2016
DOI: 10.1016/j.trd.2015.11.001
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Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

Abstract: a b s t r a c tIn the process of rapid development and urbanization in Beijing, identifying the potential factors of carbon emissions in the transportation sector is an important prerequisite to controlling carbon emissions. Based on the expanded Kaya identity, we built a multivariate generalized Fisher index (GFI) decomposition model to measure the influence of the energy structure, energy intensity, output value of per unit traffic turnover, transportation intensity, economic growth and population size on ca… Show more

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Cited by 146 publications
(60 citation statements)
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“…To promote BEVs in China, the government announced that BEVs are exempt from purchase and driving restrictions [19]. The impact of purchase restrictions [20,21] and driving restrictions [22][23][24][25] has been widely discussed [26]; however, such studies mainly focus on the macro impact of traffic policies such as the growth in the volume of vehicles, traffic congestion mitigation, pollution and fuel consumption reduction. Jiayi and Jianxiao [27] mentioned that, from a consumer's perspective, the marginal benefits of vehicles will be reduced after driving restrictions are implemented; however, the economic intangible costs of traffic policies for personal car users have not been fully studied and are usually omitted in conventional LCC methods because the effects of these non-economic incentive policies cannot be easily quantified.…”
Section: Introductionmentioning
confidence: 99%
“…To promote BEVs in China, the government announced that BEVs are exempt from purchase and driving restrictions [19]. The impact of purchase restrictions [20,21] and driving restrictions [22][23][24][25] has been widely discussed [26]; however, such studies mainly focus on the macro impact of traffic policies such as the growth in the volume of vehicles, traffic congestion mitigation, pollution and fuel consumption reduction. Jiayi and Jianxiao [27] mentioned that, from a consumer's perspective, the marginal benefits of vehicles will be reduced after driving restrictions are implemented; however, the economic intangible costs of traffic policies for personal car users have not been fully studied and are usually omitted in conventional LCC methods because the effects of these non-economic incentive policies cannot be easily quantified.…”
Section: Introductionmentioning
confidence: 99%
“…When the decreasing value of indicator raised the level of urban public transportation infrastructure utilization benefit, the formula (2) is applied. In this paper, the values of 31 U , 32 U , 33 U and 35 U are calculated by formula (2) and the values of …”
Section: Calculation Of Urban Public Transportation Infrastructure Utmentioning
confidence: 99%
“…The scholars mainly paid attention to the relationship between transportation infrastructure and economic growth and applied different econometric methods to analyze it. Hong et al [1] estimated the linkage between transportation infrastructure and regional economic growth using panel data of 31 Chinese provinces from 1998 to 2007. Yu et al [2] analyzed the causal linkages between transportation infrastructure and economic growth in China at national and regional levels.…”
Section: Introductionmentioning
confidence: 99%
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“…The second class of related studies focuses on assessing the factors that will likely influence the amount of transport carbon emissions. Economic growth, energy intensity (i.e., the ratio of energy use and economic output), and population size have been suggested as the main factors influencing carbon emissions of the Beijing transport sector, using a generalized fisher index (GFI) decomposition model [17]; using a model of stochastic impacts via regression on population, affluence, and technology (STIRPAT), the factors affecting historical trends of carbon emissions in the Xinjiang transport sector were determined [18]. The third class of related studies centers on providing effective policy measures and improving energy efficiency.…”
Section: Introductionmentioning
confidence: 99%