The carbon implications of declining household scale economies

Underwood, Anthony; Zahran, Sammy

doi:10.1016/j.ecolecon.2015.04.028

Cited by 59 publications

(27 citation statements)

References 34 publications

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“…Moreover, the share of households with persons aged 65+ shows some importance, but with contrary impact on the municipal footprint. This corresponds well with the findings of Froemelt, Dürrenmatt, and Hellweg (), which showed that—except for very‐high‐income families—families show lowest per‐capita GHG compared to other archetypes; this is most probably due to effects of economy of scale and is similarly confirmed by Underwood and Zahran () and Weber and Matthews (). (4) Related to the previous finding, the percentage of 2‐persons households compared to 3+‐persons households seems to be another important determinant for environmental impacts.…”

Section: Resultssupporting

confidence: 90%

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Froemelt

Buffat

Hellweg

2019

J of Industrial Ecology

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As major drivers of economy, households induce a large share of worldwide environmental impacts. The variability of local consumption patterns and associated environmental impacts needs to be quantified as an important starting point to devise targeted measures aimed at reducing household environmental footprints. The goal of this article is the development and appraisal of a comprehensive regionalized bottom-up model that assesses realistic environmental profiles for individual households in a specific region. For this purpose, a physically based building energy model, the results of an agent-based transport simulation, and a data-driven household consumption model were interlinked within a new probability-based classification framework and applied to the case of Switzerland. The resulting model predicts the demands in about 400 different consumption areas for each Swiss household by considering its particular circumstances and produces a realistic picture of variability in household environmental footprints. An analysis of the model results on a municipal level reveals per-capita income, population density, buildings' age, and household structure as possible drivers of municipal carbon footprints. Whilehigher-emission municipalities are located in rural areas and tend to show higher shares of older buildings, lower-emission communities have larger proportions of families and can be found in highly populated regions by trend. However, the opposing effects of various variables observed in this analysis confirm the importance of a model that is able to capture regional distinctions.The overall model constitutes a comprehensive information base supporting policymakers in understanding consumption patterns in their region and deriving environmental strategies tailored to their specific population.

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Section: Resultssupporting

confidence: 90%

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Froemelt

Buffat

Hellweg

2019

J of Industrial Ecology

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“…In contrast, expenditure on and share of transport fuels appear not to be systematically correlated with household age composition. These findings are consistent with those of Ironmonger et al (1995) and Underwood and Zahran (2015). As the authors suggest, the global trend toward smaller family size may offset the potential gain in energy efficiency.…”

Section: Determinants Of Energy Expendituresupporting

confidence: 82%

“…Ironmonger et al (1995) study the direction of EOS in residential energy use and expenditure in Australia, and find significant EOS with respect to family age composition. Underwood and Zahran (2015) study the case of the United States and find similar results; they further discuss the implications for trends in carbon dioxide emissions.…”

Section: Introductionsupporting

confidence: 53%

“…To the best of our knowledge, Rodriguez-Oreggia and Yepez-Garcia (2014), for the case of Mexico, is the only study that addresses the use of multiple fuels, including domestic gas, electricity, and gasoline. 2 Another line of study, which has received noticeably less attention, focuses on economies of scale (EOS) in energy consumption (i.e., Ironmonger et al 1995;Underwood and Zahran 2015).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Drivers of Household Energy Spending: Micro Evidence for Latin America

Jiménez¹,

Yépez-García²

2017

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work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail.Note that link provided above includes additional terms and conditions of the license. The paper investigates the determinants of household energy spending and energy budget shares, with a focus on understanding their non-linear relationship with income, and the presence of economies of scale. The analysis is based on a unique, harmonized collection of official household surveys from 13 Latin American countries. This dataset allows distinguishing between expenditures on electricity, domestic gas, and fuel for private transportation, providing a comprehensive distributional view of the energy spending profile of the residential sector. The estimated empirical Engel curves behave similarly; however, the derived income elasticities show marked distinctions by fuel, and their actual values depend on the households' relative position over the income distribution. For electricity, the elasticity tends to increase in income but stabilize at the wealthiest segments. For gas and transport fuel, it decreases under different income paths. In this dataset, the examination returns income elasticities on the (0,1) interval, suggesting that energy commodities are necessity goods. However, the distribution of aggregate energy expenditure needs to be considered. Specifically, there is a great concentration among the richer groups, particularly for transport fuels, where the top quintile gathers more than half of the aggregate spending. The results also indicate economies of scale--for electricity and domestic gas--with respect to family-age composition, and to a lesser extent with respect to dwelling size. In the case of electricity, these economies are more pronounced fo...

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“…Sustainable urban planning covers other aspects aside from urban density and transport planning. Several recent papers highlight the potential of cities to facilitate the sharing economy (Underwood and Zahran 2015, Ala-Mantila et al 2016, Fremstad et al 2018, Jones et al 2018. Decreasing household size in cities is a global trend.…”

Section: City Policiesmentioning

confidence: 99%

What can we learn from consumption-based carbon footprints at different spatial scales? Review of policy implications

Ottelin

Ala-Mantila

Heinonen

et al. 2019

Environ. Res. Lett.

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Background: Current climate change mitigation policies, including the Paris Agreement, are based on territorial greenhouse gas (GHG) accounting. This neglects the understanding of GHG emissions embodied in trade. As a solution, consumption-based accounting (CBA) that reveals the lifecycle emissions, including transboundary flows, is gaining support as a complementary information tool. CBA is particularly relevant in cities that tend to outsource a large part of their production-based emissions to their hinterlands. While CBA has so far been used relatively little in practical policymaking, it has been used widely by scientists. Methods and design: The purpose of this systematic review, which covers more than 100 studies, is to reflect the policy implications of consumption-based carbon footprint (CBCF) studies at different spatial scales. The review was conducted by reading through the discussion sections of the reviewed studies and systematically collecting the given policy suggestions for different spatial scales. We used both numerical and qualitative methods to organize and interpret the findings of the review. Review results and discussion: The motivation for the review was to investigate whether the unique consumption perspective of CBA leads to similarly unique policy features. We found that various carbon pricing policies are the most widely supported policy instrument in the relevant literature. However, overall, there is a shortage of discussion on policy instruments, since the policy discussions focus on policy outcomes, such as behavioral change or technological solutions. In addition, some policy recommendations are conflicting. Particularly, urban density and compact city policies are supported by some studies and questioned by others. To clarify the issue, we examined how the results regarding the relationship between urban development and the CBCF vary. The review provides a concise starting point for policymakers and future research by summarizing the timely policy implications.

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The carbon implications of declining household scale economies

Cited by 59 publications

References 34 publications

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

Understanding the Drivers of Household Energy Spending: Micro Evidence for Latin America

What can we learn from consumption-based carbon footprints at different spatial scales? Review of policy implications

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