良。SHAPにより移動距離・滞在日数・予算・計画スコアが主要因と判明。LINE Mini Appで実装し、実地評価でCTR53.05%、受容率50.44%を達成した。

English

A trip-level carbon prediction framework for community-based tourism using only 34 pre-travel proxy features to avoid target leakage. Trained on 13,000 synthetic profiles from 650 respondents, CatBoost achieved MAE 1.28 kgCO2e and R²=0.948, outperforming linear baselines. SHAP identified travel distance, stay duration, budget, and planning score as key drivers. Deployed via a LINE Mini App, field testing showed 53% CTR and 50% acceptance.

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

Scope 3のカテゴリ6（出張）やカテゴリ1に通じる旅行単位の炭素推計手法として、日本企業の観光・旅行業や自治体の脱炭素観光政策、SSBJ開示における間接排出算定の精緻化に示唆を与える。

In the global GX context

Offers a scalable, explainable ML approach to activity-level carbon accounting relevant to Scope 3 Category 6 (business travel) and tourism-sector disclosure under CSRD/ISSB. Demonstrates how XAI can support consumer-facing decision architecture for emissions reduction.

👥 読者別の含意

🔬研究者:旅行単位の炭素予測における特徴量設計とXAI適用の実証例として参照可能。

🏢実務担当者:旅行・観光事業者が顧客向け炭素推計・推奨機能を組込む際の設計指針になる。

🏛政策担当者:地域観光の脱炭素政策において行動変容ナッジと炭素可視化の組合せを検討する材料。

📄 Abstract（原文）

Tourism contributes substantially to greenhouse gas emissions. This study developed a trip-level carbon prediction framework for community-based tourism, serving as a surrogate model for activity-based carbon accounting equations. To prevent target leakage, predictor features were restricted to 34 pre-travel proxies, excluding post-travel parameters. Five machine learning algorithms, including Multiple Linear Regression, Random Forest, eXtreme Gradient Boosting, Light Gradient-Boosting Machine, and Categorical Boosting, were evaluated using 13,000 synthetic trip profiles generated from 650 empirical baseline respondents (partitioned into 10,400 development scenarios from 520 respondents and 2600 hold-out scenarios from 130 respondents). LINE Mini App provided instantaneous carbon estimations and personalized recommendations. Categorical Boosting achieved predictive performance, with a mean absolute error of 1.28 kilograms of carbon dioxide equivalent (kgCO2e), root mean square error of 1.96 kgCO2e, mean absolute percentage error of 6.90%, and coefficient of determination of 0.948, outperforming linear baselines. Explainable Artificial Intelligence using SHapley Additive exPlanations indicated that the model captures primary pre-travel drivers, led by travel distance, stay duration, travel budget, and travel planning score. External field evaluation: 105 independent cohort users generated 1297 recommendation impressions, 688 clicks, and 347 acceptances, achieving a 53.05% click-through rate, a 50.44% acceptance rate, a 247.64 ms mean response time, and an 86.40% user feedback score, supporting proactive decision choice architecture for sustainable community-based tourism.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.3390/su182111037first seen 2026-11-01 04:59:55 · last seen 2026-11-06 04:56:25

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。