Switching on auxiliary devices in vehicular fuel efficiency tests can help cut CO2 emissions by millions of tons

Zhang, Da; Gao, Jinyang; Ding, Tao; Wu, Ye; Shi, Junye; Chen, Jiangping; Peng, Yinghong; Zhang, Shaojun

doi:10.1016/j.oneear.2020.12.010

Cited by 13 publications

(5 citation statements)

References 49 publications

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“…Several strategies have been proposed, evaluated, and implemented to address the climate impact of car development in the past decades, which can be categorized largely into two transition pathways [18][19][20] : technology-oriented [21][22][23][24][25][26][27] and demand-oriented pathways. [28][29][30][31] Along the technology-oriented transition pathway, fuel economy improvement [32][33][34] has long been the most effective and widely discussed strategy. This can be achieved mainly by lightweight design (e.g., substituting standard steel with aluminum, magnesium, or carbon fiber) to reduce curb weight and thus the drive-cycle energy use and emissions, [35][36][37][38][39][40][41] or by powertrain technology innovation (e.g., electrification) to reduce the fossil fuel use and increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Sun²,

Liu³

et al. 2022

One Earth

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Section: Introductionmentioning

confidence: 99%

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Sun²,

Liu³

et al. 2022

One Earth

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“…SUEWS adopts static traffic EFs and neglects the relationship between traffic emission and T air as reported by Alvarez and Weilenmann (2012) and Fontaras et al (2017). In order to examine the impact of seasonal variation of T air in traffic emission, correction is conducted using the regression function following Zhang et al (2021), but only a marginal difference is seen at monthly scale: a difference of 3 % in winter, −2 % in spring, ∼ 0 % in summer, and −1 % in autumn. Therefore, we believe that the static traffic EFs adopted by SUEWS can provide reasonable traffic emission values without considering the seasonal dynamics of T air .…”

Section: Model Performancementioning

confidence: 99%

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance

Zheng,

Havu,

Liu

et al. 2023

Geosci. Model Dev.

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Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (CO2) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of sensible heat (QH), latent heat (QE), and CO2 (FC) in a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (gmax) and leaf area index (LAI) is examined. Site-specific gmax is obtained from observations over local vegetation species, and LAI parameters are extracted by optimization with remotely sensed LAI obtained from a Landsat 7 data product. For the simulation of anthropogenic CO2 components, local traffic and population data are collected. In the model evaluation, the mismatch between the measurement source area and simulation domain is also considered. Using the optimized gmax and LAI, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of QE and QH. The effect of the gmax adjustment is more significant than the LAI adjustment. Compared to heat fluxes, the FC module shows lower sensitivity to the choices of gmax and LAI. This can be explained by the low relative contribution of vegetation to the net FC in the modeled area. SUEWS successfully reproduces the average diurnal cycle of FC and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated FC ranges from 7.4 to 8.7 kgCm-2yr-1, compared to 7.5 kgCm-2yr-1 observed by EC. Traffic is the dominant CO2 source, contributing 59 %–70 % to the annual total CO2 emissions, followed by human metabolism (14 %–18 %), buildings (11 %–14 %), and CO2 release by vegetation and soil respiration (6 %–10 %). Vegetation photosynthesis offsets only 5 %–10 % of the total CO2 emissions. We highlight the importance of choosing the optimal LAI parameters and gmax when SUEWS is used to model surface fluxes. The FC module of SUEWS is a promising tool in quantifying urban CO2 emissions at the local scale and therefore assisting in mitigating urban CO2 emissions.

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“…SUEWS adopts static traffic EFs, and neglects the relationship between traffic emission and T air as reported by Alvarez and Weilenmann (2012) and Fontaras et al (2017). In order to examine the impact of seasonal variation of T air in traffic emission, correction is conducted using the regression function following Zhang et al (2021), but only a marginal difference is seen at monthly scale: a difference of 3% in winter,…”

Section: Model Uncertaintiesmentioning

confidence: 99%

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance

Zheng

Havu

Liu

et al. 2023

Preprint

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Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottomup approach to modelling carbon dioxide (CO2) emissions and sink in urban areas. In this study, SUEWS is evaluated against radiation flux observations and eddy covariance (EC) measured turbulent fluxes of sensible heat (QH), latent heat (QE), and CO2 (FC) at a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (gmax) and leaf area index (LAI) is examined. Site-specific gmax is obtained from observations over local vegetation species, and LAI parameters by optimization with remotely sensed LAI obtained from a MODIS/Terra data product. For simulation of anthropogenic CO2 components, local traffic and population data are collected. In model evaluation, the mismatch between the measurement source area and simulation domain is also considered. Using the optimized gmax and LAI, the modelling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of QE and QH. In comparison to heat fluxes, the FC module shows lower sensitivity to the choice of gmax and LAI. This can be explained by the low relative contribution of vegetation to net FC in the modelled area. SUEWS successfully reproduces the average diurnal cycle of FC and annual cumulative sums. Depending on the size of the simulation domain, the modelled annual accumulated FC ranges from 7.2 to 8.5 kg C m−2 yr−1, when compared to 7.5 kg C m−2 yr−1 observed by EC. Traffic is the dominant CO2 source, contributing 63–73 % to the annual total CO2 emissions, followed by human metabolism (14–18 %), respiration released by vegetation and soil (6–11 %) and building heating (6–9 %). Vegetation photosynthesis offsets only 4–8 % of the total CO2 emissions. We highlight the importance of choosing optimal LAI parameters and gmax when SUEWS is used to model surface fluxes. The FC module of SUEWS is a promising tool in quantifying urban CO2 emissions at the local scale, and therefore assisting to mitigate urban CO2 emissions.

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Switching on auxiliary devices in vehicular fuel efficiency tests can help cut CO2 emissions by millions of tons

Cited by 13 publications

References 49 publications

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance

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Switching on auxiliary devices in vehicular fuel efficiency tests can help cut CO2 emissions by millions of tons

Cited by 13 publications

References 49 publications

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Carbon neutrality of China’s passenger car sector requires coordinated short-term behavioral changes and long-term technological solutions

Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance

Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance

Contact Info

Product

Resources

About

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance

Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance