Contributing to local policy making on GHG emission reduction through inventorying and attribution: A case study of Shenyang, China

Xi, Fengming; Geng, Yong; Chen, Xudong; Zhang, Yunsong; Wang, Xinbei; Xue, Bing; Dong, Hongmin; Liu, Zhu; Ren, Wanxia; Fujita, Tsuyoshi; Zhu, Qiankun

doi:10.1016/j.enpol.2011.06.063

Cited by 108 publications

(31 citation statements)

References 23 publications

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“…Greenhouse gas emissions for one industry are usually defined using these two scopes [26][27][28][29][30]. Scope 1 only includes emissions that are produced within the boundaries of the industry sectors.…”

Section: Scope Of Emissions Accountingmentioning

confidence: 99%

CO2 emissions from China’s lime industry

Liu²,

2016

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Section: Scope Of Emissions Accountingmentioning

confidence: 99%

CO2 emissions from China’s lime industry

Liu²,

2016

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“…These coefficients are shown in Table A1. [76]; b Data resource: [77]; c Data resource: [78]. Xinjiang Statistical Yearbooks (2001-2015) [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] list about 39 industrial sub-sectors, and the sub-sectors listed in Xinjiang Statistical Yearbooks [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] for different years were not completely identical.…”

Section: Appendixmentioning

confidence: 99%

Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China

et al. 2017

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Xinjiang's industrial sector accounted for more than 80% of the total energy-related carbon emissions. A further understanding of each industrial sub-sector's carbon intensity is very necessary to make differentiated policies and measures. This paper applied index decomposition analysis and attribution analysis to examine the influencing factors and each sub-sector's contributions to the changes in influencing factors. The results demonstrated the following: (1) energy intensity effect contributed most to the decreases in industrial carbon intensity, and mining and quarrying, foods and tobacco, and other manufactures were the most representative industrial sub-sectors; (2) energy structure effect showed a positive effect on industrial carbon intensity, but its effect was not significant, and fuel processing, smelting and pressing of metals, metal products, and textile were mainly responsible for the increases in energy structure effect; (3) industrial structure effect showed significant fluctuations, but its accumulative effect promoted the increases in industrial carbon intensity, and fuel processing, mining and quarrying, and textiles were the main sub-sectors, which exerted negative effects on the decreases in industrial structure effect; (4) fuel processing, smelting and pressing of metals, and mining and quarrying significantly influenced these three decomposed factors from 2000 to 2014; (5) since 2009, energy-intensive sub-sectors increased rapidly, and the energy structure was not optimized, while attention was not paid to controlling the energy efficiency, thus all decomposed factors promoted the increases in industrial carbon intensity; and (6) mining and quarrying, textiles, fuel processing, and transport equipment were primarily responsible for the increases in energy structure effect. Fuel processing, chemicals, and smelting and pressing of metals were primarily responsible for the increases in energy intensity effect. Fuel processing, chemicals, smelting and pressing of metals, and other manufactures were primarily responsible for the increases in industrial structure effect.

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“…Sovacool and Brown (2010) offered a preliminary comparison of carbon footprints in 12 metropolitan areas. Xi et al (2011) studied the carbon footprint of Shenyang city, China by using IPCC method. Lin et al (2013) studied the carbon footprint of Xiamen City in China by integrating both IPCC method and EIOA method.…”

Section: Carbon Footprint (Cf) Analysismentioning

confidence: 99%