Quality of Life and the Carbon Footprint: A Zip-Code Level Study Across the United States

Clement, Matthew Thomas; Smith, Chad L.; Leverenz, Tyler

doi:10.1177/10704965211052130

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Although the links between social factors and consumption-based emissions are well-studied [6,10,14-21,27,59-62], the spatial aspects of consumption-based emissions are not well-understood. Although some existing research already highlights the benefits of using spatial models for emission analyses [71][72][73]118], to our knowledge, this paper is the first to investigate spatial heterogeneity in the relationship between social factors and consumption-based emissions, highlighting the important contribution spatial statistics can make to the field of industrial ecology. In this paper, we find that geographically weighted regression models should be used in all tested instances of this paper, as our data exhibit spatial dependency.…”

Section: Geographically Weighted Regression As a Tool For Emissions A...mentioning

confidence: 99%

Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

et al. 2022

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Recent years have seen an increased interest in demand-side mitigation of greenhouse gas emissions. Despite the oftentimes spatial nature of emissions research, links to social factors and infrastructure are often not analysed geographically. To reach substantial and lasting emission reductions without further disadvantaging vulnerable populations, the design of effective mitigation policies on the local level requires considerations of spatial and social inequalities as well as the context of well-being. Consequently, we explore spatial variations in the links between consumption-based transport emissions with infrastructural factors, such as workplace distance and public transport density, and with risk-factors of transport poverty, including income, age, ethnicity, mobility constraints in London. We find that linear models report significant spatial autocorrelation at p ≤ 0.01 in their model residuals, indicating spatial dependency. Using geographically weighted regression models improves model fits by an adjusted R2 value of 9–70% compared to linear models. Here, modelling flight emissions generally sees the lowest improvements, while those models modelling emissions from cars and vans see the highest improvements in model fit. We conclude that using geographically weighted regression to assess the links between social factors and emissions offers insights which global, linear models overlook. Moreover, this type of analysis enables an assessment of where, spatially, different types of policy interventions may be most effective in reducing not only emissions, but transport poverty risks. Patterns of spatial heterogeneity and policy implications of this research are discussed.

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Section: Geographically Weighted Regression As a Tool For Emissions A...mentioning

confidence: 99%

Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

et al. 2022

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Drivers of household carbon footprints across EU regions, from 2010 to 2015

Lee

Shigetomi²,

Kanemoto³

2023

Environ. Res. Lett.

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Urban regions are responsible for a significant proportion of carbon emissions. The carbon footprint (CF) is a practical measure to identify the responsibility of individuals, cities, or nations in climate change. Numerous CF studies have focused on national accounts, and a few combined consumer consumption and global supply chains to estimate additionally detailed spatial CF. However, the drivers of temporal change in detailed spatial CF are largely unknown, along with regional, spatial, and socioeconomic disparities.Here, we uncovered the drivers of changes in household CFs in EU regions, at the finest scale currently available, between 2010 and 2015. This study mapped the household CFs of 83 macro-regions across 27 EU nations and identified the driving factors underlying their temporal change. We combined multi-regional input-output tables and micro-consumption data from 275,247 and 272,045 households in 2010 and 2015, respectively. We decomposed EU regional CF, employing Structural Decomposition Analysis, into five driving factors: emission intensity, supply chain structure, population, per capita consumption, and final demand share. For a deeper assessment of changes in the contribution of consumption patterns, we further categorized the regional CF into 15 factors, including 11 per capita consumption categories. We found that household CF drivers vary depending on region, population density, income, and consumption patterns. Our results can help policymakers adopt climate policies at the regional level by reflecting on the residents' socioeconomic, spatial, and consumption conditions, for further ambitious climate actions.

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Quality of Life and the Carbon Footprint: A Zip-Code Level Study Across the United States

Cited by 2 publications

References 47 publications

Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

Exploring Transport Consumption-Based Emissions: Spatial Patterns, Social Factors, Well-Being, and Policy Implications

Drivers of household carbon footprints across EU regions, from 2010 to 2015

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