A two-stage clustering approach to investigate lifestyle carbon footprints in two Australian cities
オーストラリアの2都市におけるライフスタイル炭素足跡を調査する2段階クラスタリング手法 (AI 翻訳)
Andreas Froemelt, Thomas Wiedmann
🤖 gxceed AI 要約
日本語
本研究は、シドニーとメルボルンの世帯の消費行動とライフスタイル由来の炭素足跡を、自己組織化マップとウォード法による2段階クラスタリングで分析した。所得や社会経済的属性と支出データを組み合わせることで、類似した特性を持つライフスタイル類型を識別し、都市間比較を行った。高所得層と低所得層で特に顕著な差異が見られ、都市固有のサプライチェーンと地域別評価の重要性が示された。
English
This study employs a two-stage clustering approach (Self-Organising Map and Ward clustering) to analyze household consumption patterns and lifestyle carbon footprints in Sydney and Melbourne. By combining expenditure data with socio-economic attributes, it identifies lifestyle archetypes and compares them across the two cities. Distinct patterns emerge in high- and low-income segments, highlighting the need for city-specific analyses and regionalized environmental assessments.
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
日本の都市部でも世帯消費によるCO2排出の多様性が課題となっており、本手法は日本の家計ミクロデータへの応用が可能。自治体の脱炭素政策立案や、SSBJに基づくサプライチェーン排出量の把握に示唆を与える。
In the global GX context
This study provides robust empirical evidence on consumption-based carbon footprints in high-income cities, reinforcing the need for city-specific climate policy. It offers a replicable methodology valuable for global disclosure frameworks that increasingly consider sub-national and household-level emissions.
👥 読者別の含意
🔬研究者:A clear methodological template for combining ML clustering with carbon footprint analysis to derive household archetypes.
🏛政策担当者:Demonstrates how lifestyle archetypes can inform targeted urban consumption policies and regional environmental assessments.
📄 Abstract（原文）
Abstract Given the key role of households in driving global emissions and resource use, a change in their consumption behaviours towards more sustainable levels is essential to reduce worldwide adverse environmental impacts. Thereby, focusing on cities is especially important because of today’s large share of the global population living in cities and because local authorities are close to the needs of their residents. However, devising targeted and effective policy measures implies a thorough understanding of prevailing consumption patterns and associated environmental consequences. The goal of this article is to investigate and compare household behaviours and lifestyle-induced carbon footprints in Sydney and Melbourne in order to enhance today’s understanding of household consumption in cities of a high-income, high-emission country. For this purpose, we employed a two-stage clustering approach with a Self-Organising Map and a subsequent Ward-clustering. This allowed for including expenditure data along with socio-economic attributes and thus for recognising lifestyle-archetypes. These emerging archetypes represent households with similar characteristics and comparable consumption patterns. Analysing the archetypes in detail and performing a city-comparison based on multi-dimensional scaling revealed similarities and dissimilarities between the two metropoles. ‘Older’ archetypes seem to behave more alike across cities but show different carbon footprints emphasising the importance of regionalised environmental assessments and of city-specific supply chains. Distinct patterns especially emerged in the high- and low-income segments highlighting the different importance of different lifestyles in each city. Socio-economically similar family-archetypes were found in both cities, but some of them showed diverging consumption behaviours. This article showed that studying household-induced environmental impacts in cities should not rely on macro-trends but should rather be based on city-specific analyses that capture local peculiarities and consider socio-economic characteristics and consumption data simultaneously.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/1748-9326/abb502first seen 2026-08-02 17:30:34
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。