Climate-driven changes in CO2 emissions associated with residential heating and cooling demand by end-century in China

Zhang, Nan; Zhou, Yuntao

doi:10.1088/1748-9326/ab31de

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…This could possibly caused by reduced emissions from residential heating due to the decrease in heating degree days (see SI Fig. S8), which can be used as a proxy for estimating emissions from residential heating (Zhang and Zhou, 2019).…”

Section: Case Study: Effect Of Pandemic Lockdowns On Near-surface Nomentioning

confidence: 99%

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Kim¹,

Kuhlmann²,

Emmenegger³

et al. 2021

Preprint

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Nitrogen oxides (NOx&#160; = NO+ NO2) are harmful to human health and are precursors of other key air pollutants like ozone (O3) and particulate matter (PM). Since the lifetime of NOx is short and its main sources are anthropogenic emissions like fuel combustion from traffic and industry, near-surface NOx concentrations are highly variable in space and time. To assess the impact of NO2 on public health, maps of high spatial and temporal resolution are critical. In this study, we present hourly near-surface NO2 concentrations at 100 m resolution for Switzerland and northern Italy that are produced using machine learning, specifically an extreme gradient-boosted tree ensemble. The model was trained with in situ observations from European Air Quality e-Reporting data repositories (Airbase). Satellite NO2 observations from the TROPospheric Monitoring Instrument (TROPOMI) were compiled together with land use data, meteorological data and topography as covariates. Evaluation against in situ observations not used for the training shows that the dynamic maps produced in this study reproduce the spatio-temporal variation in near-surface NO2 concentrations with high accuracy (R2 = 0.59, MAE = 7.69 &#181;g/m3). In addition, we demonstrate how public health studies can utilize such high-resolution maps for unbiased assessment of population exposure that can account for home addresses and mobility of individuals. Comparing the relative importance of the different covariates based on two different metrics, total information gain and averaged local feature importance, show a leading contribution of the TROPOMI observations despite their rather coarse resolution (3.5 km &#215; 5.5 km) and daily update. TROPOMI NO2 observations were particularly important for the quality of the NO2 maps during periods of unusual NO2 reductions (e.g., during COVID19 lockdown) and when detailed emission-related covariates like traffic density, that may not be available in other regions of the globe, were not included in the model. Since all data used in our study are publicly available, our approach can be readily extended to other regions in Europe or applied worldwide.

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Section: Case Study: Effect Of Pandemic Lockdowns On Near-surface Nomentioning

confidence: 99%

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Kim¹,

Kuhlmann²,

Emmenegger³

et al. 2021

Preprint

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show abstract

“…Climate has a profound impact on human life, production and social activities, leading to significant regional CO 2 emission disparities in different regions [19]. Previous papers have studied the impacts of climate on CO 2 emissions of various sectors such as building [20,21], agriculture [22], electricity [23] and residential [24]. Using data from 30 provinces of China's transportation sector, Liu et al [25] evaluated the regional differences of carbon emission intensity (CEI).…”

Section: Introductionmentioning

confidence: 99%

Carbon Mitigation Pathways of Urban Transportation under Cold Climatic Conditions

Wang

Qin

Wang

et al. 2022

IJERPH

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Climate heterogeneity has enormous impacts on CO2 emissions of the transportation sector, especially in cold regions where the demand for in-car heating and anti-skid measures leads to high energy consumption, and the penetration rate of electric vehicles is low. It entails to propose targeted emission reduction measures in cold regions for peaking CO2 emissions as soon as possible. This paper constructs an integrated long-range energy alternatives planning system (LEAP) model that incorporates multi-transportation modes and multi-energy types to predict the CO2 emission trend of the urban transportation sector in a typical cold province of China. Five scenarios are set based on distinct level emission control for simulating the future trends during 2017–2050. The results indicate that the peak value is 704.7–742.1 thousand metric tons (TMT), and the peak time is 2023–2035. Energy-saving–low-carbon scenario (ELS) is the optimal scenario with the peak value of 716.6 TMT in 2028. Energy intensity plays a dominant role in increasing CO2 emissions of the urban transportation sector. Under ELS, CO2 emissions can be reduced by 68.66%, 6.56% and 1.38% through decreasing energy intensity, increasing the proportion of public transportation and reducing the proportion of fossil fuels, respectively. Simultaneously, this study provides practical reference for other cold regions to formulate CO2 reduction roadmaps.

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Role of adaptation measures in addressing heatwave exposure in China

Han,

Chao,

Sun

et al. 2024

Advances in Climate Change Research

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Climate-driven changes in CO₂ emissions associated with residential heating and cooling demand by end-century in China

Cited by 8 publications

References 21 publications

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Carbon Mitigation Pathways of Urban Transportation under Cold Climatic Conditions

Role of adaptation measures in addressing heatwave exposure in China

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