Household income and carbon footprint: evidence from Taiwan

Abstract Previous studies found that households’ energy use and consumption of goods and services contribute greatly to increasing carbon emissions. This study estimated household carbon footprints, including direct and indirect emissions. Using the 2019 Taiwan Survey of Family Income and Expenditure, we investigated the heterogeneous relationship between household income and carbon emissions. Income sources were divided into six categories: compensation of employees, entrepreneurial income, property income, imputed rent income, transfer income, and miscellaneous income. Our results indicate that the income–emission elasticity for household total emission, direct emission, and indirect emission are 0.62, 0.37, and 0.84, respectively. Household income is positively associated with household carbon footprint, especially among households with higher imputed rent income. The proportion of household indirect emissions rises as income increases. Household income exhibits a stronger relationship with indirect carbon emissions than with direct carbon emissions. These findings suggest an important avenue for reducing the carbon footprint of high-income households: promoting energy-saving measures and encouraging the use of low-carbon footprint goods and services. Examples include those produced with recycled materials or low-carbon energy sources.

Authors

Institutions

Publication Details

Journal
Environmental Economics and Policy Studies
Published
2026-03-09
DOI
https://doi.org/10.1007/s10018-026-00463-w
Primary Topic
Environmental Impact and Sustainability
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Household income and carbon footprint: evidence from Taiwan

Yen‐Lien Kuo, Yu-Min CHUANG, Ya‐Ming Liu, Ching-Wen Yang
Environmental Economics and Policy Studies
Environmental Impact and Sustainability
article

Household income and carbon footprint: evidence from Taiwan

Yen‐Lien Kuo, Yu-Min CHUANG, Ya‐Ming Liu, Ching-Wen Yang
article en

Abstract

Abstract Previous studies found that households’ energy use and consumption of goods and services contribute greatly to increasing carbon emissions. This study estimated household carbon footprints, including direct and indirect emissions. Using the 2019 Taiwan Survey of Family Income and Expenditure, we investigated the heterogeneous relationship between household income and carbon emissions. Income sources were divided into six categories: compensation of employees, entrepreneurial income, property income, imputed rent income, transfer income, and miscellaneous income. Our results indicate that the income–emission elasticity for household total emission, direct emission, and indirect emission are 0.62, 0.37, and 0.84, respectively. Household income is positively associated with household carbon footprint, especially among households with higher imputed rent income. The proportion of household indirect emissions rises as income increases. Household income exhibits a stronger relationship with indirect carbon emissions than with direct carbon emissions. These findings suggest an important avenue for reducing the carbon footprint of high-income households: promoting energy-saving measures and encouraging the use of low-carbon footprint goods and services. Examples include those produced with recycled materials or low-carbon energy sources.

Environmental Economics and Policy Studies
National Cheng Kung University (TW)
National Cheng Kung University
Openalex Percentile: Top 39%
Environmental Impact and Sustainability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.