Spatially differentiated effects of socioeconomic factors on China's NOx generation from energy consumption: implications for mitigation policy

Wang, Junfeng; Ma, Yupei; Qiu, Ye; Liu, Lingxuan; Dong, Zhanfeng

doi:10.1016/j.jenvman.2019.109417

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…According to the estimates, carbon dioxide emissions from fossil fuel combustion and industrial processes contributed about 78% of the total increase in greenhouse gas emissions between 1970 and 2010 [7]. Moreover, energy consumption, especially fossil energy consumption, is also the main cause of air pollution [8,9]. Specifically, the problem of haze pollution has become more and more serious recently, which has captured the attention of wider society.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Energy Consumption and Their Influencing Factors in China Based on the Integration of the DMSP-OLS and NPP-VIIRS Nighttime Light Datasets

et al. 2020

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With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Energy Consumption and Their Influencing Factors in China Based on the Integration of the DMSP-OLS and NPP-VIIRS Nighttime Light Datasets

et al. 2020

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“…High temperatures, precipitation, and higher wind speeds have been identified as beneficial factors in reducing NO 2 concentrations [18,19]. Social factors, including industrial emissions, traffic emissions, energy consumption, industrial structure, and population density, have been recognized as essential influencers affecting NO 2 concentrations [20][21][22]. Wang et al [23] analyzed the factors driving vehicle NO x emissions from 2005 to 2015 and identified economic development and road vehicle carrying capacity as the primary drivers of emissions growth.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation and Driving Factors for NO2 in Mid-Eastern China

Yi,

Jiang,

Zhao

et al. 2023

Atmosphere

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Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based monitoring stations, we investigated the impact of socioeconomic and meteorological factors on NO2 concentrations in cities in mid-eastern China from 2015 to 2021 using the Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal a notable reduction of over 10% in NO2 concentrations since 2015 in most cities, notably a 50.5% decrease in Bozhou. However, certain areas within Anhui and Jiangsu have experienced an increase in NO2 concentrations. Significant spatial heterogeneity is observed in the relationship between NO2 concentrations and influencing factors. The permanent population density (POP) and the electricity consumption (EC) of the entire society exhibited the strongest correlations with NO2 concentrations, with average coefficients of 0.431 and 0.520, respectively. Furthermore, other economic factors such as urbanization rate (UR), the share of secondary sector output in total GDP (IS), and the coverage rate of urban green areas (CG) were predominantly positively correlated, while GDP per capita (PGDP) and civil car vehicles (CV) demonstrated primarily negative correlations. Furthermore, we examined the correlations between four meteorological factors (temperature, relative humidity, wind speed, and precipitation) and NO2 concentrations. All these factors exhibited negative correlations with NO2 concentrations. Among them, temperature exhibited the strongest negative correlation, with a coefficient of −0.411. This research may contribute valuable insights and guidance for developing air emission reduction policies in various cities in mid-eastern China.

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“…Nevertheless, socioeconomic factors have been identified as driving forces, which are essential to track and evaluate emissions performance (Wang et al, 2017a). Generally, in addition to econometric techniques (Jiang et al, 2016;Wang et al, 2019a) and computable general equilibrium (CGE) models (Zhang et al, 2018), the decomposition method is popular and also helpful in finding drivers. A critical advantage of decomposition analysis is that it can distribute a temporal change or spatial difference in an aggregate indicator into components related to several driving factors.…”

Section: Introductionmentioning

confidence: 99%