Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions: A multi-scale LMDI decomposition analysis in China

Yang, Xue; Wang, Shuanjin; Zhang, Wenzhong; Li, Jiaming; Zou, Yafeng

doi:10.1016/j.apenergy.2016.11.013

Cited by 135 publications

(49 citation statements)

References 62 publications

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“…Similar conclusions have also been obtained by other researches, such as Pan et al [14], Sharmina et al [15], Song et al [16], Wang et al [17] and Nasreen et al [18]. In addition, from the perspective of structure, Yang et al [19] used the logarithmic mean Divisia index (LMDI) method to confirm that Chinese energy consumption, energy structure and processing technology affects sulfur dioxide levels and thus, have proposed that controlling energy consumption and optimizing energy structure will be the most effective method for sulfur dioxide reduction. Zhu et al [20] analyzed the dynamic relationship between the main air pollutants and nine main kinds of energy consumption factors in Beijing based on the dynamic matrix analysis.…”

Section: Introductionsupporting

confidence: 76%

Impact of Energy Consumption on Air Quality in Jiangsu Province of China

Zhong

Yin

et al. 2018

Sustainability

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Air quality has become an important sustainability concern for China's highly developed economic regions. Taking Jiangsu Province as a case, this study investigates the effect of energy consumption on air quality when both considering and not considering control variables, such as industrial structure, energy consumption structure and energy efficiency from 2006 to 2015 and further explores the influence of the related policies on air quality and the relationship between energy consumption and air quality. One important finding is that the relationship between energy consumption and the air quality index of Jiangsu Province shows a U-shaped curve and it is now in the increasing part of the curve, which reveals the effectiveness of controlling energy consumption in improving air quality. Another important finding is that, when considering the effect of related polices, there is still a U-shaped curve relationship between energy consumption and air quality index, with the opening of curve becoming bigger and the influence of energy consumption on air quality becoming more obvious. Moreover, although air quality is improved whether considering policies separately or together, the effect of combined policies is better than is the effect of a separate policy. This not only reflects the effectiveness of related polices but also shows the importance of policy coordination. Furthermore, a 1% optimization of industrial structure inhibits air quality index by 0.0054%, while the relationship of energy efficiency and energy consumption structure with air quality is inconsistent with theoretical analysis. This implies that, in practice, there is still room for improvement in both energy saving and emission reduction and even in air quality improvement. This study may help the stakeholders, whether from Jiangsu Province, other provinces in China or other countries at the global level, identify the impacts of energy consumption on air quality and formulate effective energy saving and air quality improvement policies in conjunction with their economic characteristics.

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Section: Introductionsupporting

confidence: 76%

Impact of Energy Consumption on Air Quality in Jiangsu Province of China

Zhong

Yin

et al. 2018

Sustainability

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“…Numerous studies have explored the key factors that drive the high releases in China from various perspectives, including energy consumption (Yang et al, 2016a), socioeconomic levels (He, 2009;He et al, 2016), coal-fired power plants (Liu and Donalds, 2013;Xu et al, 2017), and vehicles (Lang et al, 2016), etc. These studies mainly focused China's atmospheric pollutant emissions from the production side, i.e., total direct emissions from production entities.…”

Section: Introductionmentioning

confidence: 99%

The temporal variation of SO2 emissions embodied in Chinese supply chains, 2002–2012

Yang

Zhang

Fan

et al. 2018

Environmental Pollution

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Whilst attention is increasingly being focused on embodied pollutant emissions along supply chains in China, relatively little attention has been paid to dynamic changes in this process. This study utilized environmental extended input-output analysis (EEIOA) and structural path analysis (SPA) to investigate the dynamic variation of the SO emissions embodied in 28 economic sectors in Chinese supply chains during 2002-2012. The main conclusions are summarized as follows: (1) The dominant SO emission sectors differed under production and consumption perspectives. Electricity and heat production dominated SO emissions from the point of view of production, while construction contributed most from the consumption perspective. (2) The embodied SO emissions tended to change from the path (staring from consumption side to production side): "Services→Services→Power" in 2002 to the path: "Construction and Manufacturing→Metal and Nonmetal→Power" in 2012. (3) Metal-driven emissions raised dramatically from 15% in 2002 to 22% in 2012, due to increasing demand for metal products in construction and manufacturing activities. (4) Power generation was found to result in the greatest volume of production-based emissions, a burden it tended to transfer to upstream sectors in 2012. Controlling construction activities and cutting down end-of-pipe discharges in the process of power generation represent the most radical interventions in reducing Chinese SO emissions. This study shed light on changes in SO emissions in the supply chain, providing a range of policy implications from both production and consumption perspectives.

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“…Globally, there are numerous studies on the socioeconomic driving forces of different emissions, although the majority of these studies focus on carbon emissions [22][23][24], and on PM 2.5 , SO 2 , and NO 2 [25][26][27][28]. In particular, several studies on the changes of PM 2.5 air concentrations are studied [29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

Driving Forces of Air Pollution in Ulaanbaatar City Between 2005 and 2015: An Index Decomposition Analysis

et al. 2020

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Ulaanbaatar, the capital city of Mongolia, is facing serious air pollution challengesespecially during the cold and long winter months-mainly due to fossil fuel combustion. This study investigates the socioeconomic drivers of the sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and particulate matter (PM 2.5 ) concentration changes in Ulaanbaatar between 2005 and 2015 by applying the index decomposition analysis (IDA) method. Five socio-economic driving forces are considered in the decomposition analysis. All the driving forces contributed to more air pollution concentration changes in 2015 than in 2005, despite the decreasing trends of decomposition results for the period of 2010-2015. In general, economic growth, pollution intensity, and energy intensity significantly contributed to the changes of air pollutant concentrations, while energy structure and population growth had marginal effects. Finally, appropriate policy recommendations are proposed to the local government so that they can initiate feasible policies to effectively reduce air pollution, protect human health, and respond to climate change in Ulaanbaatar.

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Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions: A multi-scale LMDI decomposition analysis in China

Cited by 135 publications

References 62 publications

Impact of Energy Consumption on Air Quality in Jiangsu Province of China

Impact of Energy Consumption on Air Quality in Jiangsu Province of China

The temporal variation of SO2 emissions embodied in Chinese supply chains, 2002–2012

Driving Forces of Air Pollution in Ulaanbaatar City Between 2005 and 2015: An Index Decomposition Analysis

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