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Cited by 450Environmental Impact and SustainabilityAgriculture Sustainability and Environmental ImpactEnvironmental Education and Sustainability

Quantifying Carbon Footprint Reduction Opportunities for U.S. Households and Communities

Jones, Christopher M., Kammen, Daniel M.·2011·Environmental Science & Technology
DOI 10.1021/es102221h
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Abstract

Carbon management is of increasing interest to individuals, households, and communities. In order to effectively assess and manage their climate impacts, individuals need information on the financial and greenhouse gas benefits of effective mitigation opportunities. We use consumption-based life cycle accounting techniques to quantify the carbon footprints of typical U.S. households in 28 cities for 6 household sizes and 12 income brackets. The model includes emissions embodied in transportation, energy, water, waste, food, goods, and services. We further quantify greenhouse gas and financial savings from 13 potential mitigation actions across all household types. The model suggests that the size and composition of carbon footprints vary dramatically between geographic regions and within regions based on basic demographic characteristics. Despite these differences, large cash-positive carbon footprint reductions are evident across all household types and locations; however, realizing this potential may require tailoring policies and programs to different population segments with very different carbon footprint profiles. The results of this model have been incorporated into an open access online carbon footprint management tool designed to enable behavior change at the household level through personalized feedback.

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Crossref · 10.1021/es102221h

Cites

36 of 36 references verified0 citation errors · 0 not checkable

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  • Data show that the size and composition of household carbon footprints varies dramatically across geographic regions and demographics.

    “all the more so because data show that the size and composition of household carbon footprints varies dramatically across geographic regions and demographics [399].”

    From Tackling climate change with machine learning (2023) · cites this paper as [399]

  • ML can predict a household's emissions in transportation, energy, water, waste, foods, goods, and services, as a function of its characteristics.

    “ML can predict a household's emissions in transportation, energy, water, waste, foods, goods, and services, as a function of its characteristics [399].”

    From Tackling climate change with machine learning (2023) · cites this paper as [399]

  • Many carbon mitigation strategies also provide cost savings to consumers.

    “studies have shown that many carbon mitigation strategies also provide cost savings to consumers [399].”

    From Tackling climate change with machine learning (2023) · cites this paper as [399]

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Authors
Jones, C. M., & Kammen, D. M.
Affiliations
University of California, Berkeley (US)
Corresponding author
  • Jones, Christopher M.
  • Kammen, Daniel M.
ORCID
Venue
Environmental Science & Technology
Publisher
American Chemical Society (ACS)
Published
2011-03-30
Volume
45
Issue
9
Pages
4088-4095
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
DOI
10.1021/es102221h
OpenAlex
W2318960647
Cited by
450
References
36
Citations by year
2026: 24 · 2025: 25 · 2024: 34 · 2023: 32 · 2022: 44 · 2021: 35 · 2020: 38 · 2019: 33 · 2018: 34 · 2017: 31 · 2016: 28 · 2015: 37 · 2014: 14 · 2013: 21 · 2012: 17
FWCI
15.59
Type
journal article
Language
en
Open access
closed
Locations
  • Environmental Science & Technology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
  • Physical Sciences › Environmental Science › Ecology › Agriculture Sustainability and Environmental Impact
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
Keywords
household carbon footprint, greenhouse gas emissions, carbon footprint reduction, financial savings, regional variation, personalized feedback, behavior change
MeSH terms
Climate, Environmental Monitoring, Family Characteristics, Models, Theoretical, Transportation, United States, Greenhouse Effect, Waste Management, Carbon Footprint

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