Strategy on China's regional coal consumption control: A case study of Shandong province

Zhang, Yiqing; Liu, Chuangeng; Li, Ke; Yong, Zhou

doi:10.1016/j.enpol.2017.10.035

Cited by 44 publications

(19 citation statements)

References 34 publications

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“…SO 2 and CO pollution in heavy industrial cities, such as iron and steel industrial cities (Anyang, Handan, and Xingtai) and coal industrial and chemical industrial cities (Hebi and Shijiazhuang), is far worse than that in Hangzhou and Beijing ( p < 0.05). In Beijing, SO 2 and CO concentrations rise significantly from summer to winter in 2016, but the average value of 2018 is lower than the other cities ( p < 0.05), which should be linked to the prohibition of coal burning except power plants (Finkelman & Tian, 2018; Y. Zhang, Liu, et al, 2018; Y. Zhang,Ye, et al, 2018; Zhao & Luo, 2018). This suggests that SO 2 and CO pollution in Beijing was mainly caused by urban biomass burning or coal burning.…”

Section: Resultsmentioning

confidence: 96%

Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants

et al. 2020

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Air quality in Northern China has become a global hot spot issue due to a series of air pollution events in the recent years. In this study, five representative air pollutants (PM 2.5 , SO 2 , NO 2 , CO, and O 3 ) were employed to reveal the spatial and temporal distribution of air pollution in Northern China. Periodic decline in PM 2.5 , SO 2 , CO, and NO 2 from 2016 to 2018 indicated that air pollution control measures have achieved desired results. In addition, PM2.5 was significantly positively correlated with SO 2 , CO, and NO 2 (p < 0.001), and O 3 had negative correlations with the other four pollutants. Furthermore, the heavy pollution phenomenon in Shijiazhuang, Anyang, Xingtai, and Handan was attributed to their industrial structures (e.g., steel industry) and geographical location based on clustering analysis. Contrary to above four pollutants, the annual average concentrations of O 3 increased in all the nine cities (1.4-35.9%) from 2016 to 2018. It is necessary to plan mitigation strategy for O 3 based on further investigation of source and formation mechanism of O 3 .Plain Language Summary Air pollution in Northern China has attracted many attentions due to continuous air pollution issues in the recent years. However, few studies focused on air pollution in the Beijing-Tianjin-Hebei transmission corridor. In this study, the spatiotemporal variation of air pollutants in nine typical cities was investigated. Pearson correlation analysis and clustering analysis were employed to analyze their relationship and reveal the pollution characteristic and the factors affecting the air pollution. This study provided an insight for the prevention and control of air pollution in Northern China.

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Section: Resultsmentioning

confidence: 96%

Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants

et al. 2020

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“…Shandong's energy consumption accounts for about 10% of China's energy consumption. Eighty percent of the region's energy consumption is covered by coal [65]. The Shandong Energy Group Company is a wholly state-owned coal-mining company and owns numerous coal and oil shale-fired power stations.…”

Section: Shandongmentioning

confidence: 99%

“…The high amount of coal on Shandong's energy mix leads to the fact that decoupling in the power sector is an important future goal of the region, which should lead to a reduction of energy-related CO 2 emissions. Measures within the framework of a regional energy transition include a gradual increase in the use of natural gas and renewable energy resources, the promotion of a multichannel energy supply network and the use of cleaner coal technology [65]. In 2017, around 5% of all electricity was generated by hydro, wind and solar power.…”

Section: Shandongmentioning

confidence: 99%

The Role of Renewable Energy in Regional Energy Transitions: An Aggregate Qualitative Analysis for the Partner Regions Bavaria, Georgia, Québec, São Paulo, Shandong, Upper Austria, and Western Cape

et al. 2020

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The study aims to identify the strengths, weaknesses, opportunities, and threats (SWOT) of renewable energy deployment in regional energy transitions for the regions of Bavaria, Georgia, Québec, São Paulo, Shandong, Upper Austria, and Western Cape, which comprise a political and scientific network called the Regional Leaders’ Summit (RLS) and RLS-Sciences, respectively. The results classify current renewable energy usage for electricity, heat and fuel production, existing renewable energy potentials, sound legal frameworks to support renewable energy, ongoing research and development activities and expertise in renewable energy conversion and storage as strengths. That fossil fuels still hold a significant share in gross inland energy consumption, energy-intensive industrial structures continue to be supported by fossil fuels and grid access is limited for renewables are identified as weaknesses. The main opportunities are green economies, climate change mitigation and technological innovations. Associated threats are demographic developments, lack of social acceptance and renewable energy resource volatility. We conclude that these regions’ energy systems could potentially enable the realization of a strong integration of renewables and cover partially distributed, decentralized energy systems with embedded energy storage, and the application of smart technologies. Furthermore, we discover that the role of governments in guiding and managing regional energy transitions is highly important.

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“…The consumption of coal also was high, with industrial coal consumption accounting for 96% of the total coal consumption. In particular, coal consumption for power generation accounted for 35% of the total coal consumption [52]. Numerous studies have shown that fossil fuel burning is closely related to primary particulate matter emissions and secondary aerosol generation [30,[53][54][55].…”

Section: Study Areamentioning

confidence: 99%

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

Luo

Chen

et al. 2019

Atmosphere

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In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOx, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter.

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Strategy on China's regional coal consumption control: A case study of Shandong province

Cited by 44 publications

References 34 publications

Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants

Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants

The Role of Renewable Energy in Regional Energy Transitions: An Aggregate Qualitative Analysis for the Partner Regions Bavaria, Georgia, Québec, São Paulo, Shandong, Upper Austria, and Western Cape

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

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