Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China

Chen, Yanan; Lin, S.-W.

doi:10.1007/s11069-014-1576-7

Cited by 29 publications

(12 citation statements)

References 34 publications

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“…Over 50 % of the world's population is now concentrated in urban areas, consuming 77 % of the world's energy and emitting 83 % of the total GHG emission (Chen and Lin 2014;Zhang and Yan 2014a). Besides, cities, with high population density, complicated infrastructure and various municipal services, are vulnerable for abrupt climate interference and also sensitive to energy conservation and GHG reduction actions.…”

mentioning

confidence: 99%

Study on factors affecting energy-related per capita carbon dioxide emission by multi-sectoral of cities: a case study of Tianjin

Chen

Lin

2015

Nat Hazards

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This paper analyzed dual perspectives of production and resident, and quantified factors affecting the energy-related per capita carbon emission from cities. Tianjin is the largest coastal city in northern China with rapid economic development and urbanization. Analysis of the CO 2 emission generated by driving forces in Tianjin can provide guidance for policy decisions on the CO 2 emission mitigation in global metropolises. Using LMDI and Kaya extension methods, this study built a decomposition model of economic output, economic structure, energy intensity of the production sectors, energy mix of the production sectors, population structure of urban and rural distribution, energy consumption per capita and energy mix of the household sectors to analyze the per capita carbon emission factors. The empirical study of Tianjin from 2005 to 2011 showed: Economic output played a positive role in the per capita carbon emission increasing, while economic structure, energy intensity of the production sectors and energy mix of the production sectors played a negative role; population structure of urban and rural distribution and energy consumption per capita promoted in the per capita carbon emission increasing, while energy mix of the household sectors inhibited the per capita carbon emission increasing.

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confidence: 99%

Study on factors affecting energy-related per capita carbon dioxide emission by multi-sectoral of cities: a case study of Tianjin

Chen

Lin

2015

Nat Hazards

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“…The result is similar to previous studies. From the previous studies, provinces like Shandong, Shanxi and Hebei have larger CO 2 emission reduction quota, while provinces like Hainan, Beijing and Qinghai have smaller CO 2 emission reduction quota (Wang et al 2011;Yi et al 2011;Sun et al 2011;Yu et al 2014;Chen and Lin 2015;Han et al 2016).…”

Section: Co 2 Emissions Reduction Quota Allocation Of Chinese Provincesmentioning

confidence: 96%

“…Hebei, Shanxi, Liaoning and Shandong will be allocated large CO 2 emission quota under preferring responsibility scenario. LMDI model was used by Chen and Lin (2015) to objectively allocate CO 2 emission reduction quota for Chinese provinces. From the LMDI structure, CO 2 emission, energy structure, energy intensity, economic output and population are chosen as the indexes based on the equity principle.…”

Section: Carbon Intensitymentioning

confidence: 99%

“…The perpetual inventory method was widely used in the measurement of Chinese capital stoke Shan 2008;Xiang and Ye 2011;Fan 2012). The labor, energy consumption and GDP are derived from the 2015 China Statistic Yearbook (CSY), 2000-2015China Energy Statistic Yearbook (CESY) and 2000-2015. Moreover, the energy consumption is estimated as standard coal by considering the standard coal coefficient.…”

Section: Datamentioning

confidence: 99%

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Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

et al. 2017

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Chinese central government made a commitment to achieve a 40-45% reduction in carbon dioxide (CO 2 ) per unit of GDP by 2020 compared with 2005. This targeted reduction was allocated averagely among all the provinces rather than individually according to different situations of each province. Though some research has been done regarding this rough allocation, two shortcomings in previous studies exist: Firstly, CO 2 marginal abatement cost (MAC) has been ignored as one of the CO 2 emission reduction allocation indexes. Secondly, either subjective or objective method has been used rather than comprehensively of both subjective and objective method to calculate the weight of each index in the previous studies. In order to fill the gaps, this paper builds a two-stage Shapley information entropy model to allocate CO 2 emission reduction quota among the Chinese provinces based on the equity and efficiency principles. Afterward, three CO 2 emission reduction quota allocation scenarios have been proposed. The results show that the CO 2 MAC is an indispensable index in CO 2 emission reduction quota allocation, because its value of CO 2 Shapley information entropy is the highest among five indexes. CO 2 emission reduction quota of lower-MAC provinces should be allocated larger, while the quota of higher-MAC provinces should be allocated smaller. Therefore, two suggested policies have been proposed: First, differential CO 2 emission reduction quota allocation should be proposed. Second, synergetic development should be promoted.

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“…The research by Hu et al (2015) showed that the discrepancy of interprovincial carbon emission was diminishing. Chen and Lin (2015) studied the regional reduction policies and the quota of carbon emission through decomposition of contributing factors of emissions during 2005-2011 in 30 provinces of China. Based on LMDI and the Tapio decoupling model, Wang and Yang (2015) quantitatively analyzed the growing trend and decoupling effect of industrial carbon emission in the Beijing-Tianjin-Hebei economic belt.…”

Section: Introductionmentioning

confidence: 99%

Contrastive analyses of the influence factors of interprovincial carbon emission induced by industry energy in China

Zhou

Zhang

2016

Nat Hazards

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As a major contributor of carbon emission in the world, China should focus on the balance between the universality of development and regional heterogeneity of carbon discharge during the transformation toward low-carbon economy. To reveal the differences among interprovincial industry energy's carbon emissions, some relevant data of carbon emissions in 29 provinces and municipalities during the period of 1996-2012 are selected in this study. Based on the Logarithmic Mean Divisia index decomposition model and hierarchical clustering method, the 29 provinces were clustered in turn by four time intervals according to some indicators, including economic intensity, energy intensity, industry structure, energy structure, demographic effect, and carbon density influence. Research results show that during 1996-2000, economic intensity has a strong positive driving effect on carbon emissions in such eastern provinces as Shanghai, Zhejiang, and Jiangsu, and other seven inland provinces, such as Hunan and Hubei. Demographic effects have strong pulling effects on carbon emissions in municipalities and eastern coastal provinces, and they also exert strong negative effects on carbon emissions in Anhui, Guangxi, Guizhou, and Sichuan in the first three time intervals. During the four periods, highly energy-efficient provinces are Jiangsu, Guangdong, Sichuan, Shandong, Hubei, etc., whose carbon emissions are significantly inhibited by their energy intensity, whereas inefficient provinces are concentrated in western regions, like Guangxi, Hainan, Gansu, Qinghai, Ningxia, Xinjiang, etc.

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Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China

Cited by 29 publications

References 34 publications

Study on factors affecting energy-related per capita carbon dioxide emission by multi-sectoral of cities: a case study of Tianjin

Study on factors affecting energy-related per capita carbon dioxide emission by multi-sectoral of cities: a case study of Tianjin

Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model

Contrastive analyses of the influence factors of interprovincial carbon emission induced by industry energy in China

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