Ex-post decomposition analysis of passenger car energy demand and associated CO 2 emissions

Dennehy, Emer R.; Gallachóir, Brian P. Ó

doi:10.1016/j.trd.2018.01.012

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…Actually, a large number of decomposition analysis have been done in transport sectors of Beijing, Shanghai, Tianjin, and Chongqing, but the majority of them preferred to investigate factors influencing carbon emission rather than the decoupling process in transport sector [41]. Regarding to Beijing, Wang and Liu [42] studied influence of individual travel behavior on urban transport carbon emission from 2000 to 2011.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of Decoupling Economic Output from Carbon Emission in the Transport Sector: A Comparison Study of Four Municipalities in China

Wang

2019

IJERPH

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Quantitative analysis on decoupling between economic output, carbon emission, and the driving factors behind decoupling states can serve to make the economy grow without increasing carbon emission in China’s transport sector. In this work, we investigate the decoupling states and driving factors of decoupling states in the transport sector of China’s four municipalities (Beijing, Shanghai, Tianjin, and Chongqing) through combining the Tapio decoupling approach with the decomposition technique. The results show that (i) the decoupling state of Beijing, Shanghai, and Tianjin improved; Beijing stabilized in weak decoupling; Shanghai and Tianjin appeared to have strong decoupling, but the decoupling state of Chongqing deteriorated from decoupling to negative decoupling. (ii) The energy-saving effect was the primary contributor to decoupling in these four municipalities, promoting transport’s economic growth strongly decouple from carbon emission. The economic scale effect was not optimized enough in Chongqing, facilitating expansive coupling, and expansive negative decoupling emerged. But it had a rather positive impact on decoupling process in Beijing, Shanghai and Tianjin, promoting economic growth to weakly decouple from carbon emission. (iii) The carbon-reduction effect promoted strong decoupling, which emerged in Shanghai’s transport sector, more so than in the other three municipalities, in which weak decoupling emerged. Finally, several relevant policy recommendations were offered to promote the decoupling of carbon emission from economic growth and low-carbon transport.

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Section: Literature Reviewmentioning

confidence: 99%

Determinants of Decoupling Economic Output from Carbon Emission in the Transport Sector: A Comparison Study of Four Municipalities in China

Wang

2019

IJERPH

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“…Usually fuel consumption is expressed in liters per hundred kilometers (l/100 km) or miles per gallon (mpg) [3]. Finding the dependence of fuel consumption on various factors allows us to forecast fuel consumption prediction and work out a method of its reduction [21,19].…”

Section: Modeling Of Fuel Consumptionmentioning

confidence: 99%

“…A sedan passenger car from the B segment was used as the research object. It was a Renault Thalia I vehicle, produced in 2003, powered by a K7J 700 spark-ignition engine with a displacement of 1,390 cm 3 . It was characterized by a rated power of 55kW (75HP).…”

Section: Fuel Consumption Testsmentioning

confidence: 99%

Modeling of fuel consumption using artificial neural networks

Witaszek¹

2020

Diagnostyka

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The article presents a model of operational fuel consumption by a passenger car from the B segment, powered by a spark ignition engine. The model was developed using artificial neural networks simulated in the Stuttgart Neural Network Simulator (SNNS) package. The data for the model was obtained from longterm operational tests, during which data from the engine control unit were recorded via the OBDII diagnostic interface. The model is based on neural networks with two hidden layers, the size of which was selected using an original iterative algorithm. During the structure selection process, a total of 576 different networks were tested. The analysis of the obtained test errors made it possible to select the optimal structure of the 6-19-17-1 model. The network input values were: vehicle speed and acceleration, road slope, throttle opening degree, selected gear number and engine speed. The networks were trained using the efficient RPROP method. A correctly trained network, based on the set parameters, was able to forecast the instantaneous fuel consumption. These forecasts showed a high correlation with the measured values. Average fuel consumption calculated on their basis was close to the real value, which was calculated on the basis of two consecutive fuelings of the vehicle.

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“…These studies have been primarily concerned with the general transportation level [26][27][28][29]. With regard to specific sub-modes, most of the studies that have looked at the transportation sector have concentrated on the road sector, since it is the most widely used door-to-door transportation mode [30][31][32]. Since CO 2 emissions from the transport sector of China have witnessed rapid growth in recent years, the LMDI method has been applied to decompose CO 2 emissions changes in the transport sector of China at national, regional, and provincial levels [33].…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

“…Because the observed period of this paper is not very long, we assumed that the CO 2 emissions factor of HFO remained steady across the entire period. The LMDI method includes two methodologies: Multiplicative decomposition and additive decomposition, and both forms are expected to obtain identical results [32]. Accordingly, an additive form was chosen for this study.…”

Section: Co 2 Emission Changing Decomposition Approachmentioning

confidence: 99%

Uncovering CO2 Emissions Patterns from China-Oriented International Maritime Transport: Decomposition and Decoupling Analysis

Yang

2019

Sustainability

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Given that most commodity transportation depends on the maritime industry, the growing economy and increasing international trade volume are expected to accelerate the development of shipping activities and thus increase associated CO2 emissions. In order to identify the driving factors of CO2 emissions from China’s international shipping and find efficient mitigation strategies, this paper first estimates the CO2 emissions and presents the CO2 emissions features from 2000 to 2017. Second, the Logarithmic Mean Divisia Index (LMDI) method is applied to decompose the changes in CO2 emissions. Finally, the decoupling index is introduced to quantitatively examine the decoupling relationship between economic growth and CO2 emissions. The factors affecting the decoupling relationship are analyzed according to the LMDI results. The results indicate that CO2 emissions in maritime transport activities have experienced rapid growth during the study period. Economic growth appears to be the principal factor driving the CO2 emissions growth, whereas the overall effects of energy intensity and the commodity structure play a significant role in inhibiting CO2 emissions. The decoupling state over the study period has experienced four decoupling stages, with a distinct tendency towards weak decoupling. Economic activity has proven to be the most significant indicator influencing the decoupling relationship during the study period.

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Ex-post decomposition analysis of passenger car energy demand and associated CO 2 emissions

Cited by 25 publications

References 33 publications

Determinants of Decoupling Economic Output from Carbon Emission in the Transport Sector: A Comparison Study of Four Municipalities in China

Determinants of Decoupling Economic Output from Carbon Emission in the Transport Sector: A Comparison Study of Four Municipalities in China

Modeling of fuel consumption using artificial neural networks

Uncovering CO2 Emissions Patterns from China-Oriented International Maritime Transport: Decomposition and Decoupling Analysis

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