Implications of uncertainty on regional CO2 mitigation policies for the U.S. onroad sector based on a high-resolution emissions estimate

Mendoza, Daniel; Gurney, K. R.; Geethakumar, S.; Chandrasekaran, Vandhana; Zhou, Yuyu; Razlivanov, I. N.

doi:10.1016/j.enpol.2012.12.027

Cited by 19 publications

(19 citation statements)

References 13 publications

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“…Low variation in emissions relative to VMT corroborates previous findings that uncertainty in vehicle fleet and fuel economy characteristics are a minor contributor to the overall uncertainty in emissions estimates from the on-road sector (35). The relatively low variation in roadway-level VMT suggests that the uncertainty associated with spatially aggregating VMT from the roadway to the county scale is relatively small, so long as stratification by functional class is maintained.…”

Section: Significancesupporting

confidence: 70%

“…Uncertainty in the magnitude of on-road emissions at the national level is estimated to be on the order of 3-5% for developed countries (4,35), but at subnational or state scales, existing inventories disagree by as much as 40% (3), and at city scales, uncertainty can be as large as 50-100% (13). Direct quantification of the uncertainty in US on-road emissions is made impossible by the absence of independent data sources against which to compare government estimates (4).…”

Section: Significancementioning

confidence: 99%

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Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Gately

Hutyra

Wing

2015

Proc. Natl. Acad. Sci. U.S.A.

134

101

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Emissions of CO 2 from road vehicles were 1.57 billion metric tons in 2012, accounting for 28% of US fossil fuel CO 2 emissions, but the spatial distributions of these emissions are highly uncertain. We develop a new emissions inventory, the Database of Road Transportation Emissions (DARTE), which estimates CO 2 emitted by US road transport at a resolution of 1 km annually for 1980-2012. DARTE reveals that urban areas are responsible for 80% of on-road emissions growth since 1980 and for 63% of total 2012 emissions. We observe nonlinearities between CO 2 emissions and population density at broad spatial/temporal scales, with total on-road CO 2 increasing nonlinearly with population density, rapidly up to 1,650 persons per square kilometer and slowly thereafter. Per capita emissions decline as density rises, but at markedly varying rates depending on existing densities. We make use of DARTE's bottom-up construction to highlight the biases associated with the common practice of using population as a linear proxy for disaggregating national-or state-scale emissions. Comparing DARTE with existing downscaled inventories, we find biases of 100% or more in the spatial distribution of urban and rural emissions, largely driven by mismatches between inventory downscaling proxies and the actual spatial patterns of vehicle activity at urban scales. Given cities' dual importance as sources of CO 2 and an emerging nexus of climate mitigation initiatives, high-resolution estimates such as DARTE are critical both for accurately quantifying surface carbon fluxes and for verifying the effectiveness of emissions mitigation efforts at urban scales.emissions | carbon dioxide | urban | transportation | on-road

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Section: Significancesupporting

confidence: 70%

Section: Significancementioning

confidence: 99%

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Gately

Hutyra

Wing

2015

Proc. Natl. Acad. Sci. U.S.A.

134

101

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“…Increased SO 2 emissions have mainly resulted from human activities, such as industrial emissions [12]. Emissions of nitrogen oxide (NO x ) and carbon monoxide (CO), and concentrations of ozone (O 3 ) increased dramatically due to the increased number of vehicles [13], and were accompanied by an increase in emissions of greenhouse gases (e.g., CO 2 ) [14,15]. Recent studies have related these spatial patterns of air pollution to environmental and socioeconomic factors depending on distinguished mechanisms [6,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Disentangling the Complex Effects of Socioeconomic, Climatic, and Urban Form Factors on Air Pollution: A Case Study of China

Liu

2018

Sustainability

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China's tremendous economic growth during the past three decades has resulted in worsening air quality in most of its cities. However, the spatiotemporal patterns and underlying drivers of air pollution in China remain poorly understood. To address this issue, we used stepwise regression to identify major socioeconomic, climatic, and urban form factors influencing air pollution in 69 major cities across China. Our results showed that social factors such as population size and density were positively correlated with emissions of PM 2.5 , PM 10 , NO x , and SO 2 . Economic factors such as Gross Domestic Product (GDP) and GDP of secondary industry were positively correlated with industry and transportation emissions but negatively correlated with residential emissions of air pollutants. Urban form attributes such as measures of urban fragmentation and contiguity were important in explaining patterns of emissions from residential, power generation, and transportation sectors. As for climatic factors, higher precipitation, higher wind speed, and higher temperatures were all negatively correlated with air pollution levels. We also found that the effects of socioeconomic, climatic, and urban from factors on air pollution levels varied considerably among seasons and between the annual and seasonal scales. Our findings have useful implications for urban planning and management for controlling air pollution in China and beyond.

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“…Moreover, for governmental bodies it is desirable to possess an effective tool, enabling the analysis of the separate constituents of complex processes of GHG emissions and absorptions, both at regional (Feliciano et al 2013), as well as at spatial levels (Hamal 2009;Mendoza et al 2013). Such a tool would give the possibility of obtaining integrated information on the actual spatial distribution of GHG sources and sinks, and thus of finding optimum ways of solving a number of economic or environment protection problems (Bucki 2010; Bun et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in Greenhouse Gas Inventories

Ometto¹,

Bun²,

Jonas³

et al. 2015

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Implications of uncertainty on regional CO2 mitigation policies for the U.S. onroad sector based on a high-resolution emissions estimate

Cited by 19 publications

References 13 publications

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Disentangling the Complex Effects of Socioeconomic, Climatic, and Urban Form Factors on Air Pollution: A Case Study of China

Uncertainties in Greenhouse Gas Inventories

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Implications of uncertainty on regional CO2 mitigation policies for the U.S. onroad sector based on a high-resolution emissions estimate

Cited by 19 publications

References 13 publications

Cities, traffic, and CO 2 : A multidecadal assessment of trends, drivers, and scaling relationships

Cities, traffic, and CO 2 : A multidecadal assessment of trends, drivers, and scaling relationships

Disentangling the Complex Effects of Socioeconomic, Climatic, and Urban Form Factors on Air Pollution: A Case Study of China

Uncertainties in Greenhouse Gas Inventories

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Product

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About

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships